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Review Biosensors in air monitoring† K. J. Mattias Sandstro�m*abc and Anthony P. F. Turnerc aUmea° University, Department of Public Health and Clinical Medicine, Occupational Medicine, S-905 81 Umea° , Sweden. E-mail: mattias.sandstrom@niwl.se; Fax:+46 90 786 50 27, Tel:+46 90 786 90 70 bNational Institute for Working Life, Department of Chemistry, P.O. Box 7654, S-907 13 Umea° , Sweden cCranfield Biotechnology Centre, Cranfield University, Cranfield, Bedford, UK MK43 0AL Received 9th April 1999, Accepted 18th June 1999 1 Introduction ments, and such instruments can be developed using biosensor 2 Methane technology. Biosensors can also oVer greater selectivity than 3 Carbon monoxide the commonly used direct-reading instruments.Many attempts 4 Formaldehyde have been made to produce biosensors with these kinds of 5 Ethanol characteristics for air monitoring and biosensors are now 6 Phenol important tools that can successfully be used for air 7 Pesticides and other hazardous chemicals monitoring. 8 Odours The first biosensor designed for air monitoring was described 9 Other sensors in 1974 in a paper by Goodson and Jacobs.1 It was a sensor 10 Conclusions for toxins that inhibit the cholinesterase enzyme.The sensor 11 Acknowledgements was used by pumping air, together with a reagent solution, 12 References through an electrochemical cell with an enzyme pad between two electrodes. The pad contained cholinesterase, which is Mr Sandstro�m gained aMSc inhibited by some chemicals. If the air sample contained a in chemistry at Umea° cholinesterase inhibitor, competition between the reagent and University in 1995.His PhD- the inhibitor occurred. Since the reagent produced an easily studies begun in 1997 with a oxidised product after reaction with cholinesterase and the project aiming to develop an inhibitors did not, the measured potential increased when an inexpensive, easy to use per- inhibitor was present.The sensor was produced for both air sonal exposure monitor using and, with some modifications, water sampling and could be biosensor technology. The used for 8 h without changing the enzyme pad. project is a collaboration between Umea° University Sweden, National Institute 2 Methane for Working Life, Sweden Another early biosensor for air monitoring used a reactor and Cranfield University, containing immobilised methane consuming micro-organisms UK.The National Institute for Working Life has a long to sample methane in air and was described by Okada and experience of air sampling co-workers in two similar articles.2,3 The sample gas was techniques, both active and pumped through the reactor and to an oxygen electrode. A reference reactor was also used to monitor the oxygen concen- passive sampling.At Cranfield University the research and development of biosensors has been successful for many years. tration in a reactor without micro-organisms. Microbial metab- Mr Sandstro�m’s research area is mainly the combination olism of methane requires oxygen resulting in a decreased of specific biological analysing methods incorporated into concentration of dissolved oxygen, which was monitored by sampling devices to simplify exposure measurements in the oxygen electrode.The sensor had a response time of 1 min occupational environments. and was said to give a constant response for 20 d. The minimum concentration of methane in air that could be detected was calculated to 13.1 mM and the linear range was up to 6.6 mM.No interfering agents were examined, however, 1 Introduction as the micro-organisms were said to use methane as their only The methods that are commonly used in air sampling today source of energy. sometimes lack some of the properties that would be considered favourable for a certain application; such properties might include high selectivity, high sensitivity, real-time moni- 3 Carbon monoxide toring, inexpensive analysis, one-step analysis, etc.Commonly An enzyme-based carbon monoxide sensor was described by used chromatographic methods usually require an air sampling Turner and co-workers.4,5 The biosensor was based on the procedure prior to the analysis. This time-consuming two-step oxidation of carbon monoxide to carbon dioxide by the analysis can be avoided by using on-site, direct-reading instruenzyme carbon monoxide oxidoreductase.Carbon monoxide oxidoreductase was placed on a conducting gel and covered with a membrane. The conducting gel consisted of graphite, †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. J. Environ. Monit., 1999, 1, 293–298 293mediator and liquid paraYn, and was in contact with a dehyde and requires visual comparison with a colour code that is supplied with the device.platinum electrode. The gas permeable membrane was used to keep the enzyme at the surface and to make it possible for In 1996, three biosensors for monitoring formaldehyde in air were described. Ha�mmerle and co-workers described a carbon monoxide to pass through to the enzyme. 1,1¾- Dimethylferrocene was used as a mediator and was oxidised biosensor based on an electrochemical cell divided into two parts by a dialysis membrane to prevent migration of the at the electrode surface at 150 mV versus Ag/AgCl.The amperometric response reached a steady-state current in less enzyme.9 FADH was put on the working electrode and prior to sampling the cell was filled with electrolyte containing than 15 s and the current in device decrease by 12% per hour.Most of the reported experiments, however, were performed cofactor and mediator. Since the electrolyte was not added until the time of sampling, the device could be stored for a in solutions. long time. The device was tested in a controlled atmosphere by measuring the equilibrium gas phase above an aqueous 4 Formaldehyde formaldehyde solution and the limit of detection was 0.3 ppm. A linear response was achieved up to 6 ppm using steady state Monitoring formaldehyde is of great importance since it is widely used in industry.It is also a known irritant and a measurements and the range was improved when initial rate data were used. The device could also be used for 7 h without possible carcinogen. In 1983, Guilbault described a biosensor for the determination of formaldehyde in air using formal- any loss of activity.Another biosensor for formaldehyde was described by dehyde dehydrogenase (FADH) coated on a piezoelectric crystal.6 The piezoelectric crystal technique has been widely Dennison and co-workers, who utilised enzymes and cofactors immobilised in a reversed micelle medium on screen-printed used in the construction of biosensors and is based on crystals that oscillate at a certain frequency when they are exposed to electrodes.10 The biosensor used FADH for the determination of formaldehyde but a similar construction was also used to an electric field.If the mass on the crystal changes, a shift in the frequency can be observed.Biological material can thereby determine alcohols with ADH. The re-oxidation of NADH to NAD+ was measured amperometrically at 0.8 V versus be attached to the surface to create a piezoelectric biosensor and the change in, or adsorption on, the biological material is Ag/AgCl. The reversed micelle medium was used to prevent water loss as the silicone oil acted as a barrier against monitored by measuring the frequency shift.The oxidation of formaldehyde to formic acid catalysed by FADH necessitated evaporation. The biosensor was found to be suitable for gasphase sensing when it was tested in controlled atmospheres. the presence of NAD+ and reduced glutathione. The stability for this device was, according to the author, 3 d or 100 Formaldehyde permeation tubes and ethanol diVusion vials, connected to a gas rig, were used to create the atmospheres.analyses, but this could be increased to 10 d if the enzyme and cofactors was chemically bound to the crystal surface. The gas concentrations were calibrated using Dra�ger tubes or by measuring the loss of sample gravimetrically. The linearity However, this was not recommended since the crystal in this case would not be reusable. The response to formaldehyde biosensors was estimated to be 1.3 ppb–1.2 ppm for formaldehyde and 50–250 ppm for ethanol and the biosensor air was linear from 10 ppb to 10 ppm.The test atmosphere was generated by a syringe injection, of a known volume of could be stored for 60 h at 4 °C without a decrease in response.An ion-sensitive field-eVect transistor (ISFET) was used by gas, into a controlled airflow and validated by formaldehyde sampling tubes and fluorimetric analysis. The biosensor was Vianello and co-workers in biosensor for formaldehyde measurement.11 The ISFET monitors H+ produced when specific to formaldehyde. No significant interference was seen from other aldehydes or alcohols.It is notable that no further formaldehyde is oxidised by FADH with NAD+ as a cofactor. The formaldehyde was removed from the atmosphere by development of this biosensor has occured. However, other researchers have used FADH in devices to monitor formal- pumping air through a glass coil together with an aqueous solution. The solution dissolved the formaldehyde and acted dehyde in air.FADH was one of the enzymes used in the diVusion badges as a carrier of the formaldehyde to the ISFET. A membrane containing FADH covered the ISFET and the solution con- developed by Rindt and Scholtissek.7 They used various enzymes lyophilised on to sintered glass rods put into vessels taining formaldehyde was transferred directly to the surface. The enrichment factor of this sampling technique was containing buVer–reagent solutions and covered with gas permeable membranes.The diVusion badges contained the 8000-fold but there were some problems with the immobilisation of the enzyme, which complicated the evaluation of buVer solutions to overcome the problem of drying. Since the device was constructed from two parts, the glass rod with the sensor. enzyme and the vessel with buVer–reagent solution, they could be stored separately.The enzyme could be stored dry, which 5 Ethanol increased the storage time. In addition to formaldehyde, the compounds determined using this type of construction were Detecting ethanol in air has an important application in determining breath alcohol. Barzana and co-workers devel- hydrogen peroxide, acetaldehyde and ethanol and the enzymes used were diaphorase, aldehyde dehydrogenase, alcohol oped a device which changes colour when it is exposed to ethanol.12 The detection was based on a visual observation of dehydrogenase (ADH) and horseradish peroxidase.The reaction between analytes and enzymes caused a dye to change a colour change. This gives a crude indication of the amount of alcohol in the breath.For quantification, the device was colour. This colour change was documented photographically and the colour was stable for hours after exposure. The gas tested using a densitometer for a more precise determination of ethanol vapour. The device was constructed by adding mixtures, used to test the badges, were generated with a perfusion vessel at controlled temperatures and verified with alcohol oxidase (AOD), peroxidase (POD) and 2,6-dichloroindophenol (DCIP) to microcrystalline cellulose.diVerent types of enzymatic reactions. The constant flow of buVer–reagent to the top of the device not only kept it moist The ethanol was oxidised by AOD and acetaldehyde and hydrogen peroxide were produced. The hydrogen peroxide but also concentrated the enzyme at the top of the glass rod, because the water slowly but constantly evaporated through reacted with POD and at the same time, the reduction of DCIP caused a colour change in the device.To make the the gas permeable membrane. The technique used in this device was later developed into a commercial product called device fast and simple to use it was optimised to give a sharp colour change after 1 min if the ethanol concentration was Bio-Check F (Dra�gerwerk AG, Lu�beck, Germany).8 The Bio- Check F is used for quantitative measurements of formal- over the legal limit for driving.Since AOD also has the ability 294 J. Environ. Monit., 1999, 1, 293–298to oxidise formaldehyde it can also be used to detect this NADH and placed in cuvettes.Fluorescence from NADH was measured with a spectrophotometer. Since the enzymatic reac- compound. However, for this application both methanol and ethanol would be serious sources of interference. tion was based on the fact that the reaction between alcohol and ADH with NAD+ as cofactor gave the corresponding A sensor for determination of alcohol and sulfur dioxide in air was described by Matuszewski and MeyerhoV.13 It was aldehyde and NADH in a reversible reaction, it would be possible to detect both alcohol and aldehyde.It could also be mainly constructed for the continuous electrochemical detection of hydrogen peroxide. By dissolving gaseous H2O2 in a possible to regenerate the sensor after exposure to one species by exposing the sensor to the other species.The device was buVer solution using coiled tubing with an internal buVer flow the H2O2 concentration could be measured when the buVer tested by gas-phase exposure to ethanol-containing gasoline and human breath containing ethanol. In both cases a detect- was pumped over an electrochemical cell. By adding an enzyme reactor containing H2O2-producing enzymes, such as AOD or able diVerence to ethanol-free control samples was achieved.sulfite oxidase (SOD), prior to the electrochemical cell, dissolved alcohol or sulfur dioxide could be detected. To increase 6 Phenol the sensitivity of the continuous flow measurements, a stopped- flow approach was investigated. The buVer flow through the Phenol is a chemical widely used in industry and exposure to phenol is known to cause irritations.Air monitoring of phenol coiled tubing was stopped for a certain time, which accumulated the compounds of interest during sampling. The ethanol is therefore very important. Saini and co-workers investigated the possibility of using biosensors to monitor phenol in air.17 and sulfur dioxide atmospheres were generated with a permeation tube and a commercial gas emitter, respectively, and An interdigitated microband electrode was chosen as the transducer and polyphenol oxidase (PPO) was immobilised diluted with air.The limit of detection was calculated for sulfur dioxide as 0.50 ppb with continuous flow and 0.15 ppb on the electrode in two diVerent materials, Nafion and tetrabutylammonium toluene-4-sulfonate (TBATS). Various electro- with 2 min stopped flow and for ethanol as 1.0 ppb with continuous flow and 0.5 ppb with 2 min stopped flow.After chemical techniques were used to investigate the device with respect to parameters such as thermodynamics and kinetics. storage for 2 weeks the SOD reactor lost more than 50% of its activity whereas the AOD reactor kept its activity for more With a view to health and safety monitoring, Dennison and co-workers further developed the biosensor for phenol.18 Their than 1 month.Mitsubayashi and co-workers constructed another type of device was constructed by immobilising PPO on a gold microelectrode using a glycerol-based gel. The phenol vapour reacts biosensor for the determination of ethanol in air.14 A reaction cell consisting of both gas- and liquid-phase compartments with the enzyme and the product (catechol ) takes part in a redox recycling reaction at the electrode surface.The authors separated by a diaphragm membrane was used in the sensor. AOD was immobilised in a cross-linked acrylamide gel and reported that good sensitivity was achieved partly by this recycling of the catechol–quinone redox couple. The limit of placed on a Clark-type oxygen electrode and covered with a polycarbonate membrane.The ethanol atmosphere in the test detection was estimated to be 29 ppb and the response was linear up to 13 ppm, both at 40% relative humidity. The chamber was generated by a gas generator connected to a computer controlled mass flow system and the calculated phenol atmosphere was generated with a phenol high-emission permeation tube mixed with humidified air.The phenol con- atmosphere was compared with a commercially available semiconductor gas sensor. According to the authors, the centration wasified with a method using an impinger to trap phenol, which was determined spectrophotometrically. biosensor measured ethanol down to 0.357 ppm and had a linear response from 1.57 to 41.5 ppm for steady state measure- Since glycerol is hygroscopic it had the ability to maintain the water content of the gel.The glycerol gel was also particularly ments and from 15.7 to 1242 ppm for maximum response slope measurements. The biosensor response decreased with suitable for phenol determination because of its ability to concentrate phenol in the biosensor.time. After 4 d, the output was 25% of the initial response. Interferences were measured for only a few compounds and In two papers, Kaisheva and co-workers described a biosensor for monitoring phenol in both the liquid and gas did not include any of the compounds known to react with AOD (methanol, propanol, formaldehyde, etc.). phase. The first paper19 mainly described the performance of the sensor in the liquid phase but preliminary experiments in In 1995, another biosensor for ethanol vapour was developed by Park and co-workers.15 This sensor, which was also con- the gas phase was also described.The second paper20 described experiments performed in the gas phase, also investigating structed mainly for measuring breath alcohol, used ADH and NAD+ immobilised on screen-printed electrodes with a mix- p-cresol and 4-chlorophenol vapours.The enzyme used in the sensor was tyrosinase, which catalyses both the reaction of ture of hydroxyethylcellulose, ethylene glycol and carbon powder. Ethanol reacts with the enzyme and at the same time phenol to catechol and the reaction of catechol to o-quinone. The electrochemical reduction of o-quinone back to catechol the NAD+ is reduced to NADH.NAD+ is then regenerated at the electrode surface from NADH. This amperometric then produced a measurable signal at the electrode. The sensors were evaluated in the gas phase over aqueous samples regeneration of NAD+ was carried out at a potential of 0.65 V versus Ag/AgCl. The simple and inexpensive technique of but the gas-phase concentrations were not calculated.However, linear calibration curves were achieved in the range screen-printing makes the sensors both disposable and easy to mass produce. The storage stability was dependent on the 5×10-7–1×10-4 M for phenol, 5×10-5–5×10-3M for pcresol and 5×10-4–5×10-2 M for 4-chlorophenol for the amount of enzyme in the biosensor. However, it could be stored for more than 35 d if the ADH/NAD+ ratio was >6.aqueous standards. The sensor could also be stored for 20 d without losing its activity. The sensor had a linear response up to 250 ppm and the vapour was generated by bubbling nitrogen through an ethanol solution. Preliminary tests were performed with people mainly 7 Pesticides and other hazardous chemicals to investigate if there were any interfering compounds in human breath.No interference was found. Monitoring of pesticides has long been of interest since they are highly toxic and widely used. Biosensors for monitoring Williams and Hupp developed a sensor based on sol–gel encapsulated ADH for the determination of alcohols and pesticides have mainly been developed for the liquid phase but some sensors have been described for gas-phase monitoring.aldehydes in air.16 The sol–gel consisted of silica and the enzyme was encapsulated in a transparent material. The ADH Ngeh-Ngwainbi and co-workers used antibodies against parathion (a known pesticide) attached to a piezoelectric crystal to was immobilised in the sol–gel with the cofactors NAD+ and J. Environ. Monit., 1999, 1, 293–298 295monitor parathion in air.21 The response to parathion was remained after 10 d.Tetrachloroethylene was used as an example of a compound in aerosol form. The aerosols were fast, usually 1–2 min, and the time to return to the baseline was 2–5 min. The linearity of the device was shown to be in produced with an atomiser, and a fan in the sampling chamber distributed the aerosols.The limit of detection for tetrachloro- the range 2–35 ppb. However, since there was a problem with the generation of parathion at higher concentrations, experi- ethylene was calculated to be 10 ppm and a linear calibration curve was achieved in the range 0–250 ppm. The device was ments were performed only up to 35.5 ppb. The test atmosphere was generated by bubbling carrier gas through a trap said to be suitable as an early warning system to protect workers from harmful chemicals, particularly where the chemi- containing liquid sample.The vapour-saturated carrier gas was then diluted with pure carrier gas and the concentration cals have not yet been identified. of the sampling atmosphere was verified using gas chromatography. Some interferences were seen from other pesticides, but 8 Odours with lower responses.It took between 3 and 20 times more of the diVerent interferents to give the same response as para- When odours are detected, the sensor is usually referred to as an electronic nose. A device using coated piezoelectric crystals thion. The lifetime of the crystals was approximately 1 week, after which the response decreased rapidly.was developed by Okahata and Shimizu to detect odours and perfumes in the gas phase.29 DiVerent coatings were tested Non-specific adsorption of compounds on antibody coated piezoelectric crystals was addressed and utilised by Rajakovic and it was found that a lipid bilayer had the best characteristics for the odours, represented by b-ionone. The response time and co-workers.22 They used piezoelectric crystals coated with diVerent proteins (valproic acid antiserum, parathion antibody, for the device was 5 min when exposed to a saturated atmosphere of b-ionone.IgG and bovine serum albumin) and exposed these to atmospheres containing diVerent hazardous compounds (valproic Piezoelectric crystals coated with four diVerent lipid films for the detection of odours represented by eight organic acid, o-nitrotoluene, toluene, parathion, malathion and disulfoton).The results showed that there was a higher sensitivity compounds (e.g., amyl acetate, b-ionone, methanol ) were described by Muramatsu and co-workers.30 The crystals were to the three pesticides (parathion, malathion and disulfoton) than valproic acid, o-nitrotoluene and toluene.The pesticides fixed in a vessel and the samples were injected into the vessel as liquids. The patterns for the diVerent odours were then also adsorbed better on an uncoated crystal. This was explained by the ability of organosulfur compounds to chemisorb normalised and compared. Mixtures of asolectin and cholesterol were used by strongly to metal surfaces. However, this does not explain the higher sensitivity of the sensors towards pesticides. The paper Muramatsu and co-workers in a device developed for odour recognition.31 The lipids were coated on piezoelectric crystals described the problems of non-specific adsorption to antibodies when used in gas-phase monitoring and demonstrated that it and the frequency shift was measured when the odours, represented by eight organic compounds, adsorbed on the was important to consider these kinds of interactions, but it also demonstrated that it was possible to construct non-specific coated crystal.The odours were vaporised by injecting liquid sample into the vessel in which the crystal was positioned and antibody biosensors for air monitoring. Another simple device is the C-probe film badge described the resonant frequency and the resonant resistance was measured both before and after injection.The patterns for the by Case and Crivello.23 This device required visual observation and might be suitable as a hazard indicator. The device was diVerent compounds were then compared with the aim of recognising the odours. described as a biological layer between a film base and a layer of dye.The chemical agents reacted with the biological layer Wu described a device for odour detection using olfactory receptors coated on a piezoelectric crystal.32 Olfactory receptor and were converted into active intermediates that triggered a colour change in the dye. The badge was said to respond to proteins (ORP) were used in an attempt to mimic the human sense of smell. Crude ORPs and ORPs fractionated into five 130 organic and inorganic compounds with a high correlation to carcinogenic hazards.It could be stored for 3 months and groups were coated on the crystals to establish the patterns from six organic compounds (e.g. caproic acid, isoamyl acetate, the sampling time was up to 8–15 h. In three papers, Albery and co-workers described an linalool ) used as odours.According to the authors the sensor did not lose sensitivity after storage for 5 months and it could inhibited enzyme electrode. The three papers deal with (a) a theoretical model for an electrochemical sensor measuring the be used continuously for 10 weeks without a decrease in sensitivity. inhibition of the enzyme activity,24 the kinetics of the cytochrome c and the cytochrome oxidase enzyme systems25 and (c) a description of an application where the sensor was 9 Other sensors used to analyse HCN and azide ion in the liquid phase and H2S in the gas phase.26 The sensor was based on the inhibition Okada and co-workers developed a biosensor for the determination of NO2 in air.33 Nitrite oxidising bacteria were immobi- of the enzyme cytochrome oxidase.H2S inhibited the enzymatic reaction producing a decrease in the current from the lised on an acetylcellulose membrane. The membrane was then attached to an oxygen electrode and covered with a gas- gold electrode. In the gas phase H2S could be measured down to 1 ppm and the linearity was said to be good up to 20 ppm. permeable Teflon membrane.The sample was prepared in a gas bag, pumped into the system and dissolved in a buVer Naessens and Tran-Minh described a whole-cell biosensor that can be used to monitor organic compounds in both which was pumped through the sample cell of the biosensor. The decrease in oxygen, caused by an increased activity of the vapours27 and aerosols.28 The sensor used a Clark oxygen electrode to monitor the oxygen produced during the photosyn- micro-organisms when NO2 was present, was measured.The minimum concentration that could be determined was calcu- thesis of immobilised micro-algae. When the algae were exposed to the organic compounds, as vapour or aerosol, lated as 0.51 ppm and the calibration curve was linear below 255 ppm. The sensor was said to be re-usable for 400 assays photosynthesis was inhibited and a decrease in oxygen was measured.The algae were flashed with an external light source or 24 d and to respond only to NO2. No experiments were performed with other inorganic gases (e.g. NO, SO2, NH3). for 1 min every 5 min to start the photosynthetic process. Methanol was used as an example of a gaseous compound A biosensor for nitrogen monoxide was described by Aylott and co-workers.34 It consisted of a sol–gel containing and the sampling was carried out in a thermostated cell with a gas–liquid equilibrium.The calculated detection limit for cytochrome c spin-coated on to a glass substrate. A gas flowthrough cell covered the sol–gel for the gaseous sample to methanol was 30 ppm and more than 50% of the algal activity 296 J.Environ. Monit., 1999, 1, 293–298come in contact with the enzyme. When the NO attached to ethanol and the signal could be measured as a steady-state current or as initial reaction rate. cytochrome c, a shift of the absorption wavelength occurred, which was measured spectrophotometrically. Since the bond between NO and cytochrome c was reversible, the sensor could 10 Conclusions be used for repeated exposures of NO.The standard deviation When looking at biosensors for air monitoring, the conclusion was calculated to be 1% of the response when five repeated can be drawn that this application has not attracted the same exposures to 10 ppm of NO were made. The limit of detection attention as other areas. This can possibly be attributed to the was calculated to be 1 ppm and the range of detecting NO fact that the major commercial area for biosensors has so far was 1–25 ppm.The authors found no evidence of interference been the field of medicine. However, biosensors are steadily from oxygen, nitrogen or carbon monoxide. However, NO2 being developed into useful tools for air monitoring but there was found to bind to cytochrome c and therefore to give rise are still some requirements that have to be fulfilled for them to interference.The authors therefore concluded that to be accepted as instruments for air monitoring. There are, cytochrome c only could be used for detecting NOx and not for instance, few articles that have a well described system for NO selectively. generating test atmospheres and there is generally a lack of A sensor for monitoring sulfur dioxide in air was described reliable reference methods to determine the gaseous concen- by Matuszewski and MeyerhoV.13 It was also used to monitor trations that are used for the tests.In addition, most of the alcohol and it has been mentioned earlier. A gas-phase sensors have not been suYciently validated, which is a require- biosensor for the direct determination of gaseous sulfur dioxide ment from the EU when it comes to developing devices for in the atmosphere was also developed by O’Sullivan.35 A air monitoring.38–40 mixture of agarose and carboxymethylcellulose was chosen The sensors themselves can oVer exquisite sensitivity and from a range of matrices as the medium for immobilisation of specificity, but the instability of isolated biological systems is sulfite oxidase.Agarose (1% w/v) and carboxymethylcellulose aggravated by the need to operate in air. Nevertheless, the (1% w/v) retained a relatively high proportion of water over literature shows a number of innovative approaches to engina 3 h period, thus preventing enzyme dehydration and allowing eering solutions to those problems and niche applications of eYcient dissolution of SO2.The sensor method was compared biosensors for air monitoring that can be expected to materialwith the standard method for SO2 determination and a corre- ise as a commercial reality in due course. The field of biosensor lation coeYcient of 0.999 was obtained, indicating eYcient research and development is rapidly expanding.The use of dissolution of SO2 in the matrix, accurate production of air– biosensors in air monitoring is mainly targeted on real-time sulfur mixtures by the gas rig and eYcient functioning of the devices for monitoring atmospheric pollution or research biosensor. The reproducibility of the biosensor was stated to applications, but there is also a need for fast and simple be extremely good; an RSD of 0.96% was obtained for n=10.measurement devices for personal exposure measurements in The linear range was 0–13.5 ppm and the LOD was 73.9 ppb. occupational environments; biosensor technology can be an There is a need to monitor not only chemical compounds important tool for this purpose. Another major advantage in air but also micro-organisms in air.In some industrial with biosensors is that the manufacturing process can be environments workers can be exposed to high levels of micro- inexpensive owing to the mass-production technology that is organisms. Biological warfare is another application that is in now widely available. Increased demand for more frequent need of fast sampling methods for micro-organisms in air.For and more varied analyses in the workplace can be expected to this purpose, Ligler and co-workers developed a light-weight catalyse biosensor developments in this area and we can expect biosensor that was monitored in a remotely piloted aero- to see commercially available devices in due course. plane.36 Aerosolised bacteria were sampled using a plastic cyclone air sampler, with a constant addition of buVer solution. 11 Acknowledgement A portion of the liquid sample was pumped over an optical fibre coated with polyclonal antibodies against the bacteria in The authors are indebted to Professor Jan-Olof Levin and question. 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O’Sullivan, PhD Thesis, Cranfield University, 1996. Biosens. Bioelectron., 1995, 10, 945. 36 F. S. Ligler, G. P. Anderson, P. T. Davidson, R. J. Foch, J. T. 18 M. J. Dennison, J. M. Hall and A. P. F. Turner, Anal. Chem., Ives, K. D. King, G. Page, D. A. Stenger and J. P. Whelan, 1995, 67, 3922. Environ. Sci. Technol., 1998, 32, 2461. 19 A. Kaisheva, I. Iliev, R. Kazareva, S. Christov, U. Wollenberger 37 J. K. Park and H. J. Lee, Eur. Pat., 94 630044.9, 1994. and F. Scheller, Sens. Actuators, B, 1996, 33, 39. 38 Ambient Air Quality—DiVusive Samplers for the Determination of 20 A. Kaisheva, I. Iliev, S. Christov and R. Kazareva, Sens. Concentrations of Gases and Vapours—Requirements and Test Actuators, B, 1997, 44, 571. Methods, PrEN XXX5draft, Comite� Europee�n de Normalisation, 21 J. Ngeh-Ngwainbi, P. H. Foley, S. S. Kuan and G. G. Guilbault, Brussels. J. Am. Chem. Soc., 1986, 108, 5444. 39 Workplace Atmospheres—General Requirements for the 22 L. Rajakovic, V. Ghaemmaghami and M. Thompson, Anal. Chim. Performance of Procedures for the Measurement of Chemical Acta, 1989, 217, 111. Agents, EN48251994, Comite� Europee�n de Normalisation, 23 G. D. Case and J. F. Crivello, Proc. Natl. Conf. Hazard. Wastes Brussels, 1994. Hazard. Mater., 1990, 7, 229. 40 Workplace Atmospheres—DiVusive Samplers for the Determination 24 W. J. Albery, A. E. G. Cass and Z. X. Shu, Biosens. Bioelectron., of Gases and Vapours—Requirements and Test Methods, 1990, 5, 367. EN83851995, Comite� Europee�n de Normalisation, Brussels, 1995. 25 W. J. Albery, A. E. G. Cass and Z. X. Shu, Biosens. Bioelectron., 1990, 5, 379. 26 W. J. Albery, A. E. G. Cass, B. P. Mangold and Z. X. Shu, Biosens. Bioelectron., 1990, 5, 397. Paper 9/02835H 298 J. Environ. Monit., 19

ISSN:1464-0325    

DOI:10.1039/a902835h

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Measurement of vapour–aerosol mixtures† Dietmar Breuer Berufsgenossenschaftliches Institut fu�r Arbeitssicherheit (BIA), Section Analytical Chemistry, Alte Heerstrasse 111, 53757 Sankt Augustin, Germany. E-mail: D.Breuer@hvbg.de Received 16th March 1999, Accepted 25th May 1999 A particular problem in connection with the measurement of hazardous substances is posed by substances or groups of substances which may occur simultaneously in vapour and aerosol form.It is possible that, during sampling, the distribution of the two phases on the sampling medium is subject to changes due to vaporisation of aerosol or condensation of vapour. For workplace assessment purposes, it makes sense to consider the sum of vapour and aerosol, which never changes. Only then can the results of diVerent sampling systems be compared.There are several ways in which vapour–aerosol mixtures can be sampled. Combining adsorption tubes or denuders with filters is the most important. Twofold requirements must be considered in the development of such sampling methods: those applying to sampling systems for aerosols as well as those for vapour sampling systems. To satisfy the requirements resulting from these substances and groups of substances, BIA’s personal sampling system (PGP) was extended.In addition to the sampling head for inhalable dust (GSP), originally designed for a flow rate of 3.5 L min-1, sampling heads for 0.5, 1 and 10 L min-1 were developed. Tests were conducted on these sampling heads, which all showed compliance with the requirements defined for inhalable fraction sampling.For the combined sampling of aerosol and vapour, a system was created which allows a filter and up to three usual adsorption tubes to be loaded simultaneously. The measuring methods for alkanolamines, inorganic acids and explosives are described to illustrate the use of the above PGP extensions and the framework conditions, account of which must be taken in the sample treatment.DiVerent types of separation system are used for particles, 1. Introduction while filters have proven to be particularly suitable. In addition, Within the framework of analytical determinations, the sam- cyclone dust separators or cascade impactors are applied. The pling procedure often poses particular problems. While, now- sampling of particles is usually carried out by active sampling, adays, most instrumental analytical methods allow errors to whereby the characteristics of diVerent particle sizes must be be limited to a few percent, serious errors may still be made taken into consideration. Nowadays, samples are taken of during the sampling procedure.inhalable or respirable dust fractions. The procedure applied to the sampling of hazardous sub- This paper will especially concentrate on aerosol–vapour stances in air primarily depends on the state of aggregation of mixtures or so-called mixed-phase aerosols, since they have the substance in the atmosphere.The following scenarios several peculiarities which must be considered in the sampling are possible. process as well as in the subsequent analytical determination.(i) At sampling temperature (room temperature), a sub- The framework conditions that must be observed will be stance is gaseous or suYciently volatile and only occurs in the treated in depth, and potential solutions will be discussed on gas phase. Examples include ethane and acetone. the basis of examples. (ii) At room temperature, a substance is not volatile and occurs as an aerosol.In this case, a solid matter may occur in 2. Sampling of gas–vapour mixtures: how can gas– the surrounding atmosphere in the form of a dust or fume, while a liquid may occur as droplets in the form of a mist. vapour mixtures be sampled? Examples include metal oxide dust and sulfuric acid. This situation may arise when a substance, because of its (iii) At room temperature, a substance is semi-volatile and physical properties, occurs at room temperature in a stable may occur as an aerosol and a vapour at the same time.vapour and particle phase over a certain period of time. The Possible distribution in the atmosphere in this case is solid– same would apply to a group of substances with similar gaseous or liquid–gaseous.chemical properties, but whose physical properties are so The first two examples are part of the routine of every air divergent that they occur as mixed-phase aerosols. This is true analyst. Numerous possibilities exist for collecting ‘pure for homologous series; classic representatives are the aliphatic vapours’ or ‘pure aerosols’. hydrocarbons and polycyclic aromatic hydrocarbons (PAHs).Gases and vapours can be bound reversibly to sorbent Highly volatile hydrocarbons, such as hexane and octane, material such as active charcoal, silica gel or tenax. They can only occur as vapour in the atmosphere, even if they are used either be separated on coated surfaces or absorbed in liquids. as liquids in the workplace. Low volatile hydrocarbons, such In active sampling, the air under examination is conducted by as eicosane (C20), only occur in particle form.The area in a pump through the sampling system. In passive sampling, between these two ends of the scale, reaching more or less separation is achieved by diVusion. from tetradecane to octadecane, is characterised by the transition from the gaseous into the particle phase. Since these hydrocarbons are always used as mixtures, it is diYcult to †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999.J. Environ. Monit., 1999, 1, 299–305 299take combined vapour–aerosol samples of high boiling mineral The total quantity of vapour and aerosols was similar for all sampling systems. oil mixtures. PAHs with three- or four-ring systems are volatile or semi- 2.2.Sampling techniques for mixed-phase aerosols volatile, while PAHs with five or more rings are non-volatile. Normally, a wide range of PAHs occur simultaneously at the In the case of combined vapour–aerosol sampling, the requireworkplace, so that combined vapour–aerosol sampling is ments of EN 481, 482, 1076 and 1232, applying to both useful. The known methods use a combined sampler with a aerosols and vapours, have to be met.4–7 For the sampling of filter for aerosol sampling and an XAD tube for vapour mixed-phase aerosols, there are various possibilities. sampling.1 2.2.1.Denuder and filter. This combination is easy to use. 2.1. Combined vapour–aerosol sampling It has to be observed, however, that the vapours must be separated first and that the intake of the denuder must comply During the last few years, a working group of CEN/TC 137 with the requirements for particle sampling systems.A serious has dealt with the problem of sampling of aerosol–vapour error can be made if the aerosol partly evaporates after being mixtures. The working group considered its main task to collected. It should be ensured that such fractions are also gather solutions for the sampling of mixed-phase aerosols.It collected. The sampling conditions applying to denuders on is intended to publish the results achieved by the working the one hand and filters on the other can be easily made group as a pre-standard.2 compatible. The following are the basic statements made by the working group. 2.2.2. Filter and pumped sorbent tube.This combination will (i) The actual phase distribution of mixed-phase aerosols in certainly be applied in most cases, as it is easy to handle and the atmosphere cannot be determined. The only indicative quite compact. Another advantage of this assembly is that the statement that can be made concerns the total vapour–aeroexperience obtained with filter sampling and tube sampling sol quantity.can be drawn upon. It can be considered a disadvantage, (ii) Although it is known that aerosols and vapour aVect however, that the quite deviating sampling conditions for people’s health diVerently, this concentration is determined filters and sorbent tubes need to be made compatible with in total. each other. (iii) The aerosol fraction of vapour–aerosol mixtures shod be collected on the basis of the inhalable dust fraction. 2.2.3.Impinger and filter. The intake of the impinger has to These basic statements derive from the knowledge that the meet the requirements for collecting aerosols. The secondary balance between the phases can easily alter with varying filter must collect small particles that may pass the impinger. sampling conditions: a high volume flow during the sampling Errors may also result from stripping eVects.procedure can entail the evaporation of collected aerosols, whereas a low volume flow can lead to the condensation of 2.2.4. Reagent-impregnated systems. This procedure is based vapours; changes in temperature can either lead to evaporation on surface-coated filters, foams or similar applications.It can (rising temperature) or condensation (falling temperature). only be used for reactive substances. Since vapour and aerosols The distribution between vapour and aerosols could be are collected on the same sampling medium, eVorts have to influenced to a certain extent by the type of sampling system be made to carefully match the sampling conditions for or could even be manipulated deliberately.A partly volatile the mixture. substance, for example, exclusively subject to an aerosol limit value, could be measured by a sampling system with an 2.2.5. Filter and diVusive sampler. In this case, the aerosol extremely high volume flow, which would lead to a ‘more is separated before the vapour. Normally, the air flow is split favourable’, but unrealistic, result. after the filter, and the minor flow is conducted past the Parallel tests on the sampling of mineral oils have led to the collector.The advantage of this procedure is the suitability of conclusion that the use of diVerent sampling systems may the aerosol collector for relatively high volume flows. distort the aerosol concentration (Table 1). The sampling system of Berufsgenossenschaftliches Institut fu� r 2.3.BIA sampling systems for mixed-phase aerosols Arbeitssicherheit (BIA) was compared with combined Millipore cassette/sorbent tubes. The obvious result was that, Applications can be developed for all the examples mentioned in simultaneous mixed-phase aerosol sampling, the BIA sam- above. The BIA, however, has concentrated on the following pling system PGP3, depletes the aerosol concentration.This three applications. can be explained by the relatively high volume flow of 3.5 L min-1 of this personal sampling system. The Millipore 2.3.1. Annular denuder. An annular denuder with a sampling head for inhalable dusts and a secondary filter holder has been cassettes with sorbent tubes were used under volume flows of 0.5 and 1.2 L min-1.The aerosol concentration obtained using developed. An application for sampling semi-volatile nitrosamines has so far been made available.8 these systems with lower volume flows was definitely higher. Table 1 Parallel sampling of metal working fluids in grinding processes Vapour+ Sampling combination Sampling rate/L min-1 Aerosol/mg m-3 Vapour/mg m-3 aerosol/mg m-3 Sampling of a water-mix metal working fluid Filter/charcoal Millipore 1.2 1.5 6.1 7.6 Filter/charcoal Millipore 0.5 4.3 3.2 7.5 Filter/XAD-2 GGP 3.5 0.6 7.2 7.8 Sampling of a mineral oil, flash point 172 °C Filter/charcoal Millipore 1.2 2.5 22.4 24.9 Filter/charcoal Millipore 0.5 6.6 18.0 24.6 Filter/XAD-2 GGP 3.5 <0.5 24.1 24.1 300 J.Environ. Monit., 1999, 1, 299–305The denuder is composed of five concentric glass tubes (Fig. 1). The tube surfaces were polished by sand blasters. The denuder can be operated under a volume flow of 8 Lmin-1. The inner surfaces have to be coated adequately. The particles are separated on the secondary filter. The intake complies with the requirements for inhalable dusts and is also supposed to ensure a laminar flow. These conditions allow a diVusion-controlled separation on the inner surfaces of the denuder.The denuder can be combined with the sampling system PGP. 2.3.2. Sampling system PGP. The sampling system PGP has been deliberately designed to allow the combination of the GSP (gesamtstaub-probenahme=inhalable dust sampling) sampling head with a vapour sampling system.3 The so-called GGP combination (gesamtstaub-gas-probenahme=inhalable dust/gas sampling), composed of a filter and a secondary sorbent tube, was developed. Initially, the only possible combination applied to a volume flow of 3.5 L min-1, implying that Fig. 2 GSP sampling heads for 0.5, 1.0, 3.5 and 10 L min-1 air flows. sorbent tubes had to be filled with suitable sorbents. This combination has proven successful for the sampling of mineral at the same time. Since no flow control has been placed oils.The experience gained with some substances, however, between the tubes, they can only be evaluated simultaneously. showed that a volume flow of 3.5 L min-1 led to a break- In cases where all three tubes are not used, the unused tubes through after a short sampling period. Consequently, the have to be shut by an unopened tube.The sorbent tube can system was further developed by inventing sampling heads for be squeezed into the seal and fastened by carefully tightening volume flows of 1 and 0.5 L min-1. the screws. The silicone insets have to be changed from time The innovative features of these combinations are the sam- to time (Fig. 3). pling heads for diVerent volume flows and the fixing for commercially available sorbent tubes. 2.3.3.Reagent-impregnated system in GSP. The sampling Our tests have shown that, by modifications, the sampling head GSP is fitted with a filter that is impregnated with a head will comply with the requirements for inhalable dusts in reagent. Sampling heads for volume flows of 3.5, 1.0 and the case of other volume flows.These modifications basically 0.5 L min-1 can be used. concern the size of the intake. Heads for volume flows of 10, 3.5, 1.0 and 0.5 L min-1 are already available, while the 3. Examples of combined vapour–aerosol sampling 10 L min-1 head was exclusively developed for particle sam- The following examples describe how mixed-phase aerosols pling (Fig. 2). are collected and analysed.It has to be considered, however, The new fixing for sorbent tubes can be adapted to every that under certain analytical conditions it may be advisable to type of tube by simply changing the plastic male fitting. There carry out combined vapour–aerosol sampling. The measure- is a whole variety of diVerently dimensioned tubes on the ment methods were developed in strict accordance with EN market.They are all compatible with the system. The tapering 481 and EN 482. The presented methods meet all requirements. seal is made of silicone. One to three tubes can be admitted 3.1. Inorganic acids In the technological field, the inorganic acids hydrochloric acid, nitric acid, sulfuric acid and phosphoric acid are used in Fig. 3 New GGP combination (GGP-U 1.0) for sampling aerosol– Fig. 1 Annular denuder with sampling head for inhalable aerosols. vapour mixtures with commercially available sampling tubes. J. Environ. Monit., 1999, 1, 299–305 301Table 2 Physical properties and limit values of inorganic acidsa Hydrochloric acid Nitric acid Phosphoric acid Sulfuric acid Molecular mass/g mol-1 36.5 63.0 98.0 98.1 Melting point/°C -114.8 -42 42.4 10.4 Boiling point/°C -84.9 83 213 (-0.5 H2O) 338 Limit value (D) [mg m-3] 8 (CLV) 5 (CLV) No LV 1 E (CLV) [ppm] 5 (CLV) 2 (CLV) Limit value (S) [mg m-3] 8(CLV) 5 1 1 [ppm] 5 (CLV) 2 Limit value (DK) [mg m-3] 7(CLV) 5 1 1 [ppm] 5 (CLV) 2 Limit value (USA-ACGIH) [mg m-3] 7 (STEL/C) 5 1 1 [ppm] 5 (STEL/C) 2 aE, inhalable dust; STEL/C, short-term exposure limit/ceiling; CLV, ceiling limit value; D, Germany; S, Sweden; DK, Denmark.a whole variety of situations. It is quite common for several 0.5 L min-1 has proven to be adequate in this case. Two aligned silica gel tubes have to be used with the universal acids to occur simultaneously. When metal surfaces are coated in galvanic enterprises, fertilisers are manufactured or acids GGP. Acids are usually analysed by ion chromatography. This method is widely used in water chemistry and has become undergo orgic synthesis, e.g.nitrating acid, two or more acids are used at the same time. the standard method for analysing inorganic anions. A clear separation of all the occurring acids takes less than 15 min.9 The physical properties of acids, however, diverge greatly. While sulfuric acid and phosphoric acid always occur as The problem of blank values plays a special role in the context of this determination.Chlorides, in particular, are aerosols, hydrochloric and nitric acids usually occur in the working atmosphere as vapours. A common feature is their contained in numerous materials, so that an ongoing and careful control of blank values is indispensable.All receptacles, highly caustic and thus irritating eVect on the airways. In this context, it is important to note that, in Germany, the aerosol solutions and materials must be examined. This even includes the silica gel that was purified especially for this purpose. fraction that has to be collected is specified for all substances occurring in a particle form. As far as sulfuric acid is con- Teflon filters are the only non-problematic material.Another problem when sampling acids is caused by ubiqui- cerned, for example, the fraction that has to be collected is the inhalable one (Table 2). tously occurring chlorides, sulfates, nitrates or phosphates, because a distinction cannot be made through analysis. A combination of a filter and a secondary sorbent tube is used for the sampling of acids (Table 3).Teflon has proven DiVerentiation is only possible for hydrochloric acid and nitric acid, since these substances occur in the vapour phase, while the to be a suitable filter material. It is inert and the blank test shows more or less no numerical result. An especially purified salts occur in particle form. This diVerentiation does, however, not apply to sulfuric acid and phosphoric acid, so that the silica gel is used to absorb the vapours.A volume flow of measuring results always contain the ubiquitous salts as well. The measuring procedure has been conceived as flexibly as Table 3 Inorganic acids—measurement conditions possible. In working environments where acids only occur in Sampling particle form, the sorbent tube can be omitted and the volume GGP-U 0.5 0.45 mm Teflon filter/ORBO 53 silica gel tube flow can be raised to 1.0 L min-1.If no acids in particle form Flow rate 0.5 L min-1 are expected, the filter samples do not have to be analysed. In Sampling time 2 h this case, the filters are exclusively intended to collect ubiqui- Sample preparation tous salts. Filter 10 mL solution of NaHCO3 (0.0003 M)/Na2CO3 Measurements were conducted at all workplaces with expo- (0.0027 M) sure to inorganic acids.Silica gel 10 mL solution of NaHCO3 (0.0003 M)/Na2CO3 The limit value for sulfuric acid was shown to be exceeded (0.0027 M) Treatment 10 min ultrasonic bath, in two industries: lead accumulator manufacturing and electro- filtration Teflon filter (0.45 mm) plating. Excess concentrations were generally found to be only slightly above the limit value (cmax=1.2 mg m-3).Ion chromatography Injection volume 50 mL Concentrations of hydrochloric acids in chemical fibre pro- Column IonPac AS12A (Dionex) duction, however, achieved values up to twice the limit value. Mobile phase NaHCO3 (0.0003 M)/Na2CO3 (0.0027 M) Pickling plants (special steel ) and electroplating plants are Flow rate 1.5 mL min-1 workplaces where the limit value of nitric acid is frequently Detection Conductivity (chemical suppression) exceeded (five times the limit value).Reliability of the method Atmospheric workplace concentrations in other industries Detection limit HCl, HNO3 : 0.05 mg m-3 were generally in compliance with the relevant limit values.H3PO4: H2SO4 0.08 mg m-3 Precision HCl: 5–16% (blind value) HNO3 6–8% 3.2. Alkanolamines H3PO4 5–10% The alkanolamines 2-aminoethanol (ethanolamine, MEA), H2SO4 4–11% Recovery >95% diethanolamine (DEA) and triethanolamine (TEA) are used in Range 0.1–2.0 LV industrial technology in numerous areas. Technical soaps based Storage 4 weeks on alkanolamines are characterised by a lowlevel of alkalescence, Specificity High (interferences of ubiquitous salts) a high emulsifying eVect and solubility in organic solvents.These Remarks Blind values—control of all materials soaps are used as impregnating compounds for textiles, wood 302 J. Environ. Monit., 1999, 1, 299–305Table 4 Physical properties and limit values of alkanolaminesa 2-Aminoethanol Diethanolamine Triethanolamine Molecular mass/g mol-1 61.09 105.14 149.2 Melting point/°C 10.3 28 21 Boiling point/°C 170.58 271 277 (370 hPa) Vapour pressure/hPa 0.320 b <0.01320; 6138 b 2.7100 b Limit value (D) [mg m-3] 5.1 (CLV) 15 E 5 E [ppm] 2 (CLV) Limit value (S) [mg m-3] 8 15 5 [ppm] 3 3 Limit value (DK) [mg m-3] 2.5 2 3.1 [ppm] 1 0.46 0.5 Limit value (USA-ACGIH) [mg m-3] 8 2.5 5 [ppm] 3 aE, inhalable dust; CLV, ceiling limit value; D, Germany; S, Sweden; DK, Denmark.b0.320, vapour pressure at 20 °C. and leather, as well as in colouring vehicles, cleaning materials, diVerent countries. MEA only occurs in the vapour phase, whereas TEA solely occurs in particle form. DEA may occur medical soaps and creams. In water-miscible cooling lubricants, alkanolamines are either used freely as a protection against either as a vapour or as an aerosol depending on the sampling; it is thus a typical representative of a semi-volatile organic corrosion or in the form of fatty acid amides as compounds of emulsifying agents.In Germany, the use of DEA is prohibited, substance (Table 4). Samples of alkanolamines are taken by use of a glass fibre since this substance can easily be converted, as a secondary amine, into carcinogenic N-nitrosamines.filter impregnated with phosphoric acid. The amines are thus collected as ammonium salts. The glass fibre filter is impreg- Amines have a local caustic eVect on the skin, the eyes and the mucous membranes. nated by soaking it entirely in 0.1M phosphoric acid and sodium octanesulfonate, leaving it overnight to dry at room Amines are produced by transforming ethylene oxide with ammonia.All three amines are produced at the same time. temperature (Table 5). The dried filter is then fitted in the GSP and sampling is performed under a volume flow of The purification is performed by distillation. Industry mainly uses technically, less pure amines. In the majority of cases 0.5 L min-1.This low volume flow must be observed in order to avoid overcharging the filter. where these amines are used in industrial technology, it can be assumed that they occur simultaneously in the working The filter must be eluted immediately after the sampling procedure to anticipate a possible loss of MEA and DEA. environment. Mixtures are also quite frequently used.10 The physical properties of amines diverge greatly, in particu- Tests have shown that the loss of MEA within 24 h may reach up to 30% and up to 10% for DEA if immediate stabilisation lar as far as their volatility is concerned. The volatility was taken into consideration when limit values were fixed in of the sample in a solution is not ensured.The concentrated solvent agent used in ion pair chromatography serves as elution substance.The final ion pair chromatography is quite Table 5 Alkanolamines—measurement conditions simple and allows rapid separation of the alkanolamines.11 Sampling Measurements were carried out in the metal working indus- GSP 0.5 Glass fibre filter, impregnated with H3PO4 try in cases where cooling lubricants were used, as well as in (0.1 M)/C8H17SO3Na (0.02 M) the chemical industry for the manufacturing and further Flow rate 0.5 L min-1 processing of alkanolamines.While the results in the metal Sampling time 2 h working industry were clearly below the occupational exposure Filter Immediately after sampling: filter stabilisation (10 mL desorption solution) limit values, the concentrations found in the manufacturing and further processing of pure alkanolamines sometimes Sample preparation reached twice the limit value.Filter 10 mL solution of H3PO4 (0.005 M)/ C8H17SO3Na (0.001 M) Treatment 15 min ultrasonic bath, filtration Teflon filter 3.3. Explosives (0.45 mm) The last example involves the measuring procedure for the Ion chromatography explosives dinitrotoluene (DNT) and trinitrotoluene (TNT). Injection volume 50 mL Column Nucleosil 100–5C18 , L 125 mm, id 4 mm These explosives are still manufactured and used in large Mobile phase H3PO4 (0.005 M)/C8H17SO3Na (0.001 M) quantities, especially as military explosives or admixtures to Flow rate 1.0 mL min-1 commercial explosives for tunnelling or mines.In Germany, Detection Conductivity (electronic suppression) there is the additional problem of numerous military sites Reliability of the method dating from the Second World War or those of the former Detection limit MEA: 0.17 mg m-3 Russian army which await redevelopment.The soil and DEA, TEA: 0.33 mg m-3 groundwater of these sites are often highly contaminated by Recovery >90% explosives. During the redevelopment of soil, the upper layer Precision MEA: 1.5–3% of the soil is removed, cleaned and finally refilled.Germany DEA: 1.5–3% TEA: 1.5–4% must also deal with the huge ammunition pools of the former Range 0.1–2.0 LV Eastern German and Russian armies. The workers who will Storage 2 weeks (after stabilisation) be involved in these activities are likely to be exposed to large Specificity High quantities of explosives, while the type of exposure varies Remarks Sterile water is recommended considerably.J. Environ. Monit., 1999, 1, 299–305 303Table 6 Physical properties and limit values of explosivesa Dinitrotoluene 2,4,6-Trinitrotoluene Molecular mass/g mol-1 182.1 227.1 Melting point/°C 70 (2,4-DNT); 55–77 (technical mixture) 81 Boiling point/°C 319 (2,4-DNT, decomp.) 212 (16 h Pa) 287 (2,6-DNT, decomp.) Vapour pressure20 b/Pa 0.0113 (2,4-DNT) 0.74×10-3 0.031 (2,6-DNT) (0.05781) Limit value (D) [mg m-3] 0.05 (2,6-DNT) 0.1 1.5 (3,4-DNT) Limit value (S) [mg m-3] 0.15 (mixture) 0.1 Limit value (DK) [mg m-3] 0.15 (2,4-DNT) 0.1 0.15 (2,6-DNT) 0.15 (mixture) Limit value (USA-ACGIH) [mg m-3] 0.2 (mixture) 0.1 aD, Germany; S, Sweden; DK, Denmark.bVapour pressure20, vapour pressure at 20 °C.(i) In the manufacturing of military blasting charges, the DNT. Since the distribution of the substance in the soil is very heterogeneous, it is diYcult to predictwhere emissions will occur. propelling charge or the blasting charge is usually filled into the blasting cap when the material is liquid and hot. In such The requirements concerning the measuring procedure are quite diverse because of the diVerent possible uses.In order working environments, the explosives TNT and DNT normally occur as vapour or, less commonly, as a fume. to ensure the collection of all explosives, a combined vapour– aerosol sampling procedure had to be developed. Another (ii) When military blasting charges are being disposed of, the individual components (propelling charge, blasting charge diYculty that had to be tackled was the extremely low limit values applying in some of these cases (Table 6).Moreover, and ignition head for example) are manually separated and continuously burnt in small quantities. During the course of the measuring procedure had to be universally applicable, because a whole variety of explosives other than nitrotoluenes, this activity, the workers are sometimes exposed to high aerosol concentrations.with their own limit values, occur in all working environments [examples: tetryl, RDX (hexogen), HMX (octogen), picric (iii) During the redevelopment of former explosive factories, the removal of the upper soil layers may lead to high concen- acid ]. The procedure had to include the option of measuring these substances as well.trations of explosives contained in the dusts produced. Depending on the general conditions, these explosivesmay partly Finally, a combination of a quartz fibre filter and tenax tubes with a volume flow of 1 L min-1 proved to be suitable evaporate, especially in the summer and when the soil contains for sampling (Table 7). Stabilisation of the filter immediately after sampling is required to anticipate a possible loss of Table 7 Explosives—measurement conditions collected aerosol.An HPLC procedure was developed as an analysing method. Although presenting less indicative results, Sampling this procedure has the advantage of analysing nearly all GGP-U 1.0 Quartz fibre filter/tenax tube explosives simultaneously (Fig. 4). Flow rate 1.0 L min-1 Sampling time 2 h Remarks Immediately after sampling: filter stabilisation (4 mL methanol ) Sample preparation Filter 4 mL methanol Tenax 2 mL methanol Treatment 15 min ultrasonic bath Filtration Teflon filter (0.45 mm) Ion chromatography Injection volume 5 mL Column Hypersil-ODS, L 250 mm, id 2 mm Mobile phase Methanol/water 30/70 v/v Flow rate 0.2 mL min-1 Detection UV/VIS: TNT 230 nm, DNT 203 nm Reliability of the method Detection limit TNT: 0.007 mg m-3 2,4- and 2,6-DNT: 0.005 mg m-3 Precision TNT: 1.5–3% 2,4-DNT: 2–4% 2,6-DNT: 2–3.5% Recovery TNT: (95–80%) Fig. 4 Separation of explosives: HMX, octahydro- 2,4- and 2,6-DNT: >90% 1,3,5,7-tetranitro-1,3,5,7-tetrazoan; RDX, hexahydro-1,3,5-trinitro- Range 0.1–2.0 LV 1,3,5-triazin; Tetryl, N-methyl-N-2,4,6-tetranitroanilin; column, Storage 2 weeks (after stabilisation) Hypersil-ODS (250 mm), id 2 mm, particular size 3 mm; mobile Specificity High phase, methanol/water 30/70 v/v; flow rate, 0.2 mL min-1; tempera- Remarks Recovery of TNT varies with air humidity ture, 40 °C, injection volume, 5 mL; concentrations, TNT (decrease rel.humidity >40%) 0.75 mg mL-1, all others 1.25 mg mL-1. 304 J. Environ. Monit., 1999, 1, 299–305The measurements show the expected results. During the References course of hot processing, mainly vapours were determined; 1 NIOSH Manual of Analytical Methods, Polycyclic Aromatic TNT was detected and the concentration sometimes reached Hydrocarbons, Total (PACs), No. 5800, NIOSH publications, five times the limit value.The disposal of ammunition basically Cincinnati, 4th edn., 1998. required the determination of high aerosol concentrations for 2 CEN/TC137/WG3/N217, Workplace Atmospheres— TNT, sometimes up to ten times the limit value, and low Measurement of Chemical Agents Present as Mixtures of Airborne concentrations of DNT vapours. In the areas linked to the Particles and Vapour—Requirements and Test Methods, draft 1998. 3 H. Siekmann, H. Blome and W. Heisig, Staub–Reinhalt. Luft, redevelopment of soil, only traces of DNT vapours were found. 1988, 48, 89. 4 EN 482, Workplace Atmosphere—General Requirements for the Performance of Procedures for the Measurement of Chemical 4. Conclusion Agents, European Committee for Standardization, Brussels, 1994. 5 EN 481, Workplace Atmosphere—Size Fraction Definitions for The experience regarding the sampling of substances that Measurement of Airborne Particles, European Committee for occur in the vapour and aerosol phase can be summed up by Standardization, Brussels, 1993. 6 EN 1076, Workplace Atmosphere—Pumped Sorbent Tubes for the the following principles. determination of Gases and Vapours: Requirements and Test (i) The only indicative statement that can be made for Methods, European Committee for Standardization, Brussels, mixed-phase aerosols concerns their total quantity. 1997. (ii) The aerosol–vapour distribution depends considerably 7 EN 1232, Workplace Atmosphere—Pumps for Personal Sampling on the sampling conditions. of Chemical Agents: Requirements and Test Methods, European (iii) The storage and transport of collected aerosols have Committee for Standardization, Brussels, 1997. 8 B.Ha� ger and D. Breuer, GefahrstoVe–Reinhalt. Luft, 1997, 57, proven to be particularly critical, as the vapour pressure of 143. aerosols is not negligible. Therefore, immediate stabilisation 9 Flu� chtige Anorganische Sa�uren and Partikula�re Anorganische of the samples seems to be indicated. Sa�uren, in Analytische Methoden zur Pru�fung gesundheitsscha�d- When developing measuring procedures for mixed-phase licher ArbeitsstoVe–Wiley-VCH, Weinheim, 11th aerosols, particular attention has to be paid to the environmen- edn., 1998. tal conditions. 10 Ethanolamines and propanolamines, in Ullmann’s Encyclopedia of Industrial Chemistry, electronic release, Wiley-VCH, Weinheim, It is quite simple to combine filter sampling with secondary 1998, 6th edn. sorbent tubes. The same applies to impregnated sampling 11 W. Maurer, P. Nu�nnerich, E. Hohaus, B. Schleser and D. Breuer, media. These combinations have the advantage that the experi- Staub–Reinhalt. Luft, 1994, 54, 239. ence gained with the individual sampling media can be drawn upon. The ‘only’ requirement for such combinations are the additional tests to be carried out for the complete system. Paper 9/02081K J. Environ. Monit., 1999, 1, 299–305 3

ISSN:1464-0325    

DOI:10.1039/a902081k

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Determination of carbonyls using liquid chromatography-mass spectrometry with atmospheric pressure chemical ionization† Christine Kempter, Gabriela Zurek and Uwe Karst* Westfa�lische Wilhelms-Universita�t Mu� nster, Anorganisch-Chemisches Institut, Abteilung Analytische Chemie, Wilhelm-Klemm-Str. 8, D–48149 Mu�nster, Germany Received 7th April 1999, Accepted 3rd June 1999 A liquid chromatographic method for the determination of aldehydes and ketones based on mass spectrometric detection is described.Recently developed modular derivatizing agents are employed for analysis. These hydrazine reagents, e.g. 4-dimethylamino-6-(4-methoxy-1-naphthyl )-1,3,5-triazine-2-hydrazine (DMNTH), react with the carbonyl compounds with the formation of the respective hydrazones, which are separated by HPLC-MS with atmospheric pressure chemical ionization in the positive mode.Electrospray ionization may also be used for analysis. Particular focus is directed on various calibration approaches, including external calibration with standard solutions and internal calibration with a hydrazone standard of cyclobutanone, an aldehyde not likely to occur in real samples.A second approach for internal calibration is based on the 13C2-labelled acetaldehyde hydrazone standard. DiVerent calibration approaches may then be used for the analysis of real samples. Limits of detection range from 2×10-8 to 5×10-8 mol L-1 for a series of hydrazones, including hydrazones of saturated aldehydes with alkyl chain lengths from 1 to 7 carbon atoms, and hydrazones of selected unsaturated and aromatic aldehydes as well as ketone hydrazones.The determination of aldehydes and ketones is of great HPLC-MS instrumentation importance in various fields, including workplace monitoring, The HPLC-MS system from Shimadzu (Duisburg, Germany) emissions testing and process control. For the selective and consisted of the following components: controller unit simultaneous determination of a series of aldehydes and SCL-10Avp, degasser DGU-14A, two pumps LC-10ADvp, ketones, hydrazine reagents have been shown to be advantamixing chamber Model SUS (0.5 mL), autosampler SIL-10A, geous in recent years.1–10 They react readily with the carbonyl UV/VIS detector SPD-10AV, single quadrupole mass spec- compounds with the formation of the respective hydrazones, trometer LCMS QP8000 with atmospheric pressure ionization which are typically separated by reversed-phase liquid chromaand software Class 8000 Version 1.01.tography and detected photometrically1–8 or fluorescence spectroscopically. 9,10 Although mass spectrometric detection HPLC conditions appears to be promising to achieve a higher selectivity in combination with low limits of detection, only a few reports All separations were performed using a Discovery C18 column on the mass spectrometric determination of hydrazones have equipped with a guard column of the same material (Supelco, been published.11–13 Ko� lliker et al.12 have recently described Deisenhofen, Germany) with the following dimensions: particle the LC-MS-MS identification of 2,4-dinitrophenylhydrazones size, 5 mm; pore size, 100 A° ; length, 150 mm; id, 2.1 mm.using atmospheric pressure chemical ionization (APCI) in the Eluent A of the mobile phase was a solution of 1380 mL negative mode. Zurek et al.13 have reported the quantitative triethylamine and 557 mL acetic acid in 500 mL water analysis of these substances using standards based on stable (pH#7.5); eluent B was acetonitrile.A binary gradient at a isotopes.13 However, LC-MS publications on aldehyde deter- flow rate of 0.4 mL min-1 with the profile given in Table 1 mination have been limited up to now to the use of was used. The injection volume was 5 mL. UV detection was 2,4-dinitrophenylhydrazine (DNPH) derivatives. carried out at 313 nm. For fluorescence detection, the exci- Kempter et al.14 describe a modular derivatizing agent as a tation wavelength was 332 nm and the emission wavelength possible alternative to DNPH, because this new type of reagent was 395 nm.is more versatile and oVers easy access to fluorescence spectroscopic detection. The present work focuses on the development MS conditions of an HPLC-MS method with APCI and mass spectrometric All MS measurements were recorded using APCI in the detection to achieve higher selectivity of detection for the positive mode under the following conditions: nebulizer gas modular derivatizing agent.flow (N2), 2 L min-1; probe voltage, 4 kV; temperature of the APCI probe, 450 °C; curved desolvation line (CDL) voltage, Experimental -40 V; CDL temperature, 250 °C; deflector voltages, 55 V; detector gain, 1.5 kV.For SCAN mode measurements, a mass Chemicals range from 250 to 450 m/z was chosen; the integration time All chemicals were purchased from Aldrich Chemie (Steinheim, Germany) in the highest quality available. As solvent for LC, acetonitrile gradient grade from Merck (Darmstadt, Germany) Table 1 HPLC profile was used. Time/min 0.03 1 8.5 17 20.5 21.5 23.5 c(CH3CN) (%) 38 38 46 90 90 38 Stop †Presented at AIRMON’99, Geilo, Norway, February 10–14, 1999.J. Environ. Monit., 1999, 1, 307–311 307was 1.2 s. For selected ion monitoring (SIM) measurements, 2×10-8 to 2×10-5 mol L-1 were spiked with the internal standard solution resulting in concentrations of 10-5, 10-6 the integration time was 1 s. and 10-7 mol L-1 of the internal standard.Therefore, mixing ratios of the internal standards compared to the Linear range of the MS detection DMNThydrazones were obtained in the range of 10051 to A calibration curve for 4-dimethylamino-6-(4-methoxy- 152. All solutions were analysed by HPLC-MS using the same 1-naphthyl )-1,3,5-triazine-2-hydrazone (DMNThydrazone) time programme for the SIM traces as for the determination standards in acetonitrile was recorded three times in the range of the linear range of the MS detection.from 2×10-8 to 5×10-5 mol L-1. The mixture contained the The peak areas of the SIM traces were integrated separately. standards of formaldehyde, acetaldehyde, propanal, butanal, The response factors were calculated as the ratio of the peak pentanal, hexanal, heptanal, crotonaldehyde, acetone and p- areas of the DMNThydrazone to the internal standard with tolylaldehyde.The MS detection was carried out in SIM mode consideration of the respective concentration ratio. using the time programme given in Table 2 of the SIM traces. Preparation of the 4-dimethylamino-6-(4-methoxy- Synthesis and characterization of DMNThydrazones 1-naphthyl )-1,3,5-triazine-2-hydrazine (DMNTH) solution for The DMNThydrazones were prepared according to the real sample analysis procedure described in ref. 14. A 3.2×10-3 mol L-1 DMNTH solution was prepared by adding 100 mg DMNTH to 50 mL concentrated sulfuric acid in 10 mL distilled water and 89.95 mL acetonitrile. Sample preparation and air sampling procedure Air sampling was performed using a personal air sampler pump model I.H. (A.P. Buck, Inc., Orlando, FL, USA). The impingers contained 50 mL DMNTH solution. The sampling volume of the sample of a disinfected room was 10.0 L at a Ha Hb OCH3 N N N HN N C R1 R2 N H3C CH3 flow rate of 1.25 L min-1. The impinger was equipped with a backup impinger to control incomplete recovery. 13C2 Acetaldehyde DMNThydrazone. 1H NMR (200 MHz, CDCl3, TMS): d 1.24 (s, 1H, NH), 1.58, 2.06 (d, 3H, Analysis of the sample in workplace air after floor disinfection 13CH–13CH3), 3.27 (s, 6H, N(CH3)2), 4.02 (s, 3H, OCH3), Each sample was analysed in four diVerent ways: (i) SCAN 6.85 (d, 1H, Ar-Hb), 7.20 (m, 1H, NL13CH), 7.46 (m, 2H, mode with external calibration; (ii) SIM mode with addition Ar-H), 8.24 (m, 2H, Ar-H), 9.03 (d, 1H, Ar-Ha); MS (electron of 13C2 acetaldehyde DMNThydrazone as internal standard; impact, EI, 70 eV): m/z 338 (M+, 43%), 322 (M+-13CH3, (iii) UV/VIS detection at 313 nm with external calibration; 57%), 294 (M+-N13C2H4, 17%), 280 (M+-N213C2H4, 20%), and (iv) fluorescence detection (excitation; 332 nm; emission, 209 (10%), 184 (18%), 139 (100%), 96 (18%), 71 (15%), 55 395 nm) with external caSample preparation for (i), (8%); IR (KBr): 3440, 3228, 2998, 2936, 1575, 1529, 1511, (iii) and (iv): 5 mL of the sample solution was directly injected 1464, 1408, 1373, 1339, 1322, 1263, 1244, 1216, 1201, 1185, into the LC system without further dilution. Sample prep- 1160, 1132, 1033, 1019, 914, 878, 811, 771, 730, 724, 709, 653, aration for (ii): 100 mL of the sample was spiked with 100 mL 618, 590, 474 cm-1; analysis calc.for 13C2C16H20N6O: C, of the internal standard (5×10-5 mol L-1) and made up to 64.48%; H, 5.96%; N, 24.84%; found: C, 64.32%; H, 5.88%; 1 mL with acetonitrile. This mixture was analysed with N, 24.70%. HPLC-MS using the same time programme as described above for the determination of the response factor.Cyclobutanone DMNThydrazone. 1H NMR (200 MHz, CDCl3, TMS): d 1.71 (s, 1H, NH), 1.99 (m, 1H, CH2–CH–CH2), 2.86, 2.92 (2 m, 4H, CH2–CH–CH2), 3.27 Results and discussion (s, 6H, N(CH3)2), 4.02 (s, 3H, OCH3), 6.84 (m, 2H, Ar-Hb, The reaction of aldehydes and ketones with the derivatizing NLCH), 7.49 (m, 2H, Ar-H), 8.23 (m, 2H, Ar-H), 8.99 (d, agent DMNTH with the formation of the respective hydrazone 1H, Ar-Ha); MS (EI, 70 eV): m/z 362 (M+, 11%), 333 is presented in Fig. 1. First investigations were carried out (M+-C2H5, 100%), 319 (M+-C4H7, 35%), 184 (16%), 140 regarding the use of APCI and electrospray ionization (ESI) (41%), 96 (18%), 71 (27%), 55 (24%); IR (KBr): 3434, 3279, as interfaces to liquid chromatography. It is obvious from the 3075, 2994, 2925, 1674, 1621, 1564, 1522, 1510, 1470, 1425, structures of both DMNTH and its hydrazones that these 1402, 1324, 1273, 1244, 1204, 1190, 1163, 1125, 1093, 1028, molecules contain several basic, but no acidic functional 912, 809, 772, 729, 714, 663, 618, 471 cm-1; analysis calc.for groups. Therefore, protonation of these substances appeared C20H19N6O: C, 66.28%; H, 6.12%; N, 23.19%; found: C, to be more likely than deprotonation, thus leaving the positive 66.01%; H, 6.37%; N, 23.29%.ionization mode as the more promising approach. Experiments with both interfaces combined with both the positive and Response factors using internal standards negative mode led to the following conclusions: As expected A stock solution of the internal standards 13C2 acetaldehyde from the above considerations, APCI and ESI are equally DMNThydrazone and cyclobutanone DMNThydrazone suitable for the ionization of the derivatives in the positive was prepared at a concentration of 5×10-5 mol L-1 mode, while there is almost no signal obtained in the negative DMNThydrazone standards in the concentration range mode.The proposed ionization mechanism for APCI(+) and ESI(+) is also depicted in Fig. 1. We have selected APCI(+) Table 2 MS detection programme as the favourable ionization technique for all further measurements, as the APCI interface is more compatible with higher Time/min 6.5–10.1 10.1–13.0 13.0–14.9 14.9–19.0 flow rates from the LC system compared to the ESI interface. m/z 323; 337; 351; 363 363; 365 379; 393; This allows easier downscaling of the chromatographic separa- 339; 351; 407; 413 tion from column diameters of 4.6 mm and flow rates of 308 J.Environ. Monit., 1999, 1, 307–311UV/VIS and fluorescence detection have been recorded using a diVerent chromatographic system to that of MS detection. Due to diVerent void volumes, diVerent retention times are observed. The fluorescence detector is connected in series to the UV/VIS detector.Therefore, slightly longer retention times and broader peaks are observed. It should be noted that two isomers are observed for most of the unsymmetrical hydrazones. This increases quantification problems in complex matrices containing several aldehydes. All other chromatograms represent extracts of single masses from the total ion chromatogram (TIC). It is obvious that selectivity is signifi- cantly increased, as even saturated and unsaturated aldehyde hydrazones with the same alkyl chain length may be determined selectively.The two peaks in the chromatogram of the m/z 351 trace represent acetone and propanal, which are very well separated chromatographically. The SIM mode has been used for quantification, as it is more sensitive compared to the TIC extracts. Time programming of the SIM traces has been employed as described in the Experimental section to further improve the limits of detection.Quantification in liquid chromatography by MS detection is typically associated with larger relative standard deviations of the results compared to UV/VIS detection, when external calibration is used. This is due to possible changes in the Fig. 1 Reaction of DMNTH with carbonyls and protonation of the ionization conditions, which may for example be caused by formed hydrazones in LC-MS during atmospheric pressure chemical coelutions of other compounds which may influence ionization. ionization in the positive mode. Changes in the mass spectrometric system, e.g., the vacuum, will also aVect the reproducibility of the system.15,16 We have therefore focused on diVerent calibration 1.5 mL min-1 to column diameters of 2.1 mm and flow rates approaches.External calibration with solutions of hydrazone of 0.4 mL min-1. standards is typically used in combination with UV/VIS and In Fig. 2, the APCI(+) mass spectrum of pentanal fluorescence detection of the derivatives. The use of internal DMNThydrazone is presented.In addition to the (M+H)+ standards is generally more critical in HPLC than in GC, as peak, which is the base peak, almost no fragmentation is the peak capacity of a typical chromatogram is significantly observed under the selected ionization conditions. If desired, lower, and it is more diYcult to separate chromatographically some structural information may be obtained by the use of the internal standard from the analytes.To avoid these prob- cone fragmentation, but mild ionization conditions were seleclems, we have employed two diVerent techniques of internal ted in this work to allow easy quantification of unfragmented standardization. First, a carbonyl hydrazone, which is not (M+H)+ peaks. likely to occur in real samples, has been synthesized.We have Chromatograms of DMNTH and a series of the respective selected cyclobutanone DMNThydrazone, as it elutes signifi- hydrazones are presented in Fig. 3. A chromatogram with cantly in advance of the other C4-carbonyl hydrazones and it UV/VIS detection at a wavelength of 313 nm and a chromatois characterized by the identical mass as the a,b-unsaturated gram with fluorescence detection (excitation wavelength, C4-DMNThydrazone. Using these precautions, the coelution 332 nm; emission wavelength, 395 nm) prove that low selecof the internal standard with a hydrazone of identical mass tivity with both detection techniques will be obtained in the case of complex real samples.The chromatograms with can be excluded. Second, the 13C2-labelled acetaldehyde Fig. 2 APCI mass spectrum (positive mode) of pentanal DMNThydrazone. J. Environ. Monit., 1999, 1, 307–311 309Fig. 3 Chromatograms of the separation of DMNTH and a series of DMNThydrazones using UV/VIS detection (first line), fluorescence detection (second line) and APCI(+)-MS detection at selected masses. The individual mass traces have been recorded as extracts of the TIC.Concentration of the hydrazones: 2.3×10-5 mol L-1. Concentration of DMNTH: 1.1×10-4 mol L-1. 1, DMNTH; DMNThydrazones of: 2, formaldehyde; 3, acetaldehyde; 4, 13C2 acetaldehyde; 5, acetone; 6, propanal; 7, cyclobutanone; 8, crotonaldehyde; 9, butanal; 10, pentanal; 11, hexanal; 12, p-tolualdehyde; 13, heptanal. DMNThydrazone has been synthesized. It coelutes with the Calibration curves have been recorded for a series of hydrazones using the separation and mass spectrometric con- non-labelled acetaldehyde DMNThydrazone, but is discriminated by its mass.Due to coelution, identical ionization ditions stated above. The calibration functions for four representative hydrazones are provided in Fig. 4. It is obvious conditions for both internal standard and analyte should yield excellent recoveries.In addition, the isotope-labelled standard that similar, although not identical, limits of detection are obtained for the hydrazones. Table 3 summarizes the limits of may also be employed as classical internal standard as in the case of the cyclobutanone hydrazone. detection determined as a signal-to-noise ratio 351. Limits Fig. 4 Calibration function for selected DMNThydrazones. 310 J. Environ. Monit., 1999, 1, 307–311Table 3 Limits of detection (LOD) for HPLC-MS determination of four ways: UV/VIS detection (3.8 mg L-1), fluorescence detecselected DMNThydrazones tion (4.2 mg L-1), MS detection with external calibration (3.4 mg L-1) and MS detection with internal calibration DMNThydrazone of LOD (APCI-MS)/mol L-1 (4.2 mg L-1).While the data obtained by fluorescence detection and MS detection with internal calibration correlate well, Formaldehyde 5×10-8 Acetaldehyde 2×10-8 lower values are observed for UV/VIS detection and MS Propanal 5×10-8 detection with external calibration. This may be due to elution Butanal 2×10-8 of the formaldehyde hydrazone peak on the tailing of the Pentanal 2×10-8 excess reagent peak in the case of UV/VIS detection and due Hexanal 2×10-8 to limited reproducibility in the case of MS detection with Heptanal 2×10-8 external calibration.13 Acetone 2×10-8 Crotonaldehyde 5×10-8 The mass spectrometric detection of DMNThydrazones with p-Tolualdehyde 2×10-8 diVerent calibration techniques is therefore a promising alternative to UV/VIS and fluorescence detection approaches. Table 4 Response factors for internal calibration using the DMNThydrazones of 13C2-acetaldehyde and cyclobutanone Acknowledgements DMNThydrazone of 13C2 acetaldehyde Cyclobutanone Financial support by the Deutsche Forschungsgemeinschaft (DFG) under project numbers Ka1093/2–1 and Ka1093/2–2 Formaldehyde 0.55 0.79 is gratefully acknowledged.C. K.thanks the Deutsche Acetaldehyde 1.03 1.52 Bundesstiftung Umwelt for a scholarship, G. Z. thanks the Propanal 0.85 1.17 Stiftung der Deutschen Wirtschaft for a scholarship. Butanal 0.86 1.14 Pentanal 1.07 1.48 Hexanal 0.99 1.35 References Heptanal 1.07 1.43 Acetone 0.88 1.26 1 R. H. Beasley, C. E. HoVmann, M. L. Rueppel and J. W. Worley, Crotonaldehyde 0.75 1.00 Anal. Chem., 1980, 52, 1110.p-Tolualdehyde 1.06 1.47 2 K. Kuwata, M. Uebori and H. J. Yamasaki, J. Chromatogr. Sci., 1979, 17, 264. 3 D. Grosjean and K. Fung, Anal. Chem., 1982, 54, 1221. of quantification are three times higher. Linearity was observed 4 W.Po� tter and U. Karst, Anal. Chem., 1996, 68, 3354. 5 J.-O. Levin, K. Andersson, R. Lindahl and C. A. Nilsson, Anal. up to 5×10-5 mol L-1, being limited by the solubility of the Chem., 1985, 57, 1032.substances. 6 R. R. Miksch, D. W. Anthon, L. Z. Fanning, C. D. Hollowell, For the use of internal standards, response factors have K. Revzan and J. Glanville, Anal. Chem., 1981, 53, 2118. been calculated for all investigated hydrazones based on the 7 I. Ahonen, E. Priha and M.-L. A� ija�la�, Chemosphere, 1984, 13, 521. two internal standard compounds.The response factors are 8 C. H. Risner and P. J. Martin, Chromatogr. Sci., 1994, 32, 76. listed in Table 4. All response factors may be applied for the 9 W. Schmied, M. Przewosnik and K. Ba�chmann, Fresenius’ Z. Anal. Chem., 1989, 335, 464. complete concentration range mentioned above. It should be 10 D. R. Rodier, L. Nondek and J. W. Birks, Environ.Sci. Technol., noted, however, that the concentrations of the internal stan- 1993, 27, 2814. dards should optimally be in the same concentration range as 11 K. L. Olson and S. J. Swarin, J. Chromatogr., 1985, 333, 337. the analyte concentrations. Best results are typically obtained 12 S. Ko� lliker, M. Oehme and C. Dye, Anal. Chem., 1998, 70, 1979. when the analyte and standard concentrations do not diVer 13 G. Zurek, H. Luftmann and U. Karst, 1999, submitted for by more than a factor of 5. The internal standard concentration publication. 14 C. Kempter, W. Po� tter, N. Binding, H. Kla�ning, U. Witting and should not be too low, especially in the case of low analyte U. Karst, 1999, submitted for publication. concentrations, as deviations caused by the integration of 15 R. Willoughby, E. Sheehan and S. Mitrovich, A Global View of small peaks for both analyte and standard may end up with LC/MS, Pittsburgh, Global View Publications, 1998, p. 351. very high total relative standard deviations (RSDs). 16 R. Baldwin, R. A. Bethem, R. K. Boyol, W. L. Budde, T. Cairns, A real air sample taken immediately after floor disinfection R. D. Gibbens, J. D. Henion, M. A. Kaiser, D. L. Lewis, of a small room with poor ventilation with aldehyde-containing J. E. Matusik, J. A. Sphon, R. W. Stephany and R. K. Trubey, J. Am. Soc. Mass Spectrom., 1997, 8, 1180. disinfectants was obtained using impingers. Significant formaldehyde concentrations were detected in the air sample. No breakthrough of the analyte into the backup impinger was observed. The formaldehyde concentration was determined in Paper 9/02766A J. Environ. Monit., 1999, 1, 3

ISSN:1464-0325    

DOI:10.1039/a902766a

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimating Part I. Integrated air quality monitoring system† Jocelyne Clench-Aas,* Alena Bartonova, Trond Bøhler, Knut E. Grønskei, Bjarne Sivertsen and Steinar Larssen Norwegian Institute for Air Research, PO Box 100, N-2027, Kjeller, Norway. E-mail; jocelyne.clench.aas@nilu.no; Fax:+47 6389 8050; Tel:+47 6389 8000 Received 7th April 1999, Accepted 25th June 1999 This paper presents an integrated exposure monitoring system, based on an expansion of existing air quality monitoring systems using dispersion modelling.The system allows: (1) identifying geographical areas whose inhabitants are most exposed to ambient pollution; (2) identifying how many people in an area are exposed to concentrations of pollution exceeding air quality guidelines; (3) describing the exposure of population subgroups (e.g.children); (4) planning pollution abatement measures and quantifying their eVects; (5) establishing risk assessment and management programs, and (6) investigating the short- and long-term eVects of both pollutants and pollution sources on public health. The eVect of pollution is rarely very large and in order to discover it, exposure estimation must provide data that reflects both spatial and temporal variations.Estimates of pollution exposure are obtained using an integrated approach that combines results of measurements from monitoring programs with dispersion calculations. These values can serve as estimates for individual short-term or long-term exposure. The grouped data allows the expression of ambient pollution concentrations as the spatial distribution of estimates such as the mean or 98th percentile of such compounds as SO2, O3, NO2, PM10 and PM2.5. This integrated approach has been combined into a single software package, AirQUIS.ments from monitoring networks can be improved by sup- Introduction plementing measurements with information obtained by The possible role of ambient air pollution on the development dispersion modelling.Should a network of measuring stations or aggravation of chronic diseases such as asthma needs to be be chosen as a basis for exposure estimates without including clarified. However, in the majority of studies, exposure has dispersion models, a very dense network of stations would be been determined without using data on spatial and temporal needed to specify the location of pollution gradients such as variation in pollution concentrations.Improved estimates of those close to roads in urban areas. exposure are needed to attempt to causally relate individual Many large cities have pollution monitoring systems to pollutant exposure to development and/or aggravation of monitor the population’s exposure, that in particular are used chronic disease.to initiate pollution reduction measures and to follow develop- There is a need for information on the distribution of ment of the pollution situation in the area. Adding an intepollution in geographical areas. Potential stakeholders include: grated air quality monitoring system, using results of dispersion (1) public authorities at diVerent levels (municipal, regional, modelling as supplementary information allows: (1) identifying national, international ); (2) industrial users; (3) schools, uni- geographical areas whose inhabitants are most exposed to versities and the scientific community; (4) various organis- ambient pollution; (2) identifying how many people in an area ations; (5) the public and media.are exposed to concentrations of pollution exceeding air quality This paper describes a method developed over 15 years, the guidelines; (3) describing the exposure of population subintegrated air quality monitoring system, that allows a detailed groups (e.g. children); (4) planning pollution abatement estimate of pollution exposure both for populations and for measures and quantifying their eVects; (5) establishing risk individuals using as a base, concentration measurements and assessment and management programs, and (6) investigating data on emissions, wind and dispersion to specify spatial the short- and long-term eVects of either pollutants or pollution variations.In an urban area, emissions from home heating, sources on public health.roads and often industry cause a complex distribution of pollution, with concentration gradients between polluted and less polluted areas. Data on wind conditions and spatial The integrated air quality monitoring method distribution of emissions give information on pollution gradi- The integrated air quality monitoring method combines ents within the city, important when population and individual information from measurement monitoring systems with a exposure is being assessed.dispersion model, using a digital map of a geographic area. Pollution concentrations vary substantially with time, due The method can also be coupled to information concerning to variations in source strength (i.e. traYc flow) and dispersion the population distribution, input data concerning the geo- (e.g.wind velocity). These features can be accounted for by graphical placement of all homes and workplaces using a dispersion calculations. The quality of the continuous measure- Geographical Information System (GIS) system, and also be coupled to global positioning system (GPS) sensors to follow individuals in time. †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999.J. Environ. Monit., 1999, 1, 313–319 313The central feature of the integrated air quality monitoring To use wind observations for interpolation in a complex terrain, the terrain influenced wind speed model Mathew is system is the air quality measurements. Dispersion modelling completes the description of the spatial variation of pollution included, as a first approximation.This model is fast and can on an hourly basis estimate inhomogeneous wind fields based concentrations. The dispersion model combines geographical and topographical information with emissions and meteor- on wind observations as input to the dispersion models for concentration calculations. ology. This results in estimates that reflect the pollution situation both spatially and temporally.Empirical data indicate that the vertical gradient of temperature (inversion intensity) and urban scale convergence of The dispersion model horizontal wind are important parameters for describing urban scale dispersion, in particular during pollution episodes. The dispersion model used, EPISODE, combines a finite diVerence model, with a point source and line model, to Emission inventory.To describe spatial concentration distri- account for both stationary and mobile sources. Dispersion butions, including maxima and minima between monitoring modelling can be done combining information from diVerent stations, emissions must be quantified on the same time frame. scales. The local scale involves street segments, area surround- The emission inventory is based on the following features ing chimneys, or street canyons.An urban scale model is also for the appropriate compounds: TraYc—standard emission important when describing air pollution episodes. In Oslo the factors for diVerent types of vehicles, both diesel and gasoline model was run for the urban scale.1,2 However, in Grenland,3 driven, for diVerent speeds and gradients of the roads.Home given in another example, two towns were combined in a heating—standard temperature-dependent emission factors regional model. A regional scale model may also be important based on consumption of wood and heating oil, distributed describing the pollution coming into the area of calculation spatially according to number and type of heating units.by long-range transport or accumulation within the airshed Shipping—based on average number of ships in the harbour. covering a larger area. Industry—location specific emission inventory based on ques- Currently, the model has been run for CO, NOx, NO2, SO2, tionnaires to industry, that includes direct emissions through Cl2, PM2.5, and PM10. It is planned to expand the model to stacks and indirect through leakage from roofs and in loading, include photochemistry, O3, volatile organic compounds transfer or other known processes.(VOCs) and aerosol properties. Emissions are collected either on an annual basis, or patterns In some cases, for example for O3 and NO2, the model of emissions that reflect hourly emissions are provided. For includes a photochemical transformation module that adjusts example, average daily traYc on unmeasured roads is distrib- the concentrations for the presence of other chemically active uted over 24 h using typical time trends obtained by traYc agents in the presence of sunlight.counts. The PM10 model accounts for road dust as a function of As part of the model evaluation, the diVerent elements of road wetness.This is especially important in the Scandinavian the calculation procedure are controlled and evaluated. countries where studded tyres are in use during the winter. Dispersion calculations. Dispersion modelling accounts for Geographical and topographical information. The dispersion background pollution from for example long-range transport, model estimates the concentration of each compound in each from point sources such as industrial emissions, emissions grid square.The dimensions of the grid square are chosen to from domestic heating, tunnel outlets, and from line sources reflect the uses of the estimates and it is usually 1 km2 in the such as roads. Dispersion modelling allows for high spatial centre of cities and possibly 2×2 km2 in more rural areas.resolution. For the spatial model, the topographical contours of the The model computes on an hourly basis concentrations area are given using x–y–z co-ordinates. This information is based on the emission inventory and the meteorological para- an important contributor to spatially distributing pollution in, meters on the same time frame. This allows, especially in areas for example, valleys.with industrial sources, the separation of compounds that are A building register is included that contains building emitted by geographically distinct sources. Fig. 1 shows the co-ordinates, height of roof, number of floors and number of concentration distribution for the same hour of four com- residents. Building height is the basis for stack emissions pounds emitted in an industrial region by geographically connected to home heating.The building centres are defined distinct sources in the Grenland area of southern Norway. as receptor points. Industrial point sources are depicted using Fig. 2 shows how changing wind directions in the area can x–y co-ordinates, and include extra information as to stack influence the geographic distribution of NOx concentrations. height, and height of roofs.At 0600 and 0800 the wind was blowing from the south-west Information for the line model concerning traYc is also up the valley. At 1000 the wind direction began to change. necessary in the dispersion model. Roads are divided into From 1200 the wind was primarily from the north leading to segments delineated by nodes.The parameters needed for each lower NOx values in the northern versus the southern area. segment are: (1) the height of the building associated to the Dispersion models compute pollution concentrations based roads; (2) the distance from the road centre to the buildings; on a short time interval, for example hour by hour. These (3) the width of the road; (4) the slope of the road; (5) the values can be used as is, for individual short-term estimates.presence of important crossings; (6) the presence of crossing However, the data can be aggregated to provide long-term lights and other traYc impediments; (7) the orientation of the exposure estimates. The aggregation time is dependent on the street segment; (8) number of lanes; (9) permitted speed on use of the estimate.For comparison to air quality guidelines, the segment; (10) direction of traYc flow. yearly and daily averages are the most usual. However, for epidemiological studies, the aggregating time may be the time Meteorological data. Standard meteorological information is needed for key points in the geographic area. It is necessary course of the study or a fixed time previous to the study.The grouped data allows the expression of ambient pollution to know temperature and temperature gradients, wind speed, wind direction and stability conditions in the time frame being concentrations as the spatial distribution of estimates such as the mean, the 98th percentile and maximum. The models can estimated. More detailed information on temperature diVerences at diVerent heights, including those obtained using such be used to provide estimates for geographic regions in a grid, or provide estimates for specific receptor points (for example equipment as the SODAR, is advantageous in order to map inversion intensity, pollution flow and dilution. the home address of the participant). 314 J. Environ. Monit., 1999, 1, 313–319mg m_3 Fig. 1 Dispersion of O3, fine fraction of particulate matter (PM2.5), NO2 and SO2, March 9, 1988 at 0100. mg m_3 Fig. 2 Estimated values for NOx on March 9, 1988 in Skien and Porsgrunn, at diVerent times of day. Errors in pollution calculations. A typical root mean square the gradients well when data for the traYc intensity local wind and dispersion is known. (2) The description of dispersion error of hourly values in specified points is comparable to the long-term average value.The error in estimating long term from high chimneys causes a high stochastic error in zones where the plumes touch the ground.6 For describing pollution average values is typically 10–20%.4,5 For many applications such errors are not acceptable. distributions in urban areas, the point source model is important in discriminating between plumes characterised by high However many small-scale structures and characteristics of the pollution situation in urban areas become evident from and by low concentrations in the respective maximum zones.The influence of many high chimneys is of minor importance dispersion calculations using data on emissions and dispersion i.e. (1) Considering pollution coming from car traYc, the for the pollution distribution within the city.However a few chimneys may cause high concentrations within limited areas gradients close to roads are important for people living close to roads. A line source sub-model describes the location of depending on wind and dispersion conditions. These areas are J. Environ. Monit., 1999, 1, 313–319 315specified by the dispersion calculations.(3) An area source is composed of a high number of small sources located on e.g. the roof of each house. The error in calculating the concentration contribution from area sources shows diVerent characteristics than errors in calculating the contribution from line and point sources. The concentration contribution from area sources is high during meteorological situations characterised by low wind and inversion (poor vertical mixing in the atmosphere).Observations of pollution concentrations and results of tracer experiments show that it is diYcult to describe vertical exchange of pollution in these situations and to avoid systematic errors.7,8 The error describing the contribution from area sources does not change from one hour to the next as in the case of the contribution from point sources. When measurements are used in addition to results of dispersion calculations to estimate exposure, the results are improved at least close to the measuring stations.To improve Fig. 3 The AirQUIS system. the method of assimilation of measurements to the results of dispersion calculations the type of errors has to be considered.Further work on the assimilation procedure is expected to survey, background information and other relevant data improve the method for exposure calculations. directly or aggregated for diVerent types of users. Results of concentration measurements are also needed to The air quality data are usually linked to geographical sites. account for pollution concentration from sources outside the In particular when monitoring data are supported and supplied urban area or from an accumulation in an airshed larger than by model estimates of spatial concentration distributions and the urban area.4 impacts, it is suggested that the presentation of the results Calculated concentrations are adjusted using measured data would involve the use of maps or digitalized geographical by using simple kriging for interpolation of diVerences between information systems (GIS).measured and calculated values at and in the neighbourhood of measuring stations. Measurements from stations close to Uses of the integrated air quality monitoring system roads characterized by high traYc intensity are not used for kriging.Depending on the stakeholder, the needs for information vary The accuracy of model results, both spatially and temporally from simple concentrations in diVerent areas, to forecasting have been examined by comparing estimated and measured concentrations before and after pollution abatement measures. values at key sites.5 The common need, however, is for pollution exposure information.The AirQUIS system Estimation of exposure The dispersion model is being incorporated into an interactive air quality management system called AirQUIS9,10,11 that will Pollution exposure can be expressed on a population or individual basis. It is desirable to identify how many people include: (1) a manual and automatic data entering application; (2) an on-line monitoring system; (3) a measurement data in an area are exposed to concentrations of pollution exceeding air quality guidelines, the population estimate. On the other base for meteorology and air quality; (4) a modern consumption/ emission inventory database with emission models; hand, we need to know the exposure for each participant in an epidemiological study to develop exposure/eVect relation- (5) numerical models for transport and dispersion in air of pollutants; (6) an eVect module for population exposure; ships, the individual estimate.(7) statistical treatment and graphical presentation of measurements and modelling results; (8) import/export wizards for The population exposure estimate. The geographical distribution of pollution combined with the geographical distri- import of data and dissemination of results.These elements are integrated in a map and menu oriented bution of the population allows estimation of population exposure. Air pollution impact on health can be estimated by user friendly interface with a direct link to the databases for measurements and emissions and presentation tools. Advanced combining calculated concentrations, either in grid or receptor points such as addresses, and the population distribution. import/export wizards allows the user to easily transfer data to and from the AirQUIS system (see Fig. 3). Exposure estimates can be used to describe how many people that are exposed to air pollution above air quality guidelines The development of an associated database or metadata is important to all air quality monitoring and information sys- and for how long.These data are often used as input to local air quality assessment.5 tems. The database system may consist of several databases which serve as main storage platforms for: (1) on-line collected Population exposure can be calculated in two ways. The exposure of the number of people living in each km2 for air quality data; (2) source oriented emission data and emission modelling procedures; (3) calculated fields of emissions, con- example can be related to hour by hour concentrations in the same km2.12 However, using a GIS system, point estimates of centrations and exposure; (4) historical data with trends, background information such as land used, population distri- air pollution exposure for all homes and buildings in a geographic area can also be made.butions; (5) regulations, guideline values and information for the support and decision making process. The following exposure parameters are calculated:5 Exposure hours—number of hours a number of people is The databases contain information that enables an evaluation of the actual state of the environment and that include exposed to pollution over a selected value.Person dose— accumulated exposure of pollution over a selected value per data for establishing trend analyses, warnings and the undertaking of counter measures in case of episodic high pollution. person. Population load—accumulated exposure of pollution over a selected value for all persons within a grid square. An important part of an integrated system is to present measurements, statistical and modelling results, emission A risk assessment and management program can use popu- 316 J.Environ. Monit., 1999, 1, 313–319lation exposure estimates to assess exceedances of air quality air quality guidelines. The choice of air quality indicators can be expanded. It is not certain that the simple averages or indicators of pollution concentrations. Fig. 4 provides an example that shows an assessment of the number of individuals 98th percentile of pollution concentrations reflect how pollution influences health. It is possible that the time structure in the Oslo area that exceeded air quality guidelines, either during the hour where pollution concentrations were highest, underlying pollution concentrations is an important factor in damaging health.The dispersion model method allows the or during the hour when the greatest number of individuals exceeded the guidelines. identification of other air quality indicators (AQI) that may more correctly reflect health damage.14 Population exposure can also be estimated for the current situation, and forecast those concentrations that would occur after the implementation of pollution abatement measures.Individual pollution exposure estimate The model can quantify the relative importance of various In quantifying the health eVects of air pollution exposure, it sources of pollution in the area for the exposure values.4 This is desirable to both quantify the size of the eVect and relate information has been used to develop abatement strategies.13 this quantity to the eVects of other individual sociodemographic factors such as smoking or passive smoking, Air quality indicators income, sex, age, etc.The eVect of pollution is rarely very large15,16 and in order to discover it, exposure estimation must There is a need, especially for public authorities, to examine pollution indicators and compare them to accepted or existing reflect a natural variability as much as possible.Fig. 4 Estimation of population exposure, where the hour either with the highest calculated value of NO2 is portrayed, or the hour where the greatest number of people exposed to values exceeding the Norwegian air quality criteria value is presented. Table 1 Diagram of the dynamic exposure assessment model (DINEX) for each time unit Dispersion Model Emissions Meteorological conditions Dispersion calculation of a concentration field Adjustment of results to reflect background concentrations and measurements Exposure estimate Localization in the area Accounting for traYc Accounting for indoor environment (1) NOx and O3 chemistry (1) Ventilation—window open/closed (2) Extra suspended particles (2) Season (3) Smoking (own/passive) Estimate of personal exposure J.Environ. Monit., 1999, 1, 313–319 317Fig. 5 Relationship between pollution estimated at each child’s home using the STINEX method and the average exposure estimated for each child in the panel using the DINEX method. Several methods of measuring/estimating individual expo- Individual pollution exposure estimate—short-term.In studying the short-term eVects, a more detailed, dynamic approach sure have been used. The method most often applied to measure exposure to pollution has been to use concentrations is necessary, DINEX (Dynamic INdividual EXposure estimate). A method has been developed to estimate exposure to measured at one or several fixed stationary pollution measuring stations in pertinent locations in the area.Measurements at a air pollutants that is called the diary method.44 In this method an individual fills out the diary with information about station at a fixed site generally do not represent the pollutant concentrations people are in reality exposed to. Since it is location, time and health which is recorded chronologically.The location information from the diary is associated to the costly to do measurements, it is usually not possible to measure at a suYcient number of stations to get a more complete spatial and temporal distribution of air pollutants as described by using results of dispersion calculations.1,2 Estimated concen- picture of population/individual exposure. Other methods of measuring exposure include both passive trations are controlled by measurements and may be rejected when the deviation is large.Table 1 briefly summarises the and active portable monitoring equipment. Active portable monitoring equipment is costly and leads to altered behaviour elements in the suggested computer pollution exposure model. The DINEX method can also be used to check the estimates while individuals must carry relatively little robust and expensive equipment.It is diYcult for children to carry such equip- of exposure by population subgroups based on the concentrations calculated hour by hour as a function of where the ment. Passive equipment is more robust, but gives a cumulative concentration that does not allow measuring peak values or individuals actually are.Fig. 5 compares NO2 and PM2.5 exposure estimates for children living in Oslo either as a static other exposure over a short time period. Therefore, several methods of estimating exposure have estimate outside their home or as an aggregated estimate of dynamically estimated exposure using a diary over a 6 week been used. Generally, these methods provide pollution exposure based on statistical information for concentrations in period.The figure shows considerable variability between the two methods. This comparison provides valuable information diVerent microenvironments combined with general activity patterns in specialised population groups.17–24 These methods on the inherent variability of the more traditional exposure methods.2 In the future, other air quality indicators can also seldom allow individual pollution estimates, rarely allow continuous estimation of pollution exposure, or forecasting or be estimated based on the dynamic exposure of the individuals that will provide more detailed information on the temporal testing of eVects of pollution abatement measures.Although used only sporadically previously,25–42 dispersion pattern of exposure of the population to pollution.For some areas there is a positive correlation between agglomeration of modelling is becoming a supplementary tool in combination with concentration measurements. Seldom, however, has this people and pollution concentration. method been used to generate exposure estimates on an hour by hour basis as described in this paper.Conclusion Individual pollution estimating methods have been used in Individual pollution exposure estimate—long-term. Longterm health eVects can be related to air quality indicators, Norway, both for cross-sectional studies and for panel studies. In cross-sectional studies, health status of a selection of the calculated for the home. This can be called the STatic INdividual EXposure estimate (STINEX).The choice of air population is assessed together with a set of socio-demographic parameters, whereof pollution is one. The pollution estimate quality indicators can diVer for diVerent health end-points. Air quality indicators include peak, average, 98th percentiles used in cross-sectional studies is usually a yearly average. In panel studies, however, one follows the individual and com- of compounds such as SO2, O3, NO2, PM10 and PM2.5 .Dispersion modelling can be used to provide estimates of pares the health of the individual when exposed to pollution to the same individual when not exposed to pollution. Here ambient exposure outside the home, the work place or school, or both. These estimates can reflect the time frame of interest, the pollution estimate used is the hourly or daily concentration. These studies have been done both in areas with industrial such as yearly, seasonal or monthly averages.1 These estimates can also be used to estimate cumulative exposure over for pollution sources3 and with traYc pollution as the primary source.1,2,45 The methods have also been used in the Czech example a life time or several years, by estimating concentrations in the geographic units, based on known or estimated Republic46 and China.47 In order to estimate the proportion of the population changes in emissions of diVerent compounds.43 Since the estimate is calculated for each individual in a exposed to unsatisfactory air quality, and to quantify in exposure–eVect relationships the eVects of air pollution expo- survey, it can be used to estimate exposure in population subgroups such as children or the elderly.sure, refined methods of exposure estimating are necessary. 318 J. Environ. Monit., 1999, 1, 313–31919 M. L. Williams, Sci. Total Environ., 1995, 168(2), 169. Methods that account for the spatial and temporal aspects of 20 N. Duan and D. T.Mage, J. Expo. Anal. Environ. Epidemiol., pollution dispersion are currently commercially available. In 1997, 7(4), 439. the future, methods to accurately estimate indoor concen- 21 M. E. Korc, J. Air Waste Manag. Assoc., 1996, 46(6), 547. trations need to be incorporated into these models. 22 T. McCurdy, J. Expo. Anal. Environ. Epidemiol., 1995, 5(4), 533. 23 T. R. Johnson, J. Expo.Anal. Environ. Epidemiol., 1995, 5(4), 551. 24 D. L. MacIntosh, J. Xue, H. Ozkaynak, J. D. Spengler and P. B. References Ryan, J. Expo. Anal. Environ. Epidemiol., 1995, 5(4), 375. 25 P. J. Van den Hazel and C. H. Waegemaekers, Public Health Rev., 1 A. Bartonova, J. Clench-Aas, F. Gram, K. E. Grønskei, C. 1991–92, 19(1–4), 251. Guerreiro, S. Larssen, D. Tønnesen and S. E. Walker, J.Environ. 26 S. Larssen, D. Tønnesen, J. Clench-Aas, M. J. Aarnes, K. Monit., 1999, 1, 337. Arnesen, Sci. Total Environ., 1993, 134(1–3), 51. 2 J. Clench-Aas, A. Bartonova, K. E. Grønskei and S. E. Walker, 27 D. Zmirou, A. Deloraine, P. Saviuc, C. Tillier, A. Boucharlat and J. Environ. Monit., 1999, 1, 333. N. Maury, Arch. Environ. Health, 1994, 49(4), 228. 3 J. Clench-Aas, A.Bartonova, K. E. Grønskei, L. O. Hagen, O. A. 28 P. G. Georgopoulos, V. Purushothaman and R. Chiou, J. Expo. Braathen and S. E. Walker, J. Environ. Monit., 1999, 1, 341. Anal. Environ. Epidemiol., 1997, 7(2), 191. 4 K. E. Grønskei, S. E.Walker and F. Gram, Atmos. Environ., 1993, 29 C. Sacre, M. Chiron and J. P. Flori, Sci. Total Environ., 1995, 27B (1), 105. 169(1–3), 63. 5 S.E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram and K. E. 30 J. E. Till, Health Phys., 1988, 55(2), 331. Grønskei, J. Environ. Monit., 1999, 1, 321. 31 M. C. Hatch, J. Beyea, J. W. Nieves and M. Susser, Am. 6 H. R. Olesen., Int. J. Environ. Pollution, 1995, 5(4–6), 776. J. Epidemiol., 1990, 132(3), 397. 7 K. E. Grønskei, in Proceedings of the third International Clean Air 32 A. 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ISSN:1464-0325    

DOI:10.1039/a902775k

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimation Part II.‡ Model evaluation and population exposure† Sam-Erik Walker,* Leiv H. Slørdal, Cristina Guerreiro, Frederick Gram and Knut E. Grønskei Norwegian Institute for Air Research, PO Box 100, N-2027 Kjeller, Norway. E-mail: samerik@nilu.no; Fax:+47 63 89 80 50 Received 7th April 1999, Accepted 25th June 1999 The air pollution dispersion model EPISODE has been developed at the Norwegian Institute for Air Research (NILU) over the past several years in order to meet the needs of modern air quality management work in urban areas.The model has recently been used as a basis for exposure calculations of NOx and NO2 in order to assess the eVects of diVerent traYc diversion measures on health and well being for the residents in the Va° lerenga–Ekeberg– Gamlebyen area in Oslo.Here we describe some results from the most recent evaluations of the model for NOx and NO2 at station Nordahl Brunsgate in Oslo for the period 1 October 1996–19 November 1996. In addition examples of population exposure calculations for Oslo performed during the winter period of 1995–96, are also presented. using an explicit diVerence scheme.6 Vertical advection and 1.Introduction diVusion are solved simultaneously using a combined scheme As a basis for modern air-quality management work in urban for the vertical exchange of mass depending on divergence of areas, there is a need for urban scale dispersion models capable the horizontal wind and on the growth of Gaussian scheme of estimating time-varying (hourly) concentrations in arbitrary sz values.2 receptor points within the urban area.Such models should be The Lagrangian part of the model consists of diVerent able to specify the time-varying pollutant gradients even close Gaussian subgrid models for each of the three main emission to sources. The air pollution dispersion model EPISODE has categories: area, line and point sources.The purpose of the been developed at NILU over the past several years in order subgrid models is to calculate concentrations with finer resoto meet these needs.1 Earlier versions of the model have been lution near the diVerent individual sources. The subgrid area applied in several places in Norway and elsewhere.2,3 The source model calculates ground level concentrations in diVerent model has recently been used as a basis for exposure calcu- grid cells from area emissions in the grid cell itself using an lations of NOx and NO2 in order to assess the eVects of integrated Gaussian scheme.7 The calculation procedures for diVerent traYc diversion measures on health and well being pollution contributions from line sources and area sources to for the residents in the Va° lerenga–Ekeberg–Gamlebyen area the urban scale grid system are based on horizontal fluxes of in Oslo.4 pollution to the down wind grid cells.This calculation takes In this article we first give a short description of the into account the diVerent emission heights for the two main EPISODE model and its development over recent years. Then area source types in Oslo, domestic heating and small street results of model evaluation by comparing hourly calculated traYc.For traYc the emission height is set equal to 1 m above concentrations of the two compounds NOx and NO2 with the ground. For domestic heating the emission height is set hourly observed values at the centrally placed station Nordahl equal to the building height in the centre of Oslo which is Brunsgate in Oslo (see Fig. 1) are given. This was the only station where measurements of NOx and NO2 were taken during the period of investigation. Finally a description of a population exposure model together with results of population load in Oslo for diVerent model simulations are presented. 2. Description of the EPISODE dispersion model The EPISODE dispersion model is a combined 3D Eulerian– Lagrangian type model for urban and local-to-regional scale applications.1 The Eulerian part of the model is based on the numerical solution of the atmospheric (mass) conservation equation of the pollutant species, using a three-dimensional Eulerian grid.The equation is solved using a time-splitting approach where alternating sequences of advection and diVusion operators are called every other time step.The horizontal advection is solved using Botts positive definite and monotone 4th degree scheme.5 Horizontal diVusion is solved Fig. 1 The Oslo area with the air quality measurement station Nordahl Brunsgate and the meteorology station Valle Hovin. †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. ‡For Part I, see ref. 15. J. Environ. Monit., 1999, 1, 321–326 321estimated to be around 20 m above the ground. The building ent (stability), and wind speed and direction at the centrally placed station Valle Hovin in Oslo (see Fig. 1). The wind and height is also used to calculate the initial vertical dispersion (mixing) of pollution due to building eVects from both dom- stability data at this station are used as input to the diagnostic wind field model MATHEW.9 The hourly wind field data estic heating and traYc.The concentration contributions from most of the traYc sources (roads) are calculated using the produced by this model are then input to the EPISODE dispersion model. subgrid scale line source model in EPISODE. This subgrid model is based on using an integrated steady-state Gaussian Horizontal and vertical turbulence (sv and sw) are calculated for each grid cell using NILU’s meteorological pre-processor plume model from US EPA (HIWAY-2).8 Each line source has a user defined influence zone within which the subgrid line MEPDIM.10 This pre-processor is based on atmospheric similarity theory, using temperature- and wind-profiles together source model is capable of calculating concentrations in individual receptor points.In Oslo, all active line sources (around with surface roughness in order to calculate the above mentioned turbulence parameters.11,12 In addition it also calculates 400) have a common influence distance of 500 m. The concentration contribution in a receptor point is defined by integrated several other meteorological parameters such as mixing height, friction velocities and Monin–Obukhov lengths etc.also used Gaussian dispersion along the road. The EPISODE model also includes a subgrid scale point source model, but the point for dispersion calculations. sources in Oslo are so few, and they contribute very little to the total concentration levels of NOx and NO2, so this part 5. Model evaluation of the model will not be described here.The model also contains a module for calculating photo- The model evaluation was performed by comparing the modelcalculated concentration at the station Nordahl Brunsgate in chemical equilibrium between NO, NO2 and O3 in all grid cells and receptor points on an hourly basis. The calculation Oslo with a corresponding measured (observed) concentration at the same station each hour during the period of investi- of NO2 is based on this balance and on dispersion calculations of NOx and Ox (NO2+O3).gation, October 1–November 19, 1996, a total of 1198 h. The comparison was performed for the two pollutant species NOx For Oslo, a 22 km×18 km grid was defined with horizontal resolution of 1000 m in each direction.Vertically, 3 layers were and NO2. Model evaluation was not performed for PM2.5 or PM10 and could not be performed for O3 since it was not used with resolution 20, 30 and 150 m from the ground upwards. Background concentrations of ozone in Oslo were measured locally. Time series plots of model calculated (predicted) and meas- estimated on an hourly basis, using measured values of ozone at the two regional stations Jeløya and Prestebakke in Norway ured (observed) NOx and NO2 concentrations are shown in Figs. 2 and 3 for a representative part of the investigation (maximum at the two stations for each hour of measurements). Ideally, stations both north of and south of Oslo should have been used in order to give the best estimate for regional ozone concentration for Oslo during the investigation period. However, during the autumn, ozone gradients during northerly wind-transport episodes are generally small in these regions.Therefore no great systematic errors are introduced by using only stations south of Oslo. 3. Emission database The emission database for Oslo contains hourly emission data from the three diVerent source categories, domestic heating (area sources), road traYc (area and line sources), and industry (point sources) for the diVerent types of compounds such as NOx, NO2, and particulate matter PM2.5, PM10, etc.For domestic heating the database consists of a Fig. 2 Observed and model calculated hourly concentrations of NOx 22 km×18 km emission field (grid) with horizontal resolution at station Nordahl Brunsgate in Oslo for a part of the evaluation of 1000 m in either direction.These nominal emission fields period containing the highest concentrations. are then scaled using daily and weekly factors in order to produce actual emission field values on an hourly basis. The scaling procedure also includes a temperature dependent factor. Road traYc is the most important source of NOx and NO2 pollution in Oslo.The emission database for road traYc consists of about 2400 line sources (roads) with individual characteristics for each road such as: start and end position (co-ordinates), the total width and slope (elevation), the speed limit, types of vehicles, daily and weekly variations in traYc intensity, and the amount of heavy duty traYc. No seasonal adjustments to the traYc emissions have been included.The individual road data is input to a separate emission module for calculating emissions of NOx and NO2 on each individual road. The emission database currently does not contain any volatile organic compounds (VOCs). 4. Meteorological database Fig. 3 Observed and model calculated hourly concentrations of NO2 The meteorological database for Oslo consists of hourly meas- at station Nordahl Brunsgate in Oslo for a part of the evaluation period containing the highest concentrations.ured values of air temperature, vertical air temperature gradi- 322 J. Environ. Monit., 1999, 1, 321–326period (November 9–November 19, 1996). As can be seen observed and model-calculated values for NOx and NO2 was found to be reasonable high, 0.73 for NOx and 0.68 for NO2.from the figures, the observed and predicted concentrations corresponded fairly well for most of the period. Sensitivity This is also reflected in the index of agreement, being 0.85 and 0.78 respectively for NOx and NO2. analyses have indicated that the diVerences between observed and predicted concentrations, to a large extent can be explained The correspondence is also quite good when comparing mean values, 89 mg m-3 (observed) and 83 mg m-3 (predicted) by the non-representativeness of using only the observed thermal stability DT at the station Valle Hovin (which lies for NOx, and 39 mg m-3 (observed) and 36 mg m-3 (predicted) for NO2.For NOx the maximum concentration observed outside of the city centre), as a measure of the stability conditions at Nordahl Brunsgate.The build-up and weakening (1249 mg m-3) is somewhat higher than the maximum concentration calculated (863 mg m-3), while for NO2 the two num- of pollution concentrations in the central parts of Oslo is a result of a more complex process where mechanical turbulence, bers show a very good correspondence, 121 and 123 mg m-3 respectively.For both components the bias calculated by locally generated heat from buildings, and local turbulence from road traYc, influences the local wind- and turbulence- taking the diVerence in predicted and observed mean values divided by the observed mean value are also quite small being fields and the vertical exchange of pollution. In addition to the time series plots, a set of model evaluation 0.064 and 0.068 respectively for NOx and NO2.The correspondence is also quite good when considering the parameters have also been calculated for the whole period of investigation (October 1–November 19, 1996) (1198 h). These systematic (RMSEs) and unsystematic (RMSEu) portions of the total root mean square error RMSE. For a good model parameters are shown in Table 1.A description of these parameters with their definitions is given in the Appendix the unsystematic portion of the RMSE is much larger than the systematic, while a large systematic RMSE indicates a (Section 8). As can be seen from Table 1 the correlation between poor model.13 As can be seen from Table 1 the unsystematic Fig. 4 (a) The population distribution within the model domain.(b) The population load (hourly NO2 concentrations with 100 mg m-3 as threshold value) with all emissions included. (c) The population load with a 10% reduction in emissions from road traYc. (d) The population load without emissions from area-distributed stationary sources (i.e. mostly domestic heating). J. Environ. Monit., 1999, 1, 321–326 323Table 1 Model evaluation parameters comparing hourly observed and model predicted NOx and NO2 concentrations at Nordahl Brunsgate station in Oslo for the period October 1–November 19, 1996 (a total of 1198 h) Parameter Unit Observed NOx Predicted NOx Observed NO2 Predicted NO2 Mean mg m-3 88.8 83.2 38.5 35.9 Max mg m-3 1248.6 863.3 121.4 122.8 s mg m-3 106.4 112.1 15.7 23.9 BIAS 0.064 0.068 RMSE mg m-3 80.8 17.8 RMSEs mg m-3 25.3 2.7 RMSEu mg m-3 76.8 17.6 Correlation 0.73 0.68 Index of agreement 0.85 0.78 portion of RMSE is much larger than the systematic portion person dose basically is a concentration value, and its spatial distribution points to areas with potentially low air quality. both for NOx and NO2.This indicates that most of the deviance between observed and predicted values were of an unsystematic nature.The population load. The population load, L i,j is an exposure measure which combines the information about the person In order for the model to be more completely evaluated, the sensitivity of the above results to changes in emission dose in a grid square with the population living within this square, Pi,j . inventory or meteorological conditions (period of the year) shall be investigated in a future study.L i,j�Di,j×Pi,j (2) The spatial distribution of the population load reveals popu- 6. Population exposure estimates lated regions with concentrations above the threshold values. However, scarcely populated areas with high person dose After having established the ability of the EPISODE dispersion model to recapture measured concentration levels, the model values will not be distinguished from densely populated regions with small person dose values.A time dependent population was applied for predicting population exposure within the city of Oslo. In addition to NO2, these calculations were also distribution, Pi,j n, can easily be accounted for by the following definition of the population load: performed for PM10 and PM2.5.Since this presentation is meant merely as an example of how the model can be applied for exposure estimates, only results from the NO2 calculations L i,j� 1 N . N n=1 Pi,j n Max[(ci,j n-cT), 0] (3) will be referred to in the following. The exposure calculations presented have been simplified by A simplified time dependency of the population distribution applying a stationary population distribution.This means that would be to have one distribution valid for working hours it has been assumed that the inhabitants have stayed at their and another for the remaining hours. Increasing complexity home address during the entire calculation period. The expo- can be built in depending on available information. sure estimates have then been established by combining the calculated hourly NO2 concentration values at ground level The total population load.Both the person dose and the with the population distribution. population load are two-dimensional fields giving average The computed pollution levels have been compared with values for each grid square within the model domain. A the national air quality guidelines (AQG) given by the measure of the total population load within the domain is Norwegian State pollution control authies.At present the found by simply adding the grid square values of the popuguideline values (or threshold values) for NO2 are 100 mg m-3 lation load, i.e.: for hourly averaged values, 75 mg m-3 for daily averaged values and 50 mg m-3 for half-year averaged values.L Tot�. i,j L i,j (4) 6.1 Population exposure module By combining the calculated air pollution concentrations, the General considerations. The above definitions are just meant population distribution and the AQG values, diVerent measas examples of how various types of exposure measures can ures of population exposure can be constructed. Among these be constructed.Other possible choices of definition are for are the person dose and the population load, which are defined example to just consider episodes with a minimum duration as shown below. when calculating the person dose, or to scale the exceedances so that high concentration values are given more weight than The person dose. The person dose, Di,j is a quantity defined smaller ones. The selection of exposure measure to be used in for each grid square, (i, j), and is defined as: a given situation, or for a given pollutant, should generally be chosen in agreement with the medical community. Population Di,j� 1 N .N n=1 Max[(ci,j n-cT), 0] (1) concentrations based on sub grid calculations will be included in future calculations of a population exposure. where ci,j n is the calculated grid square concentration at time n, cT is a given threshold value (for example the AQG value) 6.2 Results and discussion and N is the total number of concentration values within the calculation period.The index n can either count hourly values, Population exposure calculations have been carried out for the city of Oslo for the winter period October 1, 1995 to daily values or other averages for which there exists a threshold value.The person dose can be interpreted as the average March 31, 1996. These calculations have been performed with a stationary population, which have been distributed according exceedance of the threshold value. With this definition no distinction is made between long episodes of small exceedances to their home addresses within the 1 km×1 km regular grid system of the model domain. Fig. 4a shows a colour contour and short periods with large exceedances. Note also that the 324 J. Environ. Monit., 1999, 1, 321–326plot of the applied population distribution. The black contour 8.1 Description of model evaluation performance parameters lines in the figure describe the topography of the model Let T denote the number of data, and let Ot and Pt denote domain. The diVerent colours indicate the number of persons the observed and model-calculated (predicted) values at time within each grid square.t, t=1, ...,T . The following model evaluation parameters may In Figs. 4b, c and d the calculated NO2 population load, then be defined: L i,j as defined by eqn. (2), is shown for three diVerent model O9 : Mean value of observations; simulations.The values shown in Fig. 4b were found when P9 : Mean value of predictions; the model was run with all available emission sources included. Omax : Maximum value of observations; The results shown in Fig. 4c were calculated for a scenario Pmax : Maximum value of predictions; with a 10% reduction in the emissions from road traYc.The sO : Standard deviation of observations; model can also be used in order to test the relative importance sP : Standard deviation of predictions; of diVerent types of sources. This may be done by selectively BIAS : Bias or normalised mean diVerence; removing diVerent source categories one at a time. An example RMSE : Root mean square error; of this is shown in Fig. 4d, where all emissions from area- RMSEs : Systematic RMSE; distributed stationary sources (i.e. emissions from domestic RMSEu : Unsystematic RMSE; heating and small industry not treated as individual point a,b : Intercept and slope of regression line; sources) have been neglected. The calculated population load Corr : Correlation coeYcient; shown in these figures was based on hourly NO2 concen- IA : Index of agreement.trations, and a threshold value of 100 mg m-3 was applied. The parameters are defined through the following set of This threshold value is equal to the AQG for hourly NO2 equations: concentrations given by the Norwegian State pollution control Mean values authorities. The maximum grid value in Fig. 4b is 3280 persons mg m-3. O9= 1 T . T t=1 Ot (A1) This maximum is reduced by almost 30% to 2305 persons mg m-3 as a consequence of the 10% reduction in the traYc emissions (Fig. 4c). By totally neglecting the emissions from P9= 1 T . T t=1 Pt (A2) the area-distributed stationary sources, the maximum calculated population load is reduced slightly more to a value of These denote the usual arithmetical average values of the time 2070 persons mg m-3 (Fig. 4d).Summing the individual grid series Ot and Pt . values in Figs. 4b, c and d, the total population load, as Maximum values defined by eqn. (4), can be computed for each experiment. In this way, total population load values of 43779, 30176 and Omax=max Ot for t=1, ...,T (A3) 29466 persons mg m-3 are found from the data shown in Pmax=max Pt for t=1, ..., T (A4) Figs. 4b, c and d, respectively. Assuming that the total population load is a good measure for population exposure, one These denote the usual maximum values of the time series Ot may infer from these results that a 10% reduction in road and Pt . traYc emissions and a 100% reduction in emissions from area- Standard deviations distributed stationary sources both lead to a 30% improvement in population exposure to NO2.It should be noted that these sO=C 1 T-1 . T t=1 (Ot-O9 )2D0.5 (A5) numbers strongly depend on the threshold value applied in eqn. (1). Calculations like those shown in Fig. 4 give valuable inforsP= C 1 T-1 . T t=1 (Pt-P9 )2D0.5 (A6) mation about possible problem areas within the modelling area. This type of information should be utilised not only These denote the usual standard deviations of the time series when designing a monitoring program, but also as a sup- Ot and Pt .plement to the monitoring program itself. As demonstrated Bias above, population exposure calculations can be utilised in diVerent forms of cost–benefit analyses, and as a tool for BIAS=(O9-P9 )/O9 (A7) quantifying the health eVects of various air quality This dimensionless parameter is a measure of the bias of P improvements.versus O. Ideally it should be zero, or close to zero. Root mean square error 7. Acknowledgements RMSE= C1 T . T t=1 (Ot-Pt)2D0.5 (A8) The authors acknowledge fruitful discussions with Dr The RMSE is another measure of the size of the error produced J. Clench-Aas and Dr A. Bartonova during the writing of this by the model.article. Parts of the study were funded by the Norwegian State Systematic and unsystematic RMSE Pollution Control Authority. RMSEs=C1 T . T t=1 (Ot-P� t)2D0.5 (A9) 8. Appendix RMSEu=C1 T . T t=1 (P� t-Pt)2D0.5 (A10) US EPA has given guidelines on procedures to be followed in evaluating air quality models, and a list of recommended where model evaluation performance parameters.14 In this study statistical parameters have been selected in accordance with P� t=a+bOt (A11) these recommendations. Selecting the parameters, results in ref. 13 were also taken into consideration. where a and b are the intercept and slope of the regression J. Environ. Monit., 1999, 1, 321–326 325line: 9. References a=P9-bO9 (A12) 1 S. E.Walker, The EPISODE air pollution dispersion model, version 2.2.Users Guide, Norwegian Institute for Air Research (NILU TR 10/97), Kjeller, Norway, 1997. b=C. T t=1 (Ot-O9 )(Pt-P9 )DNC. T t=1 (Ot-O9 )2D (A13) 2 K. E. Grønskei, S. E.Walker and F. Gram, Atmos. Environ., 1993, 27B, 105. 3 S. Larssen, K. E. Grønskei, F. Gram, L. O. Hagen and S. E. Here Walker, Verification of urban scale time dependent dispersion model with subgrid elements in Oslo, Norway, Air Pollution Modelling and RMSE2=RMSEs2+RMSEu2 (A14) Its Application X, ed.S.-E. Gryning and M. M. Millan, Plenum Press, New York, 1994, pp. 91–99. Systematic and unsystematan square errors give 4 J. Clench-Aas, A. Bartonova, R. Klæboe and M. Kolbenstvedt, in 8th International Symposium on Transport and Air Pollution, ed. valuable information on the possibility of model improve- P.J. Sturm, Technical University Graz, Report of the Institute for ment.13 For a good model the unsystematic portion of the Internal Combustion Engines and Thermodynamics, vol. 76, RMSE is much larger than the systematic, while a large Graz, Austria, 1999. systematic RMSE indicates a poor model. 5 A. Bott, Mon. Weather Rev., 1993, 121, 2637.Correlation coeYcient 6 G. D. Smith, Numerical solution of partial diVerential equations: Finite diVerence methods, Oxford University Press, Oxford, 1985. 7 K. E. Grønskei, Dispersion of pollution from area sources, Corr= 1 T . T t=1 (Ot-O9 )(Pt-P9 )/(sOsP) (A15) Norwegian Institute for Air Research (NILU TR 6/82), Lillestrøm, Norway, 1982. 8 W. B. Petersen, Users Guide for Hiway-2: A Highway Air This is the ordinary Pearson product–moment correlation Pollution Model, US Environmental Protection Agency (EPA-600/ coeYcient. 8-80-018), Research Triangle Park, NC, 1980. Index of agreement 9 C. A. Sherman, J. Appl. Meteor., 1978, 17, 312. 10 T. Bøhler, MEPDIM. The NILU meteorological processor for dispersion modelling. Version 1.0. Model description, Norwegian IA=1-C. T t=1 (Pt¾-Ot¾)2DN. T t=1 (|Pt¾|+|Ot¾|)2 (A16) Institute for Air Research (NILU TR 7/96), Kjeller, Norway, 1996. 11 S. E. Gryning, A. A. M. Holtslag, J. S. Irwin and B. Sivertsen, where Atmos. Environ., 1987, 21, 79. 12 A. P. Van Ulden and A. A. M. Holtslag, J. Appl. Meteorol., 1985, Pt¾=Pt-O9 and Ot¾=Ot-O9 (A17) 24, 1196. 13 C. J. Willmott, Am. Meteorol. Soc. Bull., 1982, 63, 1309. The index of agreement IA varies between 0 and 1, with 0 14 EPA, Interim procedures for evaluating air quality models (revised), EPA-450/4-84-023, OYce of Air Quality Planning and indicating the worst agreement and 1 indicating the best Standards, US Environmental Protection Agency, Research agreement. It has been recommended13 as a better parameter Triangle Park, NC, 1984. to describe the ‘agreement’ between the two time series Ot and 15 J. Clench-Aas, A. Bartonova, T. Bøhler, K. E. Grønskei and Pt than the correlation coeYcient. S. Larssen, J. Environ. Monit., 1999, 1, 313. Paper 9/02776I 326 J. Environ. Monit., 1999, 1, 321–326

ISSN:1464-0325    

DOI:10.1039/a902776i

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimation Part III.‡ Development of new types of air quality indicators† Cristina Guerreiro, Jocelyne Clench-Aas and Alena Bartonova Norwegian Institute for Air Research, PO Box 100, N-2027 Kjeller, Norway. E-mail: cristina.guerreiro@nilu.no; Fax:+47 63 89 80 50; Tel:+47 63 89 80 00 Received 7th April 1999, Accepted 25th June 1999 The temporal pattern of exposure to a specific compound may aVect health in several ways.Exposure to pollution can have short-term eVects or long-term eVects. For some compounds there is a threshold under which there is no presumed measurable eVect, whereas for other compounds, there is no presumed threshold. For short-term eVects, the exposure to a high concentration of a compound one day may either increase or decrease the response if values of the same compound become high again the next day.Adaptation to eVects of short-term exposure to ozone, for example, is reported. Similarly, health response to sudden high peaks of concentration may also possibly diVer in eVect from those to peaks attained more gradually. For long-term eVects of some compounds, the cumulative exposure may be more decisive in influencing health.This paper proposes and describes in detail several air quality indicators that reflect the time variability and the episodic nature of air pollution exposure, as an attempt to represent the temporal aspects of pollution exposure that may have important eVects on health. Mean concentrations, 98th percentile and maximum values are the traditional indicators for estimating exposure.The temporal variability of particulate matter (PM10) and NO2, however, is here described by means of: (1) the rate of change of pollution as the diVerence between two consecutive hourly or daily values, and of (2) episodes, described in terms of number, duration and inter-episode period, maximum concentration in the episode, and integrated episode exposure. example, is reported.5 For long-term eVects of some com- 1.Introduction pounds, the cumulative exposure may be more decisive in Health eVects are increasingly being described in the form of influencing health. the dose-response (or exposure-eVect) relationship. This If there are diVerences in physiological response in the relationship allows quantifying the altered health status of a diVerent exposure situations, then the following characteristics population by quantifying both the current and future health of exposure need to be considered, in addition to average and status based on measured and projected pollution peak exposure: (1) the temporal pattern with which concentrations.exceedances of threshold or AQG occur, (2) how many of Public authorities are currently using as air quality indicators these exceedances are in reality occurring during the same (AQI) for health eVects, average and peak concentrations of episode, (3) how many episodes have in reality occurred, or pollutants, together with the number of exceedances of existing (4) how long the episodes have lasted. air quality guidelines (AQG) (WHO, 1987),1 (WHO, 1998),2 This paper proposes several new types of AQIs, based on (Commission of the European Communities, 1998).3 However, hourly air pollution concentrations calculated with a dispersion it is more and more evident that attempts to characterise model in a 1 km2 grid system covering the city of Oslo for the health eVects using a simple relationship have not been quite winter 1994/95.eVective. The reasons for this are many. For example, it is not precluded that the temporal variation of exposure to a pol- 2. Methods lutant may be influencing the health impact. It may be necessary in the future to account for the temporal pattern of This section describes the criteria and methods used for the exposure in the setting of air quality guidelines.selection of AQIs. What should AQIs represent is discussed. The temporal pattern of exposure to a specific compound Basic concepts such as rate of change of exposure, episode, may aVect health in several ways. Exposure to pollution can and inter-episode period are defined. have short-term eVects (immediately or in the next few days) or more long-term eVects. For some compounds there is a 2.1 What should the indicator represent threshold under which there is no presumed measurable eVect, The air quality indicator (AQI) should represent both the whereas for other compounds, there is no presumed threshold spatial and temporal aspects of pollution exposure that may (for example PM10) (WHO, 1996).4 For short-term eVects, have important eVects on health. Two indicators are needed, the exposure to a high concentration of a compound one day the population air quality indicator and the individual air may possibly either increase or decrease the response if values quality indicator.of the same compound become high again the next day. An air quality indicator should provide information relevant Adaptation to eVects of short-term exposure to ozone, for in evaluating possible health eVects.It should be applicable for the evaluation of both short-term health eVects and long- †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. ‡ For Part II, see ref. 6. term to chronic health eVects. The indicator should also be J. Environ. Monit., 1999, 1, 327–332 327usable to measure or predict changes in exposure resulting considered one episode, would in this case, because of technical reasons, count as several.from pollution abatement measures. The elements necessary for the proper definition of AQI are, therefore, the spatial The episodes may then be described by (see Fig. 2): (1) peak height (maximum value in episode), (2) duration of episode, distribution of the pollution, the time structure of the exposure, and the magnitude of the exposure.(3) inter-episode period, and (4) integrated episode exposure, episode AOTx (sum of the concentration hours during an The air pollution episode can be the basic entity underlying the air quality indicators. The definition of an episode is the episode that exceeds the threshold value of x). Area over threshold (AOT) values are usually given in ppb h-1 (ozone), period of time that pollution concentrations are above a predefined level such as eVect threshold or air quality guideline.but may also be represented by mg m-3 h-1 as in this paper. Episodes have a time structure that defines when and how often episodes occur, and a magnitude that reflects both the 2.3 Choice of sites duration of the episode and the peak concentration reached Based on the EPISODE model, air quality concentrations during the episode. were calculated for Oslo for the winter 1994/956 at the square Health eVects of pollution are continuously under study.It kilometre level. Three grid squares were chosen in this study remains unknown whether the absolute concentration of polto estimate diVerent air quality indicators using the calculated lution or the rate of change of concentrations has the greatest concentrations of NO2 and PM10.These three grid squares eVect on diVerent health end-points. correspond to the squared kilometres where Carl Berners An air quality indicator may also reflect how rapidly polplass, Majorstua and Lysaker are located in Oslo. The two lution levels change.The health eVect of exposure to first areas (Carl Berners plass and Majorstua) represent areas 100 mg m-3 of a pollutant may diVer if the previous level for containing traYc in the centre of the city, while the third some days has been 20 as opposed to 90 mg m-3. square (Lysaker) represents a major traYc artery around the Exposure may be described on an individual basis, whenever city, and is located on the West border of the city.individual data are available. Population air quality indicators may be obtained from integrated exposure estimates, such as from estimates based on a square kilometre grid, or air quality 3. Results—temporal aspects measurements on a city level. Mean concentrations, 98th percentile and maximum values are the traditional indicators for estimating exposure.The 2.2 Definition of specific air quality indicators to describe temporal variability of PM10 and NO2, however, is here temporal variability described by means of: (1) the rate of change of pollution as the diVerence between two consecutive hourly or daily values, Both the temporal and spatial aspects of air pollution concentrations need to be described.To obtain relevant data, the and of (2) episodes, described in terms of number, duration and inter-episode period, maximum concentration in the epi- AirQUIS/Episode model was run hourly for 6 months. The assumptions of the model, the emissions used and type of sode, and integrated episode exposure (episode AOT35/100). model, are described in Walker et al.6 and Grønskei et al.7 The pollution concentrations (hourly and daily) were given for each selected square kilometre grid, and for the components NO2 and PM10.The statistical parameters of the time series were given as mean (mg m-3), maximum hourly and daily concentration, and 98th percentile of hourly values. In addition to these descriptors, rate of change of the time series, and description of episodes, may prove useful.The rate of change for hourly and daily data may be described as the diVerence between the two consecutive values in the time series (‘delta concentrations’) (see Fig. 1). As pollution concentrations change, episodes occur. An episode is in this study defined as the period when concentrations of pollutants exceed a threshold, here set as 100 mg m-3 for hourly NO2 and 35 mg m-3 for daily PM10.Should the values consistently lie around the threshold for several hours, a series of concentrations which is generally Fig. 2 Definition of ‘episode length’, ‘episode peak’ and ‘area over Fig. 1 Defining the rate of change (delta conc. in legend) of exposure to pollution. threshold’ (AOT). 328 J. Environ. Monit., 1999, 1, 327–332The general features of pollution exposure can be described The calculated rate of change of hourly concentrations of NO2 and daily concentrations of PM10 in the three selected grid by the more traditional measures as seen in Table 1.It is evident in this table that Lysaker has the highest concentration squares in Oslo are presented in Table 2. especially for PM10. However, from the point of view of health, this information may not be suYcient.Measures to 3.2 Episodes—time pattern protect health may need to account for the pattern in exposure Exposure to air pollution occurs as a series of episodes. people endure. The severity of pollution in an area is not Episode statistics, characterising the time pattern of exposure completely indicated by a simple 98th percentile, since health in terms of duration of episodes and length of periods between eVects may be worse if high pollution concentrations come episodes (inter-episode periods), have been computed for the sporadically, not allowing the body to adapt to them as may 3 grid squares in Oslo and are given in Table 3.be the case in one or two long episodes. Therefore, it may be Only Lysaker had episodes of NO2 that lasted longer than necessary in the future to specify, in addition to concentrations, 8 h (1% of total time), whereas for 4% of the total time they acceptable patterns of exposure.It may be necessary to control lasted only 1 to 2 h. Lysaker had only 5 periods of 5 days or the number of episodes, and the severity of the episodes, which more without an episode.For PM10, the episodes had a longer are described by the integrated exposure. duration, so that all three sites experienced episodes lasting longer than 8 h, and the inter-episode periods were shorter. 3.1 Rate of change of pollution exposure These statistics would obviously change if the threshold was The temporal pattern of exposure may aVect health in several changed.For daily PM10 over the new Norwegian guideline9 ways. An impact on health by exposure to high concentrations of 35 mg m-3, all three sites experienced episodes of 1 day can lead to a direct linear response, a potentiation (sensitis- duration. Lysaker had 2 over 6 daylong episodes. Lysaker had ation) or a reduction (adaptation) of response. The potenti- episodes 34% of the total time.ation or reduction can last for a period of time after exposure. In the future, information of this type may be used to Although the phenomenon of sensitisation is not described, specify control measures, which: (1) will not allow more than adaptation to ozone is reported.5 Similarly, peaks attained a certain number of episodes, (2) will restrict the allowable through rapid increase in pollution concentrations may also duration of the episode and (3) will not for example allow diVer in eVect from those attained more gradually.The pos- more than two episodes with an inter-episode period of 7 days sibility exists that occasional high concentrations of air pol- or more, as better information concerning the nature of the lution may lead to larger health eVects than more continuously health eVects becomes available.rising concentrations. The phenomenon of harvesting may also occur, where immediately after exposure to a peak concen- 3.3 Episodes—peak concentrations tration, most members of the potential population of sensitive individuals either enter the hospitals or die. Continued expo- Air pollution situations can be characterised by average concentrations.However, high mean values can be caused by sure to high concentrations during a latency period does not lead to further morbidity or mortality.8 occasional extra high concentrations or by more frequent, notso- high episodes. The 98th percentile of short-term concen- Urban air pollution concentrations vary periodically with time as shown in the time series plot in Fig. 3 in Walker et al.6 trations does not diVerentiate between the two situations. In judging the potential health eVects of diVerent air pollution Urban air pollution concentrations vary periodically with time. Table 1 Mean, maximum and 98th percentile of hourly concentrations of NO2 and daily concentrations of PM10 in three selected grid squares in Oslo, winter 1994/95 N Mean Maximum Standard 98th percentile Grid square cases /mg m-3 /mg m-3 deviation /mg m-3 Hourly NO2 Carl Berners plass 4368 53 139 25.8 101 Majorstua 4368 47 127 25.2 94 Lysaker 4368 63 169 28.0 117 Daily PM10 Carl Berners plass 182 17 81 13.6 60 Majorstua 182 12 47 8.6 37 Lysaker 182 30 112 23.0 63 Table 2 Calculated rate of change of hourly concentrations of NO2 and daily concentrations of PM10 in three selected grid squares in Oslo, winter 1994/95.Absolute values of both positive and negative changes in concentrations are included Maximum rate 98th percentile of N of change rate of change Standard Grid square cases /mg m-3 h-1 /mg m-3 h-1 deviation Hourly NO2 Carl Berners plass 4367 82.2 39.6 15.2 Majorstua 4367 80.7 38.8 13.9 Lysaker 4367 88.7 40.6 16.0 Daily PM10 Carl Berners plass 181 55.4 36.4 15.7 Majorstua 181 33.7 21.9 10.2 Lysaker 181 77.3 62.8 26.4 J.Environ. Monit., 1999, 1, 327–332 329Table 3 Duration of episodes and periods between episodes for NO2 and PM10 at three selected sites in Oslo. Percentages are presented both as a function of episode time and of total time Carl Berners plass Majorstua Lysaker Duration of episode Duration of episode Duration of episode % of % of % of Episode Total Episode Total Episode Total No.time time No. time time No. time time Hourly NO2>100 mg m-3 Episode length/h 1–2 51 85 1.33 18 90 0.46 135 79.4 3.98 3–7 9 15 0.82 2 10 0.14 30 17.6 2.68 8+ 0 0 0.00 0 0 0.00 5 2.9 1.01 Inter-episode period 1–2 h 7 11.5 0.25 2 9.5 0.07 35 20.4 1.12 3–8 h 5 8.2 0.73 1 4.8 0.09 34 19.9 4.12 9–24 h 10 16.4 4.42 2 9.5 0.73 60 35.1 23.53 2 days 10 16.4 7.85 2 9.5 1.81 22 12.9 19.55 3 days 8 13.1 12.09 1 4.8 1.19 9 5.3 13.76 4 days 4 6.6 7.49 1 4.8 1.81 6 3.5 12.52 5 days 7 11.5 18.20 3 14.3 7.90 2 1.2 5.43 <5 days 10 16.4 46.82 9 42.9 85.81 3 1.8 12.29 Daily PM10>35 mg m-3 Episode length/days 1 8 40 4.40 5 100 2.75 14 23 7.69 2 3 30 3.30 0 0 0.00 5 16 5.49 3 2 30 3.30 0 0 0.00 3 15 4.95 4–5 0 0 0.00 0 0 0.00 3 23 7.69 6+ 0 0 0.00 0 0 0.00 2 23 7.69 Inter-episode period/days 1–3 6 7 6.59 1 2 1.65 18 26 17.03 4–10 3 17 14.84 1 5 4.40 9 48 31.87 11–20 3 26 23.08 1 10 9.34 0 0 0.00 21–50 2 50 44.51 1 19 18.13 1 26 17.58 51+ 0 0 0.00 2 66 63.74 0 0 0.00 Table 4 Maximum hourly and daily concentrations in episodes for NO2 and PM10 at three selected grid squares in Oslo Carl Berners plass Majorstua Lysaker Episode peak/mg m-3 Frequency % Frequency % Frequency % Hourly NO2>100 mg m-3 100–110 36 60.0 15 75.0 103 60.6 111–130 23 38.3 5 25.0 49 28.8 131–150 1 1.7 0 0 15 8.8 151–170 0 0 0 0 3 1.8 >171 0 0 0 0 0 0 Daily PM10>35 mg m-3 35–40 3 23.1 4 80.0 6 22.2 41–50 5 38.5 1 20.0 4 14.8 51–70 3 23.1 0 0.0 6 22.2 71–90 2 15.4 0 0.0 7 25.9 91+ 0 0.0 0 0.0 4 14.8 situations, it is of interest to know whether the 2% of time Table 5 DiVerent expressions for the cumulative dose of hourly NO2 and daily PM10 for diVerent thresholds in three selected grid squares that values exceed the 98th percentile all occur during the same episode, as opposed to occasional but not consecutive Threshold=100 mg m-3 high values.This diVerentiating can only be carried out by No. of (hourly NO2) examining the number of episodes and the episode peak height. hours/days and 35 mg m-3 Episodes can be characterised by the peak height, the highest Grid square over threshold (daily PM10) concentration reached during the episode (Table 4). At Hourly NO2 AOT100/mg m-3 h-1 Lysaker, for NO2, as many as 10.6% of episodes had peak Carl Berners plass 94 870 values over 130 mg m-3, whereas Carl Berners plass had only Majorstua 26 174 1.7% of such episodes.The episode concentrations of PM10 Lysaker 335 4001 were higher. At Lysaker 15% of episodes exceeded 91 mg m-3 Daily PM10 AOT35/mg m-3 day-1 daily average. Carl Berners plass 20 258 In the future, control measures may be formulated as a Majorstua 5 24 restriction in the number of episode peak values over a given Lysaker 61 1290 concentration instead of the 98th percentile. 330 J. Environ. Monit., 1999, 1, 327–332Table 6 Distribution of episode AOT for NO2 and PM10 at three selected sites in Oslo. (Threshold=35 mg m-3 for daily values of PM10 and 100 mg m-3 for hourly NO2) Carl Berners plass Majorstua Lysaker Episode AOT S mg m-3 Frequency % Frequency % Frequency % NO2 AOT100/mg m-3 h-1 0–50 33 55.0 14 70.0 92 54.1 51–80 17 28.3 5 25.0 43 25.3 81–150 8 13.3 1 5.0 15 8.8 151–300 1 1.7 0 0 6 3.5 301–600 1 1.7 0 0 6 3.5 601–1000 0 0 0 0 5 2.9 1001–2000 0 0 0 0 3 1.8 Daily PM10 AOT35/mg m-3 day-1 0–10 9 45 4 80 7 11 11–30 5 25 1 20 14 23 31–60 6 30 0 0 2 3 61–100 0 0 0 0 19 31 101–150 0 0 0 0 15 25 151+ 0 0 0 0 4 7 3.4 Episodes—integrated exposure percentile).For ozone, it has been suggested to use AOT60= 0 (ppb h-1) as an approximation for health guidelines.10 For some short-term health eVects it is the concentration that As this paper illustrates, however, these air quality indicators is decisive in initiating a health eVect. However, for other do not account for the time variability of the exposure, and short-term and for long-term eVects, the cumulative or inte- do not account properly for the cumulative exposure to grated exposure may be the determinant in causing an eVect.pollution. As knowledge of the health eVects of pollution It may be of importance whether an episode having a ‘total increases, there is a basis for specifying limits based on both exposure’ of 120 mg m-3 h-1 occurs as 60 mg m-3 over 2 h or temporal patterns and cumulative exposure.as 120 mg m-3 over 1 h. In the future, it may be necessary to The following set of indicators seems useful in evaluating specify in addition to an AQG, a limited total integrated the health eVects of air pollution based on the time structure exposure, or a maximum acceptable integrated exposure over of the exposure.one single episode. (1) Annual mean, daily/hourly mean, 98th percentile, 99.9th Another and more usual way is to define the accumulated percentile (or other high percentiles). exposure over a given threshold (AOT exposure for episodes (2) Total semi-annual AOT35/AOT100, etc. (threshold or episode AOT35/100).Based on the example given above chosen separately for each component). this would mean that with a threshold of 50 mg m-3 h-1, the (3) Total semi-annual number of episodes. 2 h episode would have a value of 20 mg m-3 h-1, whereas (4) Episode AOT35/100 etc. the 1 h episode would have a value of 70 mg m-3 h-1. The These indicators will allow a more complete description of the AOT calculated over a nonzero threshold would give more air pollution situation, so that regulations may be developed weight to the higher peaks.In Table 5, the summed results of in the future that will more completely protect the population. the two ways of calculating the cumulative dose are presented, This paper attempts to show how current modelling tools together with the total number of hours of ‘episode time’. The may be used to develop and present diVerent air quality frequency distribution of the individual episode integrals is indicators.It will be the role of health authorities to specify, presented in Table 6. if necessary, what regulations need to be imposed. They will set maximum allowable integrated episode exposures, maximum number of allowable episodes, and maximum allowable 4.General discussion peak episode concentrations. Guidelines should set the limits of population exposure to air pollution to protect health and the environment. Currently, guidelines exist that use standard statistical concepts such as 5. Acknowledgements mean and maximum concentrations, or percentiles. However, This study was funded by the Norwegian Pollution Control maximum values are very diYcult to use, since errors in Authority.measurement or estimation can produce false maxima. Means may not reflect suYciently the short-term peaks in exposure. The 98th percentile (or other high percentile) is a more References stable measure. 1 WHO Air Quality Guidelines for Europe, WHO Regional The European Commission3 has recently proposed guide- Publications, European Series No. 23, Copenhagen, 1987.lines with percentiles diVerent from the 98th percentile. For 2 WHO Air Quality Guidelines for Europe, WHO Regional example for sulfur dioxide, hourly maxima cannot be exceeded Publications, European Series, Copenhagen, 1998, Internet address: http://www.who.dk/eh/airqual.htm more than 24 times per year and daily maxima, 3 times per 3 Commission of the European Communities, Common position year (equivalent to a 99.97th percentile) whereas for NO2, (EC) No 57/98, OV.J. Eur. Com. C360, 1998, pp. 99–122. hourly maximum concentrations cannot be exceeded more 4 WHO Quantification of health eVects related to SO2, NO2 , O3 than 18 times per year (equivalent to a 99.8th percentile). For and particulate matter exposure.Report from the Nordic Expert PM10 the daily maximum values cannot be exceeded more Meeting, Oslo, 15–17 October, 1995, ed. J. Clench-Aas and than 35 times per year in 2005 (equivalent to a 90.4th percen- M. Krzyzanowski, Kjeller (NILU OR63/96) (EUR/ICP/EHAZ 94 04/DT01), 1996. tile) and 7 times per year in 2010 (equivalent to a 98th J. Environ. Monit., 1999, 1, 327–332 3315 M. J. Hazucha, D. V. Bates and P. A. Bromberg J. Appl. Physiol, base, part of the follow-up process for the Helsinki Declaration, 1989, 67, 1535. Phase 2 (in Norwegian), National institute for public health, 6 S. E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram and K. E. Oslo, 1998. Grønskei, J. Environ. Monit., 1999, 1, 321. 10 United Nations Economic Commission for Europe/World Health 7 K. E. Grønskei, S. E.Walker and F. Gram, Atmos. Environ., 1993, Organization, Health eVects of ozone and nitrogen oxides in an 27B, 105. integrated assessment of air pollution, convention on Long-Range 8 J. Samet, S. L. Zeger and K. Berhane, Particulate air pollution and Transboundary Air Pollution, the proceedings of an International daily mortality: replication and validation of selected studies, the Workshop, Eastbourne, UK, 10–12 June, 1996, Institute for phase I report of the particle epidemiology evaluation project, Environment and Health, University of Leicester. Health EVects Institute, Cambridge, MA, 1995. 9 National Institute for Public Health, Norms/threshold values/standards. I: Environment and health—a research based knowledge Paper 9/02778E 332 J. Environ. Monit., 1999, 1, 327–332

ISSN:1464-0325    

DOI:10.1039/a902778e

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimation Part IV.‡ Urban exposure in children† Jocelyne Clench-Aas,* Alena Bartonova, Knut E. Grønskei and Sam-Erik Walker Norwegian Institute for Air Research, P.O. Box 100, N-2027 Kjeller, Norway. E-mail: jocelyne.clench-aas@nilu.no; Fax: +47 63 89 80 50; Tel:+47 63 89 80 00 Received 7th April 1999, Accepted 25th June 1999. In the winter of 1994, 2300 school-age children in Oslo participated in a panel study of the role of traYc pollution on the exacerbation of diseases of the respiratory system and other symptoms of reduced health and well being in children.The children filled out a diary daily with information for five time points over six weeks. In order to quantify exposure–eVect relationships for the symptoms, individual exposure to NO2 and particulate matter (PM2.5) was estimated, using the DINEX method a combination of information from the diary as to the children’s whereabouts during the five time points each day, coupled with continuous dispersion modelling.An individual exposure estimate for each time point for each child was defined. Individual exposure estimated using dispersion modelling can be used to examine patterns of exposure such as isolating geographic areas with higher concentrations or describing concentrations of pollution by time of day. The diary allowed the time-use of the children to be described.reduced general health. The study was performed during the 1. Introduction winter of 1994 at 12 schools. The chosen study design enhanced In all air pollution related studies, it is important to define the possibility of measuring dose-response relationships. and measure exposure.It is of vital interest in properly defining exposure-eVect relationships that exposure is estimated as correctly as possible. The principal source of air pollution in Oslo is vehicular traYc. It is therefore natural to focus on the 2. Materials principal pollutants from traYc, such as NOx, NO2 and 2.1 Study design particulate matter (PM).Air pollution exposure is the average concentration of air pollution that each individual is in reality To study the role of exposure to air pollution on the incidence exposed to over a period of time (hour, day, etc.).1 and prevalence of asthma and other airway diseases in children, Children are among the most sensitive sub-population both a study involving a series of investigations was initiated in Oslo.because of physiological factors that influence uptake and Ambient pollution exposure to particulate matter (PM2.5) because of their lifestyle which results in them being highly and nitrogen dioxide was estimated using dispersion models active outdoors and thus more exposed to pollutants than coupled to measurements, for each child’s home address, for are adults.schools, homes and other neighbouring places visited by the Panel studies, which investigate individuals repeatedly, may child during the 6 week period. be used to study the relationship of short-term variations in An important feature of this study is the opportunity to exposure and disease aggravation.In these studies, each indi- combine the information collected in the panel study with that vidual is his/her own control. Health and well-being while collected in an initial cross-sectional study. The cross-sectional exposed to pollution is compared to health and well-being questionnaire based investigation (the NOx–Ox study) on when not exposed. Studies done in children have usually children attending grades 1–6 in the primary schools in Oslo shown weak but significant associations between exposure to was chosen to study the eVects of air pollution on respiratory O3, SO2 , SO4 2-, PM10, PM2.5 and NO2 and symptom health.Parents of children attending two groups of schools reporting, medication use or decreased lung function.2–22 Not were invited to participate during the fall of 1993.The first all studies, however, find acute eVects of air pollution.23–28 group was in the more pollution-exposed centre of the city. As part of NILU’s (Norwegian Institute for Air Research’s) The second group lay in the peripheral higher areas of the research programme (NOx–Ox), financed by the Norwegian city, known to be less exposed to pollution.All schools in the Research Council (NFR), NILU has developed methods and centre of the city and those schools considered least exposed performed research on exposure to NOx and NO2 pollution were invited. Seventeen schools were willing to participate. in Oslo.29 The programme provided NILU with insight into The panel study, performed in Oslo during the following the dynamics of NOx and oxidant sources, distribution, and winter, forms the database for the present paper.It was aimed chemical processes. at studying the impact of air pollution on the ongoing health As part of this programme, a panel study of school-age of children, focusing on health problems related to the upper children in Oslo was initiated to study the eVects of exposure and lower airways.Symptoms were reported for 5 time periods to air pollution on symptoms of both respiratory disease and each school day, for 6 weeks during the winter by a panel of 2234 first to sixth graders attending 12 of the above mentioned schools. The children filled out the diary at school. The diary †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. ‡For Part III, see ref. 31. was developed so that it was easy for children to fill in the J. Environ. Monit., 1999, 1, 333–336 333required information. In addition, the children received concentrations of NO2 and sometimes particulate matter in ice-skating rinks. instruction in the form of an amusing story. The panel design allows short-term reversible changes in health status to be correlated with corresponding changes in 3.Results air pollution exposure. The aim is to study the temporal covariation of the health responses against a measure of 3.1 Air pollution exposure exposure. In this investigation type, each individual is the unit It is important when examining the eVects of separate com- of research. This reduces problems of confounding factors and pounds that the compounds are not too correlated.Since in allows each individual to be his/her own control. The collection Oslo, traYc is the principal source of pollution, the correlation of repeated data allows health status be compared with current between NO2 and PM2.5 had to be examined. The principal or preceding air exposure. source of NO2 is traYc and for PM2.5, traYc and home- The panel design also allows the eVects of individual air heating.One would expect with only one source to have a pollution components to be distinguished. This is dependent perfect 1 to 1 correlation between NO2 and PM2.5 . However, on choosing and measuring outcome variables sensitive to the sources diVer, and diVerent types of vehicles also diVer in small changes in air pollutant concentrations and using high the relative percentage of emissions of NO2 and PM2.5.enough time resolution. The ability to distinguish between Therefore the two compounds do not correlate completely. pollutants, or identify eVects of their interactions is dependent Fig. 1 shows the relationship between the estimates of NO2 on the pollutants not being strongly correlated with each and PM2.5 either as estimated at the home of the participant other.If they all vary uniformly, it is diYcult if not impossible or as the mean exposure of the child during 6 weeks in the to distinguish between them. winter as estimated using the diary. A study of this nature required approval from a series of As can be seen in Fig. 2 and Fig. 3 the overall geographical responsible institutions: the Director of Oslo Schools, the schools’ principals, parents’ associations, the teachers involved and the parents of each child.Information was disseminated in meetings with the involved parties. Approval for this study was obtained from the Data Inspectorate, and the Ethical Committee of the Norwegian Medical Association. 2.2 Choice of subjects Parents of all children in the participating schools from 1st through 6th grade (primarily from 7 to 12 years of age) received the selection questionnaire, accompanied by a letter explaining the panel study.The teachers delivered and collected the questionnaires. One written reminder was given. Those children with parental consent filled the diary at the school. The study sample was evenly divided by sex.There were a few children outside the typical range of 7 to 12 years. The population was considered randomly distributed since all children at chosen schools were invited to participate. 2.3 Air pollution exposure estimating The major diYculty in estimating ambient pollution exposure is that pollution concentrations locally can vary substantially. In order to handle this, a measurement programme was supplemented with results of dispersion modelling to estimate more precisely the pollution concentrations at the children’s homes, school and other places they visited (the DINEX method). The procedure used is described elsewhere.1 2.3.1 Individual pollution estimate. Modelling of air quality in the city was done on an hourly basis for the time periods and geographic places of interest.Hourly exposure to NO2 and PM2.5 was estimated for each child, for each day based on the location and other information provided in the diary, together with the ambient concentrations estimated using dispersion models and meteorological data.1,30 In the panel study, the covariance of the two compounds (NO2 and PM2.5) was 0.57. Each child was asked information for each time period concerning: being at home; being at school; being in walking or bicycling distance from home; being other places in Oslo; being outside Oslo; and being in downtown Oslo. 2.3.2 Socio-demographic parameters. In addition for each time children reported whether or not they were near heavy traYc, were in a room where people smoked, were indoors, Fig. 1 Relationship between (a) average NO2 and PM2.5 estimated at slept with the window open or had been in a skating hall.The each home, (b) average NO2 and PM2.5 exposure (STINEX) of each child and (c) (DINEX) individual time interval estimates. latter question was introduced because of the known high 334 J. Environ. Monit., 1999, 1, 333–336distributions of the estimates of NO2 and PM2.5 are not identical.This is a result of the diVerent concentration profiles of emissions from diesel and gasoline driven vehicles, and the diVerent patterns of driving of buses and trucks as opposed to cars. An additional emission source for particles is home heating. Even though the figures are based on overall estimates calculated during the winter prior to the study, the concentrations would not change substantially over 1 year.Fig. 4 shows the mean exposure of the students attending selected schools. Schools M to P lie in less polluted areas surrounding the city. The spread even shows a slight overlap between the exposed and unexposed areas of the city. Fig. 2 Estimated exposure to the 98th percentile of hourly NO2 over Box plots of exposure by time of day show a trend with the city of Olso.lowest values at night (see Fig. 5). 3.2 Time-use Diary studies oVer the possibility of describing the time-use patterns of the participants. The children in this study spent an average of: (1) 57% of the time intervals at home (at their home address, not inclusive of second parent, or other place that they spend the night); (2) 20% of the time intervals at school; (3) 10% of their time in walking or bicycling distance from home; (4) 6% of the time other places in Oslo; (5) 1% of the time downtown Oslo; and (6) 2% of the time outside of Oslo.They are 62% of the time indoors (this question was phrased in a reverse direction from the others and was most prone to misinterpretation). They are exposed to passive smoking 6% of the time; they are near heavy traYc 3% of the time; and they are less than 1% of the time at an ice-skating hall.Fig. 3 Estimated exposure to the 98th percentile of hourly PM2.5 over the city of Oslo. 4. Discussion Isolating an eVect of air pollution exposure on health requires refined methods. The eVects are often, if present, relatively Fig. 4 Box plot of exposure to NO2 and PM2.5 of the participants by the school of attendance.School M through P lie in the outskirts of Fig. 5 Box plot of NO2 and PM2.5 for the diVerent time intervals of the city in less polluted areas. The line represents the median, the box the 25th and 75th percentile and the standard deviation. the study. J. Environ. Monit., 1999, 1, 333–336 335Gnehm,M. Rutishauser and H.U.Wanner, Am. Rev. Respir. Dis., small although important. Improved measures of exposure are 1992, 145, 42. necessary to find these small eVects, and to create exposure- 10 L. M. Neas, D. W. Dockery, H. Burge, P. Koutrakis and F. E. eVect relationships. Speizer, Am. J. Epidemiol., 1996, 143, 797. In the panel study of children a diary method was chosen 11 W. S. Linn, D.A. Shamoo, H. R Anderson, R.-C. Peng, E. L. to estimate exposure. This method allows exposure to be Avol, J. D. Hackney and H. Gong, Jr., J. Expo. Anal. Environ. Epidemiol., 1996, 6, 449. estimated for a greater number of children, over a longer time 12 M. E. Gordian, H. Ozkaynak, J. Xue, S. S. Morris, and J. D. period and without changing their normal lifestyle as compared Spengler, Environ.Health Perspect, 1996, 104(3), 290–297. to personal monitoring equipment. 13 R. T. Burnett, R. E. Dales, M. E. Raizenne, D. Krewski, P. W. A more refined method of exposure estimating that takes Summers, G. R. Robergs, M. Raad-Young, T. Dann, and advantage of geographical diVerences in source profile of J. Brook, Environ. Res., 1994, 65(2), 172. emissions, allows developing dose-response functions for 13 A.Pinter, P. Rudnai, E. Sarkany, M. Goczan and A. Paldy, Cent Eur. J. Public Health, 1996, 4 Suppl, 17. eVects occurring at low concentrations. The individual pol- 14 R. Buchdahl, A. Parker, T. Stebbings, and A. Babiker, BMJ, 1996, lution exposure estimate also allows pollution exposure by for 312(7032), 661. example school or geographic district to be assessed. 15 I. Romieu, F. Meneses, S. Ruiz, J. J. Sienra, M. C. White, and Socio-demographic parameters showed wide variation in R. A. Etzel, Am. J. Respir. Crit. Care Med., 1996, 154(2 Pt 1), 300. this study. The time use information collected using a diary 16 W. S. Linn, D. A. Shamoo, H. R. Anderson, R. C. Peng, E. L. with children is fairly unique. The 60% time interval spent at Avol, J.D. Hackney, and H. Gong, Jr., J. Expo. Anal. Environ. Epidemiol., 1996, 6(4), 449. home, however, may be too low, since it did not properly 17 W. Roemer, G. Hoek and B. Brunekreef, Am. Rev. Respir. Dis., account for time spent with a second parent in divorced homes 1993, 147(1), 118. (only one home was registered, time spent at the other parent 18 A. Peters, I.F. Goldstein, U. Beyer, K. Franke, J. Heinrich, D. W. went into a more general category). However, the other Dockery, J. D. Spengler, and H. E.Wichmann, Am. J. Epidemiol., categories were more realistic, 20% of time intervals at school, 1996, 144(6), 570. and only 3% were near heavy traYc. 19 J. F. Scarlett, K. J. Abbott, J. L. Peacock, D. P. Strachan and H. R. Anderson, Thorax, 1996, 51(11), 1109.The ambient pollution exposure method is a valuable 20 A. Peters, D. W. Dockery, H. J. Heinrich, and H. E. Wichmann addition to methodology for panel studies aimed at examining Environ. Health Perspect, 1997, 105(4), 430. the health eVects of ambient pollution. 21 M. H. Gielen, S. C. van der Zee, J. H. van Wijnen, C. J. van Steen and B. Brunekreef, Am. J. Respir.Crit. Care Med., 1997, 155(6), 2105. 5. Acknowledgements 22 J. R. Goldsmith, M. D. D. Friger, and M. Abramson, Arch. The study was financed by the Norwegian Research Council Environ. Health, 1996, 51(5), 359. 23 G. Bjerknes-Haugen, J. Clench-Aas, S. O. Samuelsen, A. and the Norwegian Institute for Air Research. Bartonova and L. S. Bakketeig, Cent. Eur. J. Public Health, 1995, 3(1), 13. 6. References 24 S. Vedal, M. B. Schenker, A. Munoz, J. M. Samet, S. Batterman and F. E. Speizer, Am. J. Publ. Health, 1987, 77, 694. 1 J. Clench-Aas, A. Bartonova, T. Bøhler, K. E. Grønskei and 25 H. Luttmann, U. Gromping, L. Kreienbrock, C. Treiber-Klotzer S. Larssen, 1999, J.Environ. Monit., 1999, 1, 313. and H. E. Wichmann, Zentralbl. Hyg. Umweltmed., 1995, 198(2), 2 O. Herbarth, Environ.Health Perspect. 1995, 103(9), 852. 172. 3 L. M. Neas, D. W. Dockery, P. Koutrakis, D. J. Tollerud and 26 G. Hoek and B. Brunekreef, Environ. Res., 1994, 64, 136. F. E. Speizer, Am. J. Epidemiol., 1995, 141, 111. 27 G. Hoek and B. Brunekreef, Am. J. Respir. Crit. Care Med., 1995 4 C. A. Pope, III, D. W. Dockery, J. D. Spengler and M. E. 151, 27. Raizenne, Am. Rev. Respir. Dis., 1991, 144, 668. 28 W. Roemer. et al., Eur. Respir. Rev., 1998, 8(52), 125. 5 C. A. Pope, III and D. W. Dockery, Am. Rev. Respir. Dis., 1992, 29 S. Larssen, K. E. Grønskei, F. Gram, L.O.Hagen, and S. E. 145, 1123. Walker, in: Air Pollution Modeling and Its Application X. ed., S. E. 6 J. Schwartz, D. W. Dockery, L. M. Neas, D. Wypij, J. H. Ware, Gryning and M. M. Millan, Plenum Press, New York, 1994, J. D. Spengler, P. Koutrakis, F. E. Speizer and B. G. Ferris, Jr, pp. 91. Am. J. Respir. Crit. Care. Med., 1994, 150, 1234. 30 S. E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram and K. E. 7 M. J. Studnicka, T. Frischer, R. Meinert, A. Studnicka-Benke, Grønskei, 1999, J. Environ. Monit., 1999, 1, 321. K. Hajek, J. D. Spengler and M. G. Neumann, Am. J. Respir. 31 J. Clench-Aas, C. Guerreiro and A. Bartonova, J. Environ. Monit., Crit. Care. Med., 1995, 151, 423. 1999, 1, 327. 8 B. D. Ostro, M. J. Lipsett, J. K. Mann, H. Braxton-Owens and M. C. White, Inhal. Toxicol., 1995, 7, 711. 9 C. Braun-Fahrla�nder, U. Ackermann-Liebrich, J. Schwartz, H. P. Paper 9/02779C 336 J. Environ. Monit., 1999, 1, 333–3

ISSN:1464-0325    

DOI:10.1039/a902779c

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimation Part V.‡ TraYc exposure in adults† Alena Bartonova,* Jocelyne Clench-Aas, Frederick Gram, Knut Erik Grønskei, Cristina Guerreiro, Steinar Larssen, Dag A. Tønnesen and Sam-Erik Walker Norwegian Institute for Air Research, PO Box 100, N-2027 Kjeller, Norway. E-mail: alena.bartonova@nilu.no; fax:+47 63 89 80 50 Received 7th April 1999, Accepted 25th June 1999 In Oslo, traYc has been one of the dominating sources of air pollution in the last decade.In one part of the city where most traYc collects, two tunnels were built. A series of before and after studies was carried out in connection with the tunnels in use. Dispersion models were used as a basis for estimating exposure to nitrogen dioxide and particulate matter in two fractions.Exposure estimates were based on the results of the dispersion model providing estimates of outdoor pollutant concentrations on an hourly basis. The estimates represent concentrations in receptor points and in a square kilometre grid. The estimates were used to assess development of air pollution load in the area, compliance with air quality guidelines, and to provide a basis for quantifying exposure-eVect relationships in epidemiological studies.After both tunnels were taken in use, the pollution levels in the study area were lower than when the traYc was on the surface (a drop from 50 to 40 mg m-3). Compliance with air quality guidelines and other prescribed values has improved, even if high exposures still exist. The most important residential areas are now much less exposed, while areas around tunnel openings can be in periods exposed to high pollutant concentrations.The daily pattern of exposure shows smaller diVerences between peak and minimum concentrations than prior to the traYc changes. Exposures at home (in the investigation area) were reduced most, while exposures in other locations than at home showed only a small decrease.Highest hourly exposures are encountered in traYc. for comparison of air quality in the area before and after the 1. Introduction tunnels were built. In Oslo, traYc has been one of the dominating sources of air The investigations are designed as a sequence of a crosspollution in the last decade. The traYc authorities implemented sectional and a diary study.This method provides a powerful plans for reducing the impact of traYc on the urban environ- tool for describing patterns in exposure. The cross-sectional ment, especially in those parts of the city where most traYc study provides a detailed description of exposure in the collects. One of these parts is the Old Oslo/Oslo East, where residential area (study area proper), where the individuals the traYc was directed from the surface into tunnels.In spend most of their time. However, if the whole pattern of connection with taking the tunnels in use, three environmental exposure is to be established, information is needed about studies were carried out between 1987 and 1996 individual’s movements through all environments, including (Kolbenstvedt1). They served as before and after studies to home, at work, in traYc and other places.Only by evaluating two separate tunnel projects. Air pollution and noise were the the whole pattern of exposure, can eVective measures be central environmental indicators. Impact parameters that were suggested that would prioritise alleviating the situation least studied were annoyance, symptoms of reduced health and well advantageous from the point of view of human health or other being and chronic diseases.environmental concerns. The investigations aimed at assessing the impacts of traYc changes in a geographically small study area with substantial 2. Aim of the study pollution gradients. A method for estimating personal exposure had to provide a suYciently exact individual estimate of The study aimed at providing estimates of personal exposure pollution exposure to reflect the gradients well.It was therefore to nitrogen oxides and particulate matter for a representative natural to use dispersion models as a basis. sample of population in a restricted study area. These estimates In Oslo, traYc and spatial heating are the principal source were to reflect the pollution gradients in the study area.of air pollutants. The primary compounds of interest are Further, the estimates were to be used to provide a description therefore nitrogen oxides (NO2 and NOx) and fine and coarse of impacts of traYc alleviating measures, studied in a series particulate matter (PM). In Norway, no significant indoor of before and after studies. The estimates were to serve as a sources of these compounds exist, except for smoking (a source basis for establishing if air quality guidelines were satisfied, of particulate matter, Braathen2).It is therefore reasonable to and were to be used to quantify exposure-eVect relationships assume that personal exposure reflects well outdoor concen- between air pollution and a variety of eVect variables related trations.Estimates of outdoor concentrations can be used to to health and well being. construct suitable environmental indicators. The same air pollution estimates can be used both as indicators of personal 3. Methods exposure in a health and annoyance study, and as descriptors The three consecutive environmental surveys carried out in 1987, 1994 and 1996 have been performed as cross-sectional †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999.‡For Part IV, see ref. 15. epidemiological studies, with a subsequent diary study. The J. Environ. Monit., 1999, 1, 337–340 337surveys were carried out using almost identical design and The traYc measures were taking in use the newly built tunnels, restricting or closing roads for through-traYc, lower- methods, and the data were pooled for the final analysis.Each individual was assigned an estimate of air pollution exposure ing of speed limits on some road links, and increasing speed limit on the main throughway. and an estimate of exposure to noise, both assessed outside the individual’s home. In addition, the collected data include 3.3 Dispersion calculations and exposure estimates individual personal information about health, well being and perception of the environmental quality in the survey area, The dispersion model EPISODE (Grønskei et al.,6 Larssen and about mobility within the area and in Oslo.et al.,7 Walker8 and Walker et al.9) was used in this study. The model is a combination Lagrangian–Eulerian urban scale 3.1 Design of the study model, and provides estimates of concentration fields or recep- The surveys were designed as two-step epidemiological studies.tor point concentrations on an hourly basis for NO2, NOx , In the first step, a cross-sectional design was used to provide PM2.5 and PM10. Inputs to the model are meteorological data, a snapshot of the situation in the investigation time-point emission inventories and background pollution concentrations.(Klæboe et al.3 and Clench-Aas et al.4). For each of the three Walker et al.10 describe the model validation. consecutive years, a sample of over 1100 individuals was In order to improve the emission database for traYc in the selected for an interview carried out either by personal visit main study area, traYc counts were performed and traYc (1987), or over the phone (1994, 1996).The sample was density was estimated for each road link in the 2×3 km geographically stratified. investigation area (Hanssen and Grue11 and Hanssen12). In the second step, a sub-sample of volunteers was recruited TraYc in the area is then described as line sources. Outside from the participants in the cross-sectional study.They were the study area, a lesser-resolution traYc database for Oslo asked to fill out a diary with hourly resolution, for a period was used, with main roads as line sources, and smaller roads of 2–3 weeks. The diary contained information about the summarised as area sources. Other emission inventories include participant’s whereabouts, their activity and their well being.ship and air traYc and spatial heating defined as area sources, For each individual in the study, the home address and each and individual larger point sources. Detailed emission invenaddress indicated in the diary were recorded and coded into a tories with hourly resolution were available for NOx, PM2.5 co-ordinate system. Within the study area proper, the position and PM10 for each year.Actual meteorological data were also of the centre of the building’s fac�ade defined a receptor point. available from a combination of measurements and models, This coding was performed with a 5 m resolution using a map- on an hourly basis for the modelling periods. based GIS program. The air pollution and traYc noise estimates The exposure estimates used for the cross-sectional investiwere calculated for each such receptor point (Kolbenstvedt1 and gation (STINEX model, Clench-Aas et al.5), were for each Clench-Aas et al.5).For the addresses indicated in the diary, receptor point and year based on estimated hourly concenthat were outside the study area proper, the addresses were trations of air pollutants over a period September through coded into the co-ordinate system with a precision down to December, thus including the interview period (usually in 100 m.This was because less exact information about these October). In addition, this period is usually fairly representalocations was available, and in addition, the input data for the tive for calculating annual concentrations. Actual meteorologiexposure models were less detailed. For these additional receptor cal data were used for each hour.The exposure estimate did points, exposure to air pollution was calculated. not take into account the floor on which the actual home is located, or information on orientation of the main living and 3.2 The main study area and traYc measures sleeping quarters relative to the neighbouring streets, despite The approximately 2×3 km core study area in central Oslo, the availability of this information. where the participants in the environmental survey lived, For the diary study, exposure was estimated for each collects a substantial part of through traYc towards the individual at each indicated location for each hour (DINEX suburbs east of the city and to the north of the country (see model, Clench-Aas et al.5).For locations within the study Fig. 1). Within the area, 8 sub-areas in 1987 and 14 sub-areas area proper, the estimate was the value at the receptor point. in 1994 and 1996 were identified on the basis of the planned For locations in Oslo, but outside the study area proper, the traYc changes. The sub-areas included background areas with estimate represented average concentration in the appropriate not much expected traYc changes, areas where traYc was to grid square, for a given hour.For estimates of exposure while drop dramatically, areas where the traYc was to increase, and in traYc and while shopping, representative grid squares were areas situated by the tunnel openings. selected, describing squares with low, medium and high traYc density.If an individual was at several locations for a given hour, the exposure was represented by a weighted average of estimated outdoor concentrations at each of the locations. 3.4 Selected indicator compounds In both the cross-sectional and the diary study, the main pollutants of interest are nitrogen dioxide (NO2) and particulate matter (PM). In Oslo, particulate matter is emitted from point and area sources connected to spatial heating, and from exhaust of the vehicular traYc.In addition, the use of studded tyres also generated particulate pollution in the winter half year. In this study, particulate matter was considered from all the sources. The individual sources contribute by diVerent amounts to the total mass in the two fractions, PM2.5 and PM10.A special feature of the model made is that it is also possible to single out the studded tyres contribution. In the cross-sectional study, four compounds were estimated: NO2, Fig. 1 Map of Oslo with an enhanced view of the study area with PM2.5, PM10 and PM10–2.5. In the diary study, results are sub-areas. Scattered line marks the main roads from 1987, the broken line with two dots indicates the tunnels.shown here only for nitrogen dioxide. 338 J. Environ. Monit., 1999, 1, 337–3403.5 Air quality indicators Air quality indicators in this study may be defined quite freely, as hourly pollution estimates by a dispersion model are available for each receptor point. For the cross-sectional study, indicators included for each receptor point are the arithmetic mean, 98th percentile and maximum concentration during the calculation period.These statistics were for NO2 based on hourly concentrations, and for particles, on daily concentrations (although the model provides hourly data). In this way, the average concentration is a suitable indicator for the epidemiological study, the maximum provides a basis to compare with Norwegian legislation, and the 98th percentile provides a robust indicator of changes in the occurrence of high concentrations.Fig. 2 Cumulative distribution functions of individual period exposure 4. Results to NO2 in 1987, 1994 and 1996. 4.1 Cross-sectional study In the three cross-sectional investigations, the 3193 individuals air quality guideline (100 mg m-3 hourly NO2, 70mg m-3 that participated have lived at 488 distinct addresses divided daily PM10). into 14 sub-areas.The sub-areas were chosen along specified Fig. 3 shows that a certain improvement in compliance has road links, based on expected traYc development at these been observed. Unlike the description of the development in links. The sub-areas included areas with expected massive personal exposure above, this assessment is based on the improvement in traYc burden, areas with no expected change development of receptor points, using all existing receptor and areas with expected worsening of the situation.points encountered in the three years, and not only the receptor In the study area as a whole, the number of vehicle- points present at a given year. kilometres has increased from 639 000 in 1987 to 854 000 in 1996 and the traYc speed has somewhat increased, as the 4.2 Diary study main throughway has an increased speed limit.In the whole Individual exposure may be described in detail using a diary of Oslo, however, the traYc volume seems to be unchanged method. Here, information is recorded every hour as to where (ref. 13). the individual has been.From this account, detailed patterns The average exposure to NO2 (Table 1) has decreased from can be constructed, but most importantly, it can be assessed 51 to 40 mg m-3. A similar decrease is seen for particulate matter in both fractions, however, the fraction PM10–2.5 attributed to studded tyres did not decrease. In order to judge reasons for this development, it is necessary to consider meteorological conditions in some detail, but this information can not be easily evaluated. In general, the most favourable dispersion conditions were in 1994, while the least favourable were in 1987.The dispersion of the PM10–2.5 fraction is mainly influenced by wetness conditions of the road surface, and diVerences in the wetness explain the development in this indicator.Indicators of high but less frequent exposure, the 98th percentiles, show a similar decrease as for the average values. However, Fig. 2 shows that highly exposed homes still exist in the latter years. These are now mostly located near the tunnel outlets as opposed to the situation in 1987, when the highest exposures occurred more often and along the most traYcked road segments.Fig. 3 Receptor points (%) above the action limit (ACT), the mapping The estimated exposure at a home address may be used to limit (MAP) and the national air quality guideline (NGL). assess compliance with air quality guidelines and other reference values. In Norway, at the time of the study, the following air quality limits were set: Action limit (300 mg m-3 hourly NO2, 350 mg m-3 daily PM10), mapping limit (200 mg m-3 hourly NO2, 300 mg m-3 daily PM10), and suggested national Table 1 Development of exposure at home addresses.Numbers represent averagesver the 3 month period of calculation 1987 1994 1996 Exposure parameter mg m-3 mg m-3 mg m-3 Average hourly NO2 51 43 40 98th percentile NO2 136 97 101 Average daily PM10 30 18 20 98th percentile PM10 121 61 75 Average daily PM2.5 21 13 12 Fig. 4 Frequency distribution of hourly exposure to NO2 in a diary 98th percentile PM2.5 64 29 45 study, 1987 and 1994.J. Environ. Monit., 1999, 1, 337–340 339Table 2 Average exposure value for the two years with diary and therefore the source of the changes was not identified with average exposure duration at five types of locations, 1987 and 1994.certainty. Based on diary study The estimates of exposure outside individual homes and for each type of location, provided in this study, are an important Estimated average hourly NO2 Average input to epidemiological studies. This complex information concentration hours spent (%) can provide a basis for a socio–economic assessment of impacts 1987 1994 1987 and 1994 of large road projects, or other important changes.In addition, Home 23 19 70.8 the results provide a good approximation of outdoor pollution At work 21 36 14.2 levels, so that the eVect of changes can be evaluated against At school 31 14 2.8 the current legislation. The methodology provides a result that Other places 31 15 8.5 can be used flexibly to address diVerent scientific and regulat- While in traYc 70 40 3.8 ory needs.The study was funded by the Royal Norwegian Council for Scientific and Industrial Research/Norwegian Research Council, The Directorate of Public Roads, and the Norwegian Institute for Air Research. The study was co-ordinated by the Institute of Transport Economics in Oslo, Norway. References 1 M. Kolbenstvedt, Environmental consequences of main road diversion in Oslo East.Summary of studies 1987–1996 (in Norwegian) Institute of Transport Economics, Oslo, (TØI report 405/1998), 1998. 2 O. A. Braathen, Results of indoor/outdoor measurements, in NILU/NIPH. Air pollution and short term health eVects in an industrialised area in Norway, main report, Norwegian Institute for Air Research (NILU OR 81/91), Lillestrøm, 1991.Fig. 5 Daily pattern of exposure to NO2, diary study 1987 and 1994. 3 R. Klæboe, M. Kolbenstvedt, J. Clench-Aas and A. Bartonova, A holistic approach to assess traYc measures, in 8th International Symposium on Transport and Air Pollution, ed. P. J. Sturm, Technical University Graz, Graz, Report of the Institute for as to where to most eVectively direct measures to reduce Internal Combustion Engines and Thermodynamics, 1999, impact of outdoor air pollution. vol. 76, pp. 7–14. 4 J. Clench-Aas, A. Bartonova, M. Kolbenstvedt and R. Klæboe, In 1987 and 1994, 114 and 118 persons under 60 years Quantifying eVect of traYc measures using individual exposure mod- participated in the diary study, with a typical participation of elling, in 8th International Symposium on Transport and Air between 2 and 3 weeks.Similarly as in the cross-sectional part, Pollution, ed. P. J. Sturm, Technical University Graz, Graz, exposure to NO2 was lower in 1994 than in 1987, with a lower Report of the Institute for Internal Combustion Engines and percentage of hours registered with high exposures (Fig. 4). Thermodynamics, 1999, vol. 76. 5 J. Clench-Aas, A.Bartonova, T. Bøhler, K. E. Grønskei and The exposure may be classified as to where the participant S. Larssen, J. Environ. Monit., 1999, 1, 313. is situated: at home, at work/school, in traYc and other places. 6 K. E. Grønskei, S. E.Walker and F. Gram, Atmos. Environ., 1993, Exposure at a home address and in traYc was greatly reduced, 27B, 105. while for this particular population, exposure at work (i.e. 7 S.Larssen, K. E. Grønskei, F. Gram, L. O. Hagen and S. E. outdoor concentrations at a work address) increased somewhat Walker, Verification of urban scale time dependent dispersion model with subgrid elements in Oslo, Norway, in Air pollution modelling (see Table 2). While the work addresses in the two populations and its application X, ed. S.E. Gryning and M. M. Millan, Plenum may not be comparable, the home addresses are from the Press, New York, 1994, pp. 91–99. same general area, and the traYc exposure was estimated 8 S. E.Walker, The EPISODE air pollution dispersion model, version using the same reference traYc points. Thus, the results 2.2. Users Guide, Norwegian Institute for Air Research (NILU indicate a real decrease in the exposures.TR 10/97) Kjeller, 1997. 9 S. E. Walker, L. H. Slørdal, C. Guerreiro and K. E. Grønskei, The exposure can also be investigated for daily patterns. Development and evaluation of the urban dispersion model This is important for building epidemiological models, but EPISODE used in evaluating traYc diversion measures in Oslo, 8th also gives information about development in exposure in International Symposium on Transport and Air Pollution, general.Fig. 5 shows that the peak exposures were reduced, Technical University Graz, Graz, Report of the Institute for and that the diVerence between peak and saddle exposure, Internal Combustion Engines and Thermodynamics, 1999, vol. 76, pp. 25–32. including the night time exposure, are lessened in 1994. 10 S. E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram and K. E. Grønskei, J. Environ. Monit., 1999, 1, 321. 5. Conclusions 11 J. U. Hanssen and B. Grue, Environmental studies Ekeberg/Old Oslo 1994. TraYc system, traYc registering and road links register. The results show the information that can be provided about (in Norwegian), Institute of Transport Economics (TØI note individual personal exposure to outdoor air pollution, when 1055/1996), Oslo, 1995. employing diVerent study designs. The results also illustrate 12 J. U. Hanssen, After studies Ekeberg tunnel 1996. TraYc system, traYc registering and road links register (in Norwegian), Institute the possibilities that lie in using dispersion models as a basis of Transport Economics (TØI note 993/1995), Oslo, 1996. for personal exposure assessment. 13 Information body for traYc, Opplysningsra°det for trafikk AS: Car It has been illustrated here that the expected changes in and road statistics 1996 (in Norwegian), Oslo, (Publ. Nr. traYc burden have occurred, and that despite an increase in 1000–96), 1996. traYc volume, the pollution levels locally in the area have 14 S. Larssen and L. O. Hagen, Air quality in Norwegian cities. Development, reasons, measures, future (in Norwegian), dropped. The diary shows also that levels in Oslo generally Norwegian Institute for Air Research (NILU OR 69/98), Kjeller, have been decreased, and this is confirmed by measurements 1999. (Larssen and Hagen14). 15 J. Clench-Aas, A. Bartonova, K. E. Grønskei and S.-E. Walker, The method in principle allows one to investigate to what J. Environ. Monit., 1999, 1, 333. degree the individual factors influence the result. The model has been run for each year using the actual meteorology, and Paper 9/02780G 340 J. Environ. Monit., 1999, 1, 337–340

ISSN:1464-0325    

DOI:10.1039/a902780g

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Air pollution exposure monitoring and estimation Part VI.‡ Ambient exposure of adults in an industrialised region† Jocelyne Clench-Aas,* Alena Bartonova, Knut E. Grønskei, Leif O. Hagen, Ole-Anders Braathen and Sam-Erik Walker Norwegian Institute for Air Research, PO Box 100, N-2027 Kjeller, Norway. E-mail: jocelyne.clench-aas@nilu.no; Fax: +47 63 89 80 50; Tel:+47 63 89 80 00 Received 7th April 1999, Accepted 25th June 1999 This paper presents methodology and results of a dynamic individual air pollution exposure model (DINEX) that calculates the hourly exposure for each adult in a panel study.Each of over 260 participants, through the use of a diary, provided information used in the model to calculate his/her personal, individualised exposure. The participants filled out the diary daily, hour by hour, over two, two month periods.The exposure assessment model coupled the diary information and results of an indoor/outdoor measurement program, with the results of dispersion modelling on an hourly basis for an industrial area in Norway. The estimated air pollution concentrations from the dispersion model, based on continuous meteorological measurements, were calibrated with air pollutant concentrations measured continuously.in the region in the period after the field study was com- Introduction pleted (1988). It is methodologically challenging to assess the health impact of exposure to concentrations of air contaminants, especially those lower than air quality guidelines. The eVect of exposure to ambient air pollution needs to be Materials and methods quantified and separated from other known factors that influ- Exposure estimating ence health status, such as age, smoking habits, nutrition, preexisting disease and/or genetic constitution.The measurement Exposure estimating must account for people’s movements in or estimate needs to describe the fluctuations in concentrations areas varying substantially in pollution concentrations.individuals are exposed to, as a result of their movements in The spatial distribution of concentrations served as a basis diVerent microenvironments.1 for estimating each individual’s exposure. An exposure assess- Grenland, the study area, is a heavily industrialised area in ment model estimates exposure for each compound, for each southern Norway.Pollutants here originate from several geo- hour and for each participant, using information collected graphically distinct sources. This allows them to vary indepen- from the diary on where each individual was at given time dently of each other, facilitating their individual identification points. and quantification. Grenland is well known for its industrial An ambient pollution dispersion model for the entire geohaze leading to reduced visibility.Industrial emissions of graphic area combined information on emissions, with inforhydrochloric acid, ammonia and chlorine lead to the formation mation on meteorological conditions in the study area. Such of this haze, even though humidity is not high enough to lead a model estimated hourly concentrations of the diVerent to normal fog.This phenomenon occurs mainly on warm compounds in a square kilometre grid.1,2 The model estimated summer days in conjunction with the land–sea breeze. In the outdoor concentrations of pollution at each individual’s home, winter, industrial haze does not usually occur. place of employment or places visited. This method is described Typically important contaminants in the study region are: in more detail elsewhere.3 sulfur dioxide, nitrogen oxides, carbon monoxide, chloride, People generally spend so much of their time indoors, that ammonium, hydrocarbons, photochemical oxidants such as it is of importance to know indoor air quality. It is important ozone and peroxiacetylnitrate (PAN), polycyclic aromatic to quantify how much of outdoor air pollution penetrates into hydrocarbons (PAH), and traces of chlorinated organic com- the home, and to describe possible indoor sources of air pounds such as dibenzofurans and dioxins.pollution. Norwegian homes do not use gas cooking or heating, This paper presents a dynamic model used to estimate and therefore do not possess this important indoor source of exposure to a series of air pollutants in a panel investigation, nitrogen oxides.Tobacco smoke is the single most important designed to identify the compound or components, if any, factor for indoor pollution in Norwegian homes. Indoor responsible for adverse short-term health eVects. The dynamic measurements at home were made at selected homes, and individual pollution exposure model (DINEX) has also been algorithms derived from these measurements were used in the used in other studies.This paper is presented mainly to discuss individual exposure estimating. the method, since major improvements in pollution abatement An exposure assessment model estimated each individual’s and the closing of one of the factories has reduced pollution pollution exposure for each hour.For each of the pollutant compounds, the model took into account the following major elements: (1) geographic location, (2) proximity to traYc, †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. ‡For Part V, see ref. 14. (3) being indoors or outdoors, (4) shopping, and (5) travelling. J. Environ. Monit., 1999, 1, 341–347 341The dispersion model were available. Emissions were assessed for elemental chlorine and model calculations were performed for Cl2 and did not Grenland (see Fig. 1) lies in a long and relatively wide valley account for chemical reactions. opening to the sea. It is a highly industrialised locality of However, emissions were not known for all compounds. 16×23 km2. The topography combined with climate creates Ozone concentrations within the model area were assessed local temperature inversions in the winter, with poor atmos- from background concentrations, ozone depletion by NOx and pheric dispersion conditions, often leading to higher concen- ozone formation based on the dissociation of NO2.Longtrations of several types of pollutants. The land–sea breeze range transport that may at times contribute around 30% of primarily influences pollutant dispersion from most industrial the concentrations of NOx was also accounted for.sites in the valley in the summer and inversions in the winter. For sulfates, nitrates and suspended particles, emissions In the summer, the land–sea breeze leads the wind into the were again not known, and an appropriate model did not valley during the day and out to sea at night.exist. Therefore, the model for suspended particles distributed The main sources of air pollution in Grenland are local measured total suspended particles over a twelve hour period emissions from industry, vehicular traYc, domestic heating according to continuous measurements of visibility. and boat traYc. Fig. 2 gives an overview of the relative importance of various For modelling, the entire Grenland area was divided into a sources of emissions.Information on emissions from industry square kilometre grid system. Each participant’s home and and from both boat and car traYc was collected immediately work/school address was coded to the nearest km2. The code prior to the field study. EVorts were made to collect emissions also indicated proximity to a major road. on an hourly basis.The dispersion model (EPISODE) estimated the concen- A separate model using traYc counts estimated pollution tration of each compound in each grid square (km2) using concentrations along the major roads for diVerent hours of meteorological conditions measured continuously at two sta- the day and under diVerent meteorological conditions.tions. Measured ambient pollution concentrations at 5 stations For a more complete discussion of the elements included in were used to control the concentration field.1,2 The dispersion the dispersion model, the uncertainties in the model and tests model usually functions by dispersing known emissions at of the validation of the model, see other articles in this series.2,3 specified locations.For SO2, and NOx , emission inventories Air quality measurements Outdoor measurements. The principal compounds measured were sulfur dioxide (SO2), nitrogen oxide (NO), nitrogen oxides (NOx), ozone (O3), sulfates (SO4), nitrates (NO3), particulate matter (PM2.5) and pollen. Nine air quality stations in the area measured air quality and meteorological parameters during the two investigation periods.Fig. 1 shows the measuring sites for air quality and meteorological parameters. Indoor measurements. People in general spend most of their time indoors. If in addition windows and doors are closed, air quality indoors can be substantially diVerent from outdoors. Opening of windows for ventilation can for some compounds, influence indoor air quality.Using simultaneous measurements made indoors and outdoors, a set of algorithms was developed to estimate pollution concentrations indoors. Most notably, exposure to suspended particles was increased when people smoked or were exposed to passive smoking, or nearby traYc. Fig. 2 Average total emission intensity (Q) of SO2 , total suspended Fig. 1 Location of stations for measuring air quality and meteorologi- particles (TSP) and NOx from diVerent source groups in winter and summer.cal conditions in the Grenland area. 342 J. Environ. Monit., 1999, 1, 341–347Simultaneous indoor and outdoor measurements for a Table 2 summarises the maximum values of diVerent air pollution components measured outdoors during the period three day period for 15 typical homes, determined indoor concentrations due to penetration into the home, and/or January to June 1988.Table 3 provides the algorithms used for indoor concentrations based on the measurement pro- indoor sources (e.g. smoking). Measurements were made for three periods per day in each home, for both a winter and gramme inside selected homes. summer period.4 Estimated exposure to diVerent air pollution components in Data collected from each individual Grenland Over 260 people participated in the study both in winter and Based on information reported in the diary and summarised summer.Each person filled out a diary, once a day on an in the previous section, each participant’s exposure to each hourly basis. Each person was to indicate when and how long component was estimated each hour.It was necessary, in he/she was at home, at work or school, or visiting other places order to use these exposure estimates further in assessing the (providing address), outdoors, or indoors, with or without health eVects of air pollution, to know how contaminants open windows. Each address was coded to the nearest square correlated with each other.It was an a priori assumption that kilometre in addition to noting closeness to major traYc the unique geographical features of the region should result arteries. in a relatively independent distribution of the contaminants TraYc is one of the major pollution sources that contributes that should allow one to distinguish between the eVects of to exposure to NOx and suspended particles.Therefore, it is each component. As can be seen in the figures in Part I of also necessary to know when people are travelling and how this series,3 dispersion model results demonstrated that this much traYc they are encountering. The participants indicated assumption was correct. how many minutes they were travelling in dense, medium or Interrelations between exposure estimates for individual little traYc.These terms were defined. components are generally stronger in summer than in winter Each person was also to indicate how many minutes he/she but often of the same general dimension. Important exceptions was shopping either in downtown Skien or Porsgrunn (the are relative humidity and sulfates (slight negative correlation two major towns in the area) or other places.Skien had at in the winter, yet a stronger positive correlation in the summer). that time an outdoor shopping area that was essentially free O3 correlates negatively with all compounds other than sulfates of traYc, whereas a major road crossed Porsgrunn. Therefore, in the winter, whereas it correlates positively with all comthe model used an average of the squares that represent the pounds other than CO and chlorine in the summer.shopping area in Skien and an average of the km2 in downtown Table 4 indicates where the correlation exceeded 0.25, which Porsgrunn, plus an additional factor for vehicular traYc for is a value chosen to represent a meaningful correlation. With estimating concentrations when shopping in Porsgrunn. our amount of data, 0.001 significance level is reached for correlation coeYcient values under 0.1.Table 5 summarises participants’ exposure. The table shows Results percentiles of exposure estimates and maximum estimated Time-use information provided by the diary exposure for winter and summer. These estimated concentrations could be described as a Results of studies of the health eVect of air pollution carried function of various parameters. out in diVerent countries where individual exposure has not Changes in temperature and humidity during day and night been measured should be compared with care. Even though are especially noticeable in the summer, and aVect some of pollution levels may be lower in one country, exposure may the contaminants giving them marked daily variations.Human be higher due to cultural diVerences in ventilation of homes activities that tend to occur at routine times during the day, (i.e. sleeping with windows open) and amount of time spent that is, driving to work, working, etc. can also aVect exposure outdoors. This section summarises such features for the studied to contaminants. Norwegian population. Fig. 3 shows changes in exposure to the gaseous and particu- There is a sharp contrast in time spent with the window late air contaminants as a function of hour whereas Fig. 4 open, closed or outdoors between winter and summer shows exposure day by day during the investigation. Exposure (Table 1). Even in the winter, people spend an average of 17% to NO is higher in the winter than in the summer and exposure of their time in rooms with the window open and 3% of their to NO2 slightly lower in the winter.Concentrations of NO, time outdoors. In the summer, time spent outdoors can be as and to a lesser degree NO2, show the typical peaks associated high as 20%. Children are more outdoors than adults. Women with exposure to traYc pollution. These peaks are especially are outdoors less than men, but have the window open more noticeable in the afternoon rush hour and are slightly higher than men.Assuming that a yearly average is a direct mean of during the winter than the summer. Exposure to SO2 is only the summer and winter values given in Table 1, Norwegians slightly higher in the winter than in the summer and does not seem to be outdoors more than in most other countries.5–11 vary markedly with time of day.Exposure to O3 is, as expected, Time spent outdoors in this region does not diVer from values much higher in the summer than in the winter and shows a estimated for Norway12 as a whole. pronounced daily variation in the winter but not in the Time spent travelling is stable in both winter and summer. summer. Exposure to nitrates and sulfates is lower in the No comparison is available for time spent indoors with or winter than in the summer.without the window open, since few studies have segregated There were only minor diVerences in exposure between the these parameters. Otherwise it is evident that features of this two sexes. However, exposure to all parameters was either population reflect a society dominated by working in the equal or slightly higher in men, except for suspended particles.factories, with for example shift work influencing wake-up Men had slightly higher levels of exposure to the nitrogen time. oxide components. This is especially noticeable in younger men, possibly due to more exposure to traYc pollution. Both Measured ambient and indoor concentrations older men and women have higher exposure to ozone, due to more time spent outdoors.The higher exposure to suspended Due to a mild winter, pollution concentrations varied considerably. Increases in sulfates and suspended particles accompanied particles in women is especially evident in younger women. Younger men, however, have also higher levels of exposure the two coldest periods that occurred that winter.J. Environ. Monit., 1999, 1, 341–347 343Table 1 Time spent in diVerent microenvironments by age group of participants. Percent of registered wake-up and go to sleep time in the diVerent participant subgroups Winter Summer Adult Adult Adult Adult women men Children women men Children Type of location (% of time) Home 74.6 67.4 68.6 68.8 62.7 66.4 At work/school/kindergarten 9.7 17.4 12.4 8.7 15.2 10.4 Other places 15.7 15.2 19.0 22.5 22.1 23.2 Indoors window closed 76.5 76.3 81.2 45.5 44.0 54.9 Indoors window open 19.2 16.3 9.9 38.1 33.8 18.9 Outdoors 1.1 3.8 6.5 12.9 18.6 23.0 Travelling whole hour 3.2 3.6 2.4 3.6 3.6 3.2 Number of minutes traveling Dense traYc 3.9 6.9 6.9 6.1 8.0 6.2 Medium traYc 19.4 27.6 16.6 20.3 27.9 19.6 Light traYc 20.9 26.9 19.5 21.1 22.7 18.7 Total daily shopping 26.4 23.2 15.2 27.3 21.8 22.4 Type of activity (% of time) Sleeping 35.3 33.8 42.4 33.8 32.7 40.7 Daily activity 63.4 64.6 54.8 65.1 65.4 57.8 Hard work/training 1.2 1.5 2.8 1.0 2.0 1.7 Wake up time (% of registered cases) 06.00–07.00 0.9 3.3 0.7 0.9 4.2 0.1 07.00–08.00 12.4 25.7 4.1 15.1 26.6 2.2 08.00–09.00 26.6 28.6 46.2 29.3 30.5 39.7 09.00–10.00 24.8 17.7 24.7 27.0 19.5 32.2 10.00–11.00 18.0 11.8 12.2 14.9 11.0 15.1 11.00–12.00 6.3 4.0 6.7 5.0 2.7 5.6 12.00–13.00 2.4 1.5 2.2 1.7 1.1 2.3 Sleep time (% of registered cases) 19.00–20.00 0.1 0.1 0.3 0.3 0.0 0.1 20.00–21.00 0.2 0.2 13.1 0.2 0.1 4.2 21.00–22.00 1.4 2.3 37.0 0.6 1.1 30.8 22.00–23.00 11.8 16.7 20.0 7.1 13.8 23.5 23.00–24.00 35.9 38.8 17.7 34.6 39.1 23.8 24.00–01.00 33.6 28.2 7.6 42.8 35.7 14.2 01.00–02.00 5.2 3.7 0.5 5.3 3.8 0.1 02.00–03.00 2.3 2.0 0.6 2.3 1.4 0.8 Table 2 Summary of maximum values of diVerent air pollution components during the period January to June 1988 Averaging City hall G.Stangs gt Skien Component time/h A° s Herre Frednes Klyve Porsgrunn Nenset Skien Fire st. Kongensgt SO2/mg m-3 1 147 338 474 203 872 2027 24 32 23 37 55 26 63 134 320 121 NOx/mg m-3 1 296 761 326 820 463 551 24 110 320 104 273 167 229 NO2/mg m-3 1 192 119 191 125 102 121 24 84 70 75 61 47 59 90 Haze/10-6 m-1 1 764 1061 572 24 116 71 58 O3/mg m-3 1 185 150 8 179 141 Suspended particles/mg m-3 12 69 89 74 93 94 SO4/mg m-3 12 16.7 16.2 17.8 16.3 15.3 NO3/mg m-3 12 10.7 9.8 12.7 6.4 5.9 Cl2/mg m-3 12 6.6 4.7 3.3 4.6 5.0 Soot/mg m-3 24 31 30 79 104 Lead/mg m-3 24 1.21 NH3/mg m-3 24 9.6 NH4/mg m-3 24 8.7 5.3 Formaldehyde/mg m-3 24 0.7 than older men.This is due to higher exposure to tobacco more marked in daily smokers. There is a mean diVerence of 32 mg m-3 exposure to suspended particles in the winter, smoke in the young as opposed to the elderly, and in women as opposed to men, as seen in the data.between non-smokers and those who smoke every day; whereas that diVerence is only 10 mg m-3 in the summer. This is a Non-smokers are slightly more exposed in the winter than in the summer to particles. The diVerence between winter and direct reflection of time spent outdoors or with the window open in the summer as opposed to the winter. summer is more noticeable in occasional smokers and even 344 J.Environ. Monit., 1999, 1, 341–347Table 3 Relationships between indoor (Ci) and outdoor (Co) concentrations of selected pollutants in Norwegian homes in mg m3 Winter Summer Compound 0800–2000 2000–0800 0800–2000 2000–0800 SO2 Ci=0.49Co+5.05 NO2 Ci=0.21Co+10.5a Ci=0.28Co+6.3 Ci=0.34Co+9.55 Ci=0.56Co+7.5 PM2.5 Ci=0.73Co-0.75b Ci=0.7Co- 3.0c Ci=0.75Co+0.25d Ci=0.72Co+5.00e SO4 Ci=0.73Co+0.32 Ci=0.70Co-0.23 Ci=0.75Co+0.43 Ci=0.72Co-0.26 CO Ci=0.7Co O3 Ci=0.2Co aFor NO2 the time intervals are 1600–2400 and 0000–1600.bFor PM2.5 the values here are for non-smoking homes, the constant becomes additive and increases to 36.75 and 97.75 for homes with smoking of 1 to 10 cigarettes per day and homes with smoking of more than 10 cigarettes per day respectively.cFor PM2.5 the values here are for non-smoking homes, the constant increases to 23.0 and 60.5 for homes with smoking of 1 to 10 cigarettes per day and homes with smoking of more than 10 cigarettes per day respectively. dFor PM2.5 the values here are for non-smoking homes, the constant increases to 8.25 and 45.3 for homes with smoking of 1 to 10 cigarettes per day and homes with smoking of more than 10 cigarettes per day respectively.eFor PM2.5 the values here are for non-smoking homes, the constant increases to 23.0 and 60.5 for homes with smoking of 1 to 10 cigarettes per day and homes with smoking of more than 10 cigarettes per day respectively. Table 4 Mean weighted Pearson correlation coeYcients for the log-transformed air pollution exposure data for winter (under the diagonal ) and summer, (above the diagonal ) (only values over 0.25 listed ).Each season is based on approximately 260 individuals, with 1000 registered hours each Relative SO2 NOx NO2 O3 PM2.5 Cl2 SO4 NO3 CO Temperature humidity SO2 — 0.78 0.79 0.62 0.44 0.38 NOx 0.67 — 0.99 0.28 0.71 0.53 0.43 0.91 NO2 0.67 0.89 — 0.31 0.71 0.53 0.43 0.73 O3 — 0.35 0.31 -0.35 0.43 -0.40 S PM2.5 0.58 0.57 0.56 — 0.29 0.60 0.45 0.60 u Cl2 — m SO4 0.44 0.35 0.37 0.35 — 0.62 0.33 m NO3 0.40 0.35 0.26 0.43 — e CO 0.83 0.68 0.47 — r Temperature 0.33 — -0.53 Relative humidity -0.39 0.28 0.26 — Winter Table 5 Percentiles of the calculated exposure to air pollutants for showing the need for accounting for these facts when investigastudy population ting the eVects of air pollution on health.The diary method used in this investigation has proven itself Randomly selected 95% as a feasible basis for estimating personal exposure for an study population Median quantile Maximum investigation of short-term health eVects. It allows one to Wintera measure more individuals over a longer time period, divided Sulfur dioxide/mg m-3 9 22 900 into shorter time intervals.This method allows one to estimate Nitrogen dioxide/mg m-3 17 85 3065 more pollution compounds than an ideal method using port- Ozone/mg m-3 4 41 93 able pollution measuring equipment could possibly do. Particles (fine fr.)/mg m-3 17 108 581 However, one can argue that a combination of both these Sulfates/mg m-3 2 8 17 approaches would be advantageous.This was carried out in a Nitrate/mg m-3 0 2 8 Chlorine/mg m-3 0 7 297 later investigation in a region with traYc pollution.13 The exposure assessment model DINEX, used here, allowed Summer combining concentrations of pollution measured at fixed site Sulfur dioxide/mg m-3 7 19 1414 Nitrogen dioxide/mg m-3 16 55 2313 stations with individual diVerences in behaviour and lifestyle, Ozone/mg m-3 24 112 185 thus creating a personalised and hopefully more exact expo- Particles (fine fr.)/mg m-3 11 53 614 sure estimate.Sulfates/mg m-3 2 10 15 Nitrate/mg m-3 0 2 9 Chlorine/mg m-3 0 1 55 Acknowledgements Birch pollen/pollen m-3 0 47 833 Grass pollen/pollen m-3 0 19 2185 The work reported in this paper was carried out under contract from the Norwegian Ministry of the Environment, the aNumber of hours registered: Winter: 354 735; summer: 304 697.Norwegian State Pollution Control Authority and the Royal Norwegian Council for Scientific and Industrial Research who jointly appointed a board that had the administrative and Discussion co-ordinating responsibility for the investigation. We would like to thank these board members, Sigurd Hagen (Chairman), The most important sources in the area are industrial and high pollution concentrations occur only sporadically (prob- Lasse Hansen, Erik Dybing and Sverre Langa°rd for their constant and helpful contribution.We would also like to thank lems in operation of the plants). High exposure mainly occurs for one compound at a time.Odd F.Skogvold, whose earlier research in the area was the inspiration for the study. His constant help and advice during Variations in lifestyle (time spent outdoors, keeping windows open) etc. were large between seasons and population groups, all phases of the investigation was of utmost importance to us. J. Environ. Monit., 1999, 1, 341–347 345Fig. 3 Variations in concentrations of exposure to the gaseous contaminants (NO, NO2, O3 and SO2) and particulate contaminants (PM2.5, sulfate and nitrate) as a function of time of day and season.Fig. 4 Concentrations of mean daily exposure of the entire study population to the gaseous and particulate matter (PM2.5) contaminants as a function of day of study. Day 1=January 2, 1988. 346 J. Environ. Monit., 1999, 1, 341–3471990), Industrial Institute for Economics and Social Research, We would also like to thank the National Institute of Public Stockholm, 1990.Health, the Norwegian Computing Centre, the Telemark 7 M. D. Koontz and J. P. Robinson, Environ. Monit. Assess., 1982, Central Hospital and the local division of the State Pollution 2, 197. Control Authority for their expertise and help. 8 R. Letz, B. Ryan and J. D. Spengler, Environ. Monit. Assess., 1984, 4, 351. 9 J. J. Quackenboss, J. D. Spengler, M. S. Kanarek, R. Letz and References C. P. DuVy, Environ. Sci. Technol., 1986, 20, 775. 10 M. Schwab, A. McDermott and J. D. Spengler, Environ. Int., 1 K. E. Grønskei, S. E.Walker and F. Gram, Atmos. Environ., 1993, 1992, 18, 173. 27B, No. 1, 105. 11 A. Szalai, P. E. Converse, P. Feldheim, E. K. Scheuch and 2 S. E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram and K. E. P. J. Stone, The use of time: daily activities of urban and suburban Grønskei, J. Environ. Monit., 1999, 1, 321. population in twelve countries, Mouton, The Hague, 1972. 3 J. Clench-Aas, A. Bartonova, T. Bøhler, K. E. Grønskei and S. 12 Central Bureau of Statistics, The time budget surveys 1970–90, Larssen, J. Environ. Monit., 1999, 1, 313. Oslo, 1992. 4 O. A. Braathen, Air pollution and short-term health eVects in an 13 S. Larssen, D. Tønnesen, J. Clench-Aas, M. J. Aarnes and K. industrialized area in Norway – Relationships between indoor and Arnesen, Sci. Total. Environ., 1993, 134(1–3), 51. outdoor concentrations of air pollutants (in Norwegian), 14 A. Bartonova, J. Clench-Aas, F. Gram, K. E. Grønskei, C. Norwegian Institute for Air Research (NILU OR 8/91), Guerreiro, S. Larssen, D. A. Tønnesen and S.-E. Walker, Lillestrøm, Norway, 1991. J. Environ. Monit., 1999, 1, 337. 5 F. S. Chapin, Jr., Human activity patterns in the city. Things people do in time and space,Wiley, New York, 1974. 6 F. T. Juster and F. P. StaVord, Working Paper Series (NO. 258, Paper 9/02781E J. Environ. Monit., 1999, 1, 341–347 347

ISSN:1464-0325    

DOI:10.1039/a902781e

出版商:RSC    

年代:1999

数据来源: RSC

摘要:

Validation of a diVusive sampler for NO2† Annika Hagenbjo�rk-Gustafsson,*a Roger Lindahl,a Jan-Olof Levina and Doris Karlssonb aNational Institute for Working Life, Department of Chemistry, P.O. Box 7654, S-907 13 Umea° , Sweden bDepartment of Environmental Health, Umea° University, S-901 87 Umea° , Sweden Received 13th April 1999, Accepted 27th May 1999 A diVusive sampler for NO2, Willems badge, was validated in laboratory experiments and field tests.The collecting reagent for NO2 in the sampler is triethanolamine, and the analysis is based on a modified colorimetric method, the Saltzman method. The analysis was performed by a flow injection analysis (FIA) technique. The sampling rate for the sampler was determined to be 40.0 ml min-1. There was no eVect of NO2 concentration or relative humidity on sampling rate, and the influence of sampling time was found to be small.The detection limit was 4 mg m-3 for a 24 h sample. The capacity is high enough to allow sampling of 150 mg m-3 for 7 days, which is twice the recommended Swedish short-term (24 h) guideline value as a 98-percentile over 6 months. In field tests, the sampler performed well, even at wind speeds higher than 2 m s-1, and at low temperatures.The overall uncertainty of the method was 24%. The sensitivity and capacity of the method also make it suitable for personal sampling for 2–8 h in working environments. the spatial variation. They are also valuable for the determi- Introduction nation of background levels over longer periods of time. Nitrogen oxides (NO and NO2) are produced as by-products Palmes et al.4 developed the first diVusive sampler for NO2, in various combustion processes, especially at high tempera- the Palmes tube, with a rather low sampling rate.A badgetures. Primarily NO is produced, but it oxidizes in the atmos- type sampler for personal sampling, with higher uptake rate, phere to form the more harmful nitrogen dioxide.The main was developed by Yanagisawa and Nishimura.5 In 1998, Ferm sources of nitrogen oxides in urban areas are motor traYc and Svanberg3 introduced another design of the badge-type emissions and burning of fossil fuels in power plants. High sampler with a rather large opening-to-length ratio, resulting indoor concentrations of nitrogen dioxide can be found in in a device more sensitive than the tube-type sampler.At the association with the use of gas stoves and unvented gas and University of Wageningen in the Netherlands, Willems and kerosene space heaters.1 Hofschreuder6 developed a diVusive sampler with a geometry A number of studies have indicated that human exposure similar to Ferm’s, originally for ammonia measurements, and to nitrogen dioxide is associated with increased susceptibility later adapted for measuring NO2.The Willems badge was used in the European PEACE (Pollution EVects on Asthmatic to airway infections and impaired lung function. Correlations Children in Europe) study to measure concentrations of nitro- between NO2 exposure and respiratory illness and increased gen dioxide inside and outside the homes of asthmatic severity of asthma, as well as increased response to inhaled children.7 allergens in asthmatics, have been found.2 In a recent study, van Reeuwijk et al.8 used the badge for There are diVerent techniques for the determination of measuring the 2-week average NO2 concentrations in three nitrogen dioxide in ambient air.These techniques can be European areas within the EU SAVIAH (Small Area divided into active or passive sampling.The active methods Variations in Air Quality and Health) project. The Willems include impinger methods, where NO2 is collected by bubbling badge was compared with the tube-type sampler and a refer- air through a solution in which NO2 is reduced to NO2- and ence method. The validation was, however, insuYcient as the analysed colorimetrically.A modification is the impregnated comparison between the reference method and the diVusive sintered glass filter technique.3 Another active technique of sampler only included a total of nine measuring points. In monitoring NO2 is the chemiluminescence technique in which addition, no sampling rate was determined. emission from excited NO2* is detected photo-electrically.The No extensive laboratory test of the performance of the chemiluminescence analyser is a very sensitive technique, which sampler has been carried out. This paper describes the vali- oVers hourly time-weighted averages of the NO2 concentration. dation of the Willems badge for NO2 measurements. The aim The analysers are, however, expensive, need electricity, caliwas to experimentally determine the sampling rate of the bration and specialist maintenance, and are for that reason diVusive sampler in laboratory studies, and to investigate the not suitable to assess spatial variation.eVects of sampling time, concentration of NO2 and relative DiVusive (passive) samplers are based on the molecular humidity on sampling rate. Another objective was to confirm diVusion of the gas to a collector medium.DiVusive samplers the results from the laboratory tests in field studies, in con- are ideal for the purpose of monitoring ambient air. They are ditions of low temperature, which are common in Northern small, cheap, easy to handle and enable measurements to be Europe during the winter season. conducted at remote places, as they require no pump or electricity.They allow the measurement of NO2 levels at Experimental various places in a town at the same time, in order to analyse DiVusive sampler The Willems badge (Fig. 1) consists of a cylinder of polystyrene. The absorption filter, a Whatman GF-A glass fibre †Presented at AIRMON ’99, Geilo, Norway, February 10–14, 1999. J. Environ. Monit., 1999, 1, 349–352 349of lowest concentrations (blank, 0.03, 0.05, 0.2 mg l-1 NO2).The limit of quantification (12 mg m-3 for 24 h; 2 mg m-3 for 7 days) was determined as 10 times the mean standard deviation for the same samples.10 The repeatability (1.5%) was determined as the relative standard deviation for six replicates of the samples that exceeded the limit of quantification (0.2, 1.0, 3.0 and 5.0 mg l-1 NO2).The reproducibility (4%) of the method was determined as the relative standard deviation of a control sample, run together with normal sample analysis seven times during a period of 8 months. Fig. 1 Expanded view of the Willems badge: a, sampler base of polystyrene; b, absorption filter; c, spacer ring; d, Teflon filter; DiVusive sampler laboratory tests e, fixation ring; f, cap of polyethylene.Generation of standard atmospheres of nitrogen dioxide. filter, is placed at the bottom of the cylinder. By placing a Known concentrations of nitrogen dioxide were generated in Teflon filter on a distance ring of polystyrene (6 mm), a region an exposure chamber (70×48×900 mm) shown in Fig. 2. without turbulence is created. The Teflon filter (Schleicher & Nitrogen dioxide (23.3 ppm±3% NO2 in nitrogen; AGA, Schuell TE 38, 5 mm) is secured with a polystyrene ring of Stockholm, Sweden) from a gas cylinder regulated by a mass 3 mm.A polyethylene cap closes the badge. The absorption flow controller (0–500 ml min-1) was diluted by clean, humidi- filter is impregnated with a solution of triethanolamine–acetone fied air, controlled by a mass flow controller (0–200 l min-1) as a collector for NO2.Sampling is initiated by removing the and mixed before the entrance to the exposure chamber. In cap from the sampler, which is exposed with the open end order to produce diVerent relative humidities, the air was down. In field studies, the sampler is attached to an angled passed through one to four gas-dispersion bottles containing aluminium plate which shelters the badge from rain and snow.water. By modifying the number of bottles by means of valves, After a sampling period, the absorption filter is removed from the relative humidity of the air could be varied from 20 to the badge, extracted and analysed for nitrite. 90%. The air flow through the exposure chamber was The filters and sampler components were cleaned before use 40 l min-1, whicgave an air velocity in the chamber of 0.3 as follows.The sampler components were immersed in 96% m s-1. The exposure chamber has been described in detail ethanol. Teflon filters were dipped twice in a solution of 96% previously.11 ethanol–distilled de-ionized water 151 (v/v), dried and then immersed in 96% ethanol.Before coating, the glass fibre filters Experimental design. The sampling rate was experimentally were boiled in water for a few minutes to remove loose glass determined by exposing samplers in the exposure chamber. To fibres, dried, immersed twice in a solution of acetone and then investigate the eVects of sampling time, concentration of NO2 dried again. Both Teflon filters and glass fibre filters can be and relative humidity on the sampling rate of the diVusive stored for about 3 months in a closed vessel after cleaning. sampler, a laboratory test with a factorial design was per- The filters were coated by dipping in a fresh solution of formed.Six samplers were exposed simultaneously to nitrogen triethanolamine (Riedel de Hae�n, p.a.) in acetone (Merck, dioxide levels from 5 to 150 mg m-3, with exposure times from p.a.), 1550 (v/v), and then dried for about 1 min in a ventilated 1 day to 7 days.The relative humidity was varied between 20 desiccator equipped with an entrance filter coated with tri- and 80%. The temperature was 20 °C. Two laboratory blanks ethanolamine as absorbent for NO2. The samplers were were collected in each experiment, and the mean value was mounted directly after drying the filters. Samplers loaded with subtracted from the amount of nitrite in the exposed samples. coated filters were stored in a refrigerator (+8 °C) for up to As a reference method, NO2 was measured with a chemilumi- 1 month.nescence instrument (ECO PHYSICS CLD 700 AL med, Du� rnten, Switzerland), with the inlet placed in a port at the Analysis centre of the exposure chamber.A daily calibration of the analyser was performed using a certified gas of NO in nitro- Absorbed nitrogen dioxide on the filter was determined colorigen (AGA). metrically as nitrite. The analysis is based on a modified colorimetric method, the Saltzman method,9 and was per- Field validation formed by the flow injection analysis (FIA) technique.The absorption filter of a badge was soaked in 5 ml of 0.005 M Two studies of outdoor simultaneous measurements with NaOH (Eka Nobel, Goteborg, Sweden), shaken for 30 min passive samplers and a chemiluminescence instrument were and centrifuged at 4000 rpm for 10 min. After centrifugation, performed during the winter season. Both studies included one the centrifugation tubes were placed in an autosampler in the sampling period of 6 days and three sampling periods of 2 FIA analyser (FIA Tecator Star 5010 analyser, Controller days. Six parallel samplers were placed on an outer wall of a 5032, Sampler 5027, Ho�gano� s, Sweden).The sample was injected into a carrier stream of water with a flow of 1.5 ml min-1. On the addition of sulfanilamide (10 g l-1, Merck p.a.; flow, 0.6 ml min-1), a diazo compound is formed which then reacts with N-(1-naphthyl )ethylenediamine dihydrochloride (1.0 g l-1, Merck) provided from another reagent stream (flow, 0.6 ml min-1).An azo dye is formed and the colour intensity is measured in a 10 mm flow cell at 540 nm. To validate the FIA and to determine the detection limit, limit of quantification, repeatability and reproducibility of the method, seven samples of diVerent concentrations were run in six replicates.The detection limit (4 mg m-3 for 24 h; 0.6 mg m-3 for 7 days) was determined as three times the mean Fig. 2 Equipment for the generation of standard atmospheres of NO2. MFC, mass flow controller. standard deviation for the mass on the filters of four samples 350 J.Environ. Monit., 1999, 1, 349–352building. To protect the samplers from rain and snow, they The influence of sampling time, concentration of NO2 and relative humidity on the sampling rate of the diVusive sampler were attached to the underside of an angled aluminium plate. Blanks were included in the same manner as in the laboratory was statistically analysed by multiple regression.14 As shown in Table 2, there was a small, but statistically significant (P= experiments.The inlet of the chemiluminescence instrument was placed close to the diVusive samplers. Temperature, wind 0.05) negative eVect of sampling time, but no eVects of relative humidity or concentration of NO2 on sampling rate. The small velocity and relative humidity were recorded by a datalogger every 30 s during the sampling periods.In the first study, the influence of the sampling time does not aVect the usefulness of the method, and this influence is included in the relative mean nitrogen dioxide concentration ranged from 2 to 9 mg m-3. The mean temperature varied between -5.2 and standard deviation of 22%. -6.4 °C, the relative humidity between 48 and 75% and the mean wind velocity in the four sampling periods ranged from Field validation 0.5 to 1.7 m s-1.During the second series, the mean NO2 The field validation was performed during the winter when concentration in the four sampling periods varied between 8 the temperature was low, and the possibility to record increased and 20 mg m-3, the mean temperature between -2 and nitrogen dioxide concentrations was high, due to inversion -17.6 °C, the mean relative humidity between 73 and 82% and burning of fossil fuels.However, no inversion occurred. and the mean wind velocity range was 1.2 to 1.8 m s-1. As can be seen from Fig. 3, there was a good correlation between the concentrations of NO2 found by diVusive sampling Results and discussion and by the chemiluminescence instrument, with a coeYcient of correlation of 0.95.The mean ratio between the concen- DiVusive sampling trations obtained by Willems badge and chemiluminescence was 1.08, and the relative standard deviation for six samplers According to a simplification of Fick’s law, the concentration (C) of an analyte in air can be calculated if the uptake rate of in eight runs was 7%, as shown in Table 3.The field data given in Table 3 were submitted to multiple regression analy- the sampler, the amount of analyte (m) in the sampler and the sampling time (t) are known sis.14 No statistically significant eVects on the badge sampling rate of the exposure time, concentration of NO2, relative humidity, wind velocity or temperature were found.The wind m t =DA C L =SC speed varied within a wide range during the field experiments. Some of the wind speed variations are shown in Table 4. Run where D is the diVusion coeYcient (cm2 s-1), A is the cross- 5 covers the 6 days of measuring in runs 6, 7 and 8. As can sectional area of the badge (cm2), L is the length of the be seen from Table 4, wind speeds in excess of 2 m s-1 occurred diVusion path (cm), m is the amount of analyte on the filter 20 and 31% of the time, respectively, in runs 6 and 7.Although (g), t is the sampling time (s) and C is the concentration not statistically significant, this could be a reason for the (mg cm-3). increased ratio between the diVusive sampler and the reference The theoretical sampling rate is given by DA/L (cm3 s-1) method in runs 6 and 7 (Table 3).In run 8, where the wind and can be calculated from the geometry of the sampler. speed was <0.3 m s-1 for 38% of the time, the ratio was 0.93. However, the sampling rate of a diVusive sampler must be A wide range of temperatures was also covered in the field verified experimentally in accordance with existing stanexperiments, and the sampler performed well, although no dards.12,13 The Willems badge was partly evaluated according corrections for temperature were performed on the sampling to the draft European standard for the validation of diVusive rate.Since no significant factors were identified, temperature samplers for ambient air measurements.13 Laboratory tests Table 2 Multiple regression analysis of the influence of sampling time, concentration and relative humidity; S, significant; NS, not significant; The results obtained in the factorial laboratory study are Rh, relative humidity shown in Table 1.Thempling rate was 40.0 ml min-1 with a relative standard deviation for the 42 experiments of Variable Parameter estimate Standard error 22%. The concentrations measured by the reference method Sampling rate 40.0 2.2 (chemiluminescence instrument) were taken as the true values, Time -8.6 2.8 S and the uptake rate of the sampler was determined based on Concentration -4.8 2.8 NS these concentrations.Rh 1.8 2.4 NS Table 1 Sampling rates of Willems badge (n=6) at diVerent sampling times, relative humidities and NO2 concentrations in laboratory tests with chemiluminescence as reference method.RSD, relative standard deviation Mean Relative reference Sampling humidity concentration/ RSD Sampling time/days (%) mg m-3 (%) rate/ml min-1 7 80 190 13 27 1 20 182 9 45 1 80 180 12 54 7 20 173 8 33 2 50 147 12 40 7 20 5 8 38 7 80 5 12 43 Mean 11 40 Fig. 3 Relationship between concentrations of NO2 obtained by RSD (%) 22 Willems badge and chemiluminescence instrument in field studies.J. Environ. Monit., 1999, 1, 349–352 351Table 3 Results from field studies. Ratio between Willems badge measurements (n=6; sampling rate, 40 ml min-1) and chemiluminescence. Rh, relative humidity; RSD, relative standard deviation Mean reference Mean Mean Ratio between Sampling concentration/ RSD Mean wind Rh temperature/ Willems badge and Experiment time/days mg m-3 (%) velocity/m s-1 (%) °C reference method 1 6 15 3 1.5 78 -11.2 1.05 2 2 8 6 1.8 73 -2 1.11 3 2 16 6 1.4 82 -14 1.03 4 2 20 11 1.2 80 -17.6 1.06 5 6 8 3 1.3 64 -5.6 1.04 6 2 7 10 1.5 48 -5.2 1.32 7 2 3 12 1.7 69 -5.3 1.11 8 2 12 6 0.5 75 -6.4 0.93 Mean 7 1.08 Table 4 Variation of wind speed during field experiments 5–8, Acknowledgements expressed as percentage of total time For financial support and valuable discussions we would like Percentage of time (%) to thank Dr Bertil Forsberg, Department of Environmental Health, Umea° University, Sweden.Wind speed/m s-1 Run 5 Run 6 Run 7 Run 8 <0.3 13 1 1 38 References 0.3–2 70 79 68 61 >2 17 20 31 1 1 J. Quackenboss, J. Spengler, M. Kanarek, R. Letz and C. DuVy, Environ.Sci. Technol., 1986, 20, 775. 2 R. Helleday, PhD Thesis and references therein, Umea° University, included, the overall uncertainty (OU) of the method accord- Sweden, 1995, ISBN 91-7191-017-4. 3 M. Ferm and P-A. Svanberg, Atmos. Environ., 1998, 8, 1377. ing to existing European standards was determined to be 24% 4 E. D. Palmes, A. F. Gunnison, J. DiMattio and C. Tomczyk, Am. based on the eight series of field measurements, which is below Ind.Hyg. Assoc. J., 1976, 37, 570. the required 30%.13,15 5 Y. Yanagisawa and H. Nishimura, Environ. Intern., 1982, 8, 235. 6 J. J. H. Willems and P. Hofschreuder, in A Passive Monitor for Measuring Ammonia, ed. I. Allegrini, A. Febo and C. Perrino, Air Conclusions Pollution Research Report Nr 37, Commission of the European Communities, Brussels, 1991, pp. 113–121. The diVusive sampler validated in this study was designed for 7 A. Hagenbjo� rk-Gustafsson, B. Forsberg, G. Hestvik, D. Karlsson, short term (24 h) sampling in ambient air. It has been validated S. Wahlberg and T. Sandstro�m, Analyst, 1996, 121, 1261. in laboratory studies as well as in field tests for measurements 8 H. van Reeuwijk, P. H. Fischer, H.Harssema, D. J. Briggs, of NO2. The experimental sampling rate was determined to K. Smallbone and E. Lebret, Environ. Monit. Assess., 1992, 50, 37. be 40.0 ml min-1 with a relative standard deviation of 22%. 9 B. E. Saltzman, Anal. Chem., 1954, 26, 1949. There were no eVects of the relative humidity or concentration 10 Analytical Methods Committee, Analyst, 1987, 112, 119. 11 R. Lindahl, PhD Thesis and references therein, Umea° University, of NO2, but a small eVect of the sampling time on the sampling Sweden, 1997, ISBN 91-7191-355-6. rate was noted, which however does not influence the useful- 12 CEN, European Committee for Standardisation, Workplace ness of the method. Atmospheres—Requirements and Test Methods for DiVusive The sampling rate determined in the laboratory studies was Samplers for the Determination of Gases and Vapours, EN 838, confirmed in field studies, and the mean diVerence in the CEN, Brussels, 1995.concentrations obtained with Willems badge and the chemi- 13 CEN, European Committee for Standardisation, Ambient Air Quality—DiVusive Samplers for the Determination of luminescence reference method was less than 10%. The sampler Concentration of Gases and Vapours—Requirements and Test performed well in wind speeds higher than 2 m s-1 as well as Methods. Part 1 and Part 2, Draft EN, CEN/TC264/WG11, at low temperatures. The overall uncertainty of the method Brussels, 1998. was 24%. 14 R. Carlsson, Design and Optimization in Organic Synthesis (Data The sampler is useful for monitoring background levels of Handling in Science and Technology, Vol. 8), Elsevier, ambient nitrogen dioxide as the sensitivity for a 48 h sampling Amsterdam, 1992. 15 CEN, European Committee for Standardisation, Workplace period is 2 mg m-3. The capacity is high enough to allow Atmospheres—General Requirements for the Performance of sampling of 150 mg m-3 for 7 days, which is twice the rec- Procedures for Measurements, EN 482, CEN, Brussels, 1994. ommended Swedish short-term (24 h) guideline value as a 16 P. Hofschreuder, W. van der Meulen, P. Heeres and S. Slanina, 98-percentile over 6 months. J. Environ Monit., 1999, 1, 143. In summary, the sampler is suitable for monitoring nitrogen dioxide in urban air as well as in remote areas with sampling Paper 9/02937K times from 1 to 7 days. The sensitivity and capacity of the method also make it suitable for personal sampling for 2–8 h in working environments. 352 J. Environ. Monit., 1999, 1, 349&ndash

ISSN:1464-0325    

DOI:10.1039/a902937k

出版商:RSC    

年代:1999

数据来源: RSC