Environ. Scl. Technol. 1@83,17, 555-559
Loper, J. C. Mutat. Res. 1980, 76, 241. Cheh, A. M.; Skochdopole,J.; Heilig, C.; Koski, P. M.; Cole, L., In “Water Chlorination: Environmental Impact and Health Effects“,Jolley, R. L., Brungs, W. A., Cumming, R. B., Eds.; Ann Arbor Science: Ann Arbor, MI, 1980;Vol. 3,p 803. Rook, J. J. Environ. Sci. Technol. 1977, 11, 478. Babcock, D.B.; Singer, P. C. J . Am. Water Works Assoc. 1979, 71, 149. Christman, R. F.; Johnson, J. D.; Pfaender, F. K.; Norwood, D. L.; Webb, M. R.; Haas, J. R.; Bobenrieth, M. J. In “Water Chlorination: Environmental Impact and Health Effects”; Jolley, R. L., Brungs, W. A., Cumming, R. B., Eds.; Ann Arbor Science: Ann Arbor, MI, 1980;Vol. 3, p 75. Christman, R. F.; Liao, W. T.; Millington, D. S.; Johnson, J. D.; In “Advances in the Identification and Analysis of Organic Pollutants in Water”; Keith, L. H., Ed.; Ann Arbor Science: Ann Arbor, MI, 1981;Vol. 2,p 979. Kringstad, K. P.; Ljungquist, P. 0.;de Sousa, F.; Stromberg, L. M. Enuiron. Sci. Technol. 1981, 15, 562. Kringstad, K. P.; Ljungquist, P. 0.;de Sousa, F.; Stromberg, L. M. In ”Water Chlorination: Environmental Impact and Health Effeds”; Jolley, R. L., et al., Eds.; Ann Arbor Science: Ann Arbor, MI, 1983;Vol. 4,Book 2,p 1311. Christman, R. F.;Oglesby, R. T. In “Lignins: Occurrence, Formation, Structure and Reactions”; Sarkanen, K. V., Ludwig, C. H., Eds.; Wiley-Interscience: New York, 1971. Christman, R. F.; Johnson, J. D.; Hass, J. R.; Pfaender, F. K.; Liao, W. T.; Norwood, D. L.; Alexander, H. J. In “Water Chlorination: Environmental Impact and Health Effects”; Jolley, R. L., et al., Eds.; Ann Arbor Science: Ann Arbor, MI, 1978;VoI. 2, p 15. Hergert, H. L. In “Lignins: Occurrence, Formation, Structure and Reactions”;Sarkanen, K. V., Ludwig, C. H., Eds.; Wiley-Interscience: New York, 1971. Thorsen, T. “Vanndagene 1982”;Trondheim, Norway, Aug 1982;proceedings, p 117. Ander, P.; Eriksson, K.-E.; Kolar, M.-C.; Kringstad, K.; Rannug, U.; Ramel, C. Suen. Papperstidn. 1977,80,454. Kringstad, K. P.; Ljungquist, P.0.; de Sousa, F.; Stromberg, L. M. Environ. Sci. Technol., in press.
compounds as well as of 1,3-dichloroacetone and 2chloropropenal are very low. The mutagenicity of the chlorinated humic acid solutions and of ether extracts from some of these were determined according to the Ames test. The results obtained in testing the aqueous solutions were difficult to interpret due to low survival rates of the bacteria. However, the ether extracts of the 2:l and 4:l C12:C ratio chlorination liquors showed significant mutagenic activities. A comparison of the number of revertants found with the Ames test mutagenicity of 1,3-dichloroacetone and 2-chloropropenal (13) suggests that in particular the latter compound may be responsible for a significant part of the mutagenic activity of the humic acid chlorination solutions.
Acknowledgments We thank H. Flagstad, SINTEF, Trondheim, Norway, for a gift of reverse osmosis humic acid concentrate. Registry No. 2-Chloropropenal,683-51-2; 1,3-dichloroacetone, 53407-6;1,1,3,3-tetrachloroacetone, 632-21-3; pentachloroacetone, 1768-31-6; hexachloroacetone, 116-16-5.
Literature Cited Rook, J. J. In ”Water Chlorination: Environmental Impact and Health Effects”;Jolley, R. L., Brungs, W. A., Cumming, R. B., Eds.; Ann Arbor Science: Ann Arbor, MI, 1980;Vol. 3. D 85. Glaze, W. H.; Henderson, E. J . Water Pollut. Control. Fed. 1975.47.2511. Lin, D. C. K.; Melton, R. G.; Kopfler, F. C.; Lucas, S. V. Water, Air, Soil Pollut. 1983,19, 351-359. In “Advances in the Identification and Analysis of Organic Pollutants in Water”; Keith, L. H., Ed.; Ann Arbor Science: Ann Arbor, MI, 1981;Vol. 2,p 861. Bull. R. J. In “Application of Short-term Bioassays in the Analysis of Complex Environmental Mixtures”; Sandhu, S. S., Ed.; Plenum Press: New York, 1981;Vol. 2. Simmon, V. F.; Kauhanen, K.; Tardiff, R. G. In “Progress in Genetic Toxicology”;Scott, D., Bridges, B. A., Sobels, F. H., Eds.; ElsevierlNorth-Holland Biomedical Press: Amsterdam, 1977;p 249.
Received for review August 2,1982. Revised manuscript received March 10, 1983. Accepted April 25, 1983.
A Gaseous Tracer Model for Air Pollution from Residential Wood Burning M. A. K. Khalll,” S. A. Edgerton, and R. A. Rasmussen Department of Environmental Science, Oregon Graduate Center, Beaverton, Oregon 97006
A novel method is proposed whereby one may determine the contributions of wood burning, and other anthropogenic sources, to urban air pollution. This gaseous tracer model (GTM) relates the concentrations of methyl chloride (CH3C1) in polluted environments to the mass of fine particles emitted from wood burning. As a distinct advantage over existing methods this model can provide almost real-time assessment of the contribution of anthropogenic sources to urban pollution. When the model was applied to data obtained over the winter of 1981 at various suburban locations near Portland, OR, it showed that during peak wood-burning periods of the evening when wood smoke is evident, about 130 pg/m3 of fine particles may be contributed by this source. The effect of wood burning is reduced when averaged over 24 h, and the results agree with past studies that used different analytical methods. Introduction For centuries inhabitants of cities have been subjected to air pollution in the form of smoke and in recent years 0013-936X/83/0917-0555$01.50/0
emissions of exotic man-made gases, leading to the formation of a fine-particle haze. This smoke and haze not only obscure the view but upon long-term exposure may be extremely harmful to human health. Nowadays many diverse classes of sources contribute to urban air pollution, including industrial processes, cars, mass transportation, and in many cities, burning of wood to heat homes. A difficult problem in atmospheric chemistry and physics has been to construct theoretical and experimental methods for determining the amount of fine particles contributed by each source category so as to design optimal control strategies whereby pollution can be nipped at the source with least cost and greatest benefit. In this paper a new model will be derived to determine the contribution of various source categories to urban air pollution. The general method will be applied to the burning of wood for heating homes, which may be a significant source of air pollution during winter in many American cities and in cities all over the world (1,2). There are at present many different independent methods for estimating the contribution of various sources
0 1983 Amerlcan Chemical Society
Environ. Sci. Technol., Vol. 17, No. 9, 1983 555
to urban air pollution (31, including three well-known ones, namely, the chemical element balance (CEB) approach of Friedlander ( 4 , 5 ) ,atmospheric dispersion models (ADM) (6),and radiocarbon analyses (RCA) (7,8). These models have been applied successfully to determine the contributions of specific sources to urban air pollution, yet each method has its own drawbacks. For instance, dispersion models require detailed knowledge of emissions and atmospheric conditions which are seldom available, and the CEB method for wood burning in homes is limited by a lack of unique tracer elements. All these models usually require a large atmospheric sample that is generally collected over 12-24 h, thus providing an average value of pollution levels and making it practically impossible to estimate acute exposure of people to dangerous pollutants. The method proposed here avoids the problems mentioned above and provides a means for estimating the contribution of wood burning to urban air pollution averaged over times of less than 1 h. Our plan is to discuss first the model for pollution from wood burning and other sources and then apply it to two types of experimental data from the last two winters and estimate the contribution of wood burning to urban air pollution. Finally we discuss the uncertainties and propagation of errors in the model. Models The method, which we call gaseous tracer model (GTM), in analogous to the chemical element balance approach mentioned earlier. The general idea is that when fine particles are emitted, as in smoke from wood burning, certain gases are also produced. If the appropriate gas is chosen, its concentration at the receptor can be related to the mass of fine particles emitted by the selected source. We start with a simplified version and lead up to a general model. If Q, is the emission (grams per kilogram or grams per second) of fine particles from a source category (here wood burning) and Qg is the emission (grams per kilogram or grams per second) of a tracer gas (here CH3C1)from the same source category, then the ratio a = Qa/Qgcan be measured at the source. Some distance downwind at the receptor, the concentration of the tracer gas is (1) Cg = Cgo + f 8, where C is the background concentration (of CHBClin our casef and f is the inverse of the atmospheric dilution or dispersion. The contribution of this source to the ambient aerosol at the receptor is Ca = f Qa (2) or, upon combining eq 1 and 2, one obtains C , = aAC, (3) where AC, = C , - C . Since a is measured at the source and AC, is measurefat the receptor, eq 3 can be used to obtain C,, the contribution of fine particles from wood burning. In deriving eq 3 it has been assumed that,both the gaseous tracer and the fine particles in wood smoke are sufficiently long lived in the atmosphere that they are not removed from the atmosphere during the transit between the sources and the receptor. C , stands for the concentration of fine particles emitted by wood burning. Ca cannot be measured directly, but if the concentration of fiie particles is also measured at the receptor, call it C,,, then C , = C,, - Co = AC,, where Co is the sum of the background concentration of fine particles and the contributions of all the other natural and anthropogenic sources. ACg is obtained by measuring C , at the receptor and subtracting C , measured independently at cleaner 558
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NO. 9, 1983
locations during the same season. This simple derivation is preserved in the realistic case where many individual sources, spread out over a large region, contribute to the concentration of both pollutant (fine particles) and tracer gas (CH3C1)at the receptor; thus
and Qgk
= &&/a
therefore
where, as before, Qgk are the emissions of the gas (CH3C1) from the kth source or region, C, is the concentration of CH3C1at the receptor, Qak are the fine particulate emissions from the kth source or region produced by the same sources that emit CH3C1, and f k are the inverses of dilution factors betwen the kth source and the receptor. When many classes of sources contribute to the ambient aerosol either by primary emissions or by gas to particle conversion, long-lived gaseous tracers may also be emitted from these sources so that a generalization of eq 6 may be be the emissions of the ith derived as follows: let Qijk(g) tracer gas from the jth source category and the kth source location; similarly QJk(,)is the emission of fine aerosol from the same source; and aij = Q j k ( a ) / Q i j k ( g ) (7) Then, the concentration of the ith gas at the receptor contributed by the j t h source category at the kth location is fkQi,k(g)
cijk(g)=
=
“ij-’fk&jk(a)
=
%J-lC/k(a)
(8)
The total concentration of the tracer gas at the receptor is the sum of contributions of this gas from all the source categories (i)and locations (k) M N
(9)
where C,o is the background concentration of the ith tracer gas and Cjk(,) = fjQJk(,)is the aerosol contribution of the j t h source category from the kth location. The quantity we want to calculate is CJ(, = Cf=lCjk(,),where cj0is the aerosol contribution of the entire jth source category from all the locations affecting the concentration at the receptor. Equation 9 becomes M
ACi(,) = C~rij-~Cj(,)
(10)
j=l
or
C(,) = M-’AC(,)
(11)
where C is a vector, M-l is the inverse of the matrix M, which consists of ai;%, assuming that there are as many tracer gases (i’s) being measured as there are source categories 0”s). All the equations derived here are analogous to the CEB model. Other assumptions implicit in the model will be discussed later in this paper.
Results: Methyl Chloride as a Tracer of Wood Burning There is considerable evidence that methyl chloride (CH3C1)is produced in low-temperature combustion of wood for home heating, and in most areas there are no
other wintertime anthropogenic sources (9-13). High ambient levels of CH3Cl have been observed in rural China and in suburban areas of the U.S.when wood was being burned and smoke was hanging in the air (12). I t is therefore possible to use CH&l as a tracer of pollution from wood burning. There is also a large natural source, probably oceanic, leading to a global background concentration of about 600 pptV (1pptV = (11,14). The atmospheric lifetime of CH3Cl is between 1and 2 years. The background concentration may vary from place to place, but at a given inland location its natural seasonal variability is observed to be less than 10% (11). The results of the GTM are applied to two studies. One study deals with various samples of air polluted with wood smoke, and the other deals with simultaneous measurementa of CHsCl and bscat, the light-scattering coefficient, which is expected to be proportional to C,. Samples of air were collected during the winter of 1981-1982 in residential areas of Hillsboro and Beaverton, usually between 8 and 10 p.m., when wood smoke was visible. These are suburban locations, near Portland, OR, where during winter and spring wood is burnt for heating homes or for sitting around a fireplace. These samples of air were pumped into 0.8-L stainless steel flasks equipped with 4H4 Nupro valves. Developed over the past decade, this sampling equipment has been used extensively for ultraclean sampling of background tropospheric air (10, 15). In the second study samples were collected in the same manner throughout the winter but at a fixed location. The samples were brought to our laboratory and analyzed by 02-dopedelectron capture gas chromatography (EC/ GC) and GC/MS (10, 15). Measurements of the scattering coefficient were made with an MRI integrating nephelometer at a fured location. The results are summarized in Table I. The contribution of fine particles from wood burning was calculated by using eq 6. The emission factor CY was calculated as a ratio of two other factors:
CY=A/B
Qa
A=Qco2
QCH,C~
B=-
Qcoz
(12)
At present we have no direct measurement of the ratio Qa/CcH,ci, so use of eq 12 is necessary (Q, b the emission of fine particles per unit of time or kilogram of wood). The factor B has been measured and turns out to be about 3.9 X (10, 13) (f0.7X 10" being the standard error of the mean). The factor A can be calculated from various results of past experiments (2,1618). Q, N 10 g/kg of wood, QCOz 1.0 X lo3 g/kg of wood, so that A N = 0.01 (18). C Y , therefore, turns out to be about 2.6 X lo2 (a dimensionless quantity). The estimate of A , based on the data in the literature (2, 16,17), is uncertain by about a factor of 2. With the data available a t present, it is not possible to quantitatively evaluate the uncertainties in either A or B. We have adopted the most likely values of A and B, but large uncertainties exist. We hope to make more accurate estimates of CY in the future by measuring QCH,CI and Q, together, thus elimination the intermediate factor QCO2. For the present, though, we have to be content with A and B since in the past aerosol scientists have measured only A and trace-gas specialists have measured only B. In Table I, ACg is in pptV, which amounts to 2.26 X lov3pg/m3 for each pptV. Therefore, according to eq 6 Ca (pg/m3)
E
o.6Acg (pptv)
(13)
C , is taken to be 570 pptV (see ref 11 and Table I) as measured last winter at a rural site in Beaverton, OR. On the basis of these calculations, about 130 f 30 pg/m3 of
Table I. Calculated Contribution of Wood Burning to Air Pollutiona fine particles, CHSC1, Cay location various suburban locations (1981-1982) when wood burning was evident
date 1981 May 6 May12 Nov7 Nov8 Nov15 Nov19 Nov22 Nov26 Nov29 Dec31 1982 Jan12 Mar31 Apr12 CH,Cl, PPtV
c,,
pptV 930 753 765 703 760 927 766 864 1494b 638
PLgl
m3
217 110 117 81 115 214 117 177 556 42
145gb 532 724 91 811 143 particles, pg/m3 N
av concn wood burning suburban (1981-82, above) 785 f 55c 130 f 30 11 6 3 t 13 24 Hillsboro (winter 1982-83) 674 f 23d single location no wood burning (bckgrd concn) OGC, rural OR, winter 570 2 1 0 35 1982-83 Cape Meares, coastal site, 605 i 20e 10 winter 1980 a All samples were obtained from suburban locations in Concentrations in smoke Beaverton and Hillsboro, OR. near burning wood and on 1-12 after several days of s t a g nation. These data are not included in the averages. t values are 90% confidence limits of the mean based on the t statistic. For Ca the f values reflect observed variability and not the accuracy. The error analysis discussed in the These data text is an aid t o estimating the accuracy. are based o n 35 samples collected over the winter of 1982-1983 a t the same location (Jackson Street, Hillsboro, OR). The data are shown in Figure 1, hence not repeated in t h e table. e From Khalil and Rasmussen (11).
fine particles come from wood burning during peak energy use periods (Table I). Such periods can be sustained for 3-6 h, and depending on the wind and other meteorological conditions, it may take another 2-4 h to remove polluted air, thus making the 24-h average of particles contributed from wood burning to be only 1/2-1/5 of the peak concentration given in Table I, or 25-60 pg/m3. The factors 1/2-1/5 may be verified by a simple urban box model. This value agrees well with other independent estimates of the contribution of wood burning to particulate pollution in the Portland, OR, area (2, 7), which also show -30 pg/m3 as 24-h average concentration. The data presented in the first part of Table I were obtained from various neighborhoods when wood burning was evident and thus represent short-term contributions of wood burning in an otherwise unpolluted environment. Throughout the winter of 1982-1983, measurements of b, and CH3C1 were taken simultaneously at a fixed site in Hillsboro, OR. The applicability of CH3C1as a tracer of pollution from wood burning is clearly demonstrated by the results of this study as shown in Figure 1. bseat is the light-scattering coefficient, which under many usual atmospheric conditions is found to be proportional to the concentration of fine particles (4). During our study of b, Environ. Sci. Technol., Vol. 17, No. 9, 1983 557
800
affect the calculated fine-particles concentration (C,). The uncertainties should be evaluated in all the models before their results can be compared with each other (for CEB see ref 22). Propagating the errors in a,C, and Cg0of eq 6 shows that
I
€Ca
=
[€2+ € c y - C,/C,)-2
+ EC,2(1
- Cg/C,)-2]1’2
(16) where E, = u / 2 and CT is the standard deviation and R the average so that E , is the relative error (23). It is noteworthy ,-w 6 5 0 v that the relative error in the estimate of C,, the fine-particle contribution of wood burning, is very large for low pollution levels (C, close to Cg0)but is generally not ex0 pected to be large in the cases of greatest interest when z 0 pollution levels are high. Equation 16 shows that accurate V measurements of the background concentrations are nec600 essary since the error in C, is attenuated by the factor 1 4 - C,/C o, which from our results in Table I is -3. On t’Re basis of eq 16, we conclude that our results 550 , 1 0 1 2 3 4 5 6 7 8 9 presented in the last section are accurate only to within b,,,, m-’) a factor of 2. This result, however, can be greatly improved by additional systematic measurements of a, C,, and C,,,. Flgure 1. Relationship between light-scattering (bscat) and concen-
E
trations of methyl chloride (CH,CI), which is a tracer of pollution from wood burning.
and CH3Cl,wood smoke was present in varying amounts; thus, bscatmay be written as bscat = bo + aCa (14) where bo is the contribution to light scattering by particles from all other sources, a is a proportionality constant, and C, is, as before, the concentration in micrograms per cubic meter of fine particles from wood burning. Substituting eq 6 of the GTM in eq 14 yields becat= bo + uAC, bscat= + uIC, (15) where a. = bo - uaC& and al and aa where a is as defined in eq 6. If the other pollutant sources do not correlate with wood burning or are not as significant, then bo may be assumed to be a constant or a small quantity. Therefore eq 14 based on the GTM would predict a linear relationship between bscatand CH3C1 concentration (C ). In the case of our experiment in Hillsboro, OR, we believe that during the periods of sampling, bo was small and the major source of pollution was wood burning since the presence of wood smoke was verified by visual observation. Figure 1demonstrates that b, (lo4 m-l) N -19 O.03Cg (pptV) as in eq 15, with a correlation coefficient r = 0.8 ( r > 0, a < 0.01). Some of the variability in Figure 1is probably due to the variability of other sources which may contribute particles in the high light-scattering range. The values of bscatwe have observed agree well with similar studies in the past and reflect moderate levels of air pollution (19,20).In an aerosol characterization study (PACS) conducted in Portland, OR, it was found that b,,, m-l) = 0.13 0.046(mass) (pg/cm3), where the mass refers to fine particles (21, 22). The coefficient 0.046 is the same parameter as the a in eq 14. The value of a in our study, determined by applying the GTM, is a l / a = 0.0310.6 = 0.05, thus being in excellent agreement with the independent PACS data. Since a, is a measured quantity (Figure l), this agreement tends to support the accuracy of our estimated value for a.
+
+
Uncertainties We have shown how the gaseous tracer model (GTM) is derived and applied to two data bases. The ultimate applicability of GTM depends largely on how errors or variability in the basic observable factors of the model 558
Envlron. Sci. Technol., Vol. 17, No. 9, 1983
Discussion The contribution of wood burning to urban fine-particle pollution can be determined by using a gaseous tracer model (GTM). Tracers are gases that are emitted along with the partiles from the source. We conclude by discussing the physical properties of useful tracers and the advantages of GTM. The most useful tracers are those whose background concentrations are small or C, >> C, in polluted environments. Second, the lifetime of the tracer should be long enough that it is not removed from the atmosphere between the source and the receptor. Third, the tracer should be unique to the class of sources under study, and finally it should be accurately and precisely measurable at the levels present in polluted environments. Although CH3C1 fulfills most of these requirements, there are probably better gaseous tracers of wood burning yet to be found. The concentration of CH&l in the background atmosphere is high and variable; thus it is unlikely to provide estimates of wood burning to accuracies of better than 350%. The main advantages of the gaseous tracer model over the previous methods are first that is provides a means for almost real-time analysis of the contribution of specific sources to urban fine-particulate pollution. Second, it is practical, being inexpensive and experimentally simple. There are only a few potential sources of error. Finally, at least for wood burning, we believe this method is accurate and can be made precise. In this paper we have derived the model (GTM) and shown its applicability. This is a beginning that we hope will prove useful in understanding and controlling air pollution, not only from wood burning but from other sources as well. Acknowledgments We thank R. W. Dalluge, S. D. Hoyt, and J. J. Huntzicker for help with instrumentation. Registry No. CH,Cl, 74-87-3.
Literature Cited (1) Cooper, J. A.; Malek, D. “Residential Solid Fuels: Envi-
ronmental Impacts and Solutions”, Oregon Graduate Center: Beaverton, OR, 1982. (2) Cooper, J. A. J. Air Pollut. Control Assoc. 1980,30,855-861.
(3) Cooper, J. A.; Wataon, J. G., Jr. J. Air. Pollut. Control Assoc. 1980,30, 1116-1125. (4) Friedlander, S. K. “Smoke,dust & Haze”; Wiley New York, 1977; Chapter 5, p 11. (5) Friedlander, S. K. Environ. Sci. Technol. 1973, 7,235-240. (6) Dobbins, R. A. “Atmospheric Motion and Air Pollution”; Wiley: New York, 1979. (7) Cooper, J. A.; Currie, L. A.; Klouda, G. A. Environ. Sci. Technol. 1981,15, 1045-1050. (8) Core, J. E.; Pace, T. G. Paper presented at the 74th Annual Meeting of the Air Pollution Control Association, June 21-26, 1981. (9) Palmer, T. Y. Nature (London) 1976,263, 44-46. (10) Rasmussen, R. A,; Rasmussen, L. E.; Khalil, M. A. K.; Dalluge, R. W. J. Geophys. Res. 1980,85, 7350-7356. (11) Khalil, M. A. K.; Rasmussen, R. A. Chemosphere 1981,10, 1019-1023. (12) Rasmussen, R. A.; Khalil, M. A. K.; Chang, J. S. Environ. Sci. Technol. 1982, 16, 124-126. (13) Crutzen, P. J.; Heidt, L. E.; Krasnec, J. P.; Pollock, W. H.; Seiler, W. Nature (London) 1979,282, 253-256. (14) Singh, H. B.; Salas, L. J.;Shigeishi, H.; Scribner, E. Science (Washington, D.C.) 1979, 203, 899-903. (15) Rasmussen, R. A.; Khalil, M. A. K. “Proceedings of the NATO Advanced Study Institute on Atmospheric Ozone: Ita Variation and Human Influence”; Aikin, A. C., Ed.; US. Department of Transportation, Report FAA-EE-80-20, Washington, D.C., 1980, pp 209-231. (16) DeAngelis, D. G.; Ruffin, D. S.; Rezink, R. B. “Preliminary
(17) (18)
(19)
(20) (21) (22) (23)
Characterization of Emissions from Wood-Fired Residential Combustion Equipment”; U.S. Environmental Protection Agency: Washington, D.C., 1980. Cook, J. D.; Himel, J. H.; Moyer, R. H. ”Impact of Forestry Burning upon Air Quality”; GEOMET Report EF664; GEOMET Gaithersburg, MD, 1978. DeAngelis, D. G.; Ruffin, D. S.; Peters, J. A.; Reznik, R. B. “Source Assessment: Residential Combustion of Wood”; US. Environmental Protection Agency Contract 68-02-1874 Task 23, Washington, D.C., 1981. Griffing, G. W. “Dependence of Nephelometer Scattering Coefficient on Relative Humidity: Fronts, Nocturnal Disturbance and Wood Smoke”, EPA-600/3-82-006, U.S. Environmental Protection Agency: Washington, D.C., 1981. Shah, J. J.; Watson, J. G., Jr.; Cooper, J. A.; Huntzicker, J. J., submitted for publication in Atmos. Environ. Cooper, J. A,; Watson, J. G., Jr.; Huntzicker, J. J. Presented a t the 1979 Annual Meeting of the Air Pollution Control Association, Cincinnati, OH, 1979. Watson, J. G., Jr. Ph.D. Dissertation, Oregon Graduate Center, Beaverton, OR, 1979. Meyer, S. L. “Data Analysis for Scientists and Engineers”; Wiley: New York, 1975.
Received for review November 29, 1982. Revised manuscript received March 25,1983. Accepted May 2,1983. This work was supported in part by the Biospherics Research Corp. and the Andarz Co.
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