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Receptor Modeling Source Apportionment for Air Quality Management John G. Watson Judith C. Chow Desert Research Institute Reno, Nevada,

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Presentation on theme: "Receptor Modeling Source Apportionment for Air Quality Management John G. Watson Judith C. Chow Desert Research Institute Reno, Nevada,"— Presentation transcript:

1 Receptor Modeling Source Apportionment for Air Quality Management John G. Watson Judith C. Chow Desert Research Institute Reno, Nevada, USA Presented at: The Workshop on Air Quality Management, Measurement, Modeling, and Health Effects University of Zagreb, Zagreb, Croatia 24 May 2007

2 Objectives Review receptor models and data requirements Summarize prior uses of receptor models in air quality management Describe strategies for separating primary and secondary source contributions

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6 The First Receptor Model What you can see or smell

7 Black Carbon (BC) Remains at Mesa Verde National Park, Colorado, USA Not all BC is from diesel and other vehicular emissions “Marker” is a better term than “tracer” There’s something of everything in everything

8 Source and Receptor Models The source model uses source emissions as inputs and calculates ambient concentrations. The receptor model uses ambient concentrations as inputs and calculates source contributions. (From Watson, 1979.)

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10 Lagrangian Source Model C ikl = Σ j Σ m Σ n T ijklmn D kln F ij Q jkmn MEASURED AT SOURCE (INVENTORY) CALCULATED BY MET MODEL CALCULATED BY CHEMICAL MODEL CALCULATED AT RECEPTOR CMB Receptor Model C ikl = Σ j T ijkl F ij Σ m Σ n D kln Q jkmn S ijkl, SOURCE CONTRIBUTION ESTIMATE MEASURED AT SOURCE (T=1 OR ESTIMATED BY OTHER METHOD MEASURED AT RECEPTOR

11 Chemical Mass Balance Equation: Input: Ambient concentrations (C i ) and uncertainties (  Cj ), source profiles (F ij ), and uncertainties (  Fij ). Output: Source contributions (S j ) and uncertainties (  Sj ). Measurements: Size-classified mass, elements, ions, and carbon concentrations on both ambient and source samples.

12 ϰ 2 = minΣ i [ (C i -C i ) 2 / ϭ Ci 2 ] + Σ i Σ i [ (F ij -F ij ) 2 / ϭ Fij 2 ] Britt and Luecke, (1973), single sample, bold=true value CMB Solutions Minimize differences between calculated and measured values for overdetermined set of equations ϰ 2 =minΣ i [ (C i -Σ j F ij S j ) 2 /( ϭ Ci 2 + Σ j ϭ Fij 2 S j 2 ) ] Effective Variance, Watson et al., (1984), single sample ϰ 2 =minΣ i [ (C i -Σ j F ij S j ) 2 / ϭ Ci 2 ) ] Ordinary Weighted Least Squares, Friedlander (1973), single sample

13 S j =C i /F ij Tracer solution, Hidy and Friedlander (1971), Winchester and Nifong (1971), single sample Other CMB Solutions ϰ 2 =minΣ k [ (Mass k -Σ i C ik / F ii ) 2 ] Multiple Linear Regression, Kleinman et al (1980), multiple samples ϰ 2 =minΣ i Σ k [ (C ik -Σ j F ij S jk ) 2 / ϭ Cik 2 ) ] Positive Matrix Factorization, Paatero (1997), multiple samples

14 Receptor Models are Not Statistical They don’t test hypotheses or determine statistical significance Receptor models should be physically based with statements of simplifying assumptions and evaluation of deviations from assumptions They infer mechanisms and interactions rather than explicitly calculate them Receptor models recognize and elucidate patterns in measured components, space and time that bound the types, quantities, and locations of source contributions Some of them explicitly use input data uncertainties to weight influence of inputs and estimate uncertainties of outputs

15 Types of “Modern” Receptor Models Chemical Mass Balance  CMB with various solutions including marker (trace method, effective variance (EV), principal component analysis (PCA), UNMIX, abd positive matrix factorization (PMF) solutions Aerosol Evolution and Equilibrium  Estimates how reduction in one precursor will affect PM end-products Back Trajectory  estimates source areas for different pollutants or source contributions

16 Chemical Mass Balance Equation: Input: Ambient concentrations (C i ) and uncertainties (  Cj ), source profiles (F ij ), and uncertainties (  Fij ). Output: Source contributions (S j ) and uncertainties (  Sj ). Measurements: Size-classified mass, elements, ions, and carbon concentrations on both ambient and source samples.

17 Receptor Measurements from Ambient Samplers Airmetrics portable MiniVol sampler PM 2.5 and PM 10 BGI FRM Omni PM 1, PM 2.5, and PM 10

18 Source profiles from source testing

19 Many contributors not inventoried Real-World CookingSimulated Cooking

20 More source profiles could be obtained from certification tests Roadside compliance test in India

21 Material balance says much about sources (Mexico City, Feb/Mar 1997) (Chow et al., 2002)

22 More specificity obtained with source profiles Commonly measured elements, ions, and carbon (Zielinska et al., 1998)

23 Many toxic elements have been removed from emissions. Organic markers take their place (Chow et al. 2006)

24 Carbon fractions have been found useful and can be obtained from existing samples (Watson et al., 1994) Diesel-fueled vehiclesGasoline-fueled vehicles

25 Thermally-evolved material can be separated by chromatography and mass spectrometry Challenge is to extract information that separates sources Gasoline Diesel Roadside dust Coal power plant

26 Examples of U.S. CMB Model Air Quality Findings and Results Oregon wood stove emissions standard (Watson, 1979) Midwest contributions to east coast sulfate and ozone (Wolff et al., 1977, Lioy et al., 1980, Mueller et al., 1983, Rahn and Lowenthal, 1984) Washoe County, Nevada, stove changeout, burning ban, and “squealer” number (Chow et al., 1989) California EMFAC emissions model revisions (Fujita et al., 1992, 1994) SCAQMD (Los Angeles) grilling emission standard (Rogge, 1993) SCAQMD (Los Angeles) street sweeper specification (Chow et al., 1990)

27 Examples of U.S. CMB Model Air Quality Findings and Results (continued) SCAQMD (Los Angeles) Chino dairy reduction (NH 3 ) regulation (SCAQMD, 1996) PM 10 SIP implementation of wood burning, road dust, and industrial emission reductions (Davis and Maughan, 1984, Houck et al., 1981, 1982, Cooper et al., 1988, 1989) Navajo Generating Station SO 2 scrubbers (Malm et al., 1989) Hayden Generating Station SO 2 scrubbers (Watson et al., 1996) Mohave Generating Station shutdown (Pitchford et al., 1999) Denver Colorado urban visibility standard (Watson et al., 1988)

28 Worldwide PM Source Contribution Estimates by Chemical Mass Balance (Chow and Watson, 2002)

29 Receptor Model Results Need to be Challenged CMB Sensitivity Test (Chow et al. 2006)

30 CMB Pseudo-Inverse Normalized (MPIN) Matrix (Chow et al. 2006)

31 Light Duty Emission Rates Heavy Duty Emission Rates One Atmosphere (Gases and Particles) Also Works for Receptor Models (Gertler et al., 1996)

32 Hourly (VOC) data provide temporal corroboration of emissions and reveal unknown sources (Houston, TX, 1993) (Lu, 1996) Morning traffic Unknown event

33 High Time Resolution is Desired Spikes indicate local sources (Watson and Chow, 2001)

34 Wind Direction is Suggestive for Local Sources Conditional Probability Function (CPF) for a Selenium Factor at the Pittsburg Supersite (Pekney et al., 2006)

35 Source factors derived from ambient data by UNMIX and PMF These must be associated with measured source profiles (Chen et al., 2006)

36 Markers for Biogenic SOA (Pandis, 2001) Pinic acid, pinonic acid, norpinic acid, and norpinonic acid are products of the oxidation of most monoterpenes There are some (apparently) unique tracers: Hydropinonaldehydes for α-pinene Nopinone for β-pinene 3-caric acid for carene Sabinic acid for sabenene Several of these compounds measured in field studies in forests (usually a few nanograms per cubic meter, sometimes as much as 0.1 µg m -3 )

37 SO 4 = /SO 2 Ratio changes during Aerosol Aging (and should be Reflected in Source Profiles) (Watson et al., 2002)

38 Back trajectories indicate source regions Regression parameters for Grand Canyon National Park (2000–2002). Percent of time the parcel is in a horizontal grid cell based on back trajectories starting at 500 m. (Xu et al., 2006)

39 Receptor Models Can Estimate the Future in Some Circumstances (Denver, CO, 1997) (Watson et al., 1998) Effect of ammonia reductions on ammonium nitrate particles Effect of nitric acid reductions on ammonium nitrate particles

40 Emission Reduction Effectiveness Long-Term Trends in SO 2 Emissions and SO 4 = Levels (Malm et al., 2002)

41 Murphy’s Law of Reproducibility “If reproducibility is a problem, just use one model” Mohave Generating Station contributions to Meadview sulfate (Pitchford et al., 1999)

42 Model discrepancies help to improve inventories PM 2.5 Inventory/Receptor Model Comparison, Denver, CO (1997) (Watson et al., 2002)

43 SIP Guidance “Weight of Evidence” Approach (EPA, 2001) Form a conceptual model of the emissions, meteorology, and chemical transformations that are likely to affect exceedances Develop a modeling/data analysis protocol with stakeholders consistent with available science, measurements, and the conceptual model Construct and evaluate emission inventory for the domain as indicated by the conceptual model

44 SIP Guidance “Weight of Evidence” Approach (continued) Assemble and evaluate meteorological measurements for the domain Apply source and receptor models and to determine contributions Apply diagnostic tests and justify discarding results that are not physically reasonable

45 SIP Guidance “Weight of Evidence” Approach (continued) Modify the inventory to reflect different emission reduction strategies in consultation with stakeholders, and evaluate the effects of reductions at receptors Make models, input data, and results available to others for external review Judge the weight of evidence supporting or opposing the selected emission reduction strategy prior to implementation

46 Receptor Model Needs: A Summary Source properties that identify and quantify source contributions at a receptor (Daisey et al., 1986, Gordon et al., 1984) Better designed networks (Chow et al., 2002, Demerjian, 2000) with respect to Sampling locations Sampling periods Sample durations Particle sizes Precursor gases Chemical and physical components Meteorology

47 Receptor Model Needs (continued) Emissions profiles (with cooling and dilution including marker species and gases, (England et al., 2000) More convenient availability and documentation of source profile and ambient data (U.S. EPA, 1999) More evaluation, validation, and reconciliation of receptor and source modeling results (Javitz et al., 1988)

48 References Cabada, J.C.; Pandis, S.N.; and Robinson, A.L. (2002). Sources of atmospheric carbonaceous particulate matter in Pittsburgh, Pennsylvania. J. Air Waste Manage. Assoc., 52(6): Cabada, J.C.; Pandis, S.N.; Subramanian, R.; Robinson, A.L.; Polidori, A.; and Turpin, B.J. (2004). Estimating the secondary organic aerosol contribution to PM2.5 using the EC tracer method. Aerosol Sci. Technol., 38(Suppl. 1): ISI: Chen, L.-W.A.; Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; and Chang, M.C. (2006). Quantifying PM2.5 source contributions for the San Joaquin Valley with multivariate receptor models. Environ. Sci. Technol., submitted. Chow, J.C.; Engelbrecht, J.P.; Watson, J.G.; Wilson, W.E.; Frank, N.H.; and Zhu, T. (2002a). Designing monitoring networks to represent outdoor human exposure. Chemosphere, 49(9): ISI: Chow, J.C.; and Watson, J.G. (2002). Review of PM 2.5 and PM 10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model. Energy & Fuels, 16(2): Chow, J.C.; Watson, J.G.; Edgerton, S.A.; Vega, E.; and Ortiz, E. (2002b). Spatial differences in outdoor PM 10 mass and aerosol composition in Mexico City. J. Air Waste Manage. Assoc., 52(4): Chow, J.C.; Watson, J.G.; Egami, R.T.; Frazier, C.A.; and Lu, Z. (1989). The State of Nevada Air Pollution Study (SNAPS): Executive summary. Report No. DRI E. Prepared for State of Nevada, Carson city, NV, by Desert Research Institute, Reno, NV. Chow, J.C.; Watson, J.G.; Egami, R.T.; Frazier, C.A.; Lu, Z.; Goodrich, A.; and Bird, A. (1990). Evaluation of regenerative- air vacuum street sweeping on geological contributions to PM 10. J. Air Waste Manage. Assoc., 40(8): Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Chen, L.-W.A.; Zielinska, B.; Rinehart, L.R.; and Magliano, K.L. (2006). Evaluation of organic markers for chemical mass balance source apportionment at the Fresno Supersite. Chemosphere, submitted. Cooper, J.A.; Miller, E.A.; Redline, D.C.; Spidell, R.L.; Caldwell, L.M.; Sarver, R.H.; and Tansyy, B.L. (1989). PM 10 source apportionment of Utah Valley winter episodes before, during, and after closure of the West Orem steel plant. Prepared for Kimball, Parr, Crockett and Waddops, Salt Lake City, UT, by NEA, Inc., Beaverton, OR.

49 References Cooper, J.A.; Sherman, J.R.; Miller, E.; Redline, D.; Valdonovinos, L.; and Pollard, W.L. (1988). CMB source apportionment of PM 10 downwind of an oil-fired power plant in Chula Vista, California. In Transactions, PM 10 : Implementation of Standards, C.V. Mathai and D.H. Stonefield, Eds. Air and Waste Management Association, Pittsburgh, PA, pp Daisey, J.M.; Cheney, J.L.; and Lioy, P.J. (1986). Profiles of organic particulate emissions from air pollution sources: Status and needs for receptor source apportionment modeling. J. Air Poll. Control Assoc., 36(1): Davis, B.L.; and Maughan, A.D. (1984). Observation of heavy metal compounds in suspended particulate matter at East Helena, Montana. J. Air Poll. Control Assoc., 34(12): Demerjian, K.L. (2000). A review of national monitoring networks in North America. Atmos. Environ., 34(12-14): Eatough, D.J.; Du, A.; Joseph, J.M.; Caka, F.M.; Sun, B.; Lewis, L.; Mangelson, N.F.; Eatough, M.; Rees, L.B.; Eatough, N.L.; Farber, R.J.; and Watson, J.G. (1997). Regional source profiles of sources of SO X at the Grand Canyon during Project MOHAVE. J. Air Waste Manage. Assoc., 47(2): England, G.C.; Zielinska, B.; Loos, K.; Crane, I.; and Ritter, K. (2000). Characterizing PM 2.5 emission profiles for stationary sources: Comparison of traditional and dilution sampling techniques. Fuel Processing Technology, 65: Forrest, J.; and Newmann, L. (1973). Sampling and analysis of atmospheric sulfur compounds for isotope ratio studies. Atmos. Environ., 7(5): Fujita, E.M.; Croes, B.E.; Bennett, C.L.; Lawson, D.R.; Lurmann, F.W.; and Main, H.H. (1992). Comparison of emission inventory and ambient concentration ratios of CO, NMOG, and NO x in California's South Coast Air Basin. J. Air Waste Manage. Assoc., 42(3): Fujita, E.M.; Watson, J.G.; Chow, J.C.; and Lu, Z. (1994). Validation of the chemical mass balance receptor model applied to hydrocarbon source apportionment in the Southern California Air Quality Study. Enivron. Sci. Technol., 28(9): Gertler, A.W.; Fujita, E.M.; Pierson, W.R.; and Wittorff, D.N. (1996). Apportionment of NMHC tailpipe vs non-tailpipe emissions in the Fort McHenry and Tuscarora mountain tunnels. Atmos. Environ., 30(12):

50 References Gordon, G.E.(1984). Atmospheric tracers of opportunity from important classes of air pollution sources. In DOE Workshop on Atmospheric Tracers, Santa Fe, NM. Gray, H.A.; Cass, G.R.; Huntzicker, J.J.; Heyerdahl, E.K.; and Rau, J.A. (1986). Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Enivron. Sci. Technol., 20(6): Hidy, G.M. (1987). Conceptual design of a massive aerometric tracer experiment (MATEX). J. Air Poll. Control Assoc., 37(10): Houck, J.E.; Cooper, J.A.; Core, J.E.; Frazier, C.A.; and deCesar, R.T. (1981). Hamilton Road Dust Study: Particulate source apportionment analysis using the chemical mass balance receptor model. Prepared for Concord Scientific Corporation, by NEA Laboratories, Inc., Beaverton, OR. Houck, J.E.; Cooper, J.A.; Frazier, C.A.; and deCesar, R.T. (1982). East Helena Source Apportionment Study: Particulate source apportionment analysis using the chemical mass balance receptor model, Vol. III Appendices. Prepared for State of Montana, Dept. of Health & Environmental Sciences, Helena, MT, by NEA Laboratories, Inc., Beaverton, OR. Javitz, H.S.; Watson, J.G.; Guertin, J.P.; and Mueller, P.K. (1988). Results of a receptor modeling feasibility study. J. Air Poll. Control Assoc., 38(5): Lewis, C.W.; and Stevens, R.K. (1985). Hybrid receptor model for secondary sulfate from an SO 2 point source. Atmos. Environ., 19(6): Lioy, P.J.; Samson, P.J.; Tanner, R.L.; Leaderer, B.P.; Minnich, T.; and Lyons, W.A. (1980). The distribution and transport of sulfate "species" in the New York area during the 1977 Summer Aerosol Study. Atmos. Environ., 14: Lu, Z. (1996). Temporal and spatial analysis of VOC source contributions for Southeast Texas. Ph.D Dissertation, University of Nevada, Reno. Malm, W.C.; Pitchford, M.L.; and Iyer, H.K. (1989). Design and implementation of the Winter Haze Intensive Tracer Experiment - WHITEX. In Transactions, Receptor Models in Air Resources Management, J.G. Watson, Ed. Air & Waste Management Association, Pittsburgh, PA, pp Malm, W.C.; Schichtel, B.A.; Ames, R.B.; and Gebhart, K.A. (2002). A ten-year spatial and temporal trend of sulfate across the United States. J. Geophys. Res., 107(D22):ACH 11-1-ACH

51 References Mueller, P.K.; Hidy, G.M.; Baskett, R.L.; Fung, K.K.; Henry, R.C.; Lavery, T.F.; Nordi, N.J.; Lloyd, A.C.; Thrasher, J.W.; Warren, K.K.; and Watson, J.G. (1983). Sulfate Regional Experiment (SURE): Report of findings. Report No. EA Prepared by Electric Power Research Institute, Palo Alto, CA. Paatero, P.; Hopke, P.K.; Song, X.H.; and Ramadan, Z. (2002). Understanding and controlling rotations in factor analytical models. Chemom. Intell. Lab. Sys., 60: Pandis, S.N. (2001). Secondary organic aerosol: Precursors, composition, chemical mechanisms, and environmental conditions. Presentation at the Secondary Organic Aerosols Workshop in Durango, CO. Fort Lewis College, Durango, CO. Pekney, N.J.; Davidson, C.I.; Zhou, L.; and Hopke, P.K. (2006). Application of PSCF and CPF to PMF-modeled sources of PM2.5 in Pittsburgh. Aerosol Sci. Technol., accepted. Pitchford, M.L.; Green, M.C.; Kuhns, H.D.; Tombach, I.H.; Malm, W.C.; Scruggs, M.; Farber, R.J.; Mirabella, V.A.; White, W.H.; McDade, C.; Watson, J.G.; Koracin, D.; Hoffer, T.E.; Lowenthal, D.H.; Vimont, J.C., et al. (1999). Project MOHAVE, Final Report. Prepared by U.S. Environmental Protection Agency, Region IX, San Francisco, CA. Poirot, R.L.; Wishinski, P.R.; Hopke, P.K.; and Polissar, A.V. (2001). Comparitive application of multiple receptor methods to identify aerosol sources in northern Vermont. Environ. Sci. Technol., 35(23): Rahn, K.A.; and Lowenthal, D.H. (1984). Elemental tracers of distant regional pollution aerosols. Science, 223(4632): Rogge, W.F. (1993). Molecular tracers for sources of atmospheric carbon particles: Measurements and model predictions. Ph.D. Dissertation, California Institute of Technology, Pasadena, CA. South Coast Air Quality Management District (1996) air quality maintenance plan: Appendix V, Modeling and attainment demonstrations. Prepared by South Coast Air Quality Management District, Diamond Bar, CA. Turpin, B.J.; and Huntzicker, J.J. (1991). Secondary formation of organic aerosol in the Los Angeles Basin: A descriptive analysis of organic and elemental carbon concentrations. Atmos. Environ., 25A(2):

52 References U.S.EPA (1999). SPECIATE: EPA's repository of total organic compound and particulate matter speciated profiles for a variety of sources for use in source apportionment studies. Prepared by U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. U.S.EPA (2001). Draft guidance for demonstrating attainment of air quality goals for PM 2.5 and regional haze. Prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC. Watson, J.G. (1979). Chemical element balance receptor model methodology for assessing the sources of fine and total suspended particulate matter in Portland, Oregon. Ph.D. Dissertation, Oregon Graduate Center, Beaverton, OR. Watson, J.G. (1984). Overview of receptor model principles. J. Air Poll. Control Assoc., 34(6): Watson, J.G.; Blumenthal, D.L.; Chow, J.C.; Cahill, C.F.; Richards, L.W.; Dietrich, D.; Morris, R.; Houck, J.E.; Dickson, R.J.; and Andersen, S.R. (1996). Mt. Zirkel Wilderness Area reasonable attribution study of visibility impairment, Vol. II: Results of data analysis and modeling. Prepared for Colorado Department of Public Health and Environment, Denver, CO, by Desert Research Institute, Reno, NV. Watson, J.G.; and Chow, J.C. (2001). Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer. J. Air Waste Manage. Assoc., 51(11): Watson, J.G.; and Chow, J.C. (2005). Receptor models. In Air Quality Modeling -Theories, Methodologies, Computational Techniques, and Available Databases and Software. Vol. II - Advanced Topics, P. Zannetti, Ed. Air and Waste Management Association and the EnviroComp Institute, Pittsburgh, PA, pp Watson, J.G.; Chow, J.C.; Lowenthal, D.H.; Pritchett, L.C.; Frazier, C.A.; Neuroth, G.R.; and Robbins, R. (1994). Differences in the carbon composition of source profiles for diesel- and gasoline-powered vehicles. Atmos. Environ., 28(15): Watson, J.G.; Chow, J.C.; Lowenthal, D.H.; Robinson, N.F.; Cahill, C.F.; and Blumenthal, D.L. (2002). Simulating changes in source profiles from coal-fired power stations: Use in chemical mass balance of PM 2.5 in the Mt. Zirkel Wilderness. Energy & Fuels, 16(2): Watson, J.G.; Chow, J.C.; Richards, L.W.; Andersen, S.R.; Houck, J.E.; and Dietrich, D.L. (1988). The Metro Denver Brown Cloud Air Pollution Study, Volume III: Data interpretation. Report No. DRI Prepared for Greater Denver Chamber of Commerce, Denver, CO, by Desert Research Institute, Reno, NV.

53 References Watson, J.G.; Fujita, E.M.; Chow, J.C.; Zielinska, B.; Richards, L.W.; Neff, W.D.; and Dietrich, D. (1998). Northern Front Range Air Quality Study. Final report. Prepared for Colorado State University, Fort Collins, CO, by Desert Research Institute, Reno, NV. Wolff, G.T.; Lioy, P.J.; Wight, G.D.; Meyers, R.E.; and Cederwall, R.T. (1977). An investigation of long-range transport of ozone across the midwestern and eastern United States. Atmos. Environ., 11: Xu, J.; DuBois, D.; Pitchford, M.; Green, M.; and Etyemezian, V. (2006). Attribution of sulfate aerosols in Federal Class I areas of the western United States based on trajectory regression analysis. Atmos. Environ., 40: Zielinska, B.; McDonald, J.D.; Hayes, T.; Chow, J.C.; Fujita, E.M.; and Watson, J.G. (1998). Northern Front Range Air Quality Study, Volume B: Source measurements. Prepared for Colorado State University, Fort Collins, CO, by Desert Research Institute, Reno, NV.


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