Presentation on theme: "Receptor Modeling Source Apportionment for Air Quality Management"— Presentation transcript:
1 Receptor Modeling Source Apportionment for Air Quality Management RecModForAirResMgmt.ppt4/13/2017Receptor Modeling Source Apportionment for Air Quality ManagementJohn G. WatsonJudith C. ChowDesert Research Institute Reno, Nevada, USAPresented at: The Workshop on Air Quality Management, Measurement, Modeling, and Health EffectsUniversity of Zagreb, Zagreb, Croatia24 May 2007
2 Objectives Review receptor models and data requirements RecModForAirResMgmt.ppt4/13/2017ObjectivesReview receptor models and data requirementsSummarize prior uses of receptor models in air quality managementDescribe strategies for separating primary and secondary source contributions
6 The First Receptor Model What you can see or smell RecModForAirResMgmt.ppt4/13/2017The First Receptor Model What you can see or smell
7 Black Carbon (BC) Remains at Mesa Verde National Park, Colorado, USA RecModForAirResMgmt.ppt4/13/2017Black Carbon (BC) Remains at Mesa Verde National Park, Colorado, USANot 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 RecModForAirResMgmt.ppt4/13/2017Source and Receptor ModelsThe 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.)
11 Chemical Mass Balance Equation: Input: RecModForAirResMgmt.ppt4/13/2017Chemical Mass BalanceEquation:Input:Ambient concentrations (Ci) and uncertainties (sCj), source profiles (Fij), and uncertainties (sFij).Output:Source contributions (Sj) and uncertainties (sSj).Measurements:Size-classified mass, elements, ions, and carbon concentrations on both ambient and source samples.
12 RecModForAirResMgmt.ppt4/13/2017CMB Solutions Minimize differences between calculated and measured values for overdetermined set of equationsϰ2 = minΣi [(Ci-Ci)2/ϭCi2] + ΣiΣi [(Fij-Fij)2/ϭFij2 ]Britt and Luecke, (1973), single sample, bold=true valueϰ2 =minΣi [(Ci-ΣjFijSj)2/(ϭCi2+ΣjϭFij2Sj2)]Effective Variance, Watson et al., (1984), single sampleϰ2 =minΣi [(Ci-ΣjFijSj)2/ϭCi2)]Ordinary Weighted Least Squares, Friedlander (1973), single sample
13 Other CMB Solutions Sj=Ci/Fij ϰ2 =minΣk [(Massk-ΣiCik/Fii)2] RecModForAirResMgmt.ppt4/13/2017Other CMB SolutionsSj=Ci/FijTracer solution, Hidy and Friedlander (1971), Winchester and Nifong (1971), single sampleϰ2 =minΣk [(Massk-ΣiCik/Fii)2]Multiple Linear Regression, Kleinman et al (1980), multiple samplesϰ2 =minΣi Σk [(Cik-ΣjFijSjk)2/ϭCik2)]Positive Matrix Factorization, Paatero (1997), multiple samples
14 Receptor Models are Not Statistical RecModForAirResMgmt.ppt4/13/2017Receptor Models are Not StatisticalThey don’t test hypotheses or determine statistical significanceReceptor models should be physically based with statements of simplifying assumptions and evaluation of deviations from assumptionsThey infer mechanisms and interactions rather than explicitly calculate themReceptor models recognize and elucidate patterns in measured components, space and time that bound the types, quantities, and locations of source contributionsSome of them explicitly use input data uncertainties to weight influence of inputs and estimate uncertainties of outputs
15 Types of “Modern” Receptor Models RecModForAirResMgmt.ppt4/13/2017Types of “Modern” Receptor ModelsChemical Mass Balance CMB with various solutions including marker (trace method, effective variance (EV), principal component analysis (PCA), UNMIX, abd positive matrix factorization (PMF) solutionsAerosol Evolution and Equilibrium Estimates how reduction in one precursor will affect PM end-productsBack Trajectory estimates source areas for different pollutants or source contributions
16 Chemical Mass Balance Equation: Input: RecModForAirResMgmt.ppt4/13/2017Chemical Mass BalanceEquation:Input:Ambient concentrations (Ci) and uncertainties (sCj), source profiles (Fij), and uncertainties (sFij).Output:Source contributions (Sj) and uncertainties (sSj).Measurements:Size-classified mass, elements, ions, and carbon concentrations on both ambient and source samples.
17 Receptor Measurements from Ambient Samplers RecModForAirResMgmt.ppt4/13/2017Receptor Measurements from Ambient SamplersAirmetrics portable MiniVol samplerBGI FRM OmniPM2.5 and PM10PM1, PM2.5, and PM10
18 Source profiles from source testing RecModForAirResMgmt.ppt4/13/2017Source profiles from source testing
19 Many contributors not inventoried RecModForAirResMgmt.ppt4/13/2017Many contributors not inventoriedReal-World CookingSimulated Cooking
20 More source profiles could be obtained from certification tests RecModForAirResMgmt.ppt4/13/2017More source profiles could be obtained from certification testsRoadside compliance test in India
21 RecModForAirResMgmt.ppt4/13/2017Material balance says much about sources (Mexico City, Feb/Mar 1997) (Chow et al., 2002)
22 More specificity obtained with source profiles RecModForAirResMgmt.pptMore specificity obtained with source profiles4/13/2017Commonly measured elements, ions, and carbon (Zielinska et al., 1998)
23 RecModForAirResMgmt.ppt4/13/2017Many toxic elements have been removed from emissions. Organic markers take their place(Chow et al. 2006)
24 RecModForAirResMgmt.ppt4/13/2017Carbon fractions have been found useful and can be obtained from existing samples (Watson et al., 1994)Gasoline-fueled vehiclesDiesel-fueled vehicles
25 RecModForAirResMgmt.ppt4/13/2017Thermally-evolved material can be separated by chromatography and mass spectrometry Challenge is to extract information that separates sourcesGasolineCoal power plantDieselRoadside dust
26 Examples of U.S. CMB Model Air Quality Findings and Results RecModForAirResMgmt.ppt4/13/2017Examples of U.S. CMB Model Air Quality Findings and ResultsOregon 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 RecModForAirResMgmt.ppt4/13/2017Examples of U.S. CMB Model Air Quality Findings and Results (continued)SCAQMD (Los Angeles) Chino dairy reduction (NH3) regulation (SCAQMD, 1996)PM10 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 SO2 scrubbers (Malm et al., 1989)Hayden Generating Station SO2 scrubbers (Watson et al., 1996)Mohave Generating Station shutdown (Pitchford et al., 1999)Denver Colorado urban visibility standard (Watson et al., 1988)
28 RecModForAirResMgmt.ppt4/13/2017Worldwide PM Source Contribution Estimates by Chemical Mass Balance (Chow and Watson, 2002)
29 Receptor Model Results Need to be Challenged CMB Sensitivity Test RecModForAirResMgmt.ppt4/13/2017Receptor Model Results Need to be Challenged CMB Sensitivity Test(Chow et al. 2006)
31 Light Duty Emission Rates RecModForAirResMgmt.ppt4/13/2017One Atmosphere (Gases and Particles) Also Works for Receptor Models (Gertler et al., 1996)Light Duty Emission RatesHeavy Duty Emission Rates
32 RecModForAirResMgmt.ppt4/13/2017Hourly (VOC) data provide temporal corroboration of emissions and reveal unknown sources (Houston, TX, 1993) (Lu, 1996)Unknown eventMorning traffic
33 High Time Resolution is Desired Spikes indicate local sources RecModForAirResMgmt.ppt4/13/2017High Time Resolution is Desired Spikes indicate local sources(Watson and Chow, 2001)
34 Wind Direction is Suggestive for Local Sources RecModForAirResMgmt.ppt4/13/2017Wind Direction is Suggestive for Local SourcesConditional Probability Function (CPF) for a Selenium Factor at the Pittsburg Supersite(Pekney et al., 2006)
35 RecModForAirResMgmt.ppt4/13/2017Source 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) RecModForAirResMgmt.pptMarkers for Biogenic SOA (Pandis, 2001)4/13/2017Pinic acid, pinonic acid, norpinic acid, and norpinonic acid are products of the oxidation of most monoterpenesThere are some (apparently) unique tracers:Hydropinonaldehydes for α-pineneNopinone for β-pinene3-caric acid for careneSabinic acid for sabeneneSeveral 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 RecModForAirResMgmt.ppt4/13/2017SO4=/SO2 Ratio changes during Aerosol Aging (and should be Reflected in Source Profiles)(Watson et al., 2002)
38 Back trajectories indicate source regions RecModForAirResMgmt.ppt4/13/2017Back trajectories indicate source regions(Xu et al., 2006)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.
39 RecModForAirResMgmt.ppt4/13/2017Receptor Models Can Estimate the Future in Some Circumstances (Denver, CO, 1997) (Watson et al., 1998)Effect of ammonia reductions on ammonium nitrate particlesEffect of nitric acid reductions on ammonium nitrate particles
40 RecModForAirResMgmt.ppt4/13/2017Emission Reduction Effectiveness Long-Term Trends in SO2 Emissions and SO4= Levels (Malm et al., 2002)Comparison of the ambient sulfate 80th percentile and NET SO2 emissions aggregated overnortheastern, southeastern, south middle and western United States regions. In each plot the and SO2 emission scales have a factor of 3 change between the low and high values.
41 RecModForAirResMgmt.ppt4/13/2017Murphy’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 RecModForAirResMgmt.ppt4/13/2017Model discrepancies help to improve inventories PM2.5 Inventory/Receptor Model Comparison, Denver, CO (1997) (Watson et al., 2002)
43 SIP Guidance “Weight of Evidence” Approach (EPA, 2001) RecModForAirResMgmt.ppt4/13/2017SIP Guidance “Weight of Evidence” Approach (EPA, 2001)Form a conceptual model of the emissions, meteorology, and chemical transformations that are likely to affect exceedancesDevelop a modeling/data analysis protocol with stakeholders consistent with available science, measurements, and the conceptual modelConstruct and evaluate emission inventory for the domain as indicated by the conceptual model
44 SIP Guidance “Weight of Evidence” Approach (continued) RecModForAirResMgmt.ppt4/13/2017SIP Guidance “Weight of Evidence” Approach (continued)Assemble and evaluate meteorological measurements for the domainApply source and receptor models and to determine contributionsApply diagnostic tests and justify discarding results that are not physically reasonable
45 SIP Guidance “Weight of Evidence” Approach (continued) RecModForAirResMgmt.pptSIP Guidance “Weight of Evidence” Approach (continued)4/13/2017Modify the inventory to reflect different emission reduction strategies in consultation with stakeholders, and evaluate the effects of reductions at receptorsMake models, input data, and results available to others for external reviewJudge the weight of evidence supporting or opposing the selected emission reduction strategy prior to implementation
46 Receptor Model Needs: A Summary RecModForAirResMgmt.ppt4/13/2017Receptor Model Needs: A SummarySource 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 toSampling locationsSampling periodsSample durationsParticle sizesPrecursor gasesChemical and physical componentsMeteorology
47 Receptor Model Needs (continued) RecModForAirResMgmt.ppt4/13/2017Receptor 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 RecModForAirResMgmt.ppt 4/13/2017 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 PM2.5 and PM10 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 PM10 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 PM10. 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). PM10 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.
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