Receptor Modeling Source Apportionment for Air Quality Management

Receptor Modeling Source Apportionment for Air Quality Management
RecModForAirResMgmt.ppt 4/13/2017 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

Objectives Review receptor models and data requirements
RecModForAirResMgmt.ppt 4/13/2017 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

RecModForAirResMgmt.ppt 4/13/2017

The First Receptor Model What you can see or smell
RecModForAirResMgmt.ppt 4/13/2017 The First Receptor Model What you can see or smell

Black Carbon (BC) Remains at Mesa Verde National Park, Colorado, USA
RecModForAirResMgmt.ppt 4/13/2017 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

Source and Receptor Models
RecModForAirResMgmt.ppt 4/13/2017 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.)

RecModForAirResMgmt.ppt 4/13/2017

Lagrangian Source Model
RecModForAirResMgmt.ppt 4/13/2017 Lagrangian Source Model Cikl = ΣjΣmΣn TijklmnDklnFijQjkmn MEASURED AT SOURCE (INVENTORY) CALCULATED BY MET MODEL BY CHEMICAL MODEL AT RECEPTOR CMB Receptor Model Cikl = ΣjTijklFijΣmΣn DklnQjkmn MEASURED AT RECEPTOR MEASURED AT SOURCE (T=1 OR ESTIMATED BY OTHER METHOD Sijkl, SOURCE CONTRIBUTION ESTIMATE

Chemical Mass Balance Equation: Input:
RecModForAirResMgmt.ppt 4/13/2017 Chemical Mass Balance Equation: 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.

RecModForAirResMgmt.ppt 4/13/2017 CMB Solutions Minimize differences between calculated and measured values for overdetermined set of equations ϰ2 = minΣi [(Ci-Ci)2/ϭCi2] + ΣiΣi [(Fij-Fij)2/ϭFij2 ] Britt and Luecke, (1973), single sample, bold=true value ϰ2 =minΣi [(Ci-ΣjFijSj)2/(ϭCi2+ΣjϭFij2Sj2)] Effective Variance, Watson et al., (1984), single sample ϰ2 =minΣi [(Ci-ΣjFijSj)2/ϭCi2)] Ordinary Weighted Least Squares, Friedlander (1973), single sample

Other CMB Solutions Sj=Ci/Fij ϰ2 =minΣk [(Massk-ΣiCik/Fii)2]
RecModForAirResMgmt.ppt 4/13/2017 Other CMB Solutions Sj=Ci/Fij Tracer solution, Hidy and Friedlander (1971), Winchester and Nifong (1971), single sample ϰ2 =minΣk [(Massk-ΣiCik/Fii)2] Multiple Linear Regression, Kleinman et al (1980), multiple samples ϰ2 =minΣi Σk [(Cik-ΣjFijSjk)2/ϭCik2)] Positive Matrix Factorization, Paatero (1997), multiple samples

Receptor Models are Not Statistical
RecModForAirResMgmt.ppt 4/13/2017 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

Types of “Modern” Receptor Models
RecModForAirResMgmt.ppt 4/13/2017 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

Chemical Mass Balance Equation: Input:
RecModForAirResMgmt.ppt 4/13/2017 Chemical Mass Balance Equation: 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.

Receptor Measurements from Ambient Samplers
RecModForAirResMgmt.ppt 4/13/2017 Receptor Measurements from Ambient Samplers Airmetrics portable MiniVol sampler BGI FRM Omni PM2.5 and PM10 PM1, PM2.5, and PM10

Source profiles from source testing
RecModForAirResMgmt.ppt 4/13/2017 Source profiles from source testing

Many contributors not inventoried
RecModForAirResMgmt.ppt 4/13/2017 Many contributors not inventoried Real-World Cooking Simulated Cooking

More source profiles could be obtained from certification tests
RecModForAirResMgmt.ppt 4/13/2017 More source profiles could be obtained from certification tests Roadside compliance test in India

RecModForAirResMgmt.ppt 4/13/2017 Material balance says much about sources (Mexico City, Feb/Mar 1997) (Chow et al., 2002)

More specificity obtained with source profiles
RecModForAirResMgmt.ppt More specificity obtained with source profiles 4/13/2017 Commonly measured elements, ions, and carbon (Zielinska et al., 1998)

RecModForAirResMgmt.ppt 4/13/2017 Many toxic elements have been removed from emissions. Organic markers take their place (Chow et al. 2006)

RecModForAirResMgmt.ppt 4/13/2017 Carbon fractions have been found useful and can be obtained from existing samples (Watson et al., 1994) Gasoline-fueled vehicles Diesel-fueled vehicles

RecModForAirResMgmt.ppt 4/13/2017 Thermally-evolved material can be separated by chromatography and mass spectrometry Challenge is to extract information that separates sources Gasoline Coal power plant Diesel Roadside dust

Examples of U.S. CMB Model Air Quality Findings and Results
RecModForAirResMgmt.ppt 4/13/2017 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)

RecModForAirResMgmt.ppt 4/13/2017 Examples 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)

RecModForAirResMgmt.ppt 4/13/2017 Worldwide PM Source Contribution Estimates by Chemical Mass Balance (Chow and Watson, 2002)

Receptor Model Results Need to be Challenged CMB Sensitivity Test
RecModForAirResMgmt.ppt 4/13/2017 Receptor Model Results Need to be Challenged CMB Sensitivity Test (Chow et al. 2006)

CMB Pseudo-Inverse Normalized (MPIN) Matrix
RecModForAirResMgmt.ppt 4/13/2017 CMB Pseudo-Inverse Normalized (MPIN) Matrix (Chow et al. 2006)

Light Duty Emission Rates
RecModForAirResMgmt.ppt 4/13/2017 One Atmosphere (Gases and Particles) Also Works for Receptor Models (Gertler et al., 1996) Light Duty Emission Rates Heavy Duty Emission Rates

RecModForAirResMgmt.ppt 4/13/2017 Hourly (VOC) data provide temporal corroboration of emissions and reveal unknown sources (Houston, TX, 1993) (Lu, 1996) Unknown event Morning traffic

High Time Resolution is Desired Spikes indicate local sources
RecModForAirResMgmt.ppt 4/13/2017 High Time Resolution is Desired Spikes indicate local sources (Watson and Chow, 2001)

Wind Direction is Suggestive for Local Sources
RecModForAirResMgmt.ppt 4/13/2017 Wind Direction is Suggestive for Local Sources Conditional Probability Function (CPF) for a Selenium Factor at the Pittsburg Supersite (Pekney et al., 2006)

RecModForAirResMgmt.ppt 4/13/2017 Source factors derived from ambient data by UNMIX and PMF These must be associated with measured source profiles (Chen et al., 2006)

Markers for Biogenic SOA (Pandis, 2001)
RecModForAirResMgmt.ppt Markers for Biogenic SOA (Pandis, 2001) 4/13/2017 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)

RecModForAirResMgmt.ppt 4/13/2017 SO4=/SO2 Ratio changes during Aerosol Aging (and should be Reflected in Source Profiles) (Watson et al., 2002)

Back trajectories indicate source regions
RecModForAirResMgmt.ppt 4/13/2017 Back 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.

RecModForAirResMgmt.ppt 4/13/2017 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

RecModForAirResMgmt.ppt 4/13/2017 Emission 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 over northeastern, 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.

RecModForAirResMgmt.ppt 4/13/2017 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)

RecModForAirResMgmt.ppt 4/13/2017 Model discrepancies help to improve inventories PM2.5 Inventory/Receptor Model Comparison, Denver, CO (1997) (Watson et al., 2002)

SIP Guidance “Weight of Evidence” Approach (EPA, 2001)
RecModForAirResMgmt.ppt 4/13/2017 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

SIP Guidance “Weight of Evidence” Approach (continued)
RecModForAirResMgmt.ppt 4/13/2017 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

SIP Guidance “Weight of Evidence” Approach (continued)
RecModForAirResMgmt.ppt SIP Guidance “Weight of Evidence” Approach (continued) 4/13/2017 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

Receptor Model Needs: A Summary
RecModForAirResMgmt.ppt 4/13/2017 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

Receptor Model Needs (continued)
RecModForAirResMgmt.ppt 4/13/2017 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)

References RecModForAirResMgmt.ppt 4/13/2017
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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):

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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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