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John M. Ondov, Seung Shik Park, J. Patrick Pancras

Pseudo-Deterministic Receptor Model for PM Emission Rates from Highly Time-Resolved Ambient Concentration Measurements. John M. Ondov, Seung Shik Park, J. Patrick Pancras Department of Chemistry and Biochemistry, University of Maryland, College Park, MD Noreen Poor

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John M. Ondov, Seung Shik Park, J. Patrick Pancras

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  1. Pseudo-Deterministic Receptor Model forPM Emission Rates from Highly Time-Resolved Ambient Concentration Measurements John M. Ondov, Seung Shik Park, J. Patrick Pancras Department of Chemistry and Biochemistry, University of Maryland, College Park, MD Noreen Poor Environmental and Occupational Health, University of South Florida, Tampa, FL

  2. coal-fired power plants Se 190o 190o 160o Plume from CERRO Cu Products 212 deg, 1.6 km Highly time resolved data are available for many speciesAl, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, ZnBaltimore, Pittsburgh, St. Louis, Tampa, College Park Zn emissions from Big River Zn plant

  3. These inherently contain information on source location and distance DirectionalityandPlume width = powerful constraints exploitable with highly time-resolved data Nearby Source Distant Source

  4. New Pseudo-Deterministic Multivariate Receptor ModelDetermines emission rates of species, i, from j knownsources, using highly time-resolved concentration measurements: Eq. 1 where Eq 3 is expressed as a constraint, i. e., (Х/Q)j,tEq 1 = Cj (Х/Q)j,tMet where 0.1 < Cj < 2 Eq. 2 Eq. 3 Minimize Eq. 4 [E]i,t = Ambient conc. species i at time (sample) t,mg/m3 (SO2, Se, As, Ni, Pb, Zn, Cd, Cu, Cr, Al, Fe, Mn) ERi,j =AverageEmission Rate of species i from source j, over t periods,mg/sX/Qj,t =Dispersion Factor for each source j at time t,s/m3

  5. Gannon Bartow plant off-line • PDRM was applied to Resolve • 6 Stationary sources, 15 to 41 km, 170-270o quadrant, in Tampa 4 coal or oil Power Plants, Fertilizer plant, Battery recycling plant

  6. X = 20 km X = 38 km X = 25 km PDRM Results: Ambient fits, SO2 & Metal ERs, x/Qs Fits C/Qs Met PDRM

  7. Summary and Conclusions • Work in progress – much to be done – - Explore different ways of constraining met, improved met model, - Larger data sets - Additional constraints (force to known SO2 emission rates; mass balance eqs) - Logical front end processor – for data selection • Highly time-resolved marker data permits detection of individual plumes • - Wind angles are true (at least relatively flat areas) • Pseudo Deterministic Receptor Model highly successful • (i.e, for SO2 unknown sources of metals could cause errors given current config.) • - Robust Least squares fit – no “rotational ambiguities” - Makes use of much known information: • No. of major sources, station angle/wind angle; distance; stack hgt., wind speed, atmos. Stability- Provides RESULTS FOR INDIVIDUAL SOURCES – not generic averages • - Results readily verifiable!!! • - Produces results in native units directly - No knowledge of source composition needed in current Config • (Contrast with FA – few if any constraints, limited if any advantage taken for known information) • SPECULATION: THIS COULD BE THE FUTURE OF AIR POLUTION MANAGEMENT

  8. Comparison of SEAS-GFAAz vs Speciation-XRF

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