Source Contribution to PM 2.5 and Visibility Impairment in Two Class I Areas Using Positive Matrix Factorization Keith Rose EPA, Region 10 June 22, 2005.

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Presentation transcript:

Source Contribution to PM 2.5 and Visibility Impairment in Two Class I Areas Using Positive Matrix Factorization Keith Rose EPA, Region 10 June 22, 2005

Introduction  PMF was used to analyze IMPROVE data from and time periods for Mt. Rainier and Yosemite National Park Class I Areas  PMF identified six source profiles and the time- dependent contributions of these sources to PM 2.5 concentrations in these Class I Areas  PMF source concentrations were converted to light extinction to determine the visibility impairment due to each source

The PMF Model  PMF is a form of factor analysis with non-negative factor elements  PMF looks for co-variation of species over time to identify source profiles and source time-dependent contributions which best fit the data  PMF uses data uncertainty to “weight” the data (i.e. greater uncertainty = less weight)

The PMF Model (continued)  PMF works best with a large (>300) number of samples and several metal tracer species  The appropriate number of sources can be determined by analyzing the data first with another model (UNMIX), or comparing source profiles with previously identified profiles  The model was run in the “robust” mode in the “heuristically-computed” error mode

Data Selection  IMPROVE data from and  Species with a substantial number (>50%) of values below the MDLs were eliminated  Data reported as “0” were replaced by ½ MDL  Species used in this analysis included: Ca, Cu, EC1-2, Fe, K, H, Na, Pb, OC2-4, NO3, S, SO4, Si and Z  Number of samples used: Mt. Rainier – 476/435 and Yosemite – 473/421

Determining the Number of Sources  Used the UNMIX model to identify the number of sources and source profiles for Mt. Rainier  Compared PMF source profiles with identified source profiles from Seattle and Columbia Gorge  Examined “goodness of fit” of linear regression between measured and calculated source concentrations

Sources and Average Percent Contribution to PM 2.5 Concentrations at Mt. Rainier ( data) SourcePMFUNMIX Biomass Sulfate Nitrate Mobile Soil Marine6.74.0

Definition of Sources  Biomass = wood stoves/fireplaces, agricultural burning, wildfires, prescribed burning  Mobile = on-road and off-road gasoline and diesel powered mobile vehicles  Secondary sulfate = diesel fuel, home heating fuel, pulp mills, oil refineries  Secondary nitrate = oil refineries, commercial boilers, power plants, on and off-road mobile sources (50-66%?)

Results  Mt. Rainier and Yosemite source profiles  Time-dependent concentrations for each source  Average source light extinction (Bext)  Light extinction by source for and time periods  20% worst vs. 20% best days for 2002

Light Extinction Calculation  Bext = (3m 2 /g)Ft(RH)[sulfate] + (3m 2 /g)Ft(RH)[nitrate] + (4m 2 /g)[OC] + (10m 2 /g)[EC] + (1m 2 /g)[soil]  Ft(RH) = annual average relative humidity factor

Conclusions  At Mt. Rainier, the highest Bext is due to secondary sulfate and the second highest is due to biomass burning  At Yosemite, the highest Bext is due to biomass burning and the second highest is to secondary sulfate  At Mt. Rainier, Bext from biomass, sulfate, nitrate and mobile sources decreased between and  At Yosemite, average Bext from biomass and mobile sources increased, while secondary sulfate and nitrate sources decreased between and

Conclusions (continued)  Bext of the 20% worst days at Mt. Rainer in 2002 was due to a combination of secondary sulfate (44%) and biomass burning (27%)  Bext of the 20% worst days at Yosemite in 2002 was dominated by biomass burning (66%)  PMF is a valuable tool in determining source contribution to visibility impairment, and changes in visibility impairment over time in Class I Areas