WRAP COHA Update Seattle, WA May 25, 2006 Jin Xu

COHA Update 2003 and 2004 back-trajectories – done Assess of the representativeness of worst case days of 2002 for the base period – ongoing, will finish soon Evaluate winds used for the HYSPLIT backtrajectory analyses – ongoing, measurement data collected 8 and 16 year trends analysis - done PMF modeling by groups using 2000 to 2004 IMPROVE data – done Analysis of PMF results –General analysis and discussion: decide how many factors are reasonable for each group - done –Sensitivity Analysis: group modeling vs. individual modeling – done? –Spatial and temporal analysis – done? –Trajectory analysis – ongoing –Smoke analysis – ongoing? 2002 fire database from WRAP, other years from Dr. Tim Brown’s group in DRI. Satellite data and images archived. Case study Similar trajectory analysis as for the causes of dust resultant haze

8 year trends for light extinction coefficient in 20% worst days Available from COHA Website (following the “trends analysis” link from the homepage): earTrends/8yeartrend.html yearTrends/16yeartrend.html 8 and 16 Year Trends 16 year trends for light extinction coefficient in 20% worst days

PMF Modeling for Groups

PMF Results Available for Download from COHA Website Two excel files for each group (one for all days and one for 20% worst days) including –Source profiles for the group –Daily contribution of each source factor to PM2.5 mass and aerosol light extinction coefficient for each site –Comparison between measured and predicted PM2.5 mass concentration –Pie chart for each group Web Address (following the “PMF Modeling” link from the homepage):

PMF Results Page (under construction)

Source Profiles Daily Contributions

Measured Versus Predicted PM2.5 Mass Concentration

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group (20% worst days)

Smoke Analysis General Analysis: –Summarize the contributions of PMF smoke factor to PM2.5 and OC mass. –Compare PMF results between sites with known big contributions from smoke and the others without. –Investigate the relationship of OC / EC and the loading of the “smoke” factor. Case Study (for selected sites): –The prescreening for identification of cases where smoke is the predominant source of fine particles at the receptor site and/or in other sites located in the region –The retrieval of air mass backward trajectories for the receptor sites. –Compilation of detailed records of biomass burning events. –Integration of the aforementioned data types into a GIS tool.

Smoke Source Profiles Averaged based on profiles generated for the 18 groups in WRAP. Error bar represents one standard deviation

Average Contribution of PMF Smoke Factor to PM2.5 Mass during

Average Contribution of PMF Smoke Factor to PM2.5 in 2002 WRAP CMAQ Modeling Results (only modeled natural fire emissions) PMF Results Missing hot spot due to missing IMPROVE data because teflon filters were clogged during the peak of the Rodeo-Chediski Fire (burned 462,614 acres, the largest most severe fire in Arizona history)

PEFO1 PMF Smoke Factor Contribution to PM2.5

PEFO1 PMF Smoke Factor Contribution to Bext

PMF source factor contributions to PM2.5 at PEFO1 in 2002 Data missing on 6/22 and 6/25 Assume all (and only) OMC is from smoke on 6/22 and 6/25

Fire Detections in 2002 Web Fire Mapper displays active fires detected by the MODIS Rapid Response System, a collaboration between the NASA Goddard Space Flight Centre (GSFC) and the University of Maryland (UMD).

Smoke Case Study Hypothesis to-be-tested Are “smoke” concentrations associated with fire events in the vicinity and/or upwind of the site? Limitations Spatial variation of fire emissions and air mass trajectory, no precipitation, no plume information THUS between-cases comparison cannot be done and; no quantitative information can be obtained Methodology Air mass backward trajectories (at 500 m) and WRAP 2002 Fire Emissions Inventory; approximately 52 cases for Sawtooth, Badlands and San Gorgonio were analyzed (high, average and low “smoke” days) Product Maps of air mass trajectory and active fires during that day

Legends  Trajectory  Wildfire  Agricultural fires no-CA  Agricultural fires CA  Rangeland fires  MODIS Fires Filename: YYYYMMDD_SITE_day# SITE= SAWT, BADL, SAGO #=0Sampling Day #=1Sampling Day-1 #=2Sampling Day-2 e.g _SAGO_day0 WildfiresAg/NFRange  0-20 (0-1) tons  (1-5) tons  (5-20) tons  tons  tons  tons ftp.dri.edu/pub/ilias/smoke

Case Study – Sawtooth National Forest, ID (SAWT1) Road Dust/Mobile Nitrate-rich Secondary/Mobile Smoke Dust Sulfate-rich Secondary

Time Series of Factor Contributions to PM2.5 (ug/m 3 ) at SAWT1 in 2002

Average Contributions of Source Factors to PM2.5 Mass Concentration in Sawtooth National Forest in 2002

Measured Versus Predicted OMC Concentration at SAWT1 in 2002

Factor Contributions to OMC at SAWT1 in 2002 Gail Tonnesen and Tom Moore, Modeling Sensitivity Runs for Fire Emissions, White Paper for WRAP, December, 2004: “OC/EC ratio values on order of 3-5 (OMC/LAC ~ 4.2-7) suggest fossil fuel combustion contributions, while values greater than 7 (OMC/LAC>9.8) suggest fire emissions. High OC/EC rations suggest a source mix resulting from either inefficient combustion (vegetation fires) or secondary organic formation.” ? Biogenic Emissions (SOA), Aged Smoke Plume or Inefficient Burning (Vegetation Fires) ? 7/22

Aged Smoke Plume Sawtooth July 22, 2002 Smoke=10.0 μg/m % of PM 2.5

07/25/2002: Local Wildfires Smoke=9.7 μg/m 3 (89.16%) 07/22/2002: Aged Wildfire Smoke Plume Smoke=10.0 μg/m3 (79.38%) 8 Wildfire  Agricultural fires 8 Sampling day-2 8 Sampling day-1 8 Sampling Day

Case Study – Badlands National Park, SD (BADL1) Nitrate-rich Secondary Smoke Dust Road Dust/Mobile Sulfate-rich Secondary

Time Series of Factor Contributions to PM2.5 (ug/m 3 ) at BADL1 in 2002

Average Contributions of Source Factors to PM2.5 Mass Concentration in Badlands National Park in 2002

Measured Versus Predicted OMC Concentration at BADL1 in 2002

Factor Contributions to OMC at BADL1 in 2002 Biogenic Emissions (SOA), Aged Smoke Plume or Inefficient Burning (Vegetation Fires) ? 6/22 5/29

Aged Rangeland and Agricultural Fires Badlands May 29, 2002 Smoke=1.5 μg/m % of PM 2.5

Aged Smoke Plume Badlands Jun. 22, 2002 Smoke=5.2 μg/m % of PM 2.5

Case Study – San Gorgonio Wilderness, CA (SAGO1) Smoke/Urban Nitrate-rich Secondary Dust Mobile Sulfate-rich Secondary Road Dust/Mobile Oil Combustion/Shipping

Average Contributions of Source Factors to PM2.5 Mass Concentration in San Gorgonio Wilderness in 2002

Time Series of Factor Contributions to PM2.5 (ug/m 3 ) at SAGO1 in 2002

Measured Versus Predicted OC Concentration at SAGO1 in 2002

Factor Contributions to OC at SAGO1 in 2002 Biogenic Emissions (SOA), Aged Smoke Plume or Inefficient Burning (Vegetation Fires) ? 8/9 8/21

Agricultural (and Wild) Fires San Gorgonio Aug. 9, 2002 Smoke=5.3 μg/m % of PM 2.5

DateSmoke%Smoke/FMConfidence-Probable sources 1/20/ (+) No events 1/23/ (+) Wildfires 3/9/ (+++) NFRange 3/27/ (+++) NFRange 4/20/ (+++) NFRange 4/26/ (+++) NFRange 5/17/ (+++) NFRange 6/22/ (++) NFRange &Wildfires 6/25/ (++) NFRange&Wildfires 7/22/ (+++) Wildfires 7/25/ (+++) Wildfires 7/31/ (++) Wildfires 8/3/ (+++) Wildfires&agricultural 8/6/ (+++) Wildfires 8/18/ (++) Wildfires 8/21/ (++) Wildfires&agricultural 9/8/ (+++) NFRange 9/20/ (+++) R NFRange &Agric. 9/23/ (+) Agricultural 9/29/ (++) Ag&Range&Wildfires 11/13/ (+++) Wildfires 11/16/ (+++) Wildfires 12/16/ (+) No events Sawtooth, ID Bold= high smoke days Red = 20% worst days

DateSmoke% Smoke/Fine MassConfidence and probable sources 3/21/ (+++) NFRange 4/20/ Local NFRange 4/23/ (+++) Rangelands&Agricultural 4/29/ (+++) NFRange 5/5/ (+++) NFRange 5/17/ (+++) NFRange 5/29/ (+++) NFRange &agricultural 6/22/ (+++) NFRange &Wildfires 6/28/ (+++) NFRange &Wildfires 7/25/ (+++) Wildfires 7/31/ (+++) Wildfires 8/3/ (+++) Wildfires 8/27/ (++) Wildfires 9/5/ (+++) NFRange&Wildfires 9/17/ (+) Rangelands Badlands, SD Bold= high smoke days Red = 20% worst days

DateSmoke% Smoke/FMConfidence and probable sources 4/17/ (+++) NFRange &Wildfires 4/20/ (+) Wildfires 5/2/ (+++) NFRange 5/5/ (+) No events 5/14/ (+++) NFRange 6/13/ (+) Wildfires 6/19/ (+++) Wildfires 7/10/ (+) No events 8/9/ (+++) Wildfires&agricultural 8/12/ (+++) Wildfires&agricultural 8/18/ (+++) Wildfires&agricultural 8/21/ (+++) Wildfires&agricultural 9/5/ (+++) NFRange&Wildfires 10/17/ (+) NFRange 10/23/ (++) NFRange &Wildfires San Giorgonio, CA PMF resolved a mixed smoke/urban factor Bold= high smoke days Red = 20% worst days

For most of the examined cases, air masses intercepted fire events; only cases with very low PM2.5 mass (<1 μg/m 3 ) were not associated with fire events Based on the analysis, the contributions of the following types of fires were determined: (a) wildfires near the site (“hot” emissions); (b) wildfires upwind of the site (aged smoke); (c) agricultural emissions; (d) rangeland fires Case Study Conclusions Given the limitations of this analysis: Sawtooth: Spring/fall smoke events are due to rangeland fires; wildfires and local agricultural fires contribute to smoke during summer Badland: Spring/fall smoke events are due to rangeland fires; wildfires contribute to smoke during summer San Giorgonio: Smoke is usually mixed with urban emissions (air masses normally remain over LA for at least 12 h); Summer smoke events are usually associated with agricultural fires and upwind transport from large wildfires

Summary PMF is a useful tool for resolving aerosol source types and attributing aerosol loading to different sources based on ambient data at a receptor site. It works better in regions where sources are more distinguishable (e.g. near urban area). PMF modeling results (close to CMAQ modeling results?) suggest that smoke contributed on average ~1.5 ug/m 3 to PM2.5 in the Class I areas of the Western U.S. in 2002, much higher than the value of 0.46 ug/m 3 assumed throughout the West in the EPA natural guidance document. It is hard (if not impossible) for PMF to separate the primary and secondary OC into different factors using the IMPROVE data. Generally, higher OC/EC ratios were observed during the fire events. A relatively significant amount of OC was not apportioned by PMF modeling for some sites. The no apportioned OC usually peaks when OC/EC ratio was high. –Secondary OC from biogenic emissions can result in high OC/EC ratio. –Aged smoke plumes usually contain a significant amount of OC generated from oxidation of biogenic VOCs from fires. OC/EC ratio is expected to be higher when OC is mostly from long-range transport smoke plumes than from local fires. –The ratio also depends on the burning type (e.g. forest fire < agricultural burning) and burning conditions. It is possible to qualitatively (maybe even semi-quantitatively) attribute fire emissions to different fire types when detailed fire emissions inventory data are available.