Tools and Techniques for Identifying Contributions of PMfine to Regional Haze: Source Apportionment Techniques, CATT and FASTNET

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1 Tools and Techniques for Identifying Contributions of PMfine to Regional Haze: Source Apportionment Techniques, CATT and FASTNET Serpil Kayin, MARAMA Rich Poirot, VT DEC

2 Tools and Techniques for Identifying Contributions of PMfine to Regional Haze Overview Why data analysis Role in SIP planning Source Apportionment What is it, how does it work Interface with modeling & inventory development SA Work MANE-VU conducted CATT and FASTNET: DATAFed 2

3 Data Analysis and SIP Planning CAAAC and new EPA guidance recommend weight-of-evidence demonstration for SIP planning and implementation WOE = Models + monitoring data analysis + inventory review Specific data analysis suggested Conceptual model (qualitative description) Historic trends, meteorologically adjusted Transport assessment Observation-based methods Synthesis of Modeling/Monitoring/EI Identify important source sectors or specific pollutants Assess whether control strategies should be national, regional, or local 3

4 Source Apportionment of Ambient Data What is source apportionment? Mathematical technique for determining contributions of various sources to a given sample of air Produces a sample-specific inventory estimate Can be used to develop and validate inventory information, track effectiveness of control measures Can be used for various air pollutants: volatile organics, semivolatiles, particles, toxics, or a combination of these Many methods: CMB (Chemical Mass Balance), PMF (Positive Matrix Factorization), UNMIX 4

5 Source Apportionment of Ambient Data SA is a convenient way to extract information on pollutant sources from routinely collected ambient data (bottom up vs. top down approach) EIs generally self-reported, not measured EIs often derived from emission factors and activity estimates SA results allow independent evaluation of inventory data and model predictions (especially source apportionment results from photochemical models) 5

6 Source Apportionment of Ambient Data Fingerprints (aka source profiles) Chemical patterns of source emissions Species should be present in both ambient air and in source emissions Species should have limited reactivity or similar reaction rates. Assumptions Composition of source emissions is relatively constant Emissions do not react or selectively deposit between source and receptor (mass is conserved) Source profiles are linearly independent All major sources should be included in the model (CMB) 6

7 Example Source Profiles from PMF and Unmix Modeling at Underhill, VT and Comparison of Daily Source Contributions, (right) From Polissar et al. (2001) & Poirot & al. (2001)

8 Source Apportionment of Ambient Data Advantages Models tolerate deviations in model assumptions well (often breaking sources with primary & secondary precursors into 2 or more source components ) Useful for natural emission sources, & others with strong flavor (unique chemical composition, time series, or spatial origin) Can identify previously un-inventoried sources Disadvantages Usually identifies categories of sources, not individual sources Retrospective, not predictive Identifies only receptor contributions, not mass emission rates PMF and UNMIX require large number of samples (100+); CMB can be applied to individual samples 8

9 Source Apportionment of Ambient Data What data are available PAMS PM2.5 speciation data Special ozone and PM studies to support photochemical models (SEARCH, etc) Toxics Network data Rural Networks (IMPROVE, CASTNET) and Urban (STN) 9

10 Locations of Recent Northeastern Receptor Modeling Studies, Conducted by MANE-VU and/or by Academic Researchers Early work focused primarily on Rural IMPROVE sites. More recent analyses based on New Urban STN speciation data Comparing Urban and Rural results shows common influences on haze and PM2.5 and Also helps show key Local Urban Sources

11 Similarities & Differences between Rural Haze & Urban PM 2.5 During Summer, Rural Haze and Urban PM in the Northeast are Both Dominated by secondary Sulfates. During the Winter, Local Urban Sources of Carbon and Nitrate Compounds become Much More Important

12 SA Work MANE-VU conducted Battelle Report (May 2002) DRI Report (March 2005) NESCAUM Report (Tools and Techniques for Identifying Contributions to Regional Haze in the MANE-VU Region), Appendix B (January 2005) CATT and FASTNET web tools for additional SA analysis & Interpretation (available & in-progress) 12

13 FASTNET: Fast Aerosol Sensing Tools for Natural Event Tracking CATT: Combined Aerosol Trajectory Tools Web-Based Data Acquisition, Visualization, Analysis Tools, Developed by CAPITA (R. Husar, S. Falke, K. Hoijarvi), with significant contributions from R. Poirot (VT) Funded by the 5 US Regional Planning Organizations, Managed for MANE-VU by Gary Kleiman & Serpil Kayin Based on Data Architecture Developed with previous support from NSF and NASA Found at 13

14 CATT/DATAFed User Instructions and Tutorial CATT is under web apps CATT url has resources/discussion and user manual Tutorials available They take a while to download, and need sound turned on. Also there's the new "user file submittal option" described at: Help/UserView.html

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18 Near Real Time Data Integration Datasets Used in FASTNET Delayed Data Integration Surface Air Quality Data are accessed from autonomous, distributed providers DataFed wrappers provide uniform geo-time referencing Tools allow space/time overlay, comparisons and fusion AIRNOW O3, PM25 ASOS_STI Visibility, 300 sites METAR Visibility, 1200 sites VIEWS_OL 40+ Aerosol Parameters Satellite MODIS_AOT GASP TOMS SEAW_US Model Output AOT, Idea Project Reflectance, AOT Absorption Indx, Refl. Reflectance, AOT NAAPS Dust, Smoke, Sulfate, AOT WRF Sulfate Fire Data HMS_Fire MODIS_Fire Surface Meteorology RADAR NEXTRAD SURF_MET SURF_WIND ATAD Fire Pixels Fire Pixels Temp, Dewp, Humidity Wind vectors Trajectory, VIEWS locs.

19 A Sample of Datasets Accessible through ESIP DataFed Mediation Near Real Time (~ day) MODIS Reflectance MODIS AOT TOMS Index GOES AOT GOES 1km Reflec MODIS Fire Pix NEXTRAD Radar NWS Surf Wind, Bext NRL MODEL It has been demonstrated (project FASTNET) that these and other datasets can be accessed, repackaged and delivered by AIRNow through Consoles

20 CATT: A Community Tool! Part of an Analysis Value Chain Not There! When? Where? AEROSOL Further Analysis GIS Grid Processing Emission Comparison There! Why? How? Aerosol Sensors Weather Data Collection IMP. EPA Assimilate NWS Aerosol Data TRANSPORT Gridded Meteor. Integration VIEWS Trajectory ARL Integrated AerData Traject. Data CATT-In CAPITA CATT-In CAPITA AerData Cube TrajData Cube CATT Aggreg. Aerosol Aggreg. Traject. Next Process Next Process

21 Aerosol Event Catalog: Web pages Catalog of generic web objects pages, images, animations that relate to aerosol events Each web object is cataloged by location, time and aerosol type.

22 Evaluation and Interpretation of Receptor Model Results by Local Surface Winds or Ensemble Trajectory Techniques Sea Salt Source from Both Unmix and PMF Modeling at Brigantine Since Sea Salt comes from the Sea (Well, Duh!), It tends to contribute most on days when man-made pollutants (from inland) are lowest. But Battelle PMF results suggest sea salt is increasing over time. Why?

23 Receptor Model Results show Sea Salt Source (with High Na) at NE Sites CATT applied to Entire IMPROVE Network Shows High Na for the Sea(s)

24 Receptor Models indicate a Source of Fine Windblown Dust from an Unexpected Distant Origin. It s a Minor Contributor to Haze and PM2.5, but a good example that Transport Happens Like Sea Salt, Windblown Dust at Brigantine also seems to come from the Sea. Weird! Highest Dust at all Eastern IMPROVE sites comes from the Sahara Desert

25 SA Work MANE-VU conducted Surface Met and Trajectory Evaluation of Oil Source Identified by Unmix & PMF modeling at Brigantine, NJ Trajectory Incremental Probability for Oil Source Identified by Unmix or PMF modeling at 4 MANE-VU sites (sources are within region) 25

26 Receptor Model Results show Local Oil Source (with High Ni) at NE Sites CATT applied to Entire IMPROVE Network Shows High Ni for East Coast

27 Receptor Model Results show Wood Smoke Sources, which tend to be from Canadian summer Forest Fires & winter Residential Burning in New England, and more often from Southeastern Fires in Southern MANE-VU Quebec Fires of July 2002 were a Big Example of a Smaller, Common summer influence in New England

28 Receptor Model Results show Wood Smoke Sources, which tend to be from Canadian summer Forest Fires & winter Residential Burning in New England, and more often from Southeastern Fires in Southern MANE-VU

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31 Back Trajectories for All IMPROVE Sites on 7/7/02 Unweighted (top left), & color-weighted for OC (top right), SO4 lower right & Cl (lower left)

32 Color weighted ATADs for High SO4 (top) & Se (bottom) for 8/12-15/02 Haze event; MODIS before (top) & During (bottom)

33 Receptor Models indicate a Large source of Secondary Sulfate (i.e. Coal) at Northeast Receptor Sites. Same area is upwind of Highest SO4 throughout the IMPROVE Network. Selenium is a good Primary Coal Tracer PMF Sulfate (coal) sources, 7 NE sites SO4 > 15 ug/m3, all IMPROVE sites Average Upwind Se, all IMP. sites

34 Receptor Models show Secondary Nitrate Source which appears to be influenced by Midwestern areas of High Agricultural NO3 Emissions (at rural IMPROVE sites in MANE-VU and throughout the East)

35 Several Large-Scale Northeastern NO3 Events observed in recent years, illustrated here by ASOS visibility data from FASTNET for 2/18-21/04. Are these Winter NO3 events becoming more frequent in the Northeast?

36 SO4 sources similar for Rural sites like Shenandoah & Urban sites like DC But Urban areas have larger and different local NO3 sources SO4 at SHEN NO3 at SHEN SO4 at WASH NO3 at WASH

37 Changes in Average Upwind Sulfate (Left) and Nitrate (Right) from (Top) and (Bottom) averaged for 42 IMPROVE sites

38 Modeled Regional Source Impacts for MANE-VU IMPROVE sites, Summer 2002

39 Modeled Regional Source Impacts for MANE-VU IMPROVE sites, Summer 2002

40 Findings from analysis of speciated aerosol data combined with ensemble trajectory evaluations in MANE-VU Common source categories with impacts on PMfine mass concs. & visibility impairment in NE sites Windblown dust: minor contributor to avg. fine mass, with highest short term impacts from Saharan transport Sea Salt: minor contributor to fine mass, identified at coastal and near coastal sites. Significant at best visibility days at Acadia & Brigantine. Oil burning: minor contributor to fine mass, identified at many sites, within and downwind of the NE urban corridor.

41 Findings from analysis of speciated aerosol data combined with ensemble trajectory evaluations in MANE-VU Common source categories with impacts on PMfine mass concs. & visibility impairment in NE sites Ammonium Nitrate: a small to moderate contributor to avg fine mass, with regional influences at rural sites from upwind agricultural ammonia-emitting areas, and significant local source contributions in urban areas. Wood Smoke: a small to moderate contributor to avg fine mass, with contributions higher in rural areas, winter peaks in northern areas from residential wood burning, occasional large summer impacts at all sites from wildfires.

42 Findings from analysis of speciated aerosol data combined with ensemble trajectory evaluations in MANE-VU Common source categories with impacts on PMfine mass concs. & visibility impairment in NE sites Motor Vehicles & Secondary Organics: a moderate to large contributor to avg fine mass, with influence from both gasoline & diesel vehicles in urban areas; at forested rural sites, biogenic organics are likely to be more important. Coal Burning (incl. primary aerosol and secondary aerosol formation): the largest mass contributing and visibility-impairing source category at most sites, with contributions primarily from utility and industrial sources in western MANE-VU, northern VISTAS and the MRPO regions.