Characterization of Aerosol Events using the Federated Data System, DataFed R.B. Husar and S.R. Falke  Washington Universi...
Regional Haze Rule: Natural Aerosol <ul><li>Looking ahead to reach natural conditions  </li></ul><ul><li>…  in 60+ years!!...
NAAMS:  National Ambient Air Monitoring Strategy  and NCore … coordinated multi-pollutant real-time monitoring network
Natural and Exceptional Event Rule (Making) <ul><li>What is a legitimate Natural or Exceptional event? </li></ul><ul><li>H...
Dust, Smoke and Exceptional Events Intercontinental Dust Smoke  Event July 4 2004 July 4 2003
Long-Term Monitoring: Fine Mass, SO4, K <ul><li>Long-term speciated monitoring begun in 1988 with the IMPROVE network </li...
Evolution of Spatial Data Coverage: Fine Sulfate, 1998-2003 <ul><li>Before 1998, IMPROVE provided much of the PM2.5 sulfat...
AIRNOW PM25 -  ASOS RH- Corrected Bext  July 21, 2004 July 22, 2004 July 23, 2004 ARINOW PM25 ARINOW PM25 ARINOW PM25 ASOS...
Quebec Smoke July 7, 2002 Satellite Optical Depth & Surface ASOS RHBext
Event Detection   Temporal Signal Decomposition <ul><li>First, the median and average is obtained over a region for each h...
Seasonal PM25 by Region <ul><li>The 30-day smoothing average shows the seasonality by region </li></ul><ul><li>The Feb/Mar...
Bext Distribution Function Albany  Sigma g =  3.75 Charlotte Sigma g = 1.56 Upper 20 percentile contribution: Notheast > 5...
Application of Automatic Event Detection: A Trigger and Screening Tool <ul><li>The algorithmic aerosol detection and chara...
Causes  of Temporal Variation by Region <ul><li>The temporal signal variation is decomposable into seasonal, meteorologica...
‘Composition’ of Eastern US Events <ul><li>The bar-graph shows the various combinations of species-events that produce Rec...
The largest EUS Regional PM Event: Nov 15, 2005
Early Satellite Detection of Manmade Haze, 1976  Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B.  Mon. We...
SeaWiFS AOT – Summer 60 Percentile 1 km Resolution
Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 99 Percentile 90 Perce...
Average and 98 Percentile Pattern SO4 PM2.5 Mass PM2.5 Mass OC  OC  SO4 A V E R A G E 98 Percentile
Estimation of Smoke Mass <ul><li>The estimation of smoke mass from speciated aerosol data has eluded full quantification f...
Smoke Quantification using Chemical Data <ul><ul><li>Step 1: Carbon apportionment into SmokeBiogenic and Soot components <...
Smoke Excess OC – EC Calibration of SmokeBiogenic Composition <ul><li>Smoke (excess) PM25, EC and OC yields calibration </...
Soot OC–EC Calibration by Iteration <ul><li>EC/OC ratios for soot are more difficult to determine </li></ul><ul><li>EC/OC ...
Measured and Reconstructed PM25 Mass <ul><li>Regional ‘calibration’ constants were applied to OC and Soil  </li></ul>
OCSB, OCSoot and PM25 Seasonal Pattern Average over 2000-2004 period PM25Mass OCSB SmokeBio OCSoot Day of Year Mexican Smo...
OC SmokeBiogenic Spatial Pattern Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug
Soot Spatial Pattern Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug Jun Jul Aug
PM2.5 (blue) and SmokeBioMass (red)   Note: Smoke events are spikes superimposed on biogenic OC background Smoke Events Ka...
Example OC Smoke Events Note: Smoke events are spikes superimposed on biogenic OC background Smoke Events
GRSM Seasonal Pattern of Percentiles PM25 OC SO4 Soil Episodic Episodic OC in Fall dominates episodicity -  Smoke Organics?
Monthly Maps of Fire Pixels <ul><li>Fire pixels are necessary but not sufficient </li></ul><ul><li>Some Fire pixels produc...
Summary <ul><li>Developments (CIRA, Poirot, others)  </li></ul><ul><li>OC and EC can be reasonably apportioned between Smo...
FASTNET: Inter-RPO pilot project, through NESCAUM, 2004 Web-based data, tools for community use Built on DataFed infra-str...
Some of the DataFed Tools <ul><ul><li>Data Catalog </li></ul></ul><ul><ul><li>Data Browser </li></ul></ul><ul><ul><li>Plum...
Conceptual Diagram of an Emissions Community XML GIS Estimation Methods RDBMS Geospatial One-Stop Transport Models Emissio...
North American Commission  for Environmental Cooperation   Web Application Report http:// www.cec.org /files/PDF/ POLLUTAN...
Spatial-temporal analysis of fire counts   http:// webapps.datafed.net/dvoy_services/datafed.aspx?page =Fire_Pixel_Count_A...
BLM Area burned  - monthly average The acres burned in the BLM compiled fire history dataset are  spatially aggregated  on...
Aggregation Tools Fire Pixels June 16-23, 2004 Spatially Aggregated Monthly Sum Spatially Aggregated
Spatial-temporal Comparison of fire pixels   http://www.datafed.net/WebApps/MiscApps/ModisGoes/FireLocationComparison.htm ...
Standards Based Data Sharing Open Geospatial Specifications (OGC) for web mapping Web Map Service (images) Web Feature Ser...
Seasonality of OC Percentiles <ul><li>IMPROVE/STN Inconsistencies Not shown here </li></ul>Great Smoky Mtn: Episodic OC in...
 
Field burning particulate pollution & asthma - Shorts <ul><li>Jule Klotter For people with asthma, fine-particle pollution...
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2005-11-12 Characterization of Aerosol Events using the Federated Data System, DataFed

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  • 2005-11-12 Characterization of Aerosol Events using the Federated Data System, DataFed

    1. 1. Characterization of Aerosol Events using the Federated Data System, DataFed R.B. Husar and S.R. Falke Washington University in St. Louis Presented at EPA – OAQPS Seminar Research Triangle Park, NC, November 1, 2005
    2. 2. Regional Haze Rule: Natural Aerosol <ul><li>Looking ahead to reach natural conditions </li></ul><ul><li>… in 60+ years!!! </li></ul>
    3. 3. NAAMS: National Ambient Air Monitoring Strategy and NCore … coordinated multi-pollutant real-time monitoring network
    4. 4. Natural and Exceptional Event Rule (Making) <ul><li>What is a legitimate Natural or Exceptional event? </li></ul><ul><li>How does one document & quantify the N/E events? </li></ul><ul><li>How is an N/E event treated in NAAQS? </li></ul>
    5. 5. Dust, Smoke and Exceptional Events Intercontinental Dust Smoke Event July 4 2004 July 4 2003
    6. 6. Long-Term Monitoring: Fine Mass, SO4, K <ul><li>Long-term speciated monitoring begun in 1988 with the IMPROVE network </li></ul><ul><li>Starting in 2000, the IMPROVE and EPA networks have expanded </li></ul><ul><li>By 2003, the IMPROVE + EPA species are sampled at 350 sites </li></ul><ul><li>In 2003, the FRM/IMPROVE PM25 network is reporting data from over 1200 sites </li></ul>Sulfate Fine Mass Potassium
    7. 7. Evolution of Spatial Data Coverage: Fine Sulfate, 1998-2003 <ul><li>Before 1998, IMPROVE provided much of the PM2.5 sulfate </li></ul><ul><li>In the 1990s, the mid-section of the US was not covered </li></ul><ul><li>By 2003, the IMPROVE and EPA sulfate sites (350+) covered most of the US </li></ul>1998 1999 2000 2003 2002 2001
    8. 8. AIRNOW PM25 - ASOS RH- Corrected Bext July 21, 2004 July 22, 2004 July 23, 2004 ARINOW PM25 ARINOW PM25 ARINOW PM25 ASOS RHBext ASOS RHBext ASOS RHBext
    9. 9. Quebec Smoke July 7, 2002 Satellite Optical Depth & Surface ASOS RHBext
    10. 10. Event Detection Temporal Signal Decomposition <ul><li>First, the median and average is obtained over a region for each hour/day (thin blue line) </li></ul><ul><li>Next, the data are temporally smoothed by a 30 day moving window (spatial median - red line; spatial mean – heavy blue line). These determine the seasonal pattern. </li></ul>EUS Daily Average 50%-ile, 30 day 50%-ile smoothing Deviation from %-ile Event : Deviation > x*percentile Median Seasonal Conc. Mean Seasonal Conc. Average Median <ul><li>Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components </li></ul>
    11. 11. Seasonal PM25 by Region <ul><li>The 30-day smoothing average shows the seasonality by region </li></ul><ul><li>The Feb/Mar PM25 peak is evident for the Northeast, Great Lakes and Great Plains </li></ul><ul><li>This secondary peak is absent in the South and West </li></ul>
    12. 12. Bext Distribution Function Albany Sigma g = 3.75 Charlotte Sigma g = 1.56 Upper 20 percentile contribution: Notheast > 50% of dosage Southeast < 30% of dosage 1979
    13. 13. Application of Automatic Event Detection: A Trigger and Screening Tool <ul><li>The algorithmic aerosol detection and characterization provides only partial information about events </li></ul><ul><li>However, it can trigger further action during real-time monitoring </li></ul><ul><li>Also, it can be used as a screening tool for the further analysis </li></ul>
    14. 14. Causes of Temporal Variation by Region <ul><li>The temporal signal variation is decomposable into seasonal, meteorological noise and events </li></ul><ul><li>Assuming statistical independence, the three components are additive: </li></ul><ul><li>V 2 Total = V 2 Season + V 2 MetNoise + V 2 Event </li></ul><ul><li>The signal components have been determined for each region to assess the differences </li></ul>Northeast exhibits the largest coeff. variation (56%); seasonal, noise and events each at 30% Southeast is the least variable region (35%), with virtually no contribution from events Southwest, Northwest, S. Cal. and Great Lakes/Plains show 40-50% coeff. variation mostly, due to seasonal and meteorological noise. Interestingly, the noise is about 30% in all regions, while the events vary much more, 5-30%
    15. 15. ‘Composition’ of Eastern US Events <ul><li>The bar-graph shows the various combinations of species-events that produce Reconstructed Fine Mass (RCFM) events </li></ul><ul><li>‘ Composition’ is defined in terms of co-occurrence of multi-species events (not by average mass composition) </li></ul><ul><li>The largest EUS RCFM events are simultaneously ‘events’ (spikes) in sulfate, organics and soil! </li></ul><ul><li>Some EUS RCFM events are events in single species, e.g. 7-Jul-97 (OC), 21-Jun-97 (Soil) </li></ul>Based on VIEWS data
    16. 16. The largest EUS Regional PM Event: Nov 15, 2005
    17. 17. Early Satellite Detection of Manmade Haze, 1976 Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B. Mon. Weather Rev. 1976 SMS GOES June 30 1975
    18. 18. SeaWiFS AOT – Summer 60 Percentile 1 km Resolution
    19. 19. Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 99 Percentile 90 Percentile 60 Percentile
    20. 20. Average and 98 Percentile Pattern SO4 PM2.5 Mass PM2.5 Mass OC OC SO4 A V E R A G E 98 Percentile
    21. 21. Estimation of Smoke Mass <ul><li>The estimation of smoke mass from speciated aerosol data has eluded full quantification for many years </li></ul><ul><li>While full quantification is still not in hand, a proposed approximate approach yields reasonably consistent results </li></ul><ul><li>CIRA, Poirot and others have made most of the contributions </li></ul><ul><li>The smoke quantification consists of two steps: </li></ul><ul><ul><li>Step 1: Carbon apportionment into Smoke-Biogenic and Soot components </li></ul></ul><ul><ul><li>Step 2: Applying factors to turn Smoke-Biogenic and Soot into Mass </li></ul></ul>
    22. 22. Smoke Quantification using Chemical Data <ul><ul><li>Step 1: Carbon apportionment into SmokeBiogenic and Soot components </li></ul></ul><ul><ul><li>Carbon (OC & EC) is assumed to have only two ‘source’ types: smoke-biogenic and soot </li></ul></ul><ul><ul><li> OC = OCSB (SmokeBiogenic) + OCSoot (Soot) </li></ul></ul><ul><ul><ul><li>EC = ECSB (SmokeBiogenic) + ECSoot (Soot) </li></ul></ul></ul><ul><ul><ul><li>In each source type, the EC/OC ratio is assumed to be constant </li></ul></ul></ul><ul><ul><ul><li>ECSB/OCSB = rsb (In smoke and biogenic, EC/OC ratio rsb =0.08) </li></ul></ul></ul><ul><ul><ul><li>ECSoot/OCSoot = rs (In soot, EC/OC ratio rs = 0.4) </li></ul></ul></ul><ul><ul><ul><li>With these four equations, the value of the four unknowns can be calculated </li></ul></ul></ul><ul><ul><ul><li>OCSB = (rs*OC –EC)/(rs-rsb) = (0.4*OC – EC)/0.32 </li></ul></ul></ul><ul><ul><ul><li>OCSoot = OC-OCSB </li></ul></ul></ul><ul><ul><ul><li>ECSB = 0.08*OCSB </li></ul></ul></ul><ul><ul><ul><li>ECSoot = 0.4*OCSoot </li></ul></ul></ul><ul><ul><li>Step2: Apply a factor to turn OC into Mass </li></ul></ul><ul><ul><ul><li>The smoke and non-smoke OC is scaled by a factor to estimate the mass </li></ul></ul></ul><ul><ul><ul><li>OCSmokeBioMass = OCSB*1.5 </li></ul></ul></ul><ul><ul><ul><li>OCSootMass = OCSoot*2.4 </li></ul></ul></ul>
    23. 23. Smoke Excess OC – EC Calibration of SmokeBiogenic Composition <ul><li>Smoke (excess) PM25, EC and OC yields calibration </li></ul><ul><li>Ratios for Kansas, Big Bend and Quebec smoke are similar </li></ul><ul><li>Good news for OC apportionment </li></ul> PM25  EC  OC SmokeBiogenic:  EC/  OC = 0.08  PM25/  OC = 1.5 EC/OC Ratio
    24. 24. Soot OC–EC Calibration by Iteration <ul><li>EC/OC ratios for soot are more difficult to determine </li></ul><ul><li>EC/OC of about 0.2-0.4 is reasonable </li></ul><ul><li>Outside this range is not </li></ul>EC/OC Soot = 0.15 EC/OC Soot = 0.2 EC/OC Soot = 1 EC/OC Soot = 0.4 Negative SmokeBio – not Possible Maybe?? Maybe?? Too little soot, too much smokebio SmokeBio OC Soot OC
    25. 25. Measured and Reconstructed PM25 Mass <ul><li>Regional ‘calibration’ constants were applied to OC and Soil </li></ul>
    26. 26. OCSB, OCSoot and PM25 Seasonal Pattern Average over 2000-2004 period PM25Mass OCSB SmokeBio OCSoot Day of Year Mexican Smoke Agricultural Smoke Urban Soot
    27. 27. OC SmokeBiogenic Spatial Pattern Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug
    28. 28. Soot Spatial Pattern Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug Jun Jul Aug
    29. 29. PM2.5 (blue) and SmokeBioMass (red) Note: Smoke events are spikes superimposed on biogenic OC background Smoke Events Kansas Agric. Smoke
    30. 30. Example OC Smoke Events Note: Smoke events are spikes superimposed on biogenic OC background Smoke Events
    31. 31. GRSM Seasonal Pattern of Percentiles PM25 OC SO4 Soil Episodic Episodic OC in Fall dominates episodicity - Smoke Organics?
    32. 32. Monthly Maps of Fire Pixels <ul><li>Fire pixels are necessary but not sufficient </li></ul><ul><li>Some Fire pixels produce more smoke aerosol than others …by at least factor of 5 </li></ul>NOAA HMS – S. Falke Jan Feb Mar Apr Aug Jun Jul May Sep Oct Nov Dec Smoke Kansas Ag Smoke No Smoke
    33. 33. Summary <ul><li>Developments (CIRA, Poirot, others) </li></ul><ul><li>OC and EC can be reasonably apportioned between SmokeBiogenic and Soot components </li></ul><ul><li>The reconstructed mass can be matched to the measured PM25 </li></ul><ul><li>Problems of OC Apportionment </li></ul><ul><li>Need to separate smoke and biogenic OC! </li></ul><ul><li>IMPROVE and STN OC don’t match </li></ul><ul><li>Some coefficients may need regional/seasonal calibration </li></ul>
    34. 34. FASTNET: Inter-RPO pilot project, through NESCAUM, 2004 Web-based data, tools for community use Built on DataFed infra-structure, NSF, NASA Project fate depends on sponsor, user evaluation
    35. 35. Some of the DataFed Tools <ul><ul><li>Data Catalog </li></ul></ul><ul><ul><li>Data Browser </li></ul></ul><ul><ul><li>PlumeSim, Animator </li></ul></ul><ul><ul><li>Combined Aerosol Trajectory Tool (CATT) </li></ul></ul>Consoles: Data from diverse sources are displayed to create a rich context for exploration and analysis CATT: Combined Aerosol Trajectory Tool for the browsing backtrajectories for specified chemical conditions Viewer: General purpose spatio-temporal data browser and view editor applicable for all DataFed datasets
    36. 36. Conceptual Diagram of an Emissions Community XML GIS Estimation Methods RDBMS Geospatial One-Stop Transport Models Emissions Inventory Catalog Users & Projects Web Tools/Services Emissions Inventories Data Data Catalogs Activity Data Spatial Allocation Comparison of Emissions Methods Data Analysis Model Development Wrappers Emissions Factors Surrogates Report Generation Mediators / Portal
    37. 37. North American Commission for Environmental Cooperation Web Application Report http:// www.cec.org /files/PDF/ POLLUTANTS/ PowerPlant_AirEmission_en.pdf http:// webapps.datafed.net/dvoy_services / datafed.aspx?page = PowerPlant_Emissions 2002 North American Powerplant Emissions
    38. 38. Spatial-temporal analysis of fire counts http:// webapps.datafed.net/dvoy_services/datafed.aspx?page =Fire_Pixel_Count_AK Large fires during the summer of 2004 in Central Alaska. Spatially aggregated count of fire pixels over a 100km 2 area. The size of each red square in the map is proportional to the number of fire pixels. The spatial aggregation allows the generation of a time series for each aggregated area.
    39. 39. BLM Area burned - monthly average The acres burned in the BLM compiled fire history dataset are spatially aggregated on a 50km 2 grid and temporally aggregated to a monthly resolution. Circles are proportional to the acres burned at a location for a particular year and month. Time series plot shows the monthly total number of acres burned at a particular 50km2 area. http:// webapps.datafed.net/dvoy_services/datafed.aspx?page = BLM_AcresBurned
    40. 40. Aggregation Tools Fire Pixels June 16-23, 2004 Spatially Aggregated Monthly Sum Spatially Aggregated
    41. 41. Spatial-temporal Comparison of fire pixels http://www.datafed.net/WebApps/MiscApps/ModisGoes/FireLocationComparison.htm A red shaded square indicates a short distance separating the MODIS and GOES pixels while a blue shaded square indicates the nearest neighbor between the datasets were far apart . A red outlined square indicates the nearest neighbor was detected on the same day while a blue outlined square indicates a longer time separation . Gray shaded and/or outlined squares indicate that a nearest neighbor was not found between the two datasets given the search parameters (in this example case, 100 km and 2 days).
    42. 42. Standards Based Data Sharing Open Geospatial Specifications (OGC) for web mapping Web Map Service (images) Web Feature Service (point/vector data) Web Coverage Service (gridded data) Geospatial One-Stop – The National Map DataFed-OGC Description: http:// www.datafed.net/DataLinks/OGC/OGC.htm http://webapps.datafed.net/dvoy_services/ogc_domain_fire.wsfl?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetCapabilities DataFed OGC WMS for fire data:
    43. 43. Seasonality of OC Percentiles <ul><li>IMPROVE/STN Inconsistencies Not shown here </li></ul>Great Smoky Mtn: Episodic OC in the Fall season Chattanooga:: Elevated and Persistent OC
    44. 45. Field burning particulate pollution & asthma - Shorts <ul><li>Jule Klotter For people with asthma, fine-particle pollution caused by fires, can be deadly. A recent documented case, reported in US News & World Report (September 3, 2001), occurred in Coeur d'Alene, Idaho, in September 2000. The day after clouds of smoke from agricultural field burning covered the town, Marsha Mason, a waitress with asthma, called 911 at 4:51 am because her nebulizer was no longer working. By the time help arrived, she had died. Her doctor listed the cause of death: &quot;Victim with known asthma subjected to intense air pollution from wheat field burning.&quot; </li></ul><ul><li>Field burning after harvest is common practice in the grass fields of the Northwest; sugarcane fields of Florida, Louisiana, and Texas; and rice fields of California, Arkansas, and Missouri. Burning clears fields of plant residue, preparing the soil for planting without the need to till it. Some farmers say that burning increases crop yield and helps control weeds and pests. Unfortunately, the small soot particles from field burning and other combustion sources, such as coal-burning power plants, travel across large distances and easily enter buildings. Journalist David Whitman says: &quot;Estimates by the Natural Resources Defense Council and researchers at the Harvard School of Public Health suggest fine particulates from power plants and other combustion sources may be the nation's leading unregulated air-quality threat.&quot; </li></ul><ul><li>The EPA has not addressed field burning because air quality standards are based on 24-hour averages. The particulate pollution from field burning always falls within the 24-hour federal limits, even though it can greatly exceed safe limits for a few hours. At 8 pm, the night before Marsha Mason's death, an air quality meter near her home recorded a reading of 161 micrograms per cubic meter. Any reading above 100 micrograms means that &quot;people are going to be choking,&quot; according to Idaho officials. Instead of relying on EPA limits, Idaho's Department of Environmental Quality now halts field burning when an hourly reading reaches the 100-microgram level. </li></ul><ul><li>Whitman, David. Fields of Fire. US News & World Report 2001 September 3. </li></ul>
    45. 46. Kansas

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