2005-10-31 Characterization of Aerosol Events

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2005-10-31 Characterization of Aerosol Events

  1. 1. Characterization of Aerosol Events R.B. Husar Washington University in St. Louis Presented at EPA – OAQPS Seminar Research Triangle Park, NC, November 1, 2005
  2. 2. NAAMS: National Ambient Air Monitoring Strategy and NCore Applications
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. Quebec Smoke July 7, 2002 Satellite Optical Depth & Surface ASOS RHBext
  7. 7. Regional Haze Rule: Natural Aerosol <ul><li>The goal is to attain natural conditions by 2064; </li></ul><ul><li>Baseline during 2000-2004, first Natural Cond. SIP in 2008; </li></ul><ul><li>SIP & Natural Condition Revisions every 10 yrs </li></ul>
  8. 8. 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 event treated in NAAQS </li></ul>Smoke Event July 4 2004 July 4 2003
  9. 9. Aerosol Event Characterization <ul><li>In the past, the definition and documentation of events has been subjective, dependent on the analyst, the is event type etc. </li></ul><ul><li>The routine overall characterization of detected events is accomplished by the rich real-time data through delivered through the Analysts Consoles </li></ul><ul><li>Objective event definition is now possible through spatio-temporal statistical parameters derivable from routine monitoring data </li></ul>
  10. 10. Temporal Analysis <ul><li>The time series for typical monitoring data are ‘messy’; the signal variation occurs at various scales and the time pattern at each scale is different </li></ul><ul><li>Inherently, aerosol events are spikes in the time series of monitoring data but extracting the spikes from the noisy data is a challenging endeavor </li></ul><ul><li>The temporal signal can be meaningfully decomposed into a </li></ul><ul><ul><li>Seasonal component with stable periodic pattern </li></ul></ul><ul><ul><li>Random variation with ‘white noise’ pattern </li></ul></ul><ul><ul><li>Spikes or events that are more random in frequency and magnitude </li></ul></ul><ul><ul><li>Each signal component is caused by different combination of the key processes: emission, transport, transformations and removal </li></ul></ul>Typical time series of daily AIRNOW PM25 over the Northeastern US
  11. 11. Temporal Signal Decomposition and Event Detection <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>
  12. 12. 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>
  13. 13. Bext Distribution Function Albany Sigma g = 3.75 Charlotte Sigma g = 1.56 Upper 20 percentile contribution: Notheast > 45% of dosage Southeast < 30% of dosage 1979
  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. Aerosol Event Catalog: Web pages <ul><li>Catalog of generic ‘web objects’ – pages, images, animations that relate to aerosol events </li></ul><ul><li>Each ‘web object’ is cataloged by location, time and aerosol type. </li></ul>
  18. 18. Some of the Tools Used in FASTNET <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
  19. 19. Feb 19 2004: <ul><li>Isolated high PM25 occurs over the Midwest, Northeast and Texas </li></ul><ul><li>The aerosol patches are evident in AIRNOWPM25, ASOS and Fbext maps </li></ul><ul><li>The absence of TOMS signal indicates the lack of smoke or dust at high elevation </li></ul><ul><li>The high surface wind speed over Texas, hints on possible dust storm activity </li></ul><ul><li>The NAAPS model shows high sulfate over the Great Lakes, but no biomass smoke </li></ul><ul><li>Possible event causes: nitrate in the Upper Midwest and Northeast, sulfate around the Great Lakes and dust over Texas </li></ul>
  20. 20. Jun 6-8 <ul><li>This intensive 3-day episode covers much of the Eastern US </li></ul><ul><li>The AIRNOW, ASOS and Visibility FBext are all elevated </li></ul><ul><li>TOMS shows smoke(?) over the Gulf and Mexico; MODIS AOT over the Northeast </li></ul><ul><li>The surface winds indicate stagnation over the EUS </li></ul><ul><li>NAAPS model shows intense sulfate accumulation over the industrial Illinois-New York . </li></ul><ul><li>Possible causes: sulfate episode </li></ul>
  21. 21. 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>
  22. 22. 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
  23. 23. Analysts Consoles for Event Characterization <ul><li>Analysts consoles deliver the state of the aerosol, meteorology etc., automatically from real-time monitoring data </li></ul><ul><li>Dozens of maps depict the spatial pattern using dozens of surface and satellite-detected parameters </li></ul><ul><li>The temporal pattern are presented on time series for the regional average and for individual stations </li></ul><ul><li>The following pages illustrate the 2004 EUS events, through a subset of the monitored parameters. </li></ul><ul><li>The event-presentation includes limited interpretative comments; the full interpretation of this rich context is left to subsequent communal analysis </li></ul>Spatial Console Temporal Console
  24. 25. Average and 98 Percentile Pattern SO4 PM2.5 Mass PM2.5 Mass OC OC SO4 PM2.5 Mass A V E R A G E 98 Percentile
  25. 26. Exceptional Event Analysis for Regulatory Processes: Biomass Smoke Aerosol August-October 2005 [email_address] Exception Flaggic Waivers
  26. 27. 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>CIRA, Poirot and others have </li></ul><ul><li>While full quantification is still not in hand, a proposed approximate approach yields reasonably consistent results </li></ul><ul><li>The smoke quantification consists of two steps: </li></ul><ul><ul><li>Step 1: Carbon apportionment into Smoke and NonSmoke parts </li></ul></ul><ul><ul><li>Step 2: Applying factors to turn OCSmoke and OCNonSmoke into Mass </li></ul></ul>
  27. 28. Smoke Quantification using Chemical Data <ul><ul><li>Step 1: Carbon apportionment into Smoke and NonSmoke parts </li></ul></ul><ul><ul><ul><li>Carbon (OC & EC) is assumed to have only two forms: smoke and non-smoke </li></ul></ul></ul><ul><ul><ul><li>OC = OCS (Smoke) + OCNS (NonSmoke) </li></ul></ul></ul><ul><ul><ul><li>EC = ECS (Smoke) + ECNS (NonSmoke) </li></ul></ul></ul><ul><ul><ul><li>In each form, the EC/OC ratio is assumed to be constant </li></ul></ul></ul><ul><ul><ul><li>ECS/OCS = rs (In smoke, EC/OC ratio rs =0.08) </li></ul></ul></ul><ul><ul><ul><li>ECNS/OCNS = rns (In non-smoke, EC/OC ratio rns = 0.4) </li></ul></ul></ul><ul><ul><ul><li>With thes four equations, the value of the four unknowns can be calcualted </li></ul></ul></ul><ul><ul><ul><li>OCS = (rns*OC –EC)/(rns-rs) = (0.4*OC – EC)/0.32 </li></ul></ul></ul><ul><ul><ul><li>OCNS = OC-OCS </li></ul></ul></ul><ul><ul><ul><li>ECS = 0.08*OCS </li></ul></ul></ul><ul><ul><ul><li>ECNS = 0.4*OCNS </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>OCSmokeMass = OCS*1.5 </li></ul></ul></ul><ul><ul><ul><li>OCNonSmokeMass = OCNS*2.4 </li></ul></ul></ul>
  28. 29. OC – EC Smoke Calibration  PM25  EC  OC Smoke:  EC/  OC = 0.08  PM25/  OC = 1.5
  29. 30. OC–EC Non-Smoke Calibration EC/OC Non-Smoke = 0.15 EC/OC Non-Smoke = 0.2 EC/OC Non-Smoke = 1 EC/OC Non-Smoke = 0.4 Negative Smoke – not Possible Maybe?? Maybe?? Too little non-smoke too much smoke Smoke OC Non Smoke OC
  30. 31. OCS, OCNS and PM25 Seasonal Pattern Average over 2000-2004 period PM25Mass OCS Smoke OCNS NonSmoke Day of Year Mexican Smoke Agricultural Smoke Urban NonSmoke Carbon
  31. 32. OC Smoke Spatial Pattern Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug
  32. 33. EC NonSmoke Dec Jan Feb Sep Oct Nov Mar Apr May Jun Jul Aug
  33. 34. PM2.5 (blue) and SmokeMass (red)
  34. 35. Example Smoke Events
  35. 36. Seasonality of OC Percentiles Great Smoky Mtn: Episodic OC in the Fall season Chattanooga:: Elevated and Persistent OC
  36. 37. Monthly Maps of Fire Pixels NOAA HMS – S. Falke Jan Feb Mar Apr Aug Jun Jul May Sep Oct Nov Dec
  37. 38. Measured and Reconstructed PM25 Mass <ul><li>Regional ‘calibration’ constants we applied to OC and Soil </li></ul>
  38. 39. Measured and Reconstructed PM25 Mass, SE
  39. 40. Conclusion <ul><li>OC and EC can be apportioned between Smoke and NonSmoke parts </li></ul><ul><li>The reconstructed mass can be matched to the measured PM25 </li></ul><ul><li>Problems: </li></ul><ul><li>OC Biogenic needs to be separated from OC Smoke </li></ul><ul><li>Scaling OCSmoke and OCNonSmoke to mass needs more calibration </li></ul>
  40. 41. Monthly Maps of Fire Pixels NOAA HMS – S. Falke Jan Mar Jul May Sep Nov
  41. 42. FASTNET Fast Aerosol Sensing Tools for Natural Event Tracking DataFed Data Federation FASTNET is an open communal facility to study non-industrial (e.g. dust and smoke) aerosol events , including detection, tracking and impact on PM and haze. FASTNET output will be directly applicable, to public health protection, Regional Haze rule, SIP and model development as well as toward stimulating the scientific community. The main asset of FASTNET is the community of data analysts, modelers, managers and others participating in the production of actionable knowledge from observations, models and human reasoning The FASTNET community will be supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools evolving under the federated data system, DataFed . DataFed itself is under the umbrella of the interagency Earth Science Information Partners (ESIP) which includes NASA, NOAA and EPA (soon)
  42. 43. Co-retrieval of Aerosol and Surface Reflectance: Analysis of Daily US SeaWiFS Data for 2000-2002 Sean Raffuse, Erin Robinson and Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN
  43. 44. SeaWiFS Satellite Platform and Sensors <ul><li>Satellite maps the world daily in 24 polar swaths </li></ul><ul><li>The 8 sensors are in the transmission windows in the visible & near IR </li></ul><ul><li>Designed for ocean color but also suitable for land color detection, particularly of vegetation </li></ul>Chlorophyll Absorption Designed for Vegetation Detection Swath 2300 KM 24/day Polar Orbit: ~ 1000 km, 100 min. Equator Crossing: Local Noon
  44. 45. Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 99 Percentile 90 Percentile 60 Percentile
  45. 46. Satellite AOT – Time Fraction (0-100%) SeaWiFS Satellite, Summer 2000 - 2003 Dec, Jan Feb Sep, Oct, Nov Jun, Jul, Aug Mar, Apr, May
  46. 47. SeaWiFS AOT – Summer 60 Percentile 1 km Resolution
  47. 48. Near Real Time Public Satellite Data Delivery
  48. 50. 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
  49. 51. Temporal Scales of Aerosol Events <ul><li>A goal of the FASTNET project is to detect and document natural aerosol events in the context of the overall PM pattern </li></ul><ul><li>Inherently, aerosol events are spikes in the time series of monitoring but the definition and documentation of events has been highly subjective </li></ul><ul><ul><li>Temporal variation occurs at many scales from micro scale (minutes) to secular scale (decades) </li></ul></ul><ul><ul><li>At each scale the variation is dominated different combination of the key processes: emission, transport, transformations and removal </li></ul></ul><ul><ul><li>Natural aerosol events occur mostly at synoptic scale of 3-5 days </li></ul></ul>
  50. 52. Discussion: The Role of Averaging Region <ul><li>The size and location of the region strongly influences the event-detection; e.g. events in the Northeast occur at different times than Southwestern events. </li></ul><ul><li>‘ EUS events’ can occur either from a single contiguous ‘haze blob’ or from multiple smaller aerosol patches at different parts of the Eastern US </li></ul><ul><li>It would be desirable to develop a detection scheme that can identify events as they occur at different time and spatial scales </li></ul>
  51. 53. <ul><li>What kind of neighborhood is this anyway? </li></ul>May 9, 1998 A Really Bad Aerosol Day for N. America Asian Smoke C. American Smoke Canada Smoke

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