Characterization of Aerosol Eventsusing 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
Regional Haze Rule: Natural Aerosol
Looking ahead to reach natural conditions … in 60+ years!!!
NAAMS: National Ambient Air Monitoring Strategy and NCore
…coordinated multi-pollutant real-time monitoring network
Natural and Exceptional Event Rule (Making)
• What is a legitimate Natural or Exceptional event?
• How does one document & quantify the N/E events?
• How is an N/E event treated in NAAQS?
Dust, Smoke and Exceptional Events
Smoke EventJuly 4 2004
July 4 2003
Intercontinental Dust
Long-Term Monitoring: Fine
Mass, SO4, K
• Long-term speciated monitoring begun in 1988 with the IMPROVE network
• Starting in 2000, the IMPROVE and EPA networks have expanded
• By 2003, the IMPROVE + EPA species are sampled at 350 sites
• In 2003, the FRM/IMPROVE PM25 network is reporting data from over 1200 sites
Fine Mass
Sulfate
Potassium
Evolution of Spatial Data Coverage: Fine Sulfate, 1998-2003
1998 1999 2000
200320022001
• Before 1998, IMPROVE provided much of the PM2.5 sulfate• In the 1990s, the mid-section of the US was not covered • By 2003, the IMPROVE and EPA sulfate sites (350+) covered most of the US
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
Quebec Smoke July 7, 2002Satellite Optical Depth & Surface ASOS RHBext
Event Detection Temporal Signal Decomposition
• First, the median and average is obtained over a region for each hour/day (thin blue line)
• 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.
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
• Finally, the hourly/daily deviation from the the smooth median is used to determine the noise (blue) and event (red) components
Seasonal PM25 by Region
The 30-day smoothing average shows the seasonality by region
The Feb/Mar PM25 peak is evident for the Northeast, Great Lakes and Great Plains
This secondary peak is absent in the South and West
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
Application of Automatic Event Detection:A Trigger and Screening Tool
• The algorithmic aerosol detection and characterization provides only partial information about events
• However, it can trigger further action during real-time monitoring
• Also, it can be used as a screening tool for the further analysis
Causes of Temporal Variation by Region
The temporal signal variation is decomposable into seasonal, meteorological noise and events
Assuming statistical independence, the three components are additive:
V2Total = V2
Season + V2MetNoise + V2
Event
The signal components have been determined for each region to assess the differences
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 eventsSouthwest, 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%
‘Composition’ of Eastern US Events
• The bar-graph shows the various combinations of species-events that produce Reconstructed Fine Mass (RCFM) events
• ‘Composition’ is defined in terms of co-occurrence of multi-species events (not by average mass composition)
• The largest EUS RCFM events are simultaneously ‘events’ (spikes) in sulfate, organics and soil!
• Some EUS RCFM events are events in single species, e.g. 7-Jul-97 (OC), 21-Jun-97 (Soil)
Based on VIEWS data
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. Weather Rev. 1976
SMS GOES June 30 1975
SeaWiFS AOT – Summer 60 Percentile1 km Resolution
Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
99 Percentile90 Percentile
60 Percentile
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
• The estimation of smoke mass from speciated aerosol data has eluded full quantification for many years
• While full quantification is still not in hand, a proposed approximate approach yields reasonably consistent results
• CIRA, Poirot and others have made most of the contributions
• The smoke quantification consists of two steps:– Step 1: Carbon apportionment into Smoke-Biogenic and Soot
components– Step 2: Applying factors to turn Smoke-Biogenic and Soot into
Mass
Smoke Quantification using Chemical Data
– Step 1: Carbon apportionment into SmokeBiogenic and Soot components– Carbon (OC & EC) is assumed to have only two ‘source’ types: smoke-biogenic and
soot
OC = OCSB (SmokeBiogenic) + OCSoot (Soot)EC = ECSB (SmokeBiogenic) + ECSoot (Soot)
In each source type, the EC/OC ratio is assumed to be constant
ECSB/OCSB = rsb (In smoke and biogenic, EC/OC ratio rsb =0.08)
ECSoot/OCSoot = rs (In soot, EC/OC ratio rs = 0.4)
With these four equations, the value of the four unknowns can be calculated
OCSB = (rs*OC –EC)/(rs-rsb) = (0.4*OC – EC)/0.32
OCSoot = OC-OCSB
ECSB = 0.08*OCSB
ECSoot = 0.4*OCSoot
– Step2: Apply a factor to turn OC into MassThe smoke and non-smoke OC is scaled by a factor to estimate the mass
OCSmokeBioMass = OCSB*1.5
OCSootMass = OCSoot*2.4
Smoke Excess OC – EC Calibration of SmokeBiogenic Composition
• Smoke (excess) PM25, EC and OC yields calibration• Ratios for Kansas, Big Bend and Quebec smoke are similar• Good news for OC apportionment
PM25
ECOC
SmokeBiogenic:EC/OC = 0.08PM25/OC = 1.5
EC/OC Ratio
Soot OC–EC Calibration by Iteration
• EC/OC ratios for soot are more difficult to determine• EC/OC of about 0.2-0.4 is reasonable• Outside this range is not
EC/OC Soot = 0.15 EC/OC Soot = 0.2
EC/OC Soot = 1EC/OC Soot = 0.4
Negative SmokeBio – not Possible
Maybe??
Maybe?? Too little soot, too much smokebio
SmokeBio OC
Soot OC
Measured and Reconstructed PM25 Mass
• Regional ‘calibration’ constants were applied to OC and Soil
OCSB, OCSoot and PM25 Seasonal PatternAverage over 2000-2004 period
PM25MassOCSB
SmokeBio
OCSoot
Day of Year
Mexican Smoke
Agricultural Smoke
Urban Soot
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 AugJun Jul Aug
PM2.5 (blue) and SmokeBioMass (red) Note: Smoke events are spikes superimposed on biogenic OC background
Smoke Events
Kansas Agric. Smoke
Example OC Smoke EventsNote: 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
• Fire pixels are necessary but not sufficient• Some Fire pixels produce more smoke aerosol than others …by at least factor of 5
NOAA HMS – S. Falke
Jan Feb Mar Apr
AugJun JulMay
Sep Oct Nov Dec
Smoke
Kansas Ag Smoke
No Smoke
Summary
• Developments (CIRA, Poirot, others) • OC and EC can be reasonably apportioned between
SmokeBiogenic and Soot components
• The reconstructed mass can be matched to the measured PM25
Problems of OC Apportionment
• Need to separate smoke and biogenic OC!• IMPROVE and STN OC don’t match• Some coefficients may need regional/seasonal calibration
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
Some of the DataFed Tools
– Data Catalog– Data Browser– PlumeSim, Animator– Combined Aerosol Trajectory Tool (CATT)
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
Conceptual Diagram of an Emissions Community
XML
GIS
EstimationMethods
RDBMS
GeospatialOne-Stop
TransportModels
EmissionsInventoryCatalog
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
ReportGeneration
Mediators /Portal
North American Commission
for Environmental Cooperation
Web ApplicationReport
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
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 100km2 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.
BLM Area burned - monthly average
The acres burned in the BLM compiled fire history dataset are spatially aggregated on a 50km2 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
Aggregation Tools
Fire PixelsJune 16-23, 2004
Spatially Aggregated
Monthly Sum
Spatially Aggregated
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).
Standards Based Data SharingOpen 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:
Seasonality of OC Percentiles
• IMPROVE/STN Inconsistencies Not shown here
Great Smoky Mtn:
Episodic OC in the Fall season
Chattanooga::
Elevated and Persistent OC
Field burning particulate pollution & asthma - Shorts
• 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: "Victim with known asthma subjected to intense air pollution from wheat field burning."
• 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: "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."
• 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 "people are going to be choking," 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.
• Whitman, David. Fields of Fire. US News & World Report 2001 September 3.
Kansas
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