Exceptional Event Decision Support System (EE DSS) Approach and Demo

Dr. Rudolf B. HusarWith

Kari Hoijarvi and Erin RobinsonCenter for Air Pollution Impact and Trend Analysis (CAPITA)

Washington University, St. Louis

Webinar presented April 30, 2013 to EPA, Regional and State AQ Analysts

http://wiki.esipfed.org/index.php/EE_DSS_Webinar

EPA’s Exceptional Event Rule

The States and the EPA Regions encounter many hurdles implementing EE Rule

The technical hurdles can be minimized by a suitable Decision Support System

The Exceedance would not Occur, But For the Exceptional Event

• There is a NAAQS exceedance or violation based on FRM data

• The event concentration is in excess of the ”normal" fluctuations

• Event satisfies the definition of exceptional: The cause is not reasonably controllable or preventable.

• There is a clear causal relationship between the exceedance and the exceptional source

• The exceedance would not have occurred, ‘but for’ the exceptional source

Approach for supporting EE flagging process:Screening tool for PM2.5, PM10 and Ozone Exceedances

Data Screening

Tool

EPA Exceptional Event Rule Conditions:

EE DSS Overview Video

• http://datafedwiki.wustl.edu/index.php/2013-02-15_EE-DSS_Kansas_Use_Case_EPA

Exceptional Event Decision Support System (EE DSS)

• EE DSS offers a uniform tool set applicable nation-wide• Employs an open and transparent methodology for flagging • Uses FRM and a wide array of model, satellite and other datasets

Specific aims of the EE DSS• Aid the States identifying and documenting candidate EE samples• Assists the EPA Regions in evaluation the flag documentations• Supports collaborative tools development and EE analysis

The EE DSS was developed over the past 8 years with generous support from the NASA Applications Program, by EPA and by Washington University.

Exceptional Event Screening Approach and Tool SetFocus of April 30, 2013 Webinar

• The EE screening tool for supporting 2012 flag submissions by July 1, 2013 – detects exceedances of the daily NAAQS, PM2.5, PM10, Ozone– eliminates those that fall within normal fluctuations– identifies those attributable to

• Windblown dust events (domestic, international) • Smoke events (domestic, international)• July 4th event

The EE flag documentation is to be prepared by the States by Dec 12, 2012

Datasets Used

• Primary Data from EPA AQS, (April 20 extraction) – FRM PM2.5, PM10 (daily)– FRM Ozone (hourly) – aggregated to daily 8hr-day-max

• Supporting Datasets – Naval Res. Lab NAAPS aerosol model, dust, smoke, sulfate– NASA satellite data from 4 sensors– Continuous surface obs. AIRNOW,

EE DSS Tools: Data System Architecture

• Data are accessible from the Air Quality Data Network (ADN) by the AQ Community Catalog• ADN is facilitated by the GEO AQ Community of Practice (GEO AQ CoP), including R. Husar’s group.• The generic client tools (red boxes) are for processing and visualization; used in many applications• Specialized Application Tools are dedicated to specific applications, e.g. event detection

Normal Variation -Excess of Normal Variation Variation of PM2.5, PPM10 and Ozone is determined for each locationUsing all observations over a 3-year period 2010-2013Variation is quantified using nonparametric statistic – percentiles (16, 50, 84, 97)Long-term trends are ignored but percentiles are calculated seasonally

Raw Daily Observation(3rd-6th-day fom PM2.5, PM10)

Normal Variation (3-yr seasonal percentiles)

Raw Daily & Normal Variation (3-yr seasonal percentiles)

Exceedances above Normal(values >NAAQ and and > 84th perc

Qualified Samples

What is the Normal Variation for PM2.5?

CA Central Valley Ohio River

Idaho Tennessee

Aggregation over what time range

A reason for the 2006-2010 EE flag decline is the overall reduction in PM2.5 concentrations.

2000-2003 avg. 2009-2012 avg.

As a result of PM2.5 decline, the number of >35ug/m3 samples has also declined dramatically. No NAAQS exceedances-no EE flag.

>35 ug/m3 station count

O3 ‘Violation’ Trends Number of CONUS Stations with O3>75 ppb

2010-2013

1993-2013

Apr-Oct 2012

EE Screening tool for a specific site, day

Seasonal Percentiles

Station time seriesExceedances above normal

Anomaly MapExceedances above normal

Station Data MapData Contour Map

Specific Station

Specific Day

EE Samples and NAAPS Model Smoke, Dust

Navy Aerosol Analysis and Prediction System (NAAPS)

• Assimilates satellite data from multiple sensors • Aerosol Optical Depth (AOD; 2-D) (MODIS and MISR)• Extinction (3-D) (CALIPSO)

• Assimilation of previous forecast + remote sensing of aerosols

+ Land/Ocean MODIS+ Land/Ocean MISR

Natural run + Ocean MODIS+ land/Ocean MISR

Forecasting Dust, Smoke and Sulfate Globally

D. Westphal, NRL

4D Dust, Smoke, SulfateVertical Cross Section Views

Vertical dust cross sections at about 120 (surface plume) and 1000 km (elevated plume).Knowing the vertical structure of smoke and dust plumes is critical to EE documentationThe DataFed Browser now incorporates vertical cross section views

NAAPS Surface Smoke – IMPROVE Total Organic Carbon Comparison2006-2007, Purple Box Region

• Temporal correlation shows that NAAPS properly simulates the temporal smoke variation over NW

• Spatial correlation indicates that NAAPS reproduces the spatial distribution of smoke

• Time series of measured IMPROVE OCf and NAAPS smoke over the cursor box shows close co-variation Seasonal cycle of OCf and NAAPS averaged over the cursor box and multiple years is very similar.

• The NAAPS smoke and IMPROVE organics (OCf) correlation is excellent and the here is excellent since OCf is dominated by smoke

• At other regions of the US, OCf has biogenic and urban-industrial sources

What does this red dot represent?

Are NAAPS data presented in this figure?

NAAPS Surface Smoke – IMPROVE Total Organic Carbon Comparison2006-2007, Purple Box Region

• Temporal correlation shows that NAAPS properly simulates the temporal smoke variation over NW

• Spatial correlation indicates that NAAPS reproduces the spatial distribution of smoke

• Time series of measured IMPROVE OCf and NAAPS smoke over the cursor box shows close co-variation Seasonal cycle of OCf and NAAPS averaged over the cursor box and multiple years is very similar.

NAAPS Smoke IMPROVE OCf

• Spatial distribution NAAPS smoke and IMPROVE Ocf averaged over 2006-07

• For the NW States, both the temporal and the spatial correlations are excellent.• Over the SW US, the correlation is poor since Ocf has biogenic and man-made sources

Back-Trajectory Tool

Event Console

• Consoles are spatial representations of observations, emissions and models• All maps are synchronized spatially and temporally, and navigated by the user• Provide rich multisensory context to illuminate complex atmospheric situations

Permalink for Bookmarking an EE Sample

Once you identified a candidate EE sample, you can save the settings in a bookamark, so you can return to the EE sample ‘page’ any time.

• An EE sample bookmark will help• ‘Remembering’ and listing the EE candidates during your exploration• Sharing the EE sample with your colleagues in the office or elsewhere• Communicating with the EPA Region

If you whish to discuss a specific EE sample with the EE Community (inlc. The DSS team), send us the permalink to open a discussion topic..

Browser Video

http://datafedwiki.wustl.edu/index.php/2013-04-23_DataFed_Browser_Navigation

EE DSS Tool Demo

• PM2.5 EE Screen PM25– Bakersfild, CA - Winter peaked,

Exceedances over the normal variation, 84 percentile

– Also summer peaks– Salt Lake City July 4

• Ditto , July 4th

• PM2.5 Smoke– West Washington - Smoke

• Click Smoke Dust

• PM10 EE Screen PM10– Click Dust Smoke

• Ozone EE Screen Ozone– Sequence 2012-06-26,

Click Arkansas– 2012-06-27, 28, 29, 30,

7-01 Click Charlotte

• 2011 Ozone Kansas smoke– Click Back trajectory

• Kansas Smoke Ozone– Back trajectory

• Kansas Smoke KMZhttp://wiki.esipfed.org/index.php/2013-04-30_EE_DSS_Webinar_Demo

Many Thanks:

• Kari Hoijarvi, Washington University• Anshu Tirumali, Washington University• Rich Poirot, State of Vermont• Doug Westphal, Naval Research Lab• Neil Frank, EPA• Ali Omar, NASA Applied Sciences

Community ContactsRudy Husar, Washington University, Stl. rhusar@wustl.eduErin Robinson, Foundation for Earth Science, erinrobinson@esipfed.orgNeil Frank, EPA, Frank.Neil@epa.gov; Mark Evangelista, EPA, Evangelista.Mark@epamail.epa.gov