Collaborative Integration of Satellite and Surface Data for Characterization of Aerosol Events

E. M. RobinsonAdvisor, R. B. Husar

2010 M.S. ThesisSt. Louis, MO, Nov. 3, 2010

Illustrate the use of multi-sensory dataTechnical Challenge: Characterization

• PM characterization requires many sensors, sampling methods and analysis tools

• Each sensor/method covers only a fraction of the 7-Dimensional PM data space.

– Spatial dimensions (X, Y, Z) – Temporal Dimensions (T)– Particle size (D)– Particle Composition ( C ) – Particle Shape (S)

• Most of the 7 Dim PM data space is extrapolated from sparse measured data

• Others sensors integrate over time, space, chemistry, size etc. .

Satellite-IntegralSatellites, have high spatial resolution but integrate over height H, size D, composition C, particle shape

Kansas Agricultural Smoke, April 12, 2003

Fire Pixels PM25 Mass, FRM65 ug/m3 max

Organics35 ug/m3 max

Ag Fires

SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

Hurdles

“The user cannot find the data;

If he can find it, cannot access it;

If he can access it, ;

he doesn't know how good they are;

if he finds them good, he can not merge them with other data”

The Users View of IT, NAS 1989

To overcome the first two hurdles need:

1. Service oriented architecture

2. Standards for finding and accessing the data

3. Open, collaborative space to coordinate work

SOA ActionsActions: Register– Discover -Access

UserProvider

Broker

The data reuse is possible through the service oriented architecture of GEOSS.

SOA ActionsActions: Register– Discover -Access

UserProvider

Broker

The data reuse is possible through the service oriented architecture of GEOSS.

Preparation of Data and Metadata

MetadataDataData Protocol

WMS, WCS (netDCF CF) + conventions

UserProvider

GEOSSClearinghous

e

Access

Preparation of Data and Metadata

Register Metadata

MetadataDataData Protocol

WMS, WCS (netDCF CF) + conventions

Metadata

ISO 19115 subset for Geospatial Data

UserProvider

GEOSSClearinghous

e

Air Quality Metadata Record

Discover, Get Access Key

UserProvider

GEOSSClearinghouse

OGC CSW Queryable

Air QualitySpecific

ISO 19115CSW Profile

OGC CSW Returnable

MetadataDescription

Data Binding

Metadata for Finding and Accessing Data

Exceptional Event Rule: An air quality exceedance that would not have occurred but for the presence

of a natural event.

Transported Pollution

Transported African, Asian Dust; Smoke from Mexican

fires & Mining dust, Ag. Emissions

Natural Events

Nat. Disasters.; High Wind Events; Wild land Fires;

Stratospheric Ozone; Prescribed Fires

Human Activities

Chemical Spills; Industrial Accidents; July 4th; Structural

Fires; Terrorist Attack

Satellite remote sensors provide key observations for Exceptional Events

May 2007 Georgia FiresAn actual Exceptional Event Analysis for EPA

May 5, 2007

May 12, 2007

Observations Used:

OMI NO2 Quantifies the NO2 Emission

Sweat Water fire in S. Georgia (May 2007)

3. Evidence: Aerosol Composition

Sulfate Organics

Sulfate Organics

Measured

Modeled

5. The Exceedance would not Occur, But For the Exceptional Event

Social Media and Air Quality

SOA ActionsActions: Register– Discover -Access

UserProvider

Broker

The data reuse is possible through the service oriented architecture of GEOSS.

Social Media Listening for Air Quality

Air Twitter Aggregator

RSS Feeds

Air Twitter Filter

ESIPAQWG

Air Twitter – Event Identification

August 2009, Los Angeles Fires

Normal Weekly Trend

Air Quality EventSpacesEventSpaces are community workspaces on the ESIP wiki that are created to describe the Event

Science Data

Social Media

Google Analytics Results: August LA Fires

580 Views

Google Analytics Results: August LA Fires

Future Work: GEOSS Suitable Infrastructure