Integration of Multi-Sensory Earth Observations for Characterization of Air Quality Events

using Service Oriented Architecture

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

Networking Multiplies Value Creation

ApplicationData

1 User Stovepipe Value = 1 1 Data x 1 Program = 1

Enclosed Value-Creating Process - ‘Stovepipe’

“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

Service Oriented ArchitectureActions: Publish – Find – Bind

Applications

Data

Broker

The data reuse is possible through the service oriented architecture

ApplicationData

Application

Application

Application

Application

Stovepipe

1 User Stovepipe Value = 1 1 Data x 1 Program = 1

5 Uses of Data Value = 5 1 Data x 5 Program = 5

Networking Multiplies Value Creation

Merging data may creates new, unexpected opportunities

Not all data are equally valuable to all programs

1 User Stovepipe Value = 1 1 Data x 1 Program = 1

5 Uses of Data Value = 5 1 Data x 5 Program = 5

Open Network Value = 25 5 Data x 5 Program = 25

Data

Data

Data

Data

Data

StovepipeApplication

Application

Application

Application

Application

Networking Multiplies Value Creation

Dataset Description

Convergence Protocols

GetCapabilities

GetData

Capabilities, ‘Profile’

Data

Where? When? What? Which Format?

Server

Back End S

td.

Inte

rface

Client

Front End

Std

. In

terf

ace

Query GetData Standards

Where?

BBOX OGC, ISO

When? Time OGC, ISO

What? Temperature CF

Format netCDF, HDF.. CF, EOS, OGC

T2T1

Standards needed for Distributed Data Access

ScientistScience

DAACs

Info UsersData Providers Info System

AIRNowPublicAIRNow

ModelCompliance

Manager

‘Stovepipe’ and Federated Usage Architectures Landscape

• Data are accessed from autonomous, distributed providers• DataFed ‘wrappers’ provide uniform geo-time referencing• Tools allow space/time overlay, comparisons and fusion

DataFed: Over 100 Federated Datasets

Near Real Time Data IntegrationDelayed Data Integration

Surface Air Quality AIRNOW O3, PM25 ASOS_STI Visibility, 300 sitesVIEWS_OL 40+ Aerosol ParametersMETAR Surface Visual Range

SatelliteMODIS_AOT AOT, Idea ProjectOMI AI, NO2, O3, Refl. TOMS Absorption Indx, Refl.SEAW_US Reflectance, AOT

Model OutputNAAPS Dust, Smoke, Sulfate, AOTWRF Sulfate

Emissions InventoriesNEI Point, Area, MobileEDGAR SO2,NOx,CO2

Fire DataHMS_Fire Fire PixelsMODIS_Fire Fire Pixels

Web Services: Building Blocks of DataFed

Programming

Access, Process, Render Data by Service Chaining

NASA SeaWiFS Satellite

NOAA ATAD Trajectory

OGC Map Boundary

RPO VIEWS Chemistry

Data Access

Data Processing

Layer OverlayLAYERS

Web Service Composition

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

Evidence for Flagging Exceptional Events• A. Establish a site is in potential violation of the PM2.5 standard.

Gather qualitative or quantitative evidence showing that the violation could have been caused by a source that is not reasonably controllable or preventable

Consoles: Data from diverse sources are displayed to create a rich context for exploration and analysis

Viewer: General purpose spatio-temporal data browser and view editor applicable for all DataFed datasets

May 2007 Georgia FiresAn actual Exceptional Event Analysis for EPA

May 5, 2007

May 12, 2007

Observations Used: OMI AI, Airnow PM2.5

DataFed WMS layers overlaid on Google Earth

Evidence for Flagging Exceptional Events

• B. Demonstrate a clear causal relationship between the measured exceedance value and the exceptional event.

CATT: Combined Aerosol Trajectory Tool for the browsing backtrajectories for specified chemical conditions

Sulfate Organics

Evidence for Flagging Exceptional Events

• C. The measured high value is in excess of the normal, historical values.

-

=

Actual Day 84th Percentile

Difference

Evidence for Flagging Exceptional Events

• D. The exceedance occurred but for the contribution of the exceptional source qualify for EE flag.

Social Media and Air Quality

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

MD_Metadata

+ fileIdentifier [0..1]: CharacterString (O)+ contact [1..*] : CI_ResponsibleParty (M)+ dateStamp : Date (M)+ metadataStandardName [0..1]: CharacterString (O)+ metadataStandardVersion [0..1]: CharacterString (O)+ metadataLanguage [0..1]: CharacterString (C)+ characterSet [0..1]: MD_CharacterSetCode = "utf8“ (C)

+identificationInfo 1..*

MD_DataIdentification

+ citation : CI_Citation (M)+ abstract : CharacterString (M)+ extent: EX_Extent (C)+ pointOfContact [0..*] : CI_ResponsibleParty (O)+ language [1..*] : CharacterString (M)+ characterSet: [0..*] : MD_CharacterSetCode = "utf8“ (C)+ topicCategory [1..*] : MD_TopicCategoryCode (M)+ spatial RepresentationType: [0..*] : MD_SpatialRepresentationTypeCode (O)+spatialResolution [0..*]: MD_Resolution

CI_Citation

+ title : CharacterString (M)+ date [1..*] : CI_Date (M)

CI_ResponsibleParty

+ individualName [0..1] : CharacterString+ organisationName [0..1] : CharacterString+ positionName [0..1] : CharacterString+ contactInfo [0..1] : CI_Contact+ role : CI_RoleCode

<<Abstract>>EX_GeographicExtent

+ temporalElement [0..*] (O)+ verticalElement [0..*] (O)

EX_GeographicBoundingBox (C)

+westBoundingLongitude: Decimal+eastBoundingLongitude: Decimal+southBoundingLatiitude: Decimal+northBoundingLatiitude: Decimal

MD_Format

+ name: CharacterString (O)+ version: CharacterString (O)

MD_Distribution

EX_GeographicDescription (C)

+geographicIdentifier: MD_IdentifierISO 19115 CoreM = mandatoryO = optionalC = mandatory under certain conditions

MD_DigitalTransferOption

+ CI_OnlineResource (O) Access InformationContact Information

Spatial/Temporal Extent

Dataset Description

Metadata Description