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

E. M. RobinsonAdvisor, R. B. Husar

2010 Masters of Science ThesisSt. Louis, Missouri, November 3, 2010

Smoke Event Multi-Sensory Characterization

Service Oriented Architecture

Outline

• Background on Air Quality Event Analysis

• Service-Oriented Approach to Data Reuse

• Social Media as AQ Observations and Sensor

• Case Study for Data Reuse: Exceptional Event Rule

Kansas Agricultural Smoke, April 12, 2003

Surface Network Organics

SatelliteFire Pixels

Ag Fires

Satellite AOT

NASA

NPS

NASA

Technical Challenge: Characterization

• The Air Pollution System has 7-Dimensional PM data space.

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

• Surface observations only characterize at the point of observation

Satellite-Integral

On the other hand:

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

Hurdles to Data Use (and Reuse)

“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

Data Reuse through Service Orientation

The data reuse is possible through the standard data access

DataUser

Data Provider

Broker

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

Where?

BBOX

When? Time

What? Temperature

Format netCDF, HDF..

T2T1

Publish Find

Bind

ScientistScience

Satellite

Info UsersData Providers Info System

SurfaceManagerSurface

ModelCompliance

Manager

• Data are accessed from autonomous, distributed providers• DataFed ‘wrappers’ provide uniform geo-time referencing• Tools allow space/time overlay, comparisons and fusion(Husar and Poirot, 2005)

Data Reuse through Service Orientation

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

AQ Observations through Social Media

Social media can tell when, what and where

Social Media as an Air Quality Sensor

Air Twitter Aggregator

Subscribe to RSS Feeds

Air Twitter Filter

ESIPAQWG

Search sites for: Smoke, Air Quality, Dust

Social Media as an Air Quality Sensor

August 2009, Los Angeles Fires – highlight tweets from LA

Normal Weekly Trend hide bottom chart

This is a qualitative sensor since it doesn’t apportion tweets to events

EPA’S Exceptional Event Rule

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

An air quality exceedance that would not have occurred but for the presence of a natural event.

Data Available for Reuse

Data Reuse PoolData Reuse Pool

FRM Satellite Chem Model

EmissionMedia Rec.Model

Met. Model

Obs

Causality

Causality ExceptionExceptionEvent IDEvent ID

Event Identification• Earth Observation Requirements from: Satellites, surface observations,

models, emissions, weather, media• Analysis Participants: AQ Analysts, Satellite Analysts, Global Modelers,

Media/Public

Data Available for Reuse

Data Reuse PoolData Reuse Pool

FRM Satellite Chem Model

EmissionMedia Rec.Model

Met. Model

Obs

Causality

Causality ExceptionExceptionEvent IDEvent ID

Causality between Event and Site• Earth Observation Requirements from: Satellites, surface

observations, chem models, receptor models, emissions, weather• Analysis Participants: AQ Analysts, Satellite Analysts, Receptor

Analyst, Transport modeler, Regional Modeler

Sulfate Organics

Observation

Chem Models

CATT: Combined Aerosol Trajectory Tool

Data Available for Reuse

Data Reuse PoolData Reuse Pool

FRM Satellite Chem Model

EmissionMedia Rec.Model

Met. Model

Obs

Causality

Causality ExceptionExceptionEvent IDEvent ID

Exceedance occurred “but for” the event

Based on all of the evidence provided an AQ analyst could identify that the yellow regions were exceptional and the pink regions were due to local pollution

• 10 Earth Observation Requirements from: Chem models, surface observations, emissions, weather• Analysis Participants: AQ Analysts, Regional Modeler

Data Available for Reuse

Data Reuse PoolData Reuse Pool

FRM Satellite Chem Model

EmissionMedia Rec.Model

Met. Model

Obs

Causality

Causality ExceptionExceptionEvent IDEvent ID

AQ Event Characterization User Requirements• 68 Earth Observation Requirements• Most observations were reused for multiple parts of the analysis

Summary

• AQ event characterization needs many kinds of data

• Using a service-oriented approach allows data reuse by allowing the user to find, access and merge data

• Data reuse allows for faster, easier, better analysis

Future Work: Using SOA approach for GEOSS

Future Work: Collaborative Air Quality Analysis

Science Data

Social Media

• EventSpaces are community workspaces to harvest observations

• Event is explained in a cursory fashion by the AQ Community.

“In order to do improve these systems, […] a dramatic shift from traditional emphasis on self-reliance toward more collaborative operations — a shift that will allow the community as a whole to perform routinely at levels unachievable in the past” – Director of National Intelligence (Vision 2015, 2008)

Acknowledgements

• Dr. Rudy Husar• CAPITA Research Group: Dr. Stefan Falke, Kari

Hoijarvi, Dr. Janja Husar • Funding sources: NASA, EPA, ESIP

Extra Slides

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

Federated Data System: Datafed

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

Fire DataHMS_Fire Fire Pixels

Wrap Access&Process

Render

Google Analytics Results: August LA Fires

580 Views

Google Analytics Results: August LA Fires

May 2007 Georgia Fires:

May 5, 2007

May 12, 2007

Observations Used: OMI AI, Airnow PM2.5

DataFed WMS layers overlaid on Google Earth

D. Exceedance occurred “but for” the event

• 10 Earth Observation Requirements from: Chem models, surface observations, emissions, weather

• Analysis Participants: AQ Analysts, Regional Modeler

C. Measured Value was an Anomaly• Earth Observation Requirements from: Surface Obs.• Analysis Participants: AQ Analysts

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=

Actual Day 84th Percentile

Difference