integration of multi-sensory earth observations for characterization of air quality events e. m....
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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
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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
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Kansas Agricultural Smoke, April 12, 2003
Surface Network Organics
SatelliteFire Pixels
Ag Fires
Satellite AOT
NASA
NPS
NASA
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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
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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
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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
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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
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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
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AQ Observations through Social Media
Social media can tell when, what and where
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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
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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
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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.
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Data Available for Reuse
Data Reuse PoolData Reuse Pool
FRM Satellite Chem Model
EmissionMedia Rec.Model
Met. Model
Obs
Causality
Causality ExceptionExceptionEvent IDEvent ID
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Event Identification• Earth Observation Requirements from: Satellites, surface observations,
models, emissions, weather, media• Analysis Participants: AQ Analysts, Satellite Analysts, Global Modelers,
Media/Public
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Data Available for Reuse
Data Reuse PoolData Reuse Pool
FRM Satellite Chem Model
EmissionMedia Rec.Model
Met. Model
Obs
Causality
Causality ExceptionExceptionEvent IDEvent ID
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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
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Data Available for Reuse
Data Reuse PoolData Reuse Pool
FRM Satellite Chem Model
EmissionMedia Rec.Model
Met. Model
Obs
Causality
Causality ExceptionExceptionEvent IDEvent ID
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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
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Data Available for Reuse
Data Reuse PoolData Reuse Pool
FRM Satellite Chem Model
EmissionMedia Rec.Model
Met. Model
Obs
Causality
Causality ExceptionExceptionEvent IDEvent ID
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AQ Event Characterization User Requirements• 68 Earth Observation Requirements• Most observations were reused for multiple parts of the analysis
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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
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Future Work: Using SOA approach for GEOSS
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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)
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Acknowledgements
• Dr. Rudy Husar• CAPITA Research Group: Dr. Stefan Falke, Kari
Hoijarvi, Dr. Janja Husar • Funding sources: NASA, EPA, ESIP
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Extra Slides
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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
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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
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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
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Google Analytics Results: August LA Fires
580 Views
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Google Analytics Results: August LA Fires
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May 2007 Georgia Fires:
May 5, 2007
May 12, 2007
Observations Used: OMI AI, Airnow PM2.5
DataFed WMS layers overlaid on Google Earth
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D. Exceedance occurred “but for” the event
• 10 Earth Observation Requirements from: Chem models, surface observations, emissions, weather
• Analysis Participants: AQ Analysts, Regional Modeler
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C. Measured Value was an Anomaly• Earth Observation Requirements from: Surface Obs.• Analysis Participants: AQ Analysts
-
=
Actual Day 84th Percentile
Difference