Integration of Multi-Sensory Earth Observations for Characterization of Air Quality Events E. M. Robinson Advisor, R. B. Husar 2010 Masters of Science Thesis St. Louis, Missouri, November 3, 2010 Smoke EventMulti-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 Satellite Fire 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 Data User Data Provider Broker GetCapabilities GetData Capabilities, ‘Profile’ Data Where? When? What? Which Format? Server Back End Std. Interface Client Front End Std. Interface QueryGetData Where ? BBOX When?Time What?Temperature FormatnetCDF, HDF.. T2T1 Publish Find Bind

Scientist Science Satellite Info UsersData ProvidersInfo System Surface Manager Surface Model Compliance 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 Overlay LAYERS 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 Pool FRMSatellite Chem Model EmissionMedia Rec. Model Met. Model Obs Causalit y Exception Event 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 Pool FRMSatellite Chem Model EmissionMedia Rec. Model Met. Model Obs Causalit y Exception Event 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 SulfateOrganics Observation Chem Models CATT: Combined Aerosol Trajectory Tool

Data Available for Reuse Data Reuse Pool FRMSatellite Chem Model EmissionMedia Rec. Model Met. Model Obs Causalit y Exception Event 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 Pool FRMSatellite Chem Model EmissionMedia Rec. Model Met. Model Obs Causalit y Exception Event 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 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 Stovepipe Application Networking Multiplies Value Creation

Federated Data System: Datafed Surface Air Quality AIRNOWO3, PM25 ASOS_STIVisibility, 300 sites VIEWS_OL40+ Aerosol Parameters METARSurface Visual Range Satellite MODIS_AOTAOT, Idea Project OMIAI, NO2, O3, Refl. TOMSAbsorption Indx, Refl. SEAW_USReflectance, AOT Model Output NAAPSDust, Smoke, Sulfate, AOT WRFSulfate Fire Data HMS_FireFire 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 - = Actual Day 84 th Percentile Difference