Integration of Multi-Sensory Earth Observations for Characterization of Air Quality Events using Service Oriented Architecture E. M. Robinson Advisor,

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Integration of Multi-Sensory Earth Observations for Characterization of Air Quality Events using Service Oriented Architecture E. M. Robinson Advisor, R. B. Husar 2010 M.S. Thesis St. Louis, MO, Nov. 3, 2010

Illustrate the use of multi-sensory data Technical 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-Integral Satellites, have high spatial resolution but integrate over height H, size D, composition C, particle shape

Kansas Agricultural Smoke, April 12, 2003 Fire PixelsPM25 Mass, FRM 65 ug/m3 max Organics 35 ug/m3 max Ag Fires SeaWiFS, ReflSeaWiFS, AOT ColAOT Blue

Networking Multiplies Value Creation Application Data 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 Architecture Actions: Publish – Find – Bind Applications Data Broker The data reuse is possible through the service oriented architecture

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 Dataset Description

Convergence Protocols GetCapabilities GetData Capabilities, ‘Profile’ Data Where? When? What? Which Format? Server Back End Std. Interface Client Front End Std. Interface QueryGetData Standards Where ? BBOXOGC, ISO When?TimeOGC, ISO What?TemperatureCF FormatnetCDF, HDF..CF, EOS, OGC T2T1 Standards needed for Distributed Data Access

Scientist Science DAACs Info UsersData ProvidersInfo System AIRNow Public AIRNow Model Compliance 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 Integration Delayed Data Integration 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 Emissions Inventories NEIPoint, Area, Mobile EDGARSO2,NOx,CO2 Fire Data HMS_FireFire Pixels MODIS_FireFire 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 Overlay LAYERS 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 Fires An 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 SulfateOrganics

Evidence for Flagging Exceptional Events C. The measured high value is in excess of the normal, historical values. - = Actual Day 84 th 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 EventSpaces EventSpaces 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) +identificationInfo1..* 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 > 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_Identifier ISO Core M = mandatory O = optional C = mandatory under certain conditions MD_DigitalTransferOption + CI_OnlineResource (O) Access Information Contact Information Spatial/Temporal Extent Dataset Description Metadata Description