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Environmental Information Systems for Monitoring, Assessment, and Decision-making

Environmental Information Systems for Monitoring, Assessment, and Decision-making

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Environmental Information Systems for Monitoring, Assessment, and Decision-making

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  1. Environmental Information Systems for Monitoring, Assessment, and Decision-making Stefan Falke AAAS Science and Technology Policy Fellow U.S. EPA - Office of Environmental Information

  2. Environmental Information Systems Decision-making Monitoring Delivery/Presentation Storage/Description Analysis & Assessment

  3. Spatial Analysis Environmental Information Systems Decision-making Monitoring Delivery/Presentation Storage/Description Analysis & Assessment

  4. Environmental Information Systems Web-based Information Systems Decision-making Monitoring Delivery/Presentation Storage/Description Analysis & Assessment

  5. Environmental Information Systems Sensor Webs Decision-making Monitoring Delivery/Presentation Storage/Description Analysis & Assessment

  6. Mapping Air Quality Goal: Reduce the uncertainty in mapping air quality data from point measurements. Use a data-centric spatial interpolation that is based on physical principles. estimated continuous surface point monitoring data spatial interpolation ci is the estimated concentration at location i n is the number of monitoring sites cj is the concentration at monitoring site j wij is the weight assigned to monitoring site j

  7. Spatial Interpolation with Monitor Clusters Standard interpolation applies equal weight; each site has 1/3 of the weight on the estimate at i. There is a cluster of four sites. When applying standard distance weighted interpolation, the cluster will account for 2/3 of estimated value at i while the two single sites each only account for 1/6 of the total weight. Declustered weighting shows the proper allocation of the 1/3 weight to the cluster of sites.

  8. Cluster weight Inverse distance weight X 2 X 1 r j2 X 2 r X j1 r 1 X j2 X r X 3 X r j1 j 3 r j j3 j3 R R ij ij i i CW~ 1.00 CW~ 0.25 Declustered Interpolation

  9. Variance Aided Mapping Temporal variance is indicative of local source influenced monitoring sites. The higher a site’s variance, the lower its interpolation weight and the more restricted its radius of influence during interpolation.

  10. Variance Weighting Example Interpolation weights using distance and temporal variance of daily maximum ozone concentrations, 1991-1995 In central Ohio, most monitoring sites experience similar temporal variance in O3 and weights assigned to the sites are simply R-2. In estimating O3 near St. Louis, high variance sites (St. Louis urban sites) are used along with low variance sites (rural sites) and their respective weights are altered from R-2.

  11. Estimated Ozone Concentrations, 1991-1995

  12. Estimation Error most clustered least clustered Mean estimation error at least clustered locations with DIVID is about 10% lower than kriging and 30% lower than inverse distance.

  13. Barrier Aided Estimation Pollutants are “trapped” in valleys while mountain tops have low pollutant concentrations • Horizontal Flow Barriers (Mountains) • Vertical Flow Barriers (Scale Height)

  14. PM10 in California Without Barriers With Barriers AIRS PM10 data (1994-1996) Sierra Nevada Mountains are clearly visible with barrier aided estimation

  15. Surrogate Aided Interpolation 1991-1995 Summer 1991-1995 Summer Extinction Coefficient 1/r2 Interpolation Fine Mass Concentrations 1/r2 Interpolation 1991-1995 Summer 1991-1995 Summer Fine Mass Bext 1/r2 Interpolation Bext Aided FM = Fine Mass Bext x Bext

  16. Satellite Imagery for PM Assessment Spaceborne sensors allow near continuous aerosol monitoring throughout the world. When fused with surface data they provide information on the spatial, temporal, and chemical characteristics of aerosols than cannot be determined from any single image or surface observation. Goal: Fuse SeaWiFS and TOMS satellite data with surface observations and topographic data to describe extreme aerosol events.

  17. 1998 Asian Dust Storm The underlying color image is the surface reflectance derived from SeaWiFS. The TOMS absorbing aerosol index (level 2.0) is superimposed as green contours. The red contours represent the surface wind speed from the NRL surface observation data base. The blue circles are also from the NRL database and indicate locations where dust was observed. The high wind speeds generated the large dust front seen in the SeaWiFS, TOMS, and surface observation data.

  18. 2000 Saharan Dust Fuerteventura and Lanzarote Islands are fully blanketed by the murky yellow colored dust plume. Gran Canaria and Tenerife are partly covered by the dust layer but their higher elevations appear to protrude above the dust layer at about 1200m. A massive dust storm transports dust off the west coast of Africa into the Atlantic Ocean and across the Canary Islands.

  19. Future Research Interests • Spatial and temporal interpolation • Uncertainty / Estimation Error Maps • Integration of surface and satellite data • Development of web-based spatio-temporal tools

  20. AAAS Fellowship Program American Association for the Advancement of Science (AAAS) fellowship program to bring science and engineering PhDs to D.C. and the policy process Fellows are placed in federal agencies (EPA, State Dept., NSF, NIH, USAID…) and in Congress Goal is to provide scientific expertise to offices and to gain first hand experience in the policy process

  21. Interoperable Environmental Information Systems Advances in monitoring and information technology have resulted in the collection and archival of large quantities of environmental data. However, stove-piped systems, independently developed applications, and multiple data formats have prevented these data and the systems that serve them from being shared. Interoperable environmental information systems offer the potential for attaining systems of shared information and applications within a distributed environment.

  22. Environmental Monitoring for Public Access and Community Tracking (EMPACT) Assists communities in providing sustainable public access to environmental monitoring data and information that are clearly-communicated, available in near real-time, useful, and accurate A funded EMPACT project had three required components: • Real Time Environmental Monitoring • Data Analysis & Visualization • Information Dissemination Technology (Internet, Kiosks, Newspaper, TV, etc.)

  23. EMPACT Project Locations

  24. Publish – Make data and tools available to the Web Find – Enable the discovery of data and tools through Web-based search engines Bind - Connect data and tools to user applications for value added processing States EPA CDX Portal Others GEIA Web Portal Minimize Burden Maximize Transparency Distributed Environmental Information Network Data Users Data Sources Europe EI CEC EI

  25. XML Web Services Wrappers Network Data and Tool Description Data Description (Metadata) Data Tool Description Tools

  26. Whoville Cedar Lake Distributed Environmental Information Systems Integrated View Parcels Roads Images Boundaries ... Whoville Cedar Lake CatalogView Queries extract data from diverse sources Web Services Internet Data Wrapping Common interfaces enable interoperability Clearinghouse City Agency Fed. Agency State Agency Data Vendor Catalog that indexes data, similar to WWW’s html search engines Data Metadata Data Metadata Data Metadata Data Metadata XML

  27. WMS Connector ArcIMS Server WMS Applet AIRNOW Oracle Database Internet/Intranet Chesapeake Bay GIS Project Participants: - National Aquarium - Towson University - Maryland DNR - Chesapeake Bay Program

  28. Web-based Visibility Information System Project with EPA/OEI/EMPACT, Washington University/CAPITA, and Sonoma Technology, Inc Objective: To develop a web-based, near real time visibility and PM2.5 mapping system Phase 1: Map visibility every 6 hours using Naval Research Lab’s Surface Observation Data Phase 2: Incorporate ASOS Data into mapping system Phase 3: Use visibility as a surrogate for mapping PM2.5

  29. Quebec Fires, July 6, 2002 SeaWiFS satellite and METAR surface haze shown in the Voyager distributed data browser Satellite data are fetched from NASA GSFC; surface data from NWS/CAPITA servers SeaWiFS, METAR and TOMS Index superimposed

  30. 5-year EPA Geospatial Architecture Vision Users Data Sources EPA Geo Services Catalog EPA Geo Services States/ Tribes Interoperable Web Tools Others Geo- reporting EPA Enterprise Portal CDX Portal System of Access NSDI Node Servers Geo- processing Feds EPA Industry Mapping Geospatial One-Stop Feds States Geo Data & Tools Indexes EPA Civilian Locals Geo- Metadata Geography Network Red arrows and dotted lines indicate information flow based on standards, such as XML

  31. The Open GIS Consortium (OGC) OGC Vision A world in which everyone benefits fromgeographic information and services made available across any network, application, or platform. OGC Mission To deliverspatial interface specificationsthat are openly available for global use. • The Open GIS Consortium (OGC) is a not-for-profit, international consortium whose 250+ industry, government, and university members work to make geographic information an integral part of information systems of all kinds. • Operates a Specification Development Program that is similar to other Industry consortia (W3C, ISO, etc.). • Also operates an Interoperability Program (IP), a global, innovative, partnership-driven, hands-on engineering and testing program designed to deliver proven specifications into the Specification Development Program.

  32. Open GIS Web Services (OWS) Vision • Creates evolutionary, standards-based framework to enable seamless integration of online geoprocessing and location services. • Future applications assembled from multiple, network-enabled, self-describing geoprocessing and location services. • Break down barriers between real world, information about real world, and users.

  33. OGC Management Team OGC Architecture Team Open GIS Web Services Sponsors, Participants, and Coordinating Organizations • Coordinating Organizations • Urban Logic, CIESIN, NYC DOITT, NYC DEP,FEMA,EPA Region 2 • Sponsors • FGDC • GeoConnections Canada • Lockheed Martin • NASA • NIMA • USGS • US EPA • USACE ERDC • CANRI Participants Compusult CubeWerx Dawn Corp. DLR ESRI Galdos Systems GMU Intergraph Ionic Software Laser-Scan PCI Geomatics Polexis SAIC Social Change Online Syncline YSI University of Alabama Huntsville Vision for NY Common Architecture Working Group Demo Integration Sensor Web Working Group Web Mapping Working Group OGC IP Team BAE, LMCO, NASA, TASC, GST, Image Matters, OGC Staff

  34. Sensor Webs Sensor Webs are web-enabled sensors that can seamlessly exchange data with other web-based applications and can communicate with one another – leading to “dynamic networks” Advances in micro-electronics, nanotechnology, and wireless communication have provided the potential for the development of environmental sensors that will provide major leaps in the available coverage, timeliness, and resolution of monitoring information. Will enable spatially and temporally dense environmental monitoring Sensor Webs will reveal previously unobservable phenomena since they can be placed in areas not previously suitable for monitoring

  35. OWS Sensor Collection Service Clients

  36. Distributed Information System Workshops Distributed Data Dissemination, Access, & Processing (3DAP) July 2001 - Institutional Interoperability Web-based Environmental Information Systems for Global Emission Inventories (WEISGEI) July 2002 - Bring together Information Sciences and Atmospheric Sciences

  37. Future Research Interests • Council on Environmental Cooperation (CEC) - Integration of Emission Inventories for North America • Development of a Fire Emissions Inventory • Web Services (Tools) development • Implementation of sensor webs for air quality studies • Policy impacts of real time environmental information

  38. Data bases Data Description, Format and Interface Standards Web-based Services (Integration, Aggregation, Mapping, Modeling) Sensors Catalogs & Query Tools Browsers / Client Applications Public Industry Gov’t Future Project Interests • Advanced spatial and temporal interpolation techniques (surrogate data) and corresponding estimation error maps • Web services – going beyond placing maps on the Web interoperability • Smart Sensors and Sensor Webs • Information driven environmental management

  39. DIVID vs. Kriging

  40. ASOS Visibility Measurements Lens-to-lens 3.5 feet Prior to 1994, visual range was recorded hourly by human observations Human observations were replaced with automated light scattering instruments of the Automated Surface Observing System (ASOS) The ASOS sensor measures the extinction coefficient as one-minute averages and calculates visual range based on a running 10-minute average of the one-minute measurements projector detector photocell Forward scatter ASOS visibility sensor

  41. ASOS for Air Quality Studies • Currently, available only at a quantized resolution of 18 binned ranges with a visual range upper bound of 10 miles, even though the instrument can provide meaningful data up to 20-30 miles. • In the near future, it is anticipated that ASOS data will be available at their full resolution on the web in “real-time.” • Even at full resolution, they are of limited use in the western U.S. because visual range there is often in excess of 30 miles. • The application to “real-time” mapping (hourly or less) needs to be evaluated

  42. Surface Observations Extinction Coefficient

  43. Network Assessment and Network Design Goal: Develop methods for assessing the performance of air quality monitoring networks using a multi-objective “information value” approach. • Five measures of network performance considered: • Persons/Station measures the number of people in the ‘sampling zone’ of each station. • Spatial coverage measures the geographic surface each station covers. • Estimation uncertainty measures the ability to estimate the concentration at a station location using data from all other stations. • Pollutant Concentration is a measure of the health risk. • Deviation from NAAQS measures the station’s value for compliance evaluation.

  44. Estimation Error, E • The estimation error is determined by • selectively removing each site from the database • estimating the concentration at that site by spatial interpolation • setting the error as the difference between the estimated and measured values, E = Est.-Meas. PM2.5 Error < -3 μg/m3 -3 - -1 μg/m3 -1 - +1 μg/m3 +1 - +3 μg/m3 > +3 μg/m3

  45. PM2.5 Station Sampling Zones • Every location on the map is assigned to the closest monitoring station. • At the boundaries the distance to two stations is equal. • Following the above rules, the ‘sampling zone’ surrounding each site is a polygon. • The area (km2) of each polygon is calculated in ArcView.

  46. Census Tract Population • The population data used for determining a station’s population is from ESRI’s census tract file with estimated 1999 populations. • The centroid of each census tract is associated with a station area. • The census tract populations for all centroids that fall within a station’s area are summed.

  47. PM2.5 Network Performance Rankings Equal weighting of measures Red=High Ranking Blue=Low Ranking

  48. Basketball in German Bundesliga 1994 Bio Sketch B.A. Physics Courses that examined science and technology in the context of other fields such as law, history, and political science M.S. Engineering & Policy Courses covered economic, legal, management, and public policy dimensions of science and technology Thesis examined information flow in environmental policy making and use of “hypermedia” in the policy making process 1992 1993

  49. Bio Sketch • D.Sc. Environmental Engineering (1999) • Mapping Air Quality • OTAG Data Analysis Workgroup • PM-Fine Data Analysis Workgroup • Network Assessment & Design • Taught Geostatistics and GIS Data Analysis Lab • Research Associate (2000) • Integration of Satellite Imagery and Surface-based monitoring data 1995-2000 Center for Air Pollution Impact and Trend Analysis