1. AAAS Science and Technology Policy Fellow
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. Environmental Information Systems
Decision-making
Monitoring
Spatial Analysis
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. estimated continuous surface spatial interpolation
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. Declustered Interpolation
Cluster weight X j R ij i 1 3 2 r j3 j2 j1
CW~ 0.25
CW~ 1.00
Inverse distance weight

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,
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 ( )
Sierra Nevada Mountains are clearly visible with barrier aided estimation

15. Surrogate Aided Interpolation
Summer
Summer
Fine Mass Concentrations
1/r2 Interpolation
Extinction Coefficient
1/r2 Interpolation
Summer
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. A funded EMPACT project had three required components:
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. Distributed Environmental Information Network
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
Minimize
Burden
Maximize
Transparency
Data Users
Data Sources
States
Others
EPA
CDX Portal
Europe EI
CEC EI
GEIA
Web Portal

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

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

27. Chesapeake Bay GIS Project
Participants:
- National Aquarium
- Towson University
- Maryland DNR
- Chesapeake Bay Program
AIRNOW
Oracle Database
Internet/Intranet
ArcIMS Server
WMS Connector
WMS Applet
The NAB, in concert with EPA EMPACT and Towson University Center for GIS, have prototyped a highly integrated Web-GIS-centric information site that integrates environmental data from the National Aquarium, Towson University, Maryland DNR, and the Chesapeake Bay Program.
The main focus of the display is an interactive map frame that displays image or streaming vector data that is served dynamically from a relational database.
The database can also contain dynamic, time-relevant information served as either real-time, or as historical slices of quality-assured data.
Easy to use, well-documented and interpreted GIS tools, like pan, zoom, identify, query, and others provide the general user with enhanced ways of seeing and understanding both the maps as well as the information behind them.

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, METAR and TOMS Index superimposed
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
Geo-
processing
Servers
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)
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.
OGC Vision
A world in which everyone benefits from geographic information and services made available across any network, application, or platform.
OGC Mission
To deliver spatial interface specifications that are openly available for global use.

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. Coordinating Organizations
Open GIS Web Services Sponsors, Participants, and Coordinating Organizations
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
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
Common Architecture
Working Group
Demo Integration
Sensor Web
Working Group
Web Mapping
Working Group
OGC
Management Team
Architecture Team
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. 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
Data Description, Format and Interface Standards
Sensors
Browsers / Client Applications
Catalogs & Query Tools
(Integration, Aggregation, Mapping, Modeling)
Web-based Services
Data
bases
Public
Industry
Gov’t

40. DIVID vs. Kriging

41. 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

42. 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 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

43. Surface Observations Extinction Coefficient

44. 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.

45. Estimation Error, E PM2.5 Error 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
μg/m3
μg/m3
μg/m3
> +3 μg/m3

46. 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.

47. 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.

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

49. Bio Sketch B.A. Physics M.S. Engineering & Policy 1992 1993
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
1994
Basketball in German Bundesliga

50. Center for Air Pollution Impact and Trend Analysis
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
Center for Air Pollution Impact and Trend Analysis

51. Bio Sketch
American Association for the Advancement of Science (AAAS) Fellowship (current) –Washington D.C.
Environmental Monitoring for Public Access and Community Tracking (EMPACT) Program
Data Integration and web mapping projects including:
Open GIS Consortium Standards
Visibility/PM2.5 Web-mapping
Chesapeake Bay GIS

52. PM2.5 Estimates using Visibility Surrogate

53. 1998 Central American Fires
The smoke is transported north into the upper Midwest and to the east. The extinction coefficient is highest further north than the highest TOMS aerosol index.
SeaWiFS, TOMS, and visibility indicate high aerosol concentrations from Central America transported over the central U.S.
Smoke plumes over Central America appear over low elevation terrain, while high elevation regions remain mostly smoke free.