Environmental Information Systems for Monitoring, Assessment, and Decision-

making

Stefan FalkeAAAS Science and Technology Policy Fellow

U.S. EPA - Office of Environmental Information

Environmental Information Systems

Monitoring

Analysis & Assessment

Decision-making

Delivery/Presentation Storage/Description

Spatial Analysis

Environmental Information Systems

Monitoring

Analysis & Assessment

Decision-making

Delivery/Presentation Storage/Description

Environmental Information Systems

Monitoring

Analysis & Assessment

Decision-making

Delivery/Presentation Storage/Description

Web-based Information Systems

Environmental Information Systems

Monitoring

Analysis & Assessment

Decision-making

Delivery/Presentation Storage/Description

SensorWebs

Mapping Air Quality

point monitoring data

spatial interpolation

ci is the estimated concentration at location i

n is the number of monitoring sitescj is the concentration at monitoring site j

wij is the weight assigned to monitoring site j

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

Spatial Interpolation with Monitor Clusters

 

Declustered weighting shows the proper allocation of the 1/3 weight to the cluster of sites.

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.

Standard interpolation applies equal weight; each site has 1/3 of the weight on the estimate at i.

Declustered Interpolation

ijijij CWDw

i

pij

pij

ijR

RD

Inverse distance weight

ij

jkijk R

r

nCW

1Cluster weight

X j

Rij

i

X1

X3

X2

r j3

r j2r j1 X j

Rij

i

X1

X3

2

r j3

r j2

r j1

X

CW~ 0.25 CW~ 1.00

Variance Aided Mapping

11

n

xxV

n

ii

j

1 jijijij VCWDw

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.

Variance Weighting Example

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.

Interpolation weights using distance and temporal variance of daily maximum ozone concentrations, 1991-1995

Estimated Ozone Concentrations, 1991-1995

Estimation Error

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

3.5

4

4.5

5

5.5

6

6.5

7

0.150.30.450.60.750.9

Clusterness

Me

an

Ab

solu

te E

rro

r (p

pb

)

Kriging

DIVID

ID

most clustered least clustered

Barrier Aided Estimation

• Vertical Flow Barriers (Scale Height)

• Horizontal Flow Barriers (Mountains)

Pollutants are “trapped” in valleys while mountain tops have low pollutant concentrations

PM10 in California

Without Barriers With Barriers

AIRS PM10 data (1994-1996)

Sierra Nevada Mountains are clearly visible with barrier aided estimation

Surrogate Aided Interpolation

Fine Mass Concentrations1/r2 Interpolation

Extinction Coefficient1/r2 Interpolation

Fine Mass Bext1/r2 Interpolation

Bext Aided FM = Fine Mass Bext

x Bext

1991-1995Summer

1991-1995Summer

1991-1995Summer

1991-1995Summer

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.

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.

2000 Saharan Dust

A massive dust storm transports dust off the west coast of Africa into the Atlantic Ocean and across the Canary

Islands.

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.

Future Research Interests

•Spatial and temporal interpolation

•Uncertainty / Estimation Error Maps

•Integration of surface and satellite data

•Development of web-based spatio-temporal tools

AAAS Fellowship Program

http://fellowships.aaas.org

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

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.

Environmental Monitoring for Public Access and Community Tracking (EMPACT)

Data Analysis & Visualization Data Analysis & Visualization

Information Dissemination Technology Information Dissemination Technology (Internet, Kiosks, Newspaper, TV, etc.)(Internet, Kiosks, Newspaper, TV, etc.)

Real Time Environmental MonitoringReal Time Environmental Monitoring

Assists communities in providing sustainable public access to Assists communities in providing sustainable public access to environmental monitoring data and information that are environmental monitoring data and information that are clearly-communicated, available in near real-time, useful, and clearly-communicated, available in near real-time, useful, and accurateaccurate

A funded EMPACT project had three required components:

EMPACT Project Locations

Distributed Environmental Information Network

Data Users

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

MinimizeBurden

Maximize Transparency

Data Sources

States

Others

EPACDX Portal

GEIAWeb Portal

EuropeEI

CECEI

Network

Data and Tool Description

DataData

Description(Metadata)

Tools

Tool Description

XML

WebServices

Wrappers

Internet

Data Vendor City Agency State Agency Fed. AgencyClearinghouse

Whoville Cedar Lake

Whoville Cedar Lake

ParcelsRoadsImagesBoundaries ...

Integrated View

CatalogView

DataMetadata

DataMetadata

DataMetadata

DataMetadata

Catalog thatindexes data,

similar to WWW’s html search engines

Common interfaces enable interoperability

Queries extractdata from diversesources

Distributed Environmental Information Systems

XML

Data Wrapping

Web Services

Chesapeake Bay GIS Project

AIRNOWOracle Database

Internet/Intranet

ArcIMS ServerArcIMS ServerWMS Connector

WMS Applet

Participants:- National Aquarium - Towson University - Maryland DNR - Chesapeake Bay Program

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

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

States/Tribes

Interoperable

EPAGeo

Services

Geo-processing

5-year EPA Geospatial Architecture Vision

Users

• • •

Servers

Data Sources

Feds

Others

EnterprisePortal

CDX Portal

System of Access

NSDI Node

Geospatial One-Stop

Feds

Industry

States

CivilianLocals

Mapping

Geo-Metadata

Geo Data &Tools Indexes

Geo-reporting

EPA

EPAGeo

ServicesCatalog

EPA

EPA

Web

Tools

Red arrows and dotted lines indicate information flow based on standards, such as XML

Geography Network

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 VisionOGC Vision

A world in which A world in which everyone benefits everyone benefits

fromfromgeographic geographic

information and information and services made services made

available available across any network, across any network,

application, or application, or platform.platform.

OGC MissionOGC Mission

To deliverTo deliverspatial interface spatial interface

specificationsspecificationsthat are openly that are openly

available for global available for global use.use.

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.

Open GIS Web Services Sponsors, Participants, and Coordinating Organizations

Demo Integration

OGC IP TeamOGC IP Team

OGCOGCManagement TeamManagement Team

OGCOGCArchitecture TeamArchitecture Team

Common ArchitectureWorking Group

Web MappingWorking Group

Sensor WebWorking Group

ParticipantsCompusultCubeWerx

Dawn Corp.DLRESRI

Galdos SystemsGMU

IntergraphIonic Software

Laser-ScanPCI Geomatics

PolexisSAIC

Social Change Online

SynclineYSI

University of Alabama

HuntsvilleVision for NY

SponsorsSponsors

FGDCFGDCGeoConnections CanadaGeoConnections CanadaLockheed MartinLockheed MartinNASANASANIMANIMAUSGSUSGSUS EPAUS EPAUSACE ERDCUSACE ERDCCANRICANRI

BAE, LMCO, NASA, TASC, GST, Image Matters, OGC Staff BAE, LMCO, NASA, TASC, GST, Image Matters, OGC Staff

Coordinating OrganizationsCoordinating OrganizationsUrban Logic, CIESIN, NYC DOITT, NYC DEP,Urban Logic, CIESIN, NYC DOITT, NYC DEP,

FEMA,FEMA, EPA Region 2EPA Region 2

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

OWS Sensor Collection Service Clients

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

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

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 D

escription

, Form

at and

Interface

Stan

dard

s

Sensors

Brow

sers / Client

Applications

Catalogs &

Query

Tools W

eb-based Services

(Integration

, Aggregation

, Map

pin

g, M

odelin

g)

Databases

Public

Industry

Gov’t

DIVID vs. Kriging

ASOS Visibility Measurements

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

Forward scatter ASOS visibility sensor

photocell

detectorprojector

Lens-to-lens3.5 feet

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

Surface Observations Extinction Coefficient

Network Assessment and Network Design

Goal: Develop methods for assessing the performance of air quality monitoring networks using a multi-objective “information value” approach.

•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.

Five measures of network performance considered:

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

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.

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.

PM2.5 Network Performance Rankings

Equal weighting of measures

Red=High Ranking Blue=Low Ranking

Bio Sketch

B.A. PhysicsCourses 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

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

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

PM2.5 Estimates using Visibility Surrogate

1998 Central American Fires

SeaWiFS, TOMS, and visibility indicate high aerosol concentrations from Central America transported over the central U.S.

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.

Smoke plumes over Central America appear over low elevation terrain, while high elevation regions remain mostly smoke free.