1. Future Outlook for Air Quality Forecasting in the United States
Real Time Air Pollution Data Exchange and Forecast Workshop
Copenhagen, Denmark
April 7-8, 2005
Gary Foley, Director, USEPA,
National Exposure Research Laboratory

2. Presentation Overview
U.S. motivation in linking air pollution to health
US National Ambient Air Quality Standards are health based.
EPA’s Report on the Environment
Center for Disease Control - National Environmental Public Health Tracking Network
PHASE Project
EEA/US EPA ecoinformatics test bed
Current data sources and their challenges
Ambient monitoring
Air quality modeling
Satellite data
Current data assimilation research
Fusing modeling and ambient data
Satellite interpolation
Future directions

3. EPA’s Draft Report on the Environment 2003
Measuring the success of policies and programs to protect health and the environment (Accountability)
Describes what EPA knows - and doesn’t know
Identifies measures/indicators to report on the status and trends and, where possible, their impacts on human health and the environment; and,
Discusses the challenges that the nation faces in improving these measures.

4. What does the Report on the Environment say about Air?
“In general, there are some very good measures of outdoor air quality.”
However “There is a need for measures to compare actual and predicted human health and ecological effects related to exposure to air pollutants.”

5. Indicators Indicators Data Available Data Unavailable at present Time
Level 6
Improved Human or ecological health
Level 5
Reduced exposure or body burden
Level 4
Improved ambient conditions
Level 3
Reduced amount or toxicity of emissions
Level 2
Actions and behavioral changes by regu-lated com-munity
Level 1
Actions by EPA, State, and other regulatory agencies
Indicators
Indicators
Data Available
Data Unavailable at present Time
Output Measures
Measures of Human/Eco- Health Response

7. The Public Health Air Surveillance Evaluation (PHASE) Project
Collaboration between the US EPA and the Centers for Disease Control (CDC)
Develop and evaluate alternative air quality characterization methods for environmental public health tracking
Air Pollutants
Ozone and Particulate Matter
Health Endpoints
Asthma and Cardio Vascular Disease
Working with 3 CDC State Partners
Maine
New York
Wisconsin

8. PHASE Objectives
Provide enhanced air quality information for use in Environmental Public Health Tracking
Supplement the ambient air monitoring network data with emerging data sources
Satellites
Air Quality Modeling (Forecasts)
Improved spatial and temporal coverage
Use statistical techniques to “combine” data from the various sources
Reduce uncertainty in monitoring gaps
Produce information that can be ROUTINELY used to track potential relationships between public health and air quality

9. European Environment Agency - US EPA Ecoinformatics Cooperation
Test bed project
Evaluate the value and utility of advanced metadata management and semantic concept management
Result of Brussels, September 2004 meeting
Air quality and human health outcomes first subject area
EEA focus
Ljubljana, Slovenia and Leicester, U.K.
U.S. focus
Two eastern cities to be determined
Federal, state, public/private partnerships
Air accountability framework development and testing

10. The Air Quality Characterization Challenge and Steps Being Taken in the U.S.
Issue: Cannot monitor at all locations, but want to know air pollution characteristics and concentrations everywhere.
To better evaluate air quality attainment directly
To better relate to health and environmental improvements
Solution: Combined predictive approaches taking advantages of different data strengths

11. Sources of Air Quality Characterization / Concentration Information
Ambient air monitoring data
Air quality modeling output (e.g. CMAQ)
Satellite data (e.g. MODIS)

12. Partnerships in Characterizing Air Quality
Modeling
Satellite
Monitoring

13. Ambient Air Monitoring
True measure of air quality
Spatial and Temporal Gaps
Routinely available information

14. Satellite Data Emerging source of data (1-10 km grids)
Spatial and Temporal Gaps
Algorithm uncertainties (clouds)
Routinely available data

15. Alaskan Fire Complexes June 30, 2004
Can Satellite Data help assess influences of large wildfires on surface PM2.5 for public health assessments?
Alaskan Fire Complexes June 30, 2004
Data source: NASA MODIS-Aqua

16. 18 July 2004 Smoke from Alaskan/Yukon Fires Over U.S.
The image is from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra satellite ~10:30 local overpass. In the image a large area of haze extends from Ontario, across the mid-West, and into the Southern United States. The main source of this haze is smoke from Alaskan wildfires.
We can look at the MODIS aerosol optical depth to get a quantitative understanding of the intensity of the haze. However, we have very little information to help understand the vertical component of the haze layer.

17. 19 July 2004 Smoke from Alaskan/Yukon Fires Impact U.S.
The image is from MODIS on board NASA's Terra satellite July 19, We can see the hazy mass that extended over the central US being transported eastward. If we look at regional concentrations PM speciation data we can begin to see how the large scale haze from the smoke impacted the surface.

18. Regional PM2.5 Composition Measurements for Carbon and Sulfate in US Midwest States
Increase Carbon Mass in In-situ Speciation Trends Network indication of Alaskan Fire Influences on Regional Concentrations surface PM2.5.

19. 12 September 2002 Linear Interpolation Surface PM2.5 Monitors
MODIS AOD
Linear Interpolation Surface PM2.5 Monitors
Satellite measurements capture important spatial gradients and meteorology influences, extremely important for public health side of air quality.

20. Air Quality Modeling Estimate of air quality levels
Good spatial and temporal coverage
Air Quality Forecasting
Emerging source of routine data

21. The Community Multiscale Air Quality Model (CMAQ)
Developed in EPA’s Office of Research and Development (ORD)
Reflects State-of-the-Science
“One atmosphere" model
Treats multiple pollutants simultaneously at several spatial and temporal scales
regional to urban to “neighborhood” scales
tropospheric ozone, fine particles, air toxics, acid deposition, and visibility.

22. CMAQ Components Emissions Model Meteorological Model
Man-made and natural emissions into the atmosphere
Meteorological Model
Description of atmospheric states and motions
Chemical Transport Model
Simulation of chemical transformation, transport and fate in the atmosphere

23. CMAQ Modeling System Fifth Generation Mesoscale Model (MM5) SMOKE
(WRF in 2005)
NOAA Weather Observations
EPA Emissions Inventory
Met-Chem Interface Processor (MCIP)
Met. data prep
SMOKE
Anthro and Biogenic Emissions processing
CMAQ AQ Model-
Chemical-Transport Computations
Hourly 3-D Gridded Chemical Concentrations

24. CMAQ Output

25. Connection to Environmental Public Health Tracking
CMAQ Applications
Current applications
Air Quality Planning
National Air Toxics Assessments
Fine or “neighborhood” scale modeling for exposure assessment
Emerging applications
Air Quality Forecasting
Air Pollution Climatology
Connection to Environmental Public Health Tracking

26. Air Quality Forecasting another linkage of air quality characterization and public health
Current applications of air quality models in the regulatory framework do not generate routinely available modeling results.
However, the EPA-NOAA Air Quality Forecasting applications will generate routinely available data on various pollutants on different temporal and spatial scales.

27. Partnership in Air Quality Forecasting

28. EPA Monitoring Network
National Air Quality Forecast Capability Initial Operational Capability (IOC)
Linked numerical prediction system
Operationally integrated on NOAA/NWS’s supercomputer
NWS mesoscale model: Eta-12
NOAA/EPA community model for AQ: CMAQ
Observational Input:
NWS weather observations
EPA emissions inventory
Gridded forecast guidance products
Delivered to NWS Telecommunications Gateway and EPA for users to pull 2x daily
Verification basis
EPA ground-level
ozone observations
Customer outreach/feedback
State & Local AQ forecasters coordinated with EPA
Public and Private Sector AQ constituents
EPA Monitoring Network
AQI: Peak Aug 22

29. Forecast and Observed Surface Ozone Distributions
7/21/04: 8-hour Peak Ozone
7/22/04: 8-hour Peak Ozone
7/21/04 12z 48 hour forecast. Good agreement of peak 8 hour predicted vs observed ozone. Some overprediction over Western pa and Atlanta on 7/21.
Green=good, yellow=moderate, orange=high, red=extreme.

30. National Air Quality Forecasting Planned Capabilities
Current: 1-day forecast guidance for ozone
Developed and deployed initially for Northeastern US, September 2004
Deploy Nationwide by 2009
Intermediate (5-7 years):
Develop and test capability to forecast particulate matter concentration
Particulate size < 2.5 microns
Longer range (within 10 years):
Extend air quality forecast range to hours
Include broader range of significant pollutants

31. Current PHASE Project
First attempt at routine association of air quality and public health indicators
Collaboration of US EPA and CDC, and 3 CDC State partners; Maine, New York, and Wisconsin
Demonstrate use of spatial prediction using combined sources of data
Ambient air monitoring data (PM2.5 and O3)
Air quality numerical model output
Satellite data, e.g. MODIS aerosol optical depth

32. Approach in Fusing Monitoring Data and Modeling Outputs
Monitoring data and model output can be used simultaneously to predict the pollutant surface
Draw on strengths of each data source:
Give more weight to precise monitoring data in areas where monitoring exists
Rely on model output in non-monitored areas
Model underlying spatial dependence and measurement errors of each source
“Blind Combining” increases likelihood of incorrect decisions
Leads to more accurate predictions and prediction errors

33. Current work combining monitoring , modeling, and satellite data
Combining monitoring data with CMAQ output; two approaches - Adjusting model outputs with monitoring data (annual, species specific)
- Fusing data sets with Bayesian techniques (daily, pollutant concentrations for PHASE)
Improved air quality “surface.”
Considerably lower spatial interpolation errors
Satellite observations show potential for aerosol spatial predictions

34. Adjusted CMAQ model estimates of SO4 particulate (μg/m3) for July 2001
Adjusted CMAQ model estimates of SO4 particulate (μg/m3) for July Observed values are used to offset model biases.
Original CMAQ model estimates of SO4 particulate (μg/m3) for July Observed values are indicated, but model results are not influenced by them.

35. Daily 8-hr Maximum O3 (ppb) June 8, 2001 NAMS/SLAMS Monitoring Data and CMAQ

36. Combined Predictive O3 (ppb) Surface June 8, 2001

37. Daily PM2. 5 Concentration (ug/m3) Sept
Daily PM2.5 Concentration (ug/m3) Sept. 12, EPA FRM Monitoring Data and CMAQ

38. Combined PM2.5 (ug/m3) Surface, Sept. 12, 2001

40. Combined Model Validation using Daily STN PM2.5 Monitoring Data
For each day of 2001:
Use combined Bayesian approach based on CMAQ and FRM data to predict PM2.5 at STN sites
Use standard kriging approach based on FRM data to predict PM2.5 at STN sites
Calculate root mean squared prediction error (RMSPE) for each approach
RMSPE = square root{sum of squared (prediction-STN) differences across all sites}
Calculate and compare RMSPE for each prediction approach

41. Spatial Interpolation Service
EPA is Prototyping Algorithms that Use Aerosol Optical Depth in Spatial Predictions
Spatial Interpolation Service
Illustration Slide

42. Linking Air Quality and Public Health?
Do different air quality characterization methods improve capabilities for environmental public health tracking? ?

43. Percent increase in monthly mortality per increase in
1 µg/m3 of PM2.5 concentrations (June, 2000).

44. Change in monthly percent increase in mortality by adding ozone predictive surface

45. PHASE Process
EPA has provided CDC State partners with alternative measures to characterize air quality (End of 2004)
Ambient monitoring
Air quality modeling
Satellite data
Combinations of the above
State partners “link” the alternative measures to available health surveillance data (Early 2005)
Evaluate and compare the use various air quality characterization methods (End of 2005)

46. ES&T Nov 2003
“Accountability Within New Ozone Standards”, ES&T, Nov. 1, 2003
Today, it is possible to
Model of Population Exposures changes likely to result from AQ Control Measures
Design Accountability Programs that measure actual changes

48. New 8 hour O3 Std
80 ppm
90 million people
exposed to levels
at or above the
standard
Old 1 hour O3 Std
120 ppm
5 million people
exposed to levels
at or above the
standard

49. Future Analyses
Assess improved predictive ability by including MODIS satellite data
Combining monitoring, modeling, and satellite data into fused air quality surface
Summer 2005
Extend fused surface validations to other independent networks
IMPROVE (PM2.5) and CASTNet (rural O3)
2005
Conduct sensitivity analysis
Compare surfaces using 12km vs 36 km CMAQ grids
2005-6

50. Summary
EPA is seeking better ways to measure the ultimate success of its regulatory programs.
CDC’s Environmental Public Health Tracking program is seeking compatible air quality data to inform public health actions.
There are new possibilities for improving the way we characterize air quality and exposure.
EPA is building partnerships with public and private sectors
EPA is building a database of high-resolution spatial maps of air quality over the U.S.
EPA would like to work with EU in exploring the linkage between better air quality indicators and forecasts and human exposure and health.