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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
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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
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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.
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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.”
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Indicators
Data AvailableData Available
Data Data Unavailable at Unavailable at present Timepresent Time
Output Output MeasuresMeasures
Measures of Measures of Human/Eco- Human/Eco-
Health ResponseHealth Response
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
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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
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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
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European Environment Agency - US EPA Ecoinformatics
CooperationTest 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
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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
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Sources of Air Quality Characterization /
Concentration Information
• Ambient air monitoring data
• Air quality modeling output (e.g. CMAQ)
• Satellite data (e.g. MODIS)
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Partnerships in Characterizing Air Quality
Monitoring SatelliteModeling
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Ambient Air Monitoring
• True measure of air quality
• Spatial and Temporal Gaps
• Routinely available information
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Satellite Data• Emerging
source of data(1-10 km grids)
• Spatial and Temporal Gaps
• Algorithm uncertainties(clouds)
• Routinely available data
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Can Satellite Data help assess influences of large wildfires on surface PM2.5 for public health assessments?
Data source: NASA MODIS-Aqua
Alaskan Fire Complexes
June 30, 2004
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18 July 2004 Smoke from Alaskan/Yukon Fires Over U.S.
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19 July 2004 Smoke from Alaskan/Yukon Fires Impact U.S.
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Increase Carbon Mass in In-situ Speciation Trends Network indication of Alaskan Fire Influences on Regional Concentrations surface PM2.5.
Regional PM2.5 Composition Measurementsfor Carbon and Sulfate in US Midwest States
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12 September 2002
MODIS AODLinear Interpolation Surface PM2.5 Monitors
Satellite measurements capture important spatial gradients and meteorology influences, extremely important for public health side of air quality.
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• Estimate of air quality levels
• Good spatial and temporal coverage
• Air Quality Forecasting Emerging source
of routine data
Air Quality Modeling
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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.
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CMAQ Components
• Emissions 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
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CMAQ Modeling System
SMOKE
Anthro and Biogenic Emissions processing
Fifth Generation Mesoscale Model (MM5)
(WRF in 2005)
CMAQ AQ Model-
Chemical-Transport Computations
Met-Chem Interface Processor (MCIP)
Met. data prep
NOAA Weather Observations
EPA Emissions Inventory
Hourly 3-D Gridded Chemical Concentrations
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CMAQ Output
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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
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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.
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Partnership in Air Quality Forecasting
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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/feedbackState & Local AQ forecasters coordinated with EPAPublic and Private Sector AQ constituents AQI: Peak Aug 22AQI: Peak Aug 22
EPA Monitoring NetworkEPA Monitoring Network
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Observed Forecast
7/21/04: 8-hour Peak Ozone
7/22/04: 8-hour Peak Ozone
ForecastObserved
Forecast and Observed Surface Ozone Distributions
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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 48-72 hours Include broader range of significant pollutants
National Air Quality ForecastingPlanned Capabilities
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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
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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
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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
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A d j u s t e d C M A Q ( T o p ) a n d C M A Q ( B o t t o m ) S O 4 - L M 7
Original CMAQ model estimates of SO4 particulate (μg/m3) for July 2001. Observed values are indicated, but model results are not influenced by them.
Adjusted CMAQ model estimates of SO4 particulate (μg/m3) for July 2001. Observed values are used to offset model biases.
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Daily 8-hr Maximum O3 (ppb) June 8, 2001NAMS/SLAMS Monitoring Data and CMAQ
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Combined Predictive O3 (ppb) Surface June 8, 2001
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Daily PM2.5 Concentration (ug/m3) Sept. 12, 2001 EPA FRM Monitoring Data and CMAQ
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Combined PM2.5 (ug/m3) Surface, Sept. 12, 2001
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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
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EPA is Prototyping Algorithms that Use Aerosol Optical Depth in Spatial Predictions
Spatial Interpolation Service
Illustration Slide
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Linking Air Quality and Public Health?
• Do different air quality characterization methods improve capabilities for environmental public health tracking?
?
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Percent increase in monthly mortality per increase in 1 µg/m3 of PM2.5 concentrations (June, 2000).
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Change in monthly percent increase in mortality by adding ozone predictive surface
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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)
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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
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Old 1 hour O3 Std 120 ppm5 million peopleexposed to levelsat or above the standard
New 8 hour O3 Std 80 ppm90 million peopleexposed to levelsat or above the standard
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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
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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.