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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. Presentation Overview.

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Future outlook for air quality forecasting in the united states

Future Outlook for

Air Quality Forecastingin 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


Presentation overview
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


  • Epa s draft report on the environment 2003
    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.


    What does the report on the environment say about air
    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.”


    Indicators
    Indicators

    Level 1

    Actions by EPA, State, and other regulatory agencies

    Level 2

    Actions and behavioral changes by regu-lated com-munity

    Level 3

    Reduced amount or toxicity of emissions

    Level 4

    Improved ambient conditions

    Level 5

    Reduced exposure or body burden

    Level 6

    Improved Human or ecological health

    Data Available

    Data Unavailable at present Time

    Measures of Human/Eco- Health Response

    Output Measures

    Indicators


    The public health air surveillance evaluation phase project
    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


    Phase objectives
    PHASE Objectives Project

    • 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


  • European environment agency us epa ecoinformatics cooperation
    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


    The air quality characterization challenge and steps being taken in the u s
    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


    Sources of air quality characterization concentration information
    Sources of Air Quality Characterization / Concentration Information

    • Ambient air monitoring data

    • Air quality modeling output (e.g. CMAQ)

    • Satellite data (e.g. MODIS)


    Partnerships in characterizing air quality

    Modeling Information

    Satellite

    Partnerships in Characterizing Air Quality

    Monitoring


    Ambient air monitoring
    Ambient Air Monitoring Information

    • True measure of air quality

    • Spatial and Temporal Gaps

    • Routinely available information


    Satellite data
    Satellite Data Information

    • Emerging source of data(1-10 km grids)

    • Spatial and Temporal Gaps

    • Algorithm uncertainties(clouds)

    • Routinely available data


    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


    18 July 2004 Smoke from Alaskan/Yukon Fires Over U.S. on surface PM2.5 for public health assessments?


    19 July 2004 Smoke from Alaskan/Yukon Fires Impact U.S. on surface PM2.5 for public health assessments?


    Regional PM2.5 Composition Measurements on surface PM2.5 for public health assessments?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.


    12 September 2002 on surface PM2.5 for public health assessments?

    Linear Interpolation Surface PM2.5 Monitors

    MODIS AOD

    Satellite measurements capture important spatial gradients and meteorology influences, extremely important for public health side of air quality.


    Air quality modeling
    Air Quality Modeling on surface PM2.5 for public health assessments?

    • Estimate of air quality levels

    • Good spatial and temporal coverage

    • Air Quality Forecasting

      • Emerging source of routine data


    The community multiscale air quality model cmaq
    The Community Multiscale Air Quality Model (CMAQ) on surface PM2.5 for public health assessments?

    • 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.


    Cmaq components
    CMAQ Components on surface PM2.5 for public health assessments?

    • 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


    CMAQ Modeling System on surface PM2.5 for public health assessments?

    Fifth Generation Mesoscale Model (MM5)

    (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


    Cmaq output
    CMAQ Output on surface PM2.5 for public health assessments?


    Cmaq applications
    CMAQ Applications on surface PM2.5 for public health assessments?

    • 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


    Air quality forecasting another linkage of air quality characterization and public health
    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.


    Partnership in Air Quality Forecasting characterization and public health


    National air quality forecast capability initial operational capability ioc

    AQI: Peak Aug 22 characterization and public health

    EPA Monitoring Network

    National Air Quality Forecast CapabilityInitial 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


    7/21/04: 8-hour Peak Ozone characterization and public health

    Forecast

    Observed

    7/22/04: 8-hour Peak Ozone

    Forecast

    Observed

    Forecast and Observed Surface Ozone Distributions


    National air quality forecasting planned capabilities
    National Air Quality Forecasting characterization and public health 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 48-72 hours

    • Include broader range of significant pollutants


    Current phase project
    Current PHASE Project characterization and public health

    • 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


    Approach in fusing monitoring data and modeling outputs
    Approach in Fusing Monitoring Data and Modeling Outputs characterization and public health

    • 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


    Current work combining monitoring modeling and satellite data
    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


  • Adjusted CMAQ model estimates of SO data 4 particulate (μg/m3) for July 2001. Observed values are used to offset model biases.

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


    Daily 8 hr maximum o 3 ppb june 8 2001 nams slams monitoring data and cmaq
    Daily 8-hr Maximum O data 3 (ppb) June 8, 2001NAMS/SLAMS Monitoring Data and CMAQ


    Combined predictive o 3 ppb surface june 8 2001
    Combined Predictive O data 3(ppb) Surface June 8, 2001


    Daily pm 2 5 concentration ug m 3 sept 12 2001 epa frm monitoring data and cmaq
    Daily PM data 2.5 Concentration (ug/m3) Sept. 12, 2001 EPA FRM Monitoring Data and CMAQ


    Combined pm 2 5 ug m 3 surface sept 12 2001
    Combined PM data 2.5(ug/m3) Surface, Sept. 12, 2001


    Combined model validation using daily stn pm 2 5 monitoring data
    Combined Model Validation using Daily STN PM data 2.5 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


    Epa is prototyping algorithms that use aerosol optical depth in spatial predictions
    EPA is Prototyping Algorithms that Use data Aerosol Optical Depth in Spatial Predictions

    Spatial Interpolation Service

    Illustration Slide


    Linking air quality and public health

    ? data

    Linking Air Quality and Public Health?

    • Do different air quality characterization methods improve capabilities for environmental public health tracking?


    Percent increase in monthly mortality per increase in data

    1 µg/m3 of PM2.5 concentrations (June, 2000).



    Phase process
    PHASE Process ozone predictive surface

    • 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)


    Es t nov 2003
    ES&T Nov 2003 ozone predictive surface

    “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


    New 8 hour O3 Std ozone predictive surface

    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


    Future analyses
    Future Analyses ozone predictive surface

    • 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


  • Summary
    Summary ozone predictive surface

    • 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.


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