Grantee Research Project Results
2010 Progress Report: Applying Data Assimilation and Adjoint Sensitivity to Epidemiological and Policy Studies of Airborne Particulate Matter
EPA Grant Number: R833865Title: Applying Data Assimilation and Adjoint Sensitivity to Epidemiological and Policy Studies of Airborne Particulate Matter
Investigators: Stanier, Charles , Oleson, Jacob J. , Carmichael, Gregory R. , Field, R. William , Krewski, Daniel , Kumar, Naresh
Current Investigators: Stanier, Charles , Krewski, Daniel , Carmichael, Gregory R. , Kumar, Naresh , Field, R. William , Oleson, Jacob J.
Institution: University of Iowa , University of Ottawa
EPA Project Officer: Chung, Serena
Project Period: February 1, 2009 through January 31, 2013 (Extended to January 31, 2014)
Project Period Covered by this Report: December 1, 2009 through December 31,2010
Project Amount: $899,401
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text | Recipients Lists
Research Category: Particulate Matter , Air
Objective:
Source-resolved fine particulate matter (PM) concentrations are needed at high spatial and temporal resolutions for epidemiological studies aimed at identifying more- and less-harmful types of PM. Building on recent advances in air quality modeling, data assimilation, and satellite remote sensing, we aim to develop improved estimates of daily source-resolved PM2.5 at 36 km spatial resolution for major U.S. cities, and at 4 km for Chicago from 2001-2004. The estimates will be made available for public download by other researchers, and used in pilot-scale epidemiological studies to demonstrate one of the many applications of source resolved PM2.5 at greater spatial-temporal resolutions.Project objectives are to: (1) evaluate data assimilation (DA) methods for combining the “ground truth” of observations, especially chemically-speciated filter measurements, with the high spatial-temporal resolution of source-oriented air quality models; (2) demonstrate the utility of source-resolved PM2.5 in epidemiological studies and develop best practices for the use of assimilation in conjunction with health studies; and (3) demonstrate the utility of target-oriented and adjoint sensitivity methods for determining the spatial representativeness of samplers, and for visualization of relationships between user-defined air quality/health targets and spatially-resolved emissions.
The data assimilation techniques will integrate a variety of data (STN and IMPROVE speciated filters, FRM PM2.5 mass, MODIS satellite aerosol product) to constrain PM2.5 predictions from the CMAQ air quality model. Data assimilation will be performed using both optimal interpolation and 4-dimensional variational (4Dvar) methods. Eight primary source categories and secondary sulfate will be constrained. The approach utilizes the Chemical Mass Balance technique to estimate source-resolved factor loadings from speciated filters. An adjoint of a reduced version of CMAQ will be developed for 4Dvar calculations. The performance of data assimilation will be quantified by the improvements in predictive ability against high quality campaign measurements. Health effects datasets are (i) the ACS II cohort and (ii) daily census-tract resolved cause-specific mortality from Chicago for 2001-2004. The use of source-resolved vs. total PM2.5, and the effects of intra-urban spatial variation and data assimilation choices, will be investigated by statistical analysis of particulate air pollution in Chicago (at 4 km resolution) and observed health effects. Statistical models will include random spatial effects Cox proportional hazard and Bayesian hierarchical models.
Progress Summary:
The major elements of progress to date were to continue initial progress from year 1 in the following areas: (a) progress on monthly data assimilation using MODIS and CMAQ, with statistically significant improvements in PM2.5 in some months and regions of the U.S.; (b) completion of multiple years of WRF simulations in a large nested grid; (c) refinement of code for automated factor analysis of surface measurements as a preprocessing step to their assimilation; (d) preparation of combined and source resolved model-ready emissions; and (e) identification of model structure for the AQ-Chem adjoint utilizing the CMAQ 5.0 aerosol module (coding is underway).All of these accomplishments were reported in various presentations. The EPA project team gave 7 presentations or posters at multiple meetings this year and several draft manuscripts are underway on the modeling and data assimilation model development.
Future Activities:
The next reporting period will focus on continued forward model runs of CMAQ for total PM2.5, source-resolved PM2.5, and data assimilation of surface and satellite measurements. In addition, development of the CMAQ adjoint will continue.Journal Articles:
No journal articles submitted with this report: View all 26 publications for this projectSupplemental Keywords:
air, health effects, human health, modelingRelevant Websites:
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.