Applying Data Assimilation and Adjoint Sensitivity to Epidemiological and Policy Studies of Airborne Particulate MatterEPA Grant Number: R833865
Title: Applying Data Assimilation and Adjoint Sensitivity to Epidemiological and Policy Studies of Airborne Particulate Matter
Investigators: Stanier, Charles , Carmichael, Gregory R. , Field, R. William , Krewski, Daniel , Kumar, Naresh , Oleson, Jacob J.
Institution: University of Iowa , University of Ottawa
EPA Project Officer: Ilacqua, Vito
Project Period: February 1, 2009 through January 31, 2013 (Extended to January 31, 2014)
Project Amount: $899,401
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text | Recipients Lists
Research Category: Health Effects , Particulate Matter , Air
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.
(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. (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 proposed data assimilation 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.
The proposed activities will enable more efficient public policies through development of techniques that identify the most harmful PM fractions and sources. Specific results include: (1) assessment of the influence on PM2.5 concentration estimates when the proposed DA is used; (2) assessment of the changes in relative risks and confidence intervals in health effect studies due to selection of the source-apportionment method; (3) quantification of computational effort; and (4) demonstration of the utility of target-oriented analysis to air pollution policy questions.