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APPLYING DATA ASSIMILATION AND ADJOINT SENSITIVITY TO EPIDEMIOLOGICAL AND POLICY STUDIES OF AIRBORNE PARTICULATE MATTER
Impact/Purpose:
(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.
Description:
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.
Record Details:
Record Type:PROJECT(
ABSTRACT
)
Start Date:12/01/2008
Completion Date:11/30/2012
Record ID:
207930
Keywords:
AIR, HEALTH EFFECTS, HUMAN HEALTH, MODELING,
Related Organizations:
Role
:OWNER
Organization Name
:UNIVERSITY OF IOWA
Mailing Address
:Jessup Hall
Citation
:Iowa City
State
:IA
Zip Code
:52242
Role
:OWNER
Organization Name
:UNIVERSITY OF OTTAWA
Mailing Address
:550 Cumberland St.
Citation
:Ottawa
Project Information:
Approach
: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.
Cost
:$899,401.00