Statistical downscaling of coarse-resolution fine particulate matter predictions over the contiguous United States: model development, evaluation, and implication in health impact assessment

Authors

Kiarash Farzad, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA.
Yang Zhang, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA. Electronic address: ya.zhang@northeastern.edu.
Kai Wang, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA; Lynker Technologies, Leesburg, VA 20176, USA; NOAA/NWS/NCEP/EMC, College Park, MD 20740, USA.
Xiaoyang Chen, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA; Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510640, China.
Daniel L. Goldberg, Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA.
Alexei Lyapustin, NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA.
Yujie Wang, NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA; Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21228, USA.
Michelle L. Bell, School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA.

Document Type

Journal Article

Publication Date

8-26-2025

Journal

The Science of the total environment

Volume

999

DOI

10.1016/j.scitotenv.2025.180302

Keywords

BenMAP; High resolution PM(2.5); Statistical downscaling; WRF-Chem

Abstract

Fine particulate matter (PM) predictions at a high spatial resolution (i.e., neighborhood scale) are critically needed to better understand the health impacts of air pollution, especially at neighborhood scales. This work develops a statistical downscaling approach to predict PM at a 1-km grid resolution over the contiguous United States (CONUS) under baseline and future energy transition scenarios and estimate health benefits utilizing the Environmental Benefits Mapping and Analysis Program (BenMAP). To this end, we incorporate the satellite-based high-resolution aerosol optical depth (AOD), land use data, and PM composition predicted by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at 36-km into daily multi-linear regressions over different climate regions of the CONUS. Compared to the WRF-Chem baseline predictions in 2008-2012, 1-km PM estimates enhance the accuracy by increasing the yearly correlation coefficients from ~0.4 to ~0.8 and reducing normalized mean errors from ~47 % to ~27 %. Future 1-km PM is projected by combining the baseline 5-yr (2008-2012) monthly-averaged training coefficients with high-resolution statistically improved projected AOD and PM subsets from WRF-Chem. BenMAP with WRF-Chem predictions under future energy scenarios shows an average of 2478 fewer deaths per year in 2050 in New York City and Boston due to PM, while the downscaled PM shows less PM reduction and about half the health benefit of the WRF-Chem projections. The downscaling approach is more computationally efficient than running the 3-D air quality model with a 1-km spatial grid resolution. This work uniquely combines WRF-Chem outputs and statistical downscaling to provide high-resolution and high-fidelity PM predictions.

APA Citation

Farzad, Kiarash; Zhang, Yang; Wang, Kai; Chen, Xiaoyang; Goldberg, Daniel L.; Lyapustin, Alexei; Wang, Yujie; and Bell, Michelle L., "Statistical downscaling of coarse-resolution fine particulate matter predictions over the contiguous United States: model development, evaluation, and implication in health impact assessment" (2025). GW Authored Works. Paper 7715.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7715

Department

Environmental and Occupational Health