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

Kiarash Farzad¹, Yang Zhang², Kai Wang³, Xiaoyang Chen⁴, Daniel L Goldberg⁵, Alexei Lyapustin⁶, Yujie Wang⁷, Michelle L Bell⁸

Affiliations

¹ Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA.
² Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA. Electronic address: ya.zhang@northeastern.edu.
³ 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.
⁴ 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.
⁵ Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA.
⁶ NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA.
⁷ NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA; Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21228, USA.
⁸ School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA.

PMID: 40865438
DOI: 10.1016/j.scitotenv.2025.180302

Free article

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

Kiarash Farzad et al. Sci Total Environ. 2025.

Free article

. 2025 Oct 15:999:180302.

doi: 10.1016/j.scitotenv.2025.180302. Epub 2025 Aug 26.

Authors

Kiarash Farzad¹, Yang Zhang², Kai Wang³, Xiaoyang Chen⁴, Daniel L Goldberg⁵, Alexei Lyapustin⁶, Yujie Wang⁷, Michelle L Bell⁸

Affiliations

¹ Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA.
² Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA. Electronic address: ya.zhang@northeastern.edu.
³ 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.
⁴ 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.
⁵ Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA.
⁶ NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA.
⁷ NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA; Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21228, USA.
⁸ School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA.

PMID: 40865438
DOI: 10.1016/j.scitotenv.2025.180302

Abstract

Fine particulate matter (PM_2.5) 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_2.5 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_2.5 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_2.5 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_2.5 is projected by combining the baseline 5-yr (2008-2012) monthly-averaged training coefficients with high-resolution statistically improved projected AOD and PM_2.5 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_2.5, while the downscaled PM_2.5 shows less PM_2.5 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_2.5 predictions.

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

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

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