Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Shan Zhou; Robert J Griffin; Alexander Bui; Aaron Lilienfeld Asbun; Mercedes A Bravo; Claire Osgood; Marie Lynn Miranda

doi:10.1016/j.envres.2022.113418

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Environ Res. 2022 Sep;212(Pt C):113418. doi: 10.1016/j.envres.2022.113418. Epub 2022 May 4.

Authors

Shan Zhou¹, Robert J Griffin², Alexander Bui³, Aaron Lilienfeld Asbun⁴, Mercedes A Bravo⁵, Claire Osgood⁴, Marie Lynn Miranda⁶

Affiliations

¹ Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA. Electronic address: sz62@rice.edu.
² Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA; School of Engineering, Computing and Construction Management, Roger Williams University, Bristol, RI, USA.
³ Department of Civil and Environmental Engineering, Rice University, Houston, TX, USA.
⁴ Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA.
⁵ Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA; Global Health Institute, School of Medicine, Duke University, Durham, NC, USA.
⁶ Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, IN, USA.

Abstract

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM_2.5) and ozone (O₃) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM_2.5 and lower levels of O₃. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM_2.5 and O₃ between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM_2.5 and O₃, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PE_PM2.5 and PE_O3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM_2.5 and O₃ levels decreased and O₃ predictions became more reliable. However, the predictive uncertainty for PM_2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM_2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM_2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O₃ within the same time frame.

Keywords: Differential exposure and reliability; Downscaler uncertainty; Ozone; Particulate matter; Sociodemographic indicators.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Demography
Environmental Exposure / analysis
Income
North Carolina
Ozone* / analysis
Particulate Matter / analysis
Reproducibility of Results
Uncertainty

Substances

Air Pollutants
Particulate Matter
Ozone

Abstract

Publication types

MeSH terms

Substances

Grants and funding