National NO2 exposure models for measuring its impact on vulnerable people in the US metropolitan areas

Abstract

Epidemiological research requires accurate prediction of the concentrations of air pollutants. In this study, satellite-based estimates (OMI NO₂), distance-weighted models (DWMs), and universal kriging (UK) are applied to land use regression (LUR) to predict annually and monthly averaged NO₂ concentrations in the continental United States. In addition, to assess environmental risk, the relationship between NO₂ concentrations and people potentially exposed to NO₂ within urban areas is explored in 377 metropolitan statistical areas (MSAs). The results of this study show that the application of a combination of OMI NO₂, UK, and DWMs to LUR yielded the highest cross-validated (CV) R² values and the lowest root mean square error of prediction (RMSEP): 82.9% and 0.392 on a square root scale of ppb in the annual model and 70.4-83.5% and 0.408-0.518 on square root scale of ppb in the monthly models, respectively. Moreover, the model presented a spatially unbiased distribution of CV error terms. Models based on LUR provided more accurate NO₂ predictions with lower RMSEP in urban areas than in rural areas. In addition, this study finds that the people living in the urban areas of MSAs, with larger populations and a higher percentage of children under 18 years of age, are likely to be exposed to higher NO₂ concentrations. By contrast, people living in the urban areas of MSAs with a higher percentage of the elderly over 65 years of age are likely to be exposed to lower NO₂ concentrations.

Keywords: Distance-weighted models; NO2 prediction; Potential exposure; Satellite-based estimates; Universal kriging.