Journal of infection and public health · 2026
A three-year study reveals a strong association between NO2 and COVID-19 in the state of Oklahoma, United States.
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Nair Rahul Raveendran, Nazari Negin, Stephens Patrick R
Аннотация
BACKGROUND: Air pollution has been linked to adverse COVID-19 outcomes. However, studies of the US to-date have generally focused on the US as a whole or large cities on the east and west coasts. Few studies have considered the Midwest, and no comprehensive study of the four most common gaseous pollutants has considered Oklahoma. Oklahoma, a fossil fuel-dependent state with a relatively high respiratory disease burden, provides a unique context to evaluate pollutant impacts on COVID-19 case numbers.
METHODS: We analyzed county-level COVID-19 case numbers (2020-2022) in Oklahoma in relation to satellite-derived concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO), adjusting for population density (PD), county area, and percentage of elderly. We employed boosted regression trees (BRT), random-effects regression trees (RE-EM tree), and hierarchical generalized additive models (GAM) to capture nonlinear associations between COVID-19 case numbers and the predictor variables.
RESULTS: Among the confounding factors, PD had the strongest association with COVID-19 case numbers. Among the four gaseous pollutants we considered, all three models indicated that NO2 had the strongest correlation with COVID-19 case numbers. CO also showed a significant negative linear association with COVID-19 case numbers, whereas positive non-linear relationships were observed for PD, NO2, O3, and area. NO2 had the strongest association with case numbers when PD values ranged from 48 to 75.
CONCLUSION: Our findings provide robust evidence that contemporaneous annual county-level NO2 exposure was associated with realized COVID-19 case numbers in Oklahoma, independent of PD, area, and the proportion of elderly individuals. Our results underscore the health risks of fossil fuel-related NO₂ emissions and demonstrate the value of satellite-derived air pollution data in epidemiological research. Our study indicates that reducing NO₂ exposure may lower vulnerability, and suggest that improving public understanding of the air quality indices provided by various state and federal agencies should be a high priority.
METHODS: We analyzed county-level COVID-19 case numbers (2020-2022) in Oklahoma in relation to satellite-derived concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO), adjusting for population density (PD), county area, and percentage of elderly. We employed boosted regression trees (BRT), random-effects regression trees (RE-EM tree), and hierarchical generalized additive models (GAM) to capture nonlinear associations between COVID-19 case numbers and the predictor variables.
RESULTS: Among the confounding factors, PD had the strongest association with COVID-19 case numbers. Among the four gaseous pollutants we considered, all three models indicated that NO2 had the strongest correlation with COVID-19 case numbers. CO also showed a significant negative linear association with COVID-19 case numbers, whereas positive non-linear relationships were observed for PD, NO2, O3, and area. NO2 had the strongest association with case numbers when PD values ranged from 48 to 75.
CONCLUSION: Our findings provide robust evidence that contemporaneous annual county-level NO2 exposure was associated with realized COVID-19 case numbers in Oklahoma, independent of PD, area, and the proportion of elderly individuals. Our results underscore the health risks of fossil fuel-related NO₂ emissions and demonstrate the value of satellite-derived air pollution data in epidemiological research. Our study indicates that reducing NO₂ exposure may lower vulnerability, and suggest that improving public understanding of the air quality indices provided by various state and federal agencies should be a high priority.