Journal Article Epublish 11 citations
PubMed: 39975533 DOI: 10.1021/acsestair.4c00153
TROPOMI Satellite Data Reshape NO Air Pollution Land-Use Regression Modeling Capabilities in the United States.
ACS EST Air 2(2):187-200
Authors: M Omar Nawaz, Daniel L Goldberg, Gaige H Kerr, Susan C Anenberg
Affiliations
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington D.C. 20037, United States.
Abstract
Nitrogen dioxide (NO) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO column observations from the Tropospheric Monitoring Instrument (TROPOMI) have unprecedented spatial resolution and high accuracy over the globe. Land-use regression (LUR) models predict surface-level NO with relevance for epidemiological and environmental justice studies. We use TROPOMI NO columns in a land use regression (LUR) model to improve surface NO concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO data from a legacy satellite instrument (Adj. = 0.54 and MB = 49%; for North America). Removing TROPOMI NO from the LUR decreased by 29.1%, 8.1 times the impact of removing road system information. These findings reveal that novel Earth observing satellites can enhance surface NO surveillance by capturing pollution variation between monitors without relying heavily on other data sources.
MeSH
Air Pollution, Air, Environmental Pollution