Work

TROPOMI Satellite Data Reshape NO 2 Air Pollution Land-Use Regression Modeling Capabilities in the United States

Year: 2025

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide Nitrogen dioxide (NO 2 ) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO 2 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 2 with relevance for epidemiological and environmental justice studies. We use TROPOMI NO 2 columns in a land use regression (LUR) model to improve surface NO 2 concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. R 2 = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO 2 data from a legacy satellite instrument (Adj. R 2 = 0.54 and MB = 49%; for North America). Removing TROPOMI NO 2 from the LUR decreased R 2 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 2 surveillance by capturing pollution variation between monitors without relying heavily on other data sources.

Source: ACS ES&T Air

Authors: M. Omar Nawaz, Daniel L. Goldberg, Gaige H. Kerr, Susan C. Anenberg

Institutions: Milken Institute, George Washington University

Language: English

Topic: Air Quality and Health Impacts

Subfield: Health, Toxicology and Mutagenesis

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: National Aeronautics and Space Administration, National Institutes of Health, National Oceanic and Atmospheric Administration