Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

• Type of Material: Article
• Authors: Andrew Larkin, Oregon State University; Jeffrey A. Geddes, Boston University; Randall V. Martin, Dalhousie University; Qingyang Xiao, Emory University; Yang Liu, Emory University; Julian D. Marshall, University of Washington; Michael Brauer, University of British Columbia; Perry Hystad, Oregon State University
• Language: English
• Date: 2017-06-20
• Publisher: American Chemical Society
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 American Chemical Society.
• Final Published Version (URL): http://dx.doi.org/10.1021/acs.est.7b01148
• Title of Journal or Parent Work: Environmental Science and Technology
• ISSN: 0013-936X
• Volume: 51
• Issue: 12
• Start Page: 6957
• End Page: 6964
• Grant/Funding Information: The work of Y. Liu and Q. Xiao was partially supported by the NASA Applied Sciences Program (Grant # NNX11AI53G and NNX16AQ28G, PI: Liu).; This research supported by the Office of the Director, National Institutes of Health under Award Number DP5OD019850.
• Keywords: EXPOSURE ASSESSMENT; Environmental Sciences; Science & Technology; Life Sciences & Biomedicine; Engineering; Environmental Sciences & Ecology; Technology; URBANIZATION; NO2; LUNG-FUNCTION; METAANALYSIS; RISK; Engineering, Environmental
• Research Categories: Engineering, Environmental; Health Sciences, Public Health; Physics, Atmospheric Science

Abstract

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO2) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R2= 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R2 within 2%) but not for Africa and Oceania (adjusted R2 within 11%) where NO2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO2 monitoring data or models.