A global spatial-temporal land use regression model for nitrogen dioxide air pollution
Andrew Larkin, Susan C. Anenberg, Daniel L. Goldberg, Arash Mohegh, Michael Bräuer, Perry Hystad
Abstract
Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO 2 ) air pollution, reducing the annual mean NO 2 level to 10 μg/m 3 (5.3 ppb) and the 24-h mean to 25 μg/m 3 (13.3 ppb). NO 2 is a pollutant of global concern, but it is also a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (~hours). Current models have limited ability to capture both temporal and spatial NO 2 variation and none are available with global coverage. Land use regression (LUR) models that incorporate timevarying predictors (e.g., meteorology and satellite NO 2 measures) and land use characteristics (e.g., road density, emission sources) have significant potential to address this need. Methods: We created a daily Land use regression model with 50 × 50 m 2 spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations. Results: In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO 2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO 2 were consistently the strongest predictors of NO 2 measurements in all regions. Discussion: Daily model estimates from 2005–2019 are available and can be used for global risk assessments and health studies, particularly in countries without NO 2 monitoring.