title: National satellite-based land-use regression: NO2 in the United States.
abstract: Land-use regression models (LUR) estimate outdoor air pollution at high spatial resolution. Previous LURs have generally focused on individual cities. Here, we present an LUR for year-2006 ground-level NO(2) concentrations throughout the contiguous United States. Our approach employs ground- and satellite-based NO(2) measurements, and geographic characteristics such as population density, land-use (based on satellite data), and distance to major and minor roads. The results provide reliable estimates of ambient NO(2) air pollution as measured by the U.S. EPA (R(2) = 0.78; bias = 22%) at a spatial resolution (∼ 30 m) that is capable of capturing within-urban and near-roadway gradients in NO(2). We explore several aspects of temporal (time-of-day; day-of-week; season) and spatial (urban versus rural; U.S. region) variability in the model. Results are robust to spatial autocorrelation, to selection of an alternative input data set, and to minor perturbations in input data (using 90% of the data to predict the remaining 10%). The modeled population-weighted (unweighted) mean outdoor concentration in the United States is 10.7 (4.8) ppb. Our approach could be implemented in other areas of the world given sufficient road network and pollutant monitoring data. To facilitate future use and evaluation of the results, concentration estimates for the ∼ 8 million U.S. Census blocks in the contiguous United States are publicly available via the Supporting Information.
authors.affiliation: University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, USA.
authors.forename: Eric V
authors.initials: EV
authors.lastname: Novotny
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Bechle
authors.forename: Dylan B
authors.initials: DB
authors.lastname: Millet
authors.forename: Julian D
authors.initials: JD
authors.lastname: Marshall
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D016014:Linear Models; D009585:Nitrogen Dioxide / Q000032:analysis*; D058998:Remote Sensing Technology; D018531:Spacecraft; D014481:United States
chemical_list: D000393:Air Pollutants; D009585:Nitrogen Dioxide