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National Satellite-Based Land-Use Regression: NO2 in the United States

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journal contribution
posted on 15.05.2011, 00:00 by Eric V. Novotny, Matthew J. Bechle, Dylan B. Millet, Julian D. Marshall
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 NO2 concentrations throughout the contiguous United States. Our approach employs ground- and satellite-based NO2 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 NO2 air pollution as measured by the U.S. EPA (R2 = 0.78; bias = 22%) at a spatial resolution (∼30 m) that is capable of capturing within-urban and near-roadway gradients in NO2. 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.

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