Monthly national satellite-based land-use regression of NO2 in the United States for 2000 - 2010

Abstract

Background: Land use regression (LUR) is widely used for estimating within-urban variability in air pollution, typically at the urban level and for long-term averages. LUR techniques have recently been extended to national and continental scales. Aims: Our aim is a LUR for NO2 in the continental US that provides the excellent spatial resolution found in most LURs (~100-m scale), incorporates satellite estimates, covers 100% of US Census Blocks, and provides monthly average concentrations for one decade. Methods: Our work extends a previously-published national LUR for year-2006 in the US. We employ eleven years (2000-2010) of hourly NO2 measurements from US EPA monitors to calculate monthly scaling factors, which account for deviations relative to the reference year. We then create a spatially-varying (inverse-distance weighted) “scaling surface” for each month, to estimate monthly concentration deviations at non-measurement locations.Results: The resulting model captures, on average, 86% of the spatial variability and 72% of the temporal variability with low average bias (26%). Model performance is best at locations near monitors (e.g., spatial R2 = 0.90, mean bias 8% for the tertile with the lowest mean distance to the three nearest monitors). Urban and suburban locations perform moderately better than rural locations at predicting spatial (R2: 0.80 , 0.84, and 0.77, respectively) and temporal (R2: 0.78, 0.76, 0.63) variability and significantly better at predicting absolute concentrations (mean bias: 18%, 10%, 59%). Conclusions: Our approach reliably estimates monthly outdoor NO2 concentration at a spatial resolution capable of capturing within-urban and near-roadway variability in concentrations. We apply this technique to the ~8 million US Census blocks in the contiguous United States to provide a decade (2000 - 2010) of high-resolution monthly NO2 concentration estimates; these data are publicly available.