A National Land Use Regression Model for NO2 using Street View Imagery and Satellite-based Air Quality Estimates

Abstract

BACKGROUND AND AIM: Characterizing air pollution with high spatiotemporal resolution is important for assessing health impacts and crafting control strategies. Land Use Regression (LUR) is often used to estimate fine-scale ambient concentrations; national-scale LURs typically employ hundreds of geographic features which offer information at neighborhood or regional scales. In this study, we develop national NO₂ models with (1) built environment features derived from street view imagery and (2) satellite estimates of air quality. Both data sources are publicly available and consistent across large geographies. METHODS: We collected NO₂ concentrations at EPA monitors and satellite-based NO₂ tropospheric column abundance during 2007-2015. In a previous study, we successfully developed single city LUR models for measures of particulates using variables extracted from Google Street View (GSV) images. Following the same method, we extracted features from GSV images (n=242836) within 500m of NO₂ monitors using a deep learning model (PSPNet). We used only GSV images collected during the same year of the NO₂ observations. For comparison, we developed two random forest models: 1) a full model with both GSV features and satellite-based estimates of NO₂ and 2) a model with only GSV features. RESULTS:Our full model (GSV + satellite) had good performance (10-fold cross validation [CV] R²: 0.69; mean absolute error [MAE]: 2.04 ppb). When only GSV features within 500m of NO₂ monitors were included in the model, performance decreased (10-fold CV R²: 0.50; MAE: 2.85 ppb), consistent with previous national LUR studies. We plan to improve these models by exploring the impact of GSV image availability and density for monitor-year pairs, expanding the buffer area for collecting images, and adding kriging into our models. CONCLUSIONS:Our findings suggest that street view imagery and satellite estimates of air quality have great potential for building large scale air quality models (national or global) under a unified framework. KEYWORDS: air pollution, modeling, oxides of nitrogen