Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 55, Issue 4, Pages 2695-2704Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.0c05572
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Funding
- National Science Foundation [CBET 1943705]
- Virginia Tech BioBuild Interdisciplinary Graduate Education Program
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By leveraging Google Street View imagery and deep learning models, we developed LUR models for predicting street-level particulate air pollution, achieving higher spatial resolution and better performance compared to traditional LUR models.
Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from similar to 52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R-2 (10-fold CV R-2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained -50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.
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