Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements
Junshi Xu, Mingqian Zhang, Arman Ganji, Keni Mallinen, An Wang, Marshall Lloyd, Alessya Venuta, Leora Simon, Junwon Kang, James Gong, Yazan Zamel, Scott Weichenthal, Marianne Hatzopoulou
Abstract
(mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.