Health impacts of spatiotemporal variation in PM2.5 concentrations from heavy-duty diesel trucks in Beijing

Abstract

PM2.5 emissions from heavy-duty diesel vehicles (HDDTs) pose a serious threat to human health. Existing researches difficult to capture the spatiotemporal evolution patterns of pollutant concentrations, resulting in the health impacts of pollutant emissions under different spatiotemporal patterns remain unclear. To fill this gap, this study explores the spatiotemporal evolution patterns of PM2.5 concentrations emitted by HDDTs from a spatiotemporal integration perspective, utilizing a three-dimensional spatiotemporal cube model. On this basis, the disparities in health impacts and economic losses under various spatiotemporal patterns are quantified. The results show that: 1) Differences in spatiotemporal patterns of PM2.5 emissions from HDDTs have obvious public health impacts. 55% of the areas exhibit the Oscillating Hot Spot pattern, with health impacts and economic losses accounting for over 90% of the total. High-High Cluster pattern accounts for 26% of all spatiotemporal cluster patterns, but their health and economic consequences attribute over 80% of the total. 2) Short-term exposure to PM2.5 emissions from HDDTs have resulted in substantial human health impacts and economic losses that cannot be ignored. The results of this study can assist decision-makers in identifying spatiotemporal patterns with high health impacts and economic losses of HDDT emissions, and providing decision support for the formulation of health-oriented environmentally sustainable policies.