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原文摘要

Traffic-related air pollution is a major contributor to respiratory and cardiovascular disease, and travel time disproportionately drives daily exposure. Conventional routing algorithms minimize time or distance, ignoring street-level pollutant variability and leaving travelers exposed to avoidable risk. We develop a bi-objective routing framework that couples a high-resolution AERMOD-based PM2.5 dispersion surface with a shortest-path algorithm. The algorithm uses a deterministic, gradient-based weight-update scheme to enumerate Pareto-optimal paths along the time-exposure frontier. We apply the framework to 790,000 origin-destination pairs on New York City's road network, covering 1.48 million daily commuters. Link-level concentrations differed by a factor of two or more between adjacent parallel streets. Exposure-aware routes reduced cumulative exposure by about 38% at a median travel time increase of about 28%. The algorithm ran in 30 milliseconds per query and recovered more frontier paths than uniform weight sampling (4.5 vs. 2.4). These results show that exposure-aware routing is computationally practical at metropolitan scale and suitable for real-time navigation and infrastructure planning.