Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing

Abstract

Understanding the temporal and spatial variation of air quality (AQ) impact due to congestion pricing is important since the health and economic benefits of air quality improvements depend on the distribution of traffic-related air pollution. Aiming to improve our knowledge of the AQ impacts from congestion pricing, this study integrates a disaggregate agent-based travel demand model with a hyper-local air quality model to examine emissions, air quality, and exposure. Studying congestion pricing schemes in NYC, we find that daily single-occupancy-vehicle trips to the charging area decreases by 14.5% and 24.3% under the low and high charging schemes, respectively. Correspondingly, the PM2.5 concentration decreases by 5–25% in the Central Manhattan areas in the low-toll scenario, and by more than 10% across almost all of New York City areas in the high-toll scenario. Our results indicate non-linear relations between the adaptation of travel behavior and the resulting air quality/exposure impacts.