Dynamic grid-receptor method for regional-level near-road air quality analysis

Although various modeling tools have been developed to predict potential public exposure to harmful transportation emissions at regional scales, computational efficiency remains a critical concern in the design of modeling tools. Thus, most regional applications of microscale dispersion models for traffic-induced pollutants have predicted spatial pollutant concentration profiles with reduced receptor resolution (e.g., 200 m by 200 m resolution), to reduce computing resources and time. However, such simplified model settings were weak in identifying great variations in near-road pollutant concentration profiles. To overcome this challenge, this work proposes a strategic receptor placement method, called dynamic-receptor-grid model (DGRM), that identifies the optimal receptor positions across a region, while preserving the high-resolution pollutant concentration profiles predicted by dense receptor placement. DGRM places receptor positions considering each link's geometry and emissions characteristics. The modeling results suggest that the optimal receptor placement based on DGRM readily approximates the high-resolution PM2.5 concentration profiles.