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

Daejin Kim, Haobing Liu, Michael O. Rodgers, Randall Guensler

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number103232
JournalTransportation Research Part D: Transport and Environment
Volume105
DOIs
StatePublished - Apr 2022
Externally publishedYes

Funding

This study was supported by the National Center for Sustainable Transportation.

Keywords

  • Air quality conformity
  • Dispersion modeling
  • Dynamic grids
  • Near-road
  • Pollutant concentration
  • Receptor

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