Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM₂.₅ emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM₂.₅ pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM₂.₅ pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. Environmental Protection Agency's (EPA’s) Motor Vehicle Emissions Simulator (MOVES) and atmospheric dispersion modelling system (AERMOD) models. The heavy-duty truck released PM₂.₅ pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using the authors' methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM₂.₅ emissions are caused by trucks and also 21 percent of the urban overall ambient PM₂.₅ concentrations can be attributed to trucks in Cincinnati urban area.

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  • English

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  • Accession Number: 01603453
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 31 2016 12:29PM