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Integrating Advanced Truck Models into Mobile Source PM2.5 Air Quality Modeling
Perugu, Harikishan C

2013, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
The U.S. Environmental Protection Agency is concerned about fine particulate matter (also called as PM2.5 as the average particle size is less than 2.5 µm) pollution and its ill effects on public health. About 80 percent of the mobile-source PM2.5 emissions are released into the urban atmosphere through combustion of diesel fuel by trucks and are composed of road dust, smoke, and liquid droplets. To estimate the regional or local air quality impact of PM2.5 emissions and also to predict future PM2.5 concentrations, we often utilize atmospheric dispersion models. Application of such sophisticated dispersion models with finer details can provide us the comprehensive understanding of the air quality problem, including the quantitative effect of pollution sources. However, in the current practice the detailed truck specific pollution estimation is not easily possible due to unavailability of a modeling methodology with applied supporting data to predict the link-level hourly truck activity and corresponding emission inventory.
In the first part of this dissertation, we have proposed a methodology for estimating the disaggregated link-level hourly truck activity based on advanced statistics in light of the AERMOD based dispersion/pollution modeling process. This new proposed truck model consists of following sub models: (a) The Spatial Regression and Optimization based Truck-demand (SROT) model is developed to predict truck travel demand matrices using the spatial regression model-output truck volumes at control locations in the study area. (b) The hourly distribution factor model to convert daily truck volumes to hourly truck volumes (c) The Highway Capacity Manual (HCM) based highway assignment model for assigning the hourly truck travel demand matrices. In the second part of dissertation, we have utilized the link-level hourly truck activity to predict the typical 24-hour and maximum 1-hr PM2.5 pollution in urban atmosphere. In this AERMOD based dispersion/pollution modeling process, the gridded hourly emission inventories are estimated based on bottom-up approach using link-level hourly truck activity and emission factors from MOVES model. The proposed framework is tested using the data for the Cincinnati urban area and for four different seasonal weekdays in the analysis year 2010. The comparison with default results has revealed that the proposed models anticipate higher PM2.5 emission contribution from the heavy duty trucks.
The innovation of the current research will be reflective of the following aspects: (a) An enhanced comprehensive truck-related PM2.5 pollution modeling approach and also consistent estimation of heavy-duty trucks apportionment in urban air quality (b) More reliable estimation of spatial and temporal truck activity which takes care of peak hour congestion through application of advanced modeling techniques (c) The gridded emission inventory is better estimated as detailed truck activity and emission rates are used as part of the bottom-up approach (d) Better ground-truth prediction of PM2.5 hot-spots in the modeling area (e) A transferable methodology that can be useful in other regions in the Unites States.
Heng Wei, Ph.D. (Committee Chair)
Hazem Elzarka, Ph.D. (Committee Member)
Mingming Lu, Ph.D. (Committee Member)
Ala Tabiei, Ph.D. (Committee Member)
173 p.

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Perugu, H. (2013). Integrating Advanced Truck Models into Mobile Source PM2.5 Air Quality Modeling. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Perugu, Harikishan. "Integrating Advanced Truck Models into Mobile Source PM2.5 Air Quality Modeling." Electronic Thesis or Dissertation. University of Cincinnati, 2013. OhioLINK Electronic Theses and Dissertations Center. 16 Dec 2017.

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Perugu, Harikishan "Integrating Advanced Truck Models into Mobile Source PM2.5 Air Quality Modeling." Electronic Thesis or Dissertation. University of Cincinnati, 2013. https://etd.ohiolink.edu/

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