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contributor authorRajiv Ganguly
contributor authorDivyansh Sharma
contributor authorPrashant Kumar
contributor authorB. R. Gurjar
date accessioned2022-01-30T22:38:57Z
date available2022-01-30T22:38:57Z
date issued4/1/2021
identifier other(ASCE)HZ.2153-5515.0000574.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4269343
description abstractVehicular pollution is one of the major sources of air pollution in urban locales that have reportedly elevated concentrations of air pollutants. This study aims to examine the performance of two air quality dispersion models, STREET and CALINE 4 to predict pollutant concentrations for an urban monitoring location that is en route to the high traffic volumes in Shimla, Himachal Pradesh, India. This study will compare the predicted and observed concentrations (for the urban monitoring location) using both quantitative and statistical methods for the 2 years of the study. The pollutant selected for the study is PM10. It was observed from the modeling studies that the performance of CALINE4 was slightly better than the STREET model. The models selected to a certain extent are defined by the available parameters for successful run completions. The application of detailed modeling studies is the first of its kind for the study location, to the best of the authors’ knowledge. Hence, the application of basic and simplistic models and the examination of their performance could potentially find the best fit model to predict approximately precise concentrations. Further scope of this study should include the use of advanced air quality dispersion models for the improved prediction of concentrations.
publisherASCE
titleDispersion Modeling of Air Pollutants in a Hilly City in India
typeJournal Paper
journal volume25
journal issue2
journal titleJournal of Hazardous, Toxic, and Radioactive Waste
identifier doi10.1061/(ASCE)HZ.2153-5515.0000574
journal fristpage04020073
journal lastpage04020073-11
page11
treeJournal of Hazardous, Toxic, and Radioactive Waste:;2021:;Volume ( 025 ):;issue: 002
contenttypeFulltext


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