Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/123456789/18668
Title: Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model
Authors: KUMAR, A
PATIL, RS
DIKSHIT, AK
KUMAR, R
Keywords: Southeastern United-States
Weather Research
Climate-Change
Control Strategies
Energy Security
Control Policy
Pollution
Health
Urbanization
Emissions
Issue Date: 2017
Publisher: SPRINGER
Citation: CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY,19(7)1955-1965
Abstract: Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRFmodel and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the "air pollution control region'' by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF-AERMOD system was applied for prediction of concentration levels of pollutants SO2, NOx and PM10. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers.
URI: http://dx.doi.org/10.1007/s10098-017-1379-0
http://localhost:8080/xmlui/handle/123456789/18668
ISSN: 1618-954X
1618-9558
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