Abstract:
The accuracy of the results from an air quality model is governed by the quality of emission and meteorological data inputs in most of the cases. In the present study, two air quality models were applied for inverse modelling to determine the particulate matter emission strengths of urban and regional sources in and around Mumbai in India. The study takes outset in an existing emission inventory for Total Suspended Particulate Matter (TSPM). Since it is known that the available TSPM inventory is uncertain and incomplete, this study will aim for qualifying this inventory through an inverse modelling exercise. For use as input to the air quality models in this study, onsite meteorological data has been generated using the Weather Research Forecasting (WRF) model. The regional background concentration from regional sources is transported in the atmosphere from outside of the study domain. The regional background concentrations of particulate matter were obtained from model calculations with the Danish Eulerian Hemisphere Model (DEHM) for regional sources. The regional background concentrations obtained from DEHM were then used as boundary concentrations in AERMOD calculations of the contribution from local urban sources. The results from the AERMOD calculations were subsequently compared with observed concentrations and emission correction factors obtained by best fit of the model results to the observed concentrations. The study showed that emissions had to be up-scaled by between 14 and 55% in order to fit the observed concentrations; this is of course when assuming that the DEHM model describes the background concentration level of the right magnitude. (C) 2016 Elsevier Ltd. All rights reserved.