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Open AccessArticle

Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin

1
Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh 786004, India
2
International Centre for Integrated Mountain Development (ICIMOD), Khumaltar G.P.O. Box 3226, Lalitpur, Nepal
3
Department of Physics, Dibrugarh University, Dibrugarh 786004, India
4
Department of Physics, University of Mumbai, Mumbai 400098, India
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(11), 703; https://doi.org/10.3390/atmos10110703
Received: 22 October 2019 / Revised: 29 October 2019 / Accepted: 30 October 2019 / Published: 13 November 2019
(This article belongs to the Special Issue Numerical Weather Prediction Models in Atmospheric Dispersion)
The temporal distributions of meteorological drivers and air pollutants over Dibrugarh, a location in the upper Brahmaputra basin, are studied using observations, models and reanalysis data. The study aims to assess the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the WRF coupled with Sulfur Transport dEposition Model (WRF-STEM), and Copernicus Atmosphere Monitoring Service (CAMS) model over Dibrugarh for the first time. The meteorological variables and air pollutants viz., black carbon(BC), carbon monoxide(CO), sulphur dioxide(SO2), Ozone(O3), and oxides of Nitrogen(NOx) obtained from WRF-Chem, WRF-STEM and CAMS are evaluated with observations. The source region tagged CO simulated by WRF-STEM delineate the regional contribution of CO. The principal source region of anthropogenic CO over Dibrugarh is North-Eastern India with a 59% contribution followed by that from China (17%), Indo-Gangetic Plains (14%), Bangladesh (6%), other parts of India (3%) and other regions (1%). Further, the BC-CO regression analysis is used to delineate the local emission sources. The BC-CO correlations estimated from models (0.99 for WRF-Chem, 0.96 for WRF-STEM, 0.89 for CAMS), and reanalysis (0.8 for Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) are maximum in pre-monsoon whereas surface observations show highest correlations (0.81) in winter. In pre-monsoon season, 90% of the modeled CO is due to biomass burning over Dibrugarh. View Full-Text
Keywords: WRF-Chem; WRF-STEM; CAMS; MERRA2; regional transport; biomass burning WRF-Chem; WRF-STEM; CAMS; MERRA2; regional transport; biomass burning
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Saikia, A.; Pathak, B.; Singh, P.; Bhuyan, P.K.; Adhikary, B. Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin. Atmosphere 2019, 10, 703.

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