Faculty Publications
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Item Identification of potential sources affecting fine particulate matter concentration in delhi, india(Springer Science and Business Media Deutschland GmbH, 2021) Harsha, K.; Shiva Nagendra, S.M.S.; Deka, P.C.The seasonal air transport pathways and the potential sources contributing to air pollution in Delhi for the period March 2015 to February 2016 have been identified with the help of PM2.5 data (particulate matter with diameter less than 2.5 μm), potential source contribution function (PSCF), cluster analysis and concentration weighted trajectory (CWT) method. The local sources are identified with the help of conditional probability function (CPF). The presence of re-circulating air masses has shown that the major contributors to air pollution in winter seasons are the local sources and the neighboring states of Haryana and Punjab. Northwesterly flows can be observed throughout the year and are highest in the winter season and comparatively lower in the monsoon season. PSCF values greater than 0.7 and CWT values greater than 110 μgm−3 are observed within the state in the winter season. Haryana and some parts of Uttar Pradesh also have higher PSCF values. The frequency of occurrence of long distance pathways is less in all the seasons in Delhi. The influence of the dust pathways from the Thar Desert areas can be seen in the monsoon season. Slower moving northwesterly and southwesterly flows are associated with high concentration values and indicate high pollution along the pathways. Higher CPF values occur in the northeastern direction. Therefore, the industrial sites, traffic congestion and emission from vehicles in the roads connecting Delhi and Uttar Pradesh have high influence in the rise in pollution levels. © Springer Nature Singapore Pte Ltd 2021.Item Analysis of Concentration of Ambient Particulate Matter in the Surrounding Area of an Opencast Coal Mine using Machine Learning Techniques(Shahrood University of Technology, 2024) Podicheti, R.K.; Karra, R.C.Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological conditions. There is a need to establish a suitable model for predicting the concentration of particulate matter on a regional basis. Though a number of dispersion models exist for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling complex interactions, anomaly detection, etc. An attempt has been made to assess the dispersion of particulate matter using machine learning techniques by considering the mining and meteorological parameters. Historical data comprising of mine working parameters, meteorological conditions, and particulate matter pertaining to one of the operating opencast coal mines in southern India has been utilized for the study. The data has been analyzed using different machine learning techniques like bagging, random forest, and decision tree. The performance metrics of test data are compared for different models in order to find the best fit model among the three techniques. It is found that for PM10, many of the times bagging technique gave a better accuracy, and for PM2.5, decision tree technique gave a better accuracy. Integration of mine working parameters with meteorological conditions and historical data of particulate matter in developing the model using machine learning techniques has helped in making more accurate predictions. © 2024, Shahrood University of Technology. All rights reserved.