Faculty Publications
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Item Receptor model based source apportionment of PM10 in the metropolitan and industrialized areas of Mangalore(Elsevier B.V., 2016) Kalaiarasan, G.; Mohan Balakrishnan, R.M.; Khaparde, V.V.PM10 samples were collected from a traffic site (Town hall) and industrial site (KSPCB) of Mangalore, India during 2014. Chemical characterization using ICP-MS proclaimed the presence of twelve trace elements (Ca, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Sr, Ti, V, and Zn) from traffic site and six trace elements (Cd, Ni, Pb, K, Cr and Zn) from industrial site. Source apportionment has been done using Enrichment Factors (EF's) and Principal Component Analysis (PCA). EF's outcome using Fe as reference element showed higher enrichment for Zn, Pb, Cd, V, Cr, Ti and Cu compared to Sr, Ca, Mg and Mn. Similarly EF's calculated for industrial site using K as a reference element exhibits higher enrichment for Cd, Ni, Pb, Cr and Zn. Principal Component Analysis using varimax rotation distinguishes three sources (vehicular sources, crustal sources and brake wear emissions) for PM10 particles at traffic and two sources (steel and non-ferrous metal industries emissions and Coal/fuel oil combustion emission) at industrial site. This is the first known work for source identification of particulate matter (PM10) in coastal industrial city Mangalore. © 2016Item 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.