Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

Atmospheric particle pollution causes acute and chronic\ua0health effects. Predicting the concentrations of PM and PM, therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO, SO, CO, and O, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM and PM while ANN performed best in case of Chattogram and Sylhet for predicting PM. However, in terms of predicting PM, M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing “ensemble learning” models by combining several best models to advance application of ML in predicting pollutants’ concentration in Bangladesh

Assessing Energy-Based CO2 Emission and Workers’ Health Risks at the Shipbreaking Industries in Bangladesh

The study represents the estimation of energy-based CO2 emission and the health risks of workers involved in the shipbreaking industries in Sitakunda, Bangladesh. To calculate the carbon emission (CE) from three shipbreaking activities, i.e., metal gas cutting (GC), diesel fuel (FU) and electricity consumption (EC), we used the Intergovernmental Panel on Climate Change (IPCC) guidelines and Environmental Protection Agency (EPA)’s Emission and Generation Resource Integrated Database (eGRID) emission factors. Moreover, the geographic weighted regression (GWR) model was applied to assess the contribution of influencing factors of CE throughout the sampling points. To assess the workers’ health condition and their perceptions on environmental degradation, a semi-structured questionnaire survey among 118 respondents were performed. The results showed that total CO2 emissions from GC were 0.12 megatons (MT), 11.43 MT, and 41.39 MT for daily, monthly, and yearly respectively, and the values were significantly higher than the surrounding control area. Emissions from the FU were estimated as daily: 0.85 MT, monthly: 1.92 MT, and yearly: 17.91 MT, which were significantly higher than EC. The study also revealed that workers were very susceptible to accidental hazards especially death (91%), and pollution (79%). Environmental consequences and health risks of the workers in shipbreaking industry warrant more attention nationally and internationally at the industry-level