Despite significant contributions to the national economy of Bangladesh, various urban developments, massive industrial and growing shipping activities are making the water of many urban rivers, including Karnaphuli River, extremely polluted. To find out the pollution sources and their possible health effects, 45 water samples were collected from 15 sampling stations. Investigation of six physicochemical parameters (pH, temperature, total dissolved solids, conductivity, salinity, and turbidity) through in-situ measurements and eight heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) status using atomic absorption spectrophotometer (AAS) was carried out in this research. Both the physicochemical parameters and heavy metals exceeded the World Health Organization (WHO)’s permeable threshold limit. The calculated hazard quotient (HQ) and hazard index (HI) ingestion values indicate non-carcinogenic risk both for adults and children, but dermal exposure was within the safety limit. Carcinogenic risk analysis revealed that Cd could cause a risk of cancer in those using the river water for a long period. Spatial analysis and metal pollution index (MPI) results exhibit that downstream of the river water is more polluted than upstream of the river. Overall, the findings of this study imply that polluted water is a threat to human health and the results will also help to undertake proper management strategies and incorporate monitoring programs that study river water for the implementation of safety measures to protect human health.

Atmospheric particulate matter (PM) has major threats to global health, especially in urban regions around the world. Dhaka, Narayanganj and Gazipur of Bangladesh are positioned as top ranking polluted metropolitan cities in the world. This study assessed the performance of the application of hybrid models, that is, Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN), ARIMA-Support Vector Machine (SVM) and Principle Component Regression (PCR) along with Decision Tree (DT) and CatBoost deep learning model to predict the ambient PM_2.5 concentrations. The data from January 2013 to May 2019 with 2342 observations were utilized in this study. Eighty percent of the data was used as training and the rest of the dataset was employed as testing. The performance of the models was evaluated by R², RMSE and MAE value. Among the models, CatBoost performed best for predicting PM_2.5 for all the stations. The RMSE values during the test period were 12.39 µg m⁻³, 13.06 µg m⁻³ and 12.97 µg m⁻³ for Dhaka, Narayanganj and Gazipur, respectively. Nonetheless, the ARIMA-ANN and DT methods also provided acceptable results. The study suggests adopting deep learning models for predicting atmospheric PM_2.5 in Bangladesh.

Viral hepatitis is a serious global public health problem affecting billions of people globally, and both hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are rapidly spreading in the developing countries including Bangladesh due to the lack of health education, poverty, illiteracy and lack of hepatitis B vaccination. Also there is lack of information on their prevalence among the general population. So, a population-based serological survey was conducted in Dhaka to determine the prevalence and risk factors of HBV and HCV infections.

Drought is a widespread hazard that can tremendously affect the biodiversity, habitat of wild species, and ecosystem functioning and stability, especially in the dry region. Due to its geographic location, the north-western region of Bangladesh has a comparatively arid climate which is very much susceptible to drought occurrence and is marked as a red zone. Despite the growing evidence of the impact of drought on food security and ecosystem functioning, little effort has been paid to mitigate the drought in this region. The present study aimed to assess the drought condition of the north-western region of Bangladesh using earth observation techniques. For this purpose, Landsat data from 1990 to 2020 was used to determine various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Water Index (NDWI), Moisture Index (NDMI) and Soil Adjusted Vegetation Index (SAVI), along with Land Surface Temperature (LST). Results show that the depletion of forests (2832 km²) and water bodies (6773 km²) resulted from the expansion of settlement (6563 km²) and agricultural land (1802 km²) for the period 1990-2020. Examination of the temporal changes of vegetation indices and LST showed that the values of all indices decreased while the LST increased. The negative correlation between NDVI value and LST indicates that the vegetation in our study was subject to drought-induced shocks. This study reveals the current situation of the vegetation health in the north-western region of Bangladesh in relation to the drought conditions. The findings of this study have practical implications for the policymakers in implementing necessary measures for agriculture, forests, water development, and economic zone planning.