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.

Heavy metal pollution is one of the major environmental issues in recent decades owing to the rapid increase in urbanisation and industrialisation. Sediments usually act as sinks for heavy metals due to their complex physical and chemical adsorption mechanisms. In this study, heavy metals like lead (Pb), Zinc (Zn), Cadmium (Cd), Copper (Cu) and Iron (Fe) in the surface sediment from 15 location (upstream and downstream) on the Perak River, Malaysia were investigated by means of inductively coupled plasma optical emission spectroscopy (ICP-OES). The geostatistical prediction map showed the range of Pb, Zn, Cd, Cu and Fe concentration in upstream area was 14.56–27.0 µg/g, 20–51.27 µg/g, 1.51–3.0 µg/g, 6.6–19.12 µg/g and 20.24–56.58%, respectively, and in downstream areas was 27.6–60.76 µg/g, 49.04–160.5 µg/g, 2.77–4.02 µg/g, 9.82–59.99 µg/g and 31.34–39.5%, respectively. Based on the enrichment factor and geoaccumulation index, Cd was found to be the most dominant pollutant in the study area. Pollution load index, sediment quality guidelines and sediment environmental toxicity quotient data showed that the downstream sediment was more polluted than the upstream sediment in the Perak River. The multivariate analysis showed that Pb, Zn and Cu mainly originated from natural sources with minor contribution from human activities, whereas Fe and Cd originated from various industrial and agricultural activities along the studied area.

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 PM2.5 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 PM2.5 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 PM2.5 in Bangladesh.

Groundwater is one of the most valuable natural resources, and the most dependable source of fresh water. For sustainable groundwater management, the present study aimed to model groundwater potential zones in the north–central region of Bangladesh using GIS, remote sensing, and the analytical hierarchy process. The present study included eight thematic layers: lineament density, geomorphology, soil types, slope, land use/land cover, drainage density, elevation, and rainfall features to delineate a groundwater potential zone of the area. Integration of the eight thematic layers was performed through weighted overlay analysis, which assisted in delineating groundwater potential zones. This simple and systematic method successfully provides a satisfactory result concerning the delineation of groundwater potential zones. The study resulted in a groundwater potential zone map, which identifies about 11.51% of the study area as being under a very high groundwater potential zone, covering an area of 504.09 km2. The AHP analysis shows that the physiographical parameters, such as lineament density, slope, and drainage density, and meteorological factors such as annual rainfall, have greater influence over groundwater potentiality. The result obtained from the weighted overlay analysis was verified with actual well yield and groundwater depth data, which show a significant positive correlation. The outcome of the study will help in taking effective measures to ensure sustainable use and extraction of groundwater in this region.

The rapid growth of industrial and agricultural activities in Malaysia are leading to the impairment of most of the rivers in recent years through realising various trace metals. This leads to toxicity, particularly when the toxic has entered the food chain. Perak River is one of the most dynamic rivers for the Malaysian population. Therefore, in consideration of the safety issue, this study was conducted to assess the concentration of such metals (Cd, Cu, Zn, Fe, and Pb) in the muscles of most widely consumed fish species ( Barbonymus schwanenfeldii , Puntius bulu m, Puntius daruphani , Hexanematichthys sagor , Channa striatus , Mystacoleucus marginatus , and Devario regina ) from different locations of Perak River, Malaysia by employing inductively coupled plasma optical emission spectroscopy (ICP-OES). Among the trace metals, Fe and Cd were found to be the highest (29.33–148.01 μg/g) and lowest (0.16–0.49 μg/g) concentration in all of the studied species, respectively. Although the estimated daily intakes (μg/kg/day) of Cd (0.65–0.85), Fe (79.27–352.00) and Pb (0.95–12.17) were higher than their reference, the total target hazard quotients values suggested that the local residents would not experience any adverse health effects from its consumption. In contrast, the target cancer risk value suggested that all fish species posed a potential cancer risk due to Cd and cumulative cancer risk values, strongly implying that continuous consumption of studied fish species would cause cancer development to its consumers.