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This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen’s Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901–1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions’ also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

Community transmission of COVID-19 is happening in Bangladesh—the country which did not have a noteworthy health policy and legislative structures to combat a pandemic like COVID-19. Early strategic planning and groundwork for evolving and established challenges are crucial to assemble resources and react in an appropriate timely manner. This article, therefore, focuses on the public perception of comparative lockdown scenario analysis and how they may affect the sustainable development goals (SDGs) and the strategic management regime of COVID-19 pandemic in Bangladesh socio-economically as well as the implications of the withdrawal of partial lockdown plan. Scenario-based public perceptions were collected via a purposive sampling survey method through a questionnaire. Datasets were analysed through a set of statistical techniques including classical test theory, principal component analysis, hierarchical cluster analysis, Pearson’s correlation matrix and linear regression analysis. There were good associations among the lockdown scenarios and response strategies to be formulated. Scenario 1 describes how the death and infection rate will increase if the Bangladesh Government withdraws the existing partial lockdown. Scenario 2 outlines that limited people’s movement will enable low-level community transmission of COVID-19 with the infection and death rate will increase slowly ( r  = 0.540, p  < 0.01). Moreover, there will be less supply of necessities of daily use with a price hike ( r  = 0.680, p  < 0.01). In scenario 3, full lockdown will reduce community transmission and death from COVID-19 ( r  = 0.545, p  < 0.01). However, along with the other problems gender discrimination and gender-based violence will increase rapidly ( r  = 0.661, p  < 0.01). Due to full lockdown, the formal and informal business, economy, and education sector will be hampered severely ( R  = 0.695). Subsequently, there was a strong association between the loss of livelihood and the unemployment rate which will increase due to business shutdown ( p  < 0.01). This will lead to the severe sufferings of poor and vulnerable communities in both urban and rural areas ( p  < 0.01). All these will further aggravate the humanitarian needs of the most vulnerable groups in the country in the coming months to be followed which will undoubtedly affect the Bangladesh targets to achieve the SDGs of 2030 and other development plans that need to be adjusted. From our analysis, it was apparent that maintaining partial lockdown with business and economic activities with social distancing and public health guidelines is the best strategy to maintain. However, as the government withdrew the partial lockdown, inclusive and transparent risk communication towards the public should be followed. Recovery and strengthening of the health sector, economy, industry, agriculture, and food security should be focused on under the “new normal standard of life” following health guidelines and social distancing. Proper response plans and strategic management are necessary for the sustainability of the nation. Graphic abstract

Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

Rapid assessments have been emerging on the effects of COVID-19, yet rigorous analyses remain scant. Here, rigorous evidence of the impacts of COVID-19 on several livelihood outcomes are presented, with a particular focus on heterogenous effects of COVID-19. We use a household-level panel dataset consisting of 880 data points collected in rural Bangladesh in 2018 and 2020, and employ difference-in-differences with fixed effects regression techniques. Results suggest that COVID-19 had significant and heterogenous effects on livelihood outcomes. Agricultural production and share of production sold were reduced, especially for rice crops. Further, diet diversity and education expenditure were reduced for the total sample. Households primarily affected by (fear of) sickness had a significantly lower agricultural production, share of crop market sales, and lower health and education expenditure, compared to households affected by other COVID-19 effects, such as travel restrictions. In turn, (fear of) sickness and the correlated reduced incidence of leaving the house, resulted in higher off-farm incomes suggesting that households engage in less physically demanding and localized work. Policy-makers need to be cognizant of these heterogenous COVID-19 effects and formulate policies that are targeted at those households that are most vulnerable (e.g., unable/willing to leave the house due to (fear of) sickness).

Heavy metals occur naturally in very small amounts in living organisms, but exposure to their higher concentrations is hazardous. Heavy metals at hazardous levels are commonly found in foodstuffs of Bangladesh, mainly due to the lack of safety guidelines and poor management of industrial effluents. Several lines of evidence suggest that the level of heavy metals in foodstuffs of Bangladesh is higher than the acceptable limits set by World Health Organization/Food and Agriculture Organization. Literature survey revealed that the sources and transport pathways of heavy metals in the ecosystem and the abundance of heavy metals in the food products of Bangladesh are potential threats to food safety. However, an extensive assessment of the toxicity of heavy metals in food webs is lacking. Although widespread heavy metal contamination in various foodstuffs and environmental matrices have been summarized in some reports, a critical evaluation regarding multi-trophic transfer and the health risk of heavy metal exposure through food chain toxicity in Bangladesh has not been performed. This systematic review critically discussed heavy metal contamination, exposure toxicity, research gaps, existing legislation, and sustainable remediation strategies to enhance Bangladesh’s food safety. In particular, this study for the first time explored the potential multi-trophic transfer of heavy metals via food webs in Bangladesh. Furthermore, we recommended a conceptual policy framework to combat heavy metal contaminations in Bangladesh.

The deficiency of freshwater has become a global issue in the recent era, especially in water-scarce hard rock region including India. Groundwater (GW) as a natural resource is decreasing at an alarming rate in West Bengal, India. Sustainable use and planning for better management of groundwater resources are essential; thus, spatial modelling of GW distribution requires proper assessment to conserve and manage the groundwater resource. Machine learning algorithms in RS-GIS environment plays a crucial role in exploration, assessing, monitoring and conserving groundwater resource in this regard. Logistic regression (LR), support vector machine (SVM) and random forest (RF) were used to develop groundwater potential zone (GWPZ) of water-stressed district Purulia with the help of 10 GW controlling factors including geology, geomorphology, lineament density, slope, soil texture, drainage density, GW level, rainfall, NDVI and NDWI. Multi-collinearity analysis was also used to eliminate collinearity issues among all controlling factors. In this study, the total area has been divided into five groups under the very low, low, moderate, high and very high groundwater potential zone categories. It has been calculated that most of the area has moderate groundwater potentiality, which is 29% of the total regions. About 8%, 19%, 25% and 18% of the study area fall under the very low, low, high and very high zones. Finally, all the adopted models were validated through ROC–AUC using GW depth data from CGGB and 484 validating point datasets in this area. The AUC values of adopted LR, SVM and RF models are 0.801, 0.849 and 0.878, respectively; implies that RF is a more reliable algorithm with better predictive ability than other models in the study area. This study's findings will help decision-makers take proper strategies and planning of groundwater resource management for this particular water-scare hard rock region.

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15–300-m depth) from the study area. The results show that NO 3 − , then SO 4 2− , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

The coastal zone of Bangladesh, with a population density of 1278 people per square kilometer, is under serious threat due to heavy metal pollution. To date, many studies have been conducted on the heavy metal contamination in soils, water, aquatic animals, and plants in the coastal zone of Bangladesh; however, the available information is dispersed. In this study, previous findings on the contamination levels, distributions, risks, and sources of heavy metals in sediments and organisms were summarized for the first time to present the overall status of heavy metal pollution along coastal regions. Earlier research found that the concentrations of various heavy metals (HMs), particularly Co, Cd, Pb, Cu, Cr, Mn, Fe, and Ni in water, sediment, and fish in most coastal locations, were above their permissible limits. High concentrations of HMs were observed in sediments and water, like Cr of 55 mg/kg and 86.93 mg/l in the ship-breaking areas and Karnaphuli River, respectively, in coastal regions of Bangladesh. Heavy metals severely contaminated the Karnaphuli River estuary and ship-breaking area on the Sitakundu coast, where sediments were the ultimate sink of high concentrations of metals. Sedentary or bottom-dwelling organisms like gastropods and shrimp had higher levels of heavy metals than other organisms. As a result, the modified PRISMA review method was used to look at the critical research gap about heavy metal pollution in Bangladesh’s coastal areas by analyzing the current research trends and bottlenecks. Graphical abstract

The exponential growth in the number of flash flood events is a global threat, and detecting a flood-prone area has also become a top priority. The flash flood-susceptibility mapping can help to mitigate the worst effects of this type of risk phenomenon. However, there is an urgent need to construct precise models for predicting flash flood-susceptibility mapping, which can be useful in developing more effective flood management strategies. In this present research, support vector regression (SVR) was coupled with two meta-heuristic algorithms such as particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA), to construct new GIS-based ensemble models (SVR–PSO and SVR–GOA) for flash flood-susceptibility mapping (FFSM) in the Gandheswari River basin, West Bengal, India. In this regard, 16 topographical and environmental flood causative factors have been identified to run the models using the multicollinearity (MC) test. The entire dataset was divided into 70:30 for training and validating purposes. Statistical measures including specificity, sensitivity, PPV, NPV, AUC–ROC, kappa and Taylor diagram have been employed to validate adopted models. The SVR-based factor importance analysis was employed to choose and prioritize significant factors for the spatial analysis. Among the three modeling approaches used here, the ensemble method of SVR–GOA is the most optimal (specificity 0.97 and 0.87, sensitivity 0.99 and 0.91, PPV 0.97 and 0.86, NPV 0.99 and 0.91, AUC 0.951 and 0.938 in training and validation, respectively), followed by the SVR–PSO (specificity 0.84 and 0.84, sensitivity 0.87 and 0.86, PPV 0.85 and 0.82, NPV 0.87 and 0.87, AUC 0.951 and 0.938 in training and validation, respectively) and SVR (specificity 0.80 and 0.77, sensitivity 0.93 and 0.89, PPV 0.82 and 0.77, NPV 0.91 and 0.89, AUC 0.951 and 0.938 in training and validation, respectively) model. The result shown that 40.10 km2 (10.99%) and 25.94 km2 (7.11%) areas are under very high and high flood-prone regions, respectively. This produced reliable results that can help policymakers at the local and national levels to implement a concrete strategy with an early warning system to reduce the occurrence of floods in a region.