Explore the vast range of titles available.

The Bangladeshi readymade garment (RMG) industry is considered the main driver of economic transformation, as it employs many unskilled and underprivileged people. However, recently, the RMG industry has faced international concern because of several building collapses and fire incidents, indicating inadequacy in the structural design and preparedness measures in the factory buildings. This research aims to understand earthquake hazard knowledge, preparedness, and emergency response, which may contribute to earthquake risk reduction in the RMG industry in Bangladesh. A survey using the methods of structured and semi-structured interviews and field observations was carried out to achieve the aims of this research. The findings suggest that 43% of these workers perceived their workplace as being a highly fire-prone environment, while 55 respondents believed that they were at risk of both fires and earthquakes. Only two percent believed that the workplaces are only at risk of earthquakes because the industries they work for have a zero-tolerance policy toward fire hazards. It was noted that the preparedness and improvement strategies were exclusively focused on fire hazards and related safety programs. Finally, the research suggests that the RMG industry may strengthen its earthquake risk reduction program by improving preparedness within the current workplace safety manuals without incurring extra effort and cost.

PurposeThe small-scale artisanal fishers in coastal Bangladesh are comparatively more vulnerable to climate risks than any other communities in Bangladesh. Based on practicality, this paper aims to explain the local level climate change perception, its impact and adaptation strategies of the fisher in southeast coastal villages in Bangladesh.Design/methodology/approachTo achieve the above objective, this study used structural, semi-structured interviews and focus group discussion in two coastal communities, namely, at Salimpur in the Sitakund coast and Sarikait Sandwip Island, Bangladesh. It reviews and applies secondary data sources to compare and contrast the findings presented in this study.FindingsResults show that the fishers perceived an increase in temperature, frequency of tropical cyclones and an increase in sea level. They also perceived a decrease in monsoon rainfall. Such changes impact the decreasing amount of fish in the Bay of Bengal and the fishers’ livelihood options. Analysing seasonal calendar of fishing, findings suggest that fishers’ well-being is highly associated with the amount of fish yield, rather than climatic stress, certain non-climatic factors (such as the governmental rules, less profit, bank erosion and commercial fishing) also affected their livelihood. The major adaptation strategies undertaken include, but are not limited to, installation of tube well or rainwater harvesting plant for safe drinking water, raising plinth of the house to cope with inundation and use of solar panel/biogas for electricity.Originality/valueDespite experiencing social stress and extreme climatic events and disasters, the majority of the fishing community expressed that they would not change their profession in future. The research suggests implementing risk reduction strategies in the coastal region of Bangladesh that supports the small-scale fishers to sustain their livelihood despite climate change consequences.

This study explores the attitudes, subjective norms, and perceived behavioral control of Dhaka city residents toward plastic pollution, using the Theory of Planned Behavior (TPB) as a framework. A cross-sectional survey of 435 participants was conducted online, targeting adults (≥ 18 years) with internet access. The survey consisted of 10 items each for attitude, subjective norms, and perceived behavioral control, analyzed using descriptive statistics and multiple regression to identify associations with sociodemographic factors such as age, education, and income. Findings indicate that respondents generally hold positive attitudes toward reducing plastic pollution, with 39.77% agreeing to purchase environmentally sustainable products despite higher costs and 44.83% willing to reduce single-use plastics even when offered for free. However, adoption of reusable alternatives remains low, with only 28.97% using their bags when shopping. Subjective norms showed moderate influence, with 43.68% of respondents indicating that people around them affect their plastic usage, though many lacked social pressure to adopt environmentally friendly behaviors. The study also highlights practical barriers, including the low availability of biodegradable alternatives (4.37%), lack of effective plastic waste segregation, and limited community-level campaigns. The 36–45 age group showed a significant negative association with perceived behavioral control, while higher education positively influenced attitudes toward plastic reduction. The study emphasizes the need for supportive policies, reward systems, and infrastructure to translate positive attitudes into sustainable behaviors. These findings offer valuable insights for policymakers aiming to improve plastic pollution management in Dhaka and other developing urban areas.

This study aims to enhance the precision of climate simulations by optimizing a multi-model ensemble of General Circulation Models (GCMs) for simulating precipitation, maximum temperature (Tmax), and minimum temperature (Tmin). Bangladesh, with its susceptibility to rapid seasonal shifts and various forms of flooding, is the focal point of this research. Historical simulations of 19 CMIP6 GCMs are meticulously compared with ERA5 data for 1986–2014. The bilinear interpolation technique is used to harmonize the resolution of GCM data with the observed grid points. Seven distinct error metrics, including Kling-Gupta Efficiency and normalized root mean squared error, quantify the grid-to-grid agreement between GCMs and ERA5 data. The metrics are integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for seasonal and annual rankings of GCMs. Finally, the ensemble means of top-performing models are estimated using Bayesian Model Averaging (BMA) and Arithmetic Mean (AM) for relative comparison. The outcomes of this study underscore the variability in GCM performance across different seasons, necessitating the development of an overarching ranking system. Results reveal ACCESS.CM2 is the preeminent GCM for precipitation, with an overall rating matric of 0.99, while INM.CM4.8 and UKESM1.0.LL excel in replicating Tmax and Tmin, with rating matrices of 1.0 and 0.88. In contrast, FGOALS.g3, KACE.1.0.G, and CanESM5 are the most underperformed models in estimating precipitation, Tmx, and Tmn, respectively. Overall, there are five models, ACCESS.ESM1.5, ACCESS.CM2, UKESM1.0.LL, MRI.ESM2.0, EC.Earth3 performed best in simulating both precipitation and temperature. The relative comparison of the ensemble means of the top five models revealed that the accuracy of BMA with Kling Gupta Efficiency (KGE) of 0.82, 0.65, and 0.82 surpasses AM with KGE of 0.59, 0.28, and 0.45 in capturing the spatial pattern of precipitation, Tmax and Tmin, respectively. This study offers invaluable insights into the selection of GCMs and ensemble methodologies for climate simulations in Bangladesh. Improving the accuracy of climate projections in this region can contribute significantly to climate science.

The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses metals pollution and its threat to ecology from the mid-channel bar’s ( char ) agricultural soil in the Damodar River basin, India. For this, the contamination factor (CF), enrichment factor (EF), geoaccumulation index (I geo ), pollution index, and ecological risk index (RI) were measured on 60 soil samples at 30 stations (2 from each station, i.e., surface and sub-surface) in different parts of the mid-channel bar. The CF and EF indicate that both levels of char soil have low contamination and hence portray a higher potential for future enrichment by heavy metals. Moreover, I geo portrays that soil samples are uncontaminated to moderately contaminated. Further, pollution indices indicate that all the samples (both levels) are unpolluted with a mean of 0.062 for surface soils and 0.048 for sub-surface soils. Both levels of the char have a low potentiality for ecological risk with an average RI of 0.20 for the surface soils and 0.19 for the sub-surface soils. Moreover, Technique for order preference by similarity to ideal solution (TOPSIS) indicates that the sub-surface soils have lower pollution than the surface soils. The geostatistical modeling reveals that the simple kriging technique was estimated as the most appropriate interpolation model. The present investigation exhibits that reduced heavy metal pollution is due to the sandy nature of soils and frequent flooding. However, the limited pollution is revealed due to the intensive agricultural practices on riverine chars . Therefore, this would be helpful to regional planners, agricultural engineers, and stakeholders in a basin area.

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

This study explores the factors affecting self-reported occupational health problems among improved traditional shrimp farmers in Southwestern Bangladesh. Data was collected from 270 farmers in the districts of Khulna, Satkhira, and Bagerhat using a structured questionnaire. According to stepwise and hierarchical logistic regression analyses, health problem reporting was associated with district of residence, education level, training, self-rated health, and occupational health awareness. Health problems were more frequently reported among farmers in Satkhira (aOR = 4.41, 95% CI: 2.06–9.75, p  < 0.001) and Khulna (aOR = 3.02, 95% CI: 1.47–6.39, p  < 0.01) than in Bagerhat. Poor self-rated health (aOR = 25.46, p  < 0.001) and those with less than secondary education (aOR = 16.72, p  < 0.01) were associated with reported issues. Knowledge of occupational safety measures significantly increased the probability of reporting health problems (aOR = 87.13, p  < 0.001); trained farmers reported higher health difficulties most likely due to increased awareness (aOR = 0.13 for untrained farmers, p  < 0.001). Environmental hazards and incorrect chemical handling continue even with hygienic procedures and personal protection equipment (PPE) laws usually embraced (99.26% of individuals used masks and gloves, for example). These results underscore the need for education-sensitive, locally directed health-safety efforts and imply that more accurate health reporting could follow from better awareness.

Bangladesh has been ranked as one of the world’s top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen’s slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual T mean , T min , and T max have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective T mean , T min , and T max was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970–2013. The results suggest that crop yield variability can be explained by climate variability during Aus , Aman , and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops’ yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation.

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km 2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km 2 ) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km 2 ) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.