Transforming landscapes: Decoding the impact of universities on urbanization using advanced modeling and perception analysis.

Universities play a crucial role in urban economic and structural development. The government of Bangladesh has undertaken the initiative to establish a public university in each of the 64 districts. These newly founded universities have the potential to impact urban growth significantly. We aimed to project university-induced urban expansion to address this knowledge gap and identify the mechanisms driving urban growth. The classification of supervised and unsupervised methods was employed to analyze urban development for the years 2000, 2010, 2016, and 2022. We used the Cellular Automata and Markov Chain approach to forecast future urban growth and land transition capacity. Additionally, the driving factors and selection of the study area were derived from Focus Group Discussions (FGD), Key Informant Interviews (KII), Probit Model, and Perception Index (PI). The findings of this study reveal a 1.6% urban growth rate within ten years of the establishment of the university, while urban expansion accelerated to 29.78% after ten years. The predictions also indicate a sustained urban growth rate of 4.7% by 2042. Furthermore, the PI index demonstrates that the establishment of the university has resulted in high demand for rental housing, serving as one of the primary drivers of urban expansion. Moreover, the Probit model highlights strong economic capability, proximity to the town, railway station, hospital, and easy access to credit as vital facilitators behind the drivers of urban expansion. Policymakers, the scientific community, and urban planners can benefit from this study in pursuing sustainable city development through university establishment.

Delineating the drought vulnerability zones in Bangladesh.

The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach.

The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+Na+pH, Ca2+Mg2+NO3-, and HCO3-SO42-PO43-F-. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43- content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43- distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.

Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

Impact of climate change on vector-borne diseases: Exploring hotspots, recent trends and future outlooks in Bangladesh.

Climate change is a significant risk multiplier and profoundly influences the transmission dynamics, geographical distribution, and resurgence of vector-borne diseases (VBDs). Bangladesh has a noticeable rise in VBDs attributed to climate change. Despite the severity of this issue, the interconnections between climate change and VBDs in Bangladesh have yet to be thoroughly explored. To address this research gap, our review meticulously examined existing literature on the relationship between climate change and VBDs in Bangladesh. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we identified 3849 records from SCOPUS, Web of Science, and Google Scholar databases. Ultimately, 22 research articles meeting specific criteria were included. We identified that the literature on the subject matter of this study is non-contemporaneous, with 68% of studies investing datasets before 2014, despite studies on climate change and dengue nexus having increased recently. We pinpointed Dhaka and Chittagong Hill Tracts as the dengue and malaria research hotspots, respectively. We highlighted that the 2023 dengue outbreak illustrates a possible shift in dengue-endemic areas in Bangladesh. Moreover, dengue cases surged by 317% in 2023 compared to 2019 records, with a corresponding 607% increase in mortality compared to 2022. A weak connection was observed between dengue incidents and climate drivers, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). However, no compelling evidence supported an association between malaria cases, and Sea Surface Temperature (SST) in the Bay of Bengal, along with the NINO3 phenomenon. We observed minimal microclimatic and non-climatic data inclusion in selected studies. Our review holds implications for policymakers, urging the prioritization of mitigation measures such as year-round surveillance and early warning systems. Ultimately, it calls for resource allocation to empower researchers in advancing the understanding of VBD dynamics amidst changing climates.

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

Hydro-chemical characterization and irrigation suitability assessment of a tropical decaying river in India.

Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station's data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+-HCO3- types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3- + CO32-) outperform strong acids (Cl- + SO42-). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p < 0.05). The results also revealed that river water for irrigation during monsoon is suitable compared to the pre-monsoon season. Anthropogenic interventions including riverbed agriculture, and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river, which needs substantial attention from the various stakeholders in a participatory, and sustainable manner.

A systematic review of cyclonic disaster: Damage-loss, consequences, adaptation strategies, and future scopes.

Tropical cyclones have direct and indirect repercussions in many coastal areas worldwide. In coastal regions, several studies have identified the driving factors of cyclonic hazards and their associated impacts. However, previous studies have focused little on cyclone-induced damage and loss, consequences, and adaptation strategies. As a result, it is critical to explore the global focus areas of cyclone-related studies. This review systematically examined cyclone-induced damage and loss, its consequences, adaptation strategies in coastal regions, and associated research gaps. Results revealed eight main types of cyclone-induced damages and losses. About 46 % of studies focused on vegetation damages, followed by water and sanitation (11 %), crop damages (8 %), income or business losses (8 %), health and injuries (8 %), land use and land cover changes (8 %), infrastructural damages (5 %), and mixed damages and losses (5 %). These damages and losses led to further consequences, including disruption of biocenoses, fish death because defoliated leaves carried carbon into the water, changes in forest structure and composition, loss of timber plantation confidence, hampering the steady supply of safe drinking water, raising drinking water costs, unsanitary circumstances, an increase in infectious diseases, a decrease in protein consumption, and business and supply chain interruptions. Approximately 35 % of the studies addressed one or more of the thirteen adaptation strategies identified in this review. Most of these studies documented the use of natural regeneration and tree planting as responses to vegetation damage and water purification and the distribution of emergency-safe water in response to water and sanitation damage. The findings have led to a proposal for an adaptation framework for cyclone-induced damage and loss. This review recommended investigating cyclone-induced land use and land cover change, damage to vegetation functional traits and patterns, health and injuries, service networks, and infrastructural damages.

Exploring the barriers to implement industrial symbiosis in the apparel manufacturing industry: Implications for sustainable development.

Industrial symbiosis, a promising approach for sustainable industrial practices, has garnered attention in recent days for its ability to enhance resource efficiency, minimize waste, and preserve the environment through collaborative exchanges among industries. In emerging economies like Bangladesh, integrating industrial symbiosis in the manufacturing industries offers the potential to balance economic growth with environmental sustainability. However, this integration encounters various barriers that complicate the implementation. Despite research on industrial symbiosis in robust economies, studies on emerging and developed economies are still scarce. To date, no research has yet investigated the barriers hindering the performance of industrial symbiosis in the Bangladeshi apparel manufacturing sector. To address this gap, this study integrates the Bayes theorem and the Best-Worst Method to identify and prioritize barriers to the Bangladeshi apparel manufacturing sector. From extensive literature reviews and expert validation, 17 barriers were identified. Findings reveal the "lack of technology and infrastructure readiness" as the most significant barrier, followed by "lack of inter-company cooperation" and "lack of management support". Conquering these barriers empowers emerging economies to fortify the apparel manufacturing sector's resilience, resource efficiency, and environmental performance while fostering sustainable development via circular economy practices. This study is expected to guide policymakers and stakeholders in crafting targeted strategies for promoting steady growth and sustainable development in the apparel manufacturing sector of emerging economies like Bangladesh.

Appraising water resources for irrigation and spatial analysis based on fuzzy logic model in the tribal-prone areas of Bangladesh.

The lack of quality water resources for irrigation is one of the main threats for sustainable farming. This pioneering study focused on finding the best area for farming by looking at irrigation water quality and analyzing its location using a fuzzy logic model on a Geographic Information System platform. In the tribal-prone areas of Khagrachhari Sadar Upazila, Bangladesh, 28 surface water and 39 groundwater samples were taken from shallow tube wells, rivers, canals, ponds, lakes, and waterfalls. The samples were then analyzed for irrigation water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), magnesium hazard ratio (MHR), Kelley's ratio (KR), and permeability index (PI). Fuzzy Irrigation Water Quality Index (FIWQI) was employed to determine the irrigation suitability of water resources. Spatial maps for parameters like EC, KR, MH, Na%, PI, SAR, and RSBC were developed using fuzzy membership values for groundwater and surface water. The FIWQI results indicate that 100% of the groundwater and 75% of the surface water samples range in the categories of excellent to good for irrigation uses. A new irrigation suitability map constructed by overlaying all parameters showed that surface water (75%) and some groundwater (100%) in the northern and southwestern portions are fit for agriculture. The western and central parts are unfit for irrigation due to higher bicarbonate and magnesium contents. The Piper and Gibbs diagram also indicated that the water in the study area is magnesium-bicarbonate type and the primary mechanism of water chemistry is controlled by the weathering of rocks, respectively. This research pinpoints the irrigation spatial pattern for regional water resource practices, identifies novel suitable areas, and improves sustainable agricultural uses in tribal-prone areas.

Microplastics in multi-environmental compartments: Research advances, media, and global management scenarios.

During the past decades, microplastics (MPs) have become an emerging concern due to their persistence and potential environmental threat. MP pollution has become so drastic that it has been found in the human food chain, breast milk, polar regions, and even the Himalayan basin, lake, etc. Inflammation, pulmonary hypertension, vascular occlusions, increased coagulability and blood cell cytotoxicity, disruption of immune function, neurotoxicity, and neurodegenerative diseases can all be brought on by severe microplastic exposure. Although many MPs studies have been performed on single environmental compartments, MPs in multi-environmental compartments have yet to be explored fully. This review aims to summarize the muti-environmental media, detection tools, and global management scenarios of MPs. The study revealed that MPs could significantly alter C flow through the soil-plant system, the structure and metabolic status of the microbial community, soil pH value, biomass of plant shoots and roots, chlorophyll, leaf C and N contents, and root N contents. This review reveals that MPs may negatively affect many C-dependent soil functions. Different methods have been developed to detect the MPs from these various environmental sources, including microscopic observation, density separation, Raman, and FT-IR analysis. Several articles have focused on MPs in individual environmental sources with a developed evaluation technique. This review revealed the extensive impacts of MPs on soil-plant systems, microbial communities, and soil functions, especially on water, suggesting possible disturbances to vital ecological processes. Furthermore, the broad range of detection methods explored emphasizes the significance of reliable analytical techniques in precisely evaluating levels of MP contamination in various environmental media. This paper critically discusses MPs' sources, occurrences, and global management scenarios in all possible environmental media and ecological health impacts. Future research opportunities and required sustainable strategies have also been suggested from Bangladesh and international perspectives based on challenges faced due to MP's pollution.

Assessing river water quality for ecological risk in the context of a decaying river in India.

The decay of rivers and river water pollution are common problems worldwide. However, many works have been performed on decaying rivers in India, and the status of the water quality is still unknown in Jalangi River. To this end, the present study intends to examine the water quality of the Jalangi River to assess ecological status in both the spatial and seasonal dimensions. To depict the spatiality of ecological risks, 34 water samples were collected from the source to the sink of the Jalangi River with an interval of 10 km while 119 water samples were collected from a secondary source during 2012-2022 to capture the seasonal dynamics. In this work, the seasonality and spatiality of change in the river's water quality have been explored. This study used the eutrophication index (EI), organic pollution index (OPI), and overall index of pollution (OIP) to assess the ecological risk. The results illustrated that the values of OPI range from 7.17 to 588, and the values of EI exceed the standard of 1, indicating the critical situation of the ecological status of Jalangi River. The value of OIP ranges between 2.67 and 3.91 revealing the slightly polluted condition of the river water. The study signified the ecological status of the river is in a critical situation due to elevated concentrations of biological oxygen demand, chemical oxygen demand, and low concentrations of dissolved oxygen. The present study found that stagnation of water flow in the river, primarily driven by the eastward tilting of the Bengal basin, triggered water pollution and ecological risk. Moreover, anthropogenic interventions in the form of riverbed agriculture and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river. This decay needs substantial attention from the various stakeholders in a participatory manner.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh.

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.

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Assessing metal(loid)s-Induced long-term spatiotemporal health risks in Coastal Regions, Bay of Bengal: A chemometric study.

Despite sporadic and irregular studies on heavy metal(loid)s health risks in water, fish, and soil in the coastal areas of the Bay of Bengal, no chemometric approaches have been applied to assess the human health risks comprehensively. This review aims to employ chemometric analysis to evaluate the long-term spatiotemporal health risks of metal(loid)s e.g., Fe, Mn, Zn, Cd, As, Cr, Pb, Cu, and Ni in coastal water, fish, and soils from 2003 to 2023. Across coastal parts, studies on metal(loid)s were distributed with 40% in the southeast, 28% in the south-central, and 32% in the southwest regions. The southeastern area exhibited the highest contamination levels, primarily due to elevated Zn content (156.8 to 147.2 mg/L for Mn in water, 15.3 to 13.2 mg/kg for Cu in fish, and 50.6 to 46.4 mg/kg for Ni in soil), except for a few sites in the south-central region. Health risks associated with the ingestion of Fe, As, and Cd (water), Ni, Cr, and Pb (fish), and Cd, Cr, and Pb (soil) were identified, with non-carcinogenic risks existing exclusively through this route. Moreover, As, Cr, and Ni pose cancer risks for adults and children via ingestion in the southeastern region. Overall non-carcinogenic risks emphasized a significantly higher risk for children compared to adults, with six, two-, and six-times higher health risks through ingestion of water, fish, and soils along the southeastern coast. The study offers innovative sustainable management strategies and remediation policies aimed at reducing metal(loid)s contamination in various environmental media along coastal Bangladesh.

Exploring the environmental pathways and challenges of fluoroquinolone antibiotics: A state-of-the-art review.

Fluoroquinolone (FQ) antibiotics have become a subject of growing concern due to their increasing presence in the environment, particularly in the soil and groundwater. This review provides a comprehensive examination of the attributes, prevalence, ecotoxicity, and remediation approaches associated with FQs in environmental matrices. The paper discusses the physicochemical properties that influence the fate and transport of FQs in soil and groundwater, exploring the factors contributing to their prevalence in these environments. Furthermore, the ecotoxicological implications of FQ contamination in soil and aquatic ecosystems are reviewed, shedding light on the potential risks to environmental and human health. The latter part of the review is dedicated to an extensive analysis of remediation approaches, encompassing both in-situ and ex-situ methods employed to mitigate FQ contamination. The critical evaluation of these remediation strategies provides insights into their efficacy, limitations, and environmental implications. In this investigation, a correlation between FQ antibiotics and climate change is established, underlining its significance in addressing the Sustainable Development Goals (SDGs). The study further identifies and delineates multiple research gaps, proposing them as key areas for future investigational directions. Overall, this review aims to consolidate current knowledge on FQs in soil and groundwater, offering a valuable resource for researchers, policymakers, and practitioners engaged in environmental management and public health.

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India.

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

Climate change and geo-environmental factors influencing desertification: a critical review.

The problem of desertification (DSF) is one of the most severe environmental disasters which influence the overall condition of the environment. In Rio de Janeiro Earth Summit on Environment and Development (1922), DSF is defined as arid, semi-arid, and dry sub-humid induced LD and that is adopted at the UNEP's Nairobi ad hoc meeting in 1977. It has been seen that there is no variability in the trend of long-term rainfall, but the change has been found in the variability of temperature (avg. temp. 0-5 °C). There is no proof that the air pollution brought on by CO2 and other warming gases is the cause of this rise, which seems to be partially caused by urbanization. The two types of driving factors in DSF-CC (climate change) along with anthropogenic influences-must be compared in order to work and take action to stop DSF from spreading. The proportional contributions of human activity and CC to DSF have been extensively evaluated in this work from "qualitative, semi-quantitative, and quantitative" perspectives. In this study, we have tried to connect the drives of desertification to desertification-induced migration due to loss of biodiversity and agriculture failure. The authors discovered that several of the issues from the earlier studies persisted. The policy-makers should follow the proper SLM (soil and land management) through using the land. The afforestation with social forestry and consciousness among the people can reduce the spreading of the desertification (Badapalli et al. 2023). The green wall is also playing an important role to reduce the desertification. For instance, it was clear that assessments were subjective; they could not be readily replicated, and they always relied on administrative areas rather than being taken and displayed in a continuous space. This research is trying to fulfill the mentioned research gap with the help of the existing literatures related to this field.

Unveiling microplastics pollution in a subtropical rural recreational lake: A novel insight.

While global attention has been primarily focused on the occurrence and persistence of microplastics (MP) in urban lakes, relatively little attention has been paid to the problem of MP pollution in rural recreational lakes. This pioneering study aims to shed light on MP size, composition, abundance, spatial distribution, and contributing factors in a rural recreational lake, 'Nikli Lake' in Kishoreganj, Bangladesh. Using density separation, MPs were extracted from 30 water and 30 sediment samples taken from ten different locations in the lake. Subsequent characterization was carried out using a combination of techniques, including a stereomicroscope, Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FE-SEM). The results showed a significant prevalence of MPs in all samples, with an average amount of 109.667 ± 10.892 pieces/kg3 (dw) in the sediment and 98.167 ± 12.849 pieces/m3 in the water. Small MPs (<0.5 mm), fragments and transparent colored particles formed the majority, accounting for 80.2%, 64.5% and 55.3% in water and 78.9%, 66.4% and 64.3% in sediment, respectively. In line with global trends, polypropylene (PP) (53%) and polyethylene (PE) (43%) emerged as the predominant polymers within the MPs. MP contents in water and sediment showed positive correlations with outflow, while they correlated negatively with inflow and lake depth (p > 0.05). Local activities such as the discharge of domestic sewage, fishing waste and agricultural runoff significantly influence the distribution of polypropylene. Assessment of pollution factor, pollution risk index and pollution load index values at the sampling sites confirmed the presence of MPs, with values above 1. This study is a baseline database that provides a comprehensive understanding of MP pollution in the freshwater ecosystem of Bangladesh, particularly in a rural recreational lake. A crucial next step is to explore ecotoxicological mechanisms, legislative measures and future research challenges triggered by MP pollution.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta.

Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.