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.

Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region.

Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.

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.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy.

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

A review on sea cucumber (Bengali: Somuddro Sosha) as a bioindicator of heavy metal contamination and toxicity.

This review paper exhibits the underexplored realm of heavy metal contamination and associated risks in sea cucumbers (SCs), which hold significant importance in traditional Asian marine diets and are globally harvested for the Asian market. The assessment focuses on heavy metals (HMs) presence in various SC species, revealing a global trend in HMs concentrations across anatomical parts: Fe > Zn > As > Cu > Hg > Pb > Mn > Cr > Ni > Cd. Specific species, such as Eupentacta fraudatrix, Holothuria mammata, Holothuria polii, Holothuria tubulosa, and Holothuria atra, exhibit heightened arsenic levels, while Stichopus herrmanni raises concerns with mercury levels, notably reaching 3.75 mg/kg in some instances, posing potential risks, particularly for children. The study sheds light on anthropogenic activities such as cultivation, fishing, and shipping, releasing HMs into marine ecosystems and thereby threatening ocean and coastal environments due to the accumulation and toxicity of these elements. In response to these findings, the paper suggests SCs as promising bioindicator species for assessing metal pollution in marine environments. It underscores the adverse effects of human actions on sediment composition and advocates for ongoing monitoring efforts both at sea and along coastlines.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region.

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.

The path of microplastics through the rare biodiversity estuary region of the northern Bay of Bengal.

Due to its harmful effects on ecosystems and human health, microplastic (MP) pollution has become a significant environmental problem on a global scale. Although MPs' pollution path and toxic effects on marine habitats have been examined worldwide, the studies are limited to the rare biodiversity estuary region of Hatiya Island from the northern Bay of Bengal. This study aimed to investigate the MP pollution path and its influencing factors in estuarine sediments and water in rare biodiversity Hatiya Island in the northern Bay of Bengal. Sixty water and sediment samples were collected from 10 sampling sites on the Island and analyzed for MPs. The abundance of MPs in sediment ranged from 67 to 143 pieces/kg, while the abundance in water ranged from 24.34 to 59 pieces/m3. The average concentrations of MPs in sediment and water were 110.90 ± 20.62 pieces/kg and 38.77 ± 10.09 pieces/m3, respectively. Most identified MPs from sediment samples were transparent (51%), while about 54.1% of the identified MPs from water samples were colored. The fragment was the most common form of MP in both compartments, with a value of 64.6% in sediment samples and 60.6% in water samples. In sediment and water samples, almost 74% and 80% of MP were <0.5 mm, respectively. Polypropylene (PP) was the most abundant polymer type, accounting for 51% of all identified polymers. The contamination factor, pollution load index, polymer risk score, and pollution risk score values indicated that the study area was moderately polluted with MPs. The spatial distribution patterns and hotspots of MPs echoed profound human pathways. Based on the results, sustainable management strategies and intervention measures were proposed to reduce the pollution level in the ecologically diverse area. This study provides important insights into evaluating estuary ecosystem susceptibility and mitigation policies against persistent MP issues.

A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.

Exploring the microplastic pollution: Unveiling origins and varieties in coastal sediments and waters of the Bushehr Province, Persian Gulf, Iran.

In this study, microplastic (MP) pollution in the coastal sediments and tidal waters of Bushehr province in the Persian Gulf was comprehensively investigated. The sampling stations were selected based on their proximity to various human activities in January and February 2022, such as tourism, fishing, urban development and industry. The results showed that the abundance of MP associated with different human activities varied. The highest concentrations were observed near the petrochemical industry in Asaluyeh, followed by the densely populated Bushehr and the fishing port of Dayyer. Other areas such as Ganaveh, Deylam and Mand also showed varying levels of MP contamination. The average MP concentration was 1.67 × 104 particles/km2 in surface water and 1346.67 ± 601.69 particles/kg in dry sediment. Fiber particles were in the majority in both sediment and water samples, mainly black. The sediment samples had a size range of 100-500 µm (41.34 %), while the water samples were between 500 and 1000 µm (33.44 %). The main polymers found were polyethylene (PE) and polypropylene (PP). This assessment highlights the widespread problem of microplastic pollution in the coastal and intertidal zones of Bushehr province in the Persian Gulf.

A review of microplastic threat mitigation in Asian lentic environments.

Microplastic (MP) pollution has evolved into a significant worldwide environmental concern due to its widespread sources, enduring presence, and adverse effects on lentic ecosystems and human well-being. The growing awareness of the hidden threat posed by MPs in lentic ecosystems has emphasized the need for more in-depth research. Unlike marine environments, there remain unanswered questions about MP hotspots, ecotoxic effects, transport mechanisms, and fragmentation in lentic ecosystems. The introduction of MPs represents a novel threat to long-term environmental health, posing unresolved challenges for sustainable management. While MP pollution in lentic ecosystems has garnered global attention due to its ecotoxicity, our understanding of MP hotspots in lakes from an Asian perspective remains limited. Hence, the aim of this review is to provide a comprehensive analysis of MP hotspots, morphological attributes, ecotoxic impacts, sustainable solutions, and future challenges across Asia. The review summarizes the methods employed in previous studies and the techniques for sampling and analyzing microplastics in lake water and sediment. Notably, most studies concerning lake microplastics tend to follow the order of China > India > Pakistan > Nepal > Turkey > Bangladesh. Additionally, this review critically addresses the analysis of microplastics in lake water and sediment, shedding light on the prevalent net-based sampling methods. Ultimately, this study emphasizes the existing research gaps and suggests new research directions, taking into account recent advancements in the study of microplastics in lentic environments. In conclusion, the review advocates for sustainable interventions to mitigate MP pollution in the future, highlighting the presence of MPs in Asian lakes, water, and sediment, and their potential ecotoxicological repercussions on both the environment and human health.

Heavy metal (As, Cr, and Pb) contamination and associated human health risks in two commercial fish species in Bangladesh.

Contamination of fish with metals is a worldwide consumer safety concern. In this study, three metals such as arsenic (As), chromium (Cr), and lead (Pb) were measured in two commonly consumed fish species Oreochromis niloticus (Tilapia) and Pangasianodon hypophthalmus (Pangasius) that are commercially farmed. The concentration of the metals studied was found within the permissible limits. The concentrations of As, Cr, and Pb in tilapia fluctuated, ranging from not detected (ND) to 0.114 mg/kg, ND to 0.009 mg/kg, and ND to 0.085 mg/kg, respectively. For Pangasius, the concentrations were in the range of 0.014 to 0.118 mg/kg for As, ND to 0.02 mg/kg for Cr, and ND to 0.047 mg/kg for Pb. Hierarchical clustering revealed that As was possibly taken up by leachate and groundwater, while Cr and Pb were from contaminated feed. The results of the calculations for estimated daily intake, target hazard quotient, hazard index, and carcinogenic risk made it clear that consumption of the fish studied does not have a significant adverse effect on consumer health. In conclusion, the contamination levels of farmed tilapia and Pangasius sold in the study area are within acceptable limits, but regular monitoring is required to ensure safe production.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network.

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Micro (nano) plastics uptake, toxicity and detoxification in plants: Challenges and prospects.

Plastic pollution has emerged as a global challenge affecting ecosystem health and biodiversity conservation. Terrestrial environments exhibit significantly higher plastic concentrations compared to aquatic systems. Micro/nano plastics (MNPs) have the potential to disrupt soil biology, alter soil properties, and influence soil-borne pathogens and roundworms. However, limited research has explored the presence and impact of MNPs on aquaculture systems. MNPs have been found to inhibit plant and seedling growth and affect gene expression, leading to cytogenotoxicity through increased oxygen radical production. The article discusses the potential phytotoxicity process caused by large-scale microplastics, particularly those unable to penetrate cell pores. It also examines the available data, albeit limited, to assess the potential risks to human health through plant uptake.

The effect of short-term plants cultivation on soil organic/inorganic carbon storage in newly formed soils.

Studying total soil carbon (STC), which encompasses organic (SOC) and inorganic carbon (SIC), as well as investigating the influence of soil carbon on other soil properties, is crucial for effective global soil carbon management. This knowledge is invaluable for evaluating carbon sequestration, although its scope is currently limited. Boosting soil carbon sequestration, particularly in arid regions, has direct and indirect implications for achieving over four Sustainable Development Goals: mitigating hunger, extreme poverty, enhancing environmental preservation, and addressing global climate concerns. Research into changes within SOC and SIC across surface and subsurface soils was conducted on aeolian deposits. In this specific case study, two sites sharing similar climates and conditions were chosen as sources of wind-blown sediment parent material. The aim was to discern variations in SOC, SIC, and STC storage in surface and subsurface soils between Sistan and Baluchistan Province (with rapeseed and date orchard cultivation) and Kerman Province (with maize cultivation) in southeastern Iran. The findings highlighted an opposing pattern in SOC and storage concerning soil depth, unlike SIC. The average SOC content was higher in maize cultivation (0.2%) compared to date orchard and rapeseed cultivation (0.11%), attributed to the greater evolution of these arid soils (aridisols) in comparison to the other region (entisols). Conversely, SIC content in the three soil uses demonstrated minimal variation. The mean STC storage was greater in maize cultivation (60.35 Mg ha-1) than in date orchard (54.67 Mg ha-1) and rapeseed cultivation (53.42 Mg ha-1). Within the examined drylands, SIC, originating from aeolian deposits and soil processes, assumes a more prominent role in total carbon storage than SOC, particularly within subsurface soils. Notably, over 90% of total carbon storage exists in the form of inorganic carbon in soils.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region.

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India.

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.

Reviewing the role of biochar in paddy soils: An agricultural and environmental perspective.

The main challenge of the twenty-first century is to find a balance between environmental sustainability and crop productivity in a world with a rapidly growing population. Soil health is the backbone of a resilient environment and stable food production systems. In recent years, the use of biochar to bind nutrients, sorption of pollutants, and increase crop productivity has gained popularity. This article reviews key recent studies on the environmental impacts of biochar and the benefits of its unique physicochemical features in paddy soils. This review provides critical information on the role of biochar properties on environmental pollutants, carbon and nitrogen cycling, plant growth regulation, and microbial activities. Biochar improves the soil properties of paddy soils through increasing microbial activities and nutrient availability, accelerating carbon and nitrogen cycle, and reducing the availability of heavy metals and micropollutants. For example, a study showed that the application of a maximum of 40 t ha-1 of biochar from rice husks prior to cultivation (at high temperature and slow pyrolysis) increases nutrient utilization and rice grain yield by 40%. Biochar can be used to minimize the use of chemical fertilizers to ensure sustainable food production.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region.

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

A review of the effects of environmental hazards on humans, their remediation for sustainable development, and risk assessment.

In the race for economic development and prosperity, our earth is becoming more polluted with each passing day. Technological advances in agriculture and rapid industrialization have drastically polluted the two pillars of natural resources, land and water. Toxic chemicals and microbial contaminants/agents created by natural and anthropogenic activities are rapidly becoming environmental hazards (EH) with increased potential to affect the natural environment and human health. This review has attempted to describe the various agents (chemical, biological, and physical) responsible for environmental contamination, remediation methods, and risk assessment techniques (RA). The main focus is on finding ways to mitigate the harmful effects of EHs through the simultaneous application of remediation methods and RA for sustainable development. It is recommended to apply the combination of different remediation methods using RA techniques to promote recycling and reuse of different resources for sustainable development. The report advocates for the development of site-specific, farmer-driven, sequential, and plant-based remediation strategies along with policy support for effective decontamination. This review also focuses on the fact that the lack of knowledge about environmental health is directly related to public health risks and, therefore, focuses on promoting awareness of effective ways to reduce anthropological burden and pollution and on providing valuable data that can be used in environmental monitoring assessments and lead to sustainable development.