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 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.

Critical review on phytoremediation of polyfluoroalkyl substances from environmental matrices: Need for global concern.

Poly- and perfluoroalkyl substances (PFAS) are a class of emerging organic contaminants that are impervious to standard physicochemical treatments. The widespread use of PFAS poses serious environmental issues. PFAS pollution of soils and water has become a significant issue due to the harmful effects of these chemicals both on the environment and public health. Owing to their complex chemical structures and interaction with soil and water, PFAS are difficult to remove from the environment. Traditional soil remediation procedures have not been successful in reducing or removing them from the environment. Therefore, this review focuses on new phytoremediation techniques for PFAS contamination of soils and water. The bioaccumulation and dispersion of PFAS inside plant compartments has shown great potential for phytoremediation, which is a promising and unique technology that is realistic, cost-effective, and may be employed as a wide scale in situ remediation strategy.

Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon's entropy, fuzzy logic and automatic linear model.

The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.

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.

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.

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.

Abundance and characteristics of microplastic in some commercial species from the Persian Gulf, Iran.

Global production of plastics has increased dramatically in recent decades and is considered a major threat to marine life and human health due to their stability, persistence, and potential to move through food chains. The study was conducted to detect, identify and quantify microplastics (MP) in the gastrointestinal tract (GI) of some commercial fish species in the North Persian Gulf in Bushehr Province: Psettodes erumei, Sphyraena jello, Sillago sihama, Metapenaeus affinis and Portunus segnis. A total of 216 plastic particles were collected from 102 individuals (72.68% of all sampled individuals; MP prevalence of 85.1% for M. affinis, 80% for P. segnis, 70% for P.erumei, 60.3% for S.sihama, 45.2% for S.jello). The average number of microplastics per organism was 2.26 ± 0.38 MP/ind (considering only species that ingested plastic, n = 102) and 1.51 ± 0.40 pieces/ind (considering all species studied, n = 140). Microfibers accounted for 58.49% of the total microplastics, followed by fragments (33.02%) and pellets (8.49%). The most common color of microplastic was black (52.83%), followed by blue (22.64%) and transparent (15.09%). The length of microplastic ranged from 100 to 5000 µm with an average of 854 ± 312 µm. Microplastics were significantly (p < 0.05) abundant in two shrimp studied: M. affinis and P. segnis (plastic in 80% of individuals studied) and to a lesser extent in the pelagic barracuda fish S. jello (plastic in 45% of individuals studied). The main synthetic polymers identified by Fourier transform infrared spectroscopy (FTIR) were polyethylene (38%), polypropylene (24%), polystyrene (17%), polyethylene terephthalate (11%) and polyamide (10%). The pollutant load index and lifetime accumulation index were calculated to identify the most polluted species and their toxicity to human health. The white shrimp M. affinis was identified as the most polluted and toxic species for MP based on PLI. The present study can provide valuable data for further research and a background for the control and monitoring of this pollutant in the coastal environment of Bushehr province.

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.

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.

Mobilization and health risk assessment of fertilizer induced uranium in coastal groundwater.

Uranium (U) in groundwater is hazardous to human health, especially if it is present in drinking water. The semiarid regions of southern India chiefly depend on groundwater for drinking purposes. In this regard, a comprehensive sampling strategy was adopted to collect groundwater representing different lithologies of the region. The samples were collected in two different seasons and analysed for major and minor ions along with total U in the groundwater. Two samples during pre monsoon (PRM) and seven samples during post monsoon (POM) had U > 30 µgL-1, which is above the World Health Organization's provisional guideline value. The high concentration of U (188 µgL-1) was observed in the alluvial formation though a few samples showed the release of U near the pink granite (39 µgL-1) and the concentration was low in the lateritic formation (10 µgL-1). The uranyl carbonato complexes UO2(CO3)22- and UO2(CO3)34- were associated with high pH which facilitated the transport of U into groundwater especially during POM. U3O8 is the major form observed in groundwater compared to either UO2 or UO3 in the both seasons. The uranium oxides were observed to be more prevalent at the neutral pH. Though U concentration increases with pH, it is mainly governed by the redox conditions. The principal component analysis (PCA) analysis also suggested redox conditions in groundwater to be the major process facilitating the U release mechanism regardless of the season. The POM season has an additional source of U in groundwater due to the application of nitrogenous fertilizers in the alluvium region. Furthermore, redox mobilization factor was predominantly observed near the coastal region and in the agricultural regions. The process of infiltration of the fertilizer-induced U was enhanced by the agricultural runoff into the surface water bodies in the region. Health risk assessment was also carried out by determining annual effective dose rate, cancer mortality risk, lifetime average daily dose and hazard quotient to assess the portability of groundwater in the study area. Artificial recharge technique and reducing the usage of chemical based fertilizers for irrigation are suggested as sustainable plans to safeguard the vulnerable water resource in this region.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

A review on global status of fresh and saline groundwater discharge into the ocean.

Submarine groundwater discharge (SGD) is the groundwater flow from land to the sea across the seabed, and it includes both terrane freshwater and recirculated seawater in the sub-surface. This review (i) systematically evaluates findings of various quantification methodologies, (ii) examines the estimated SGD in scientific publications between 2000 and 2020, and (iii) quantitatively evaluates current situation of coastal zone management through the bibliometric analysis of research papers. Apart from enhancing the shortage of groundwater resources in coastal area, the SGD brings nutrients (nitrate and phosphate), toxic heavy metals, and organic compounds, and thus contaminate the seawater. Therefore, the improved understanding about location and quantity of global SGD is essential to conserve the coastal and ocean ecosystems.

Inferences on metal pollution in the natural spawning zone of Bangladesh river and pollution management strategies.

The purpose of this study was to evaluate the metal concentrations in the Halda River in Bangladesh to determine the quality of the water and sediment in the natural spawning zone. Fe > Zn > Cr > Cd > Cu was the order of the metals in water, whereas Fe > Zn > Cd > Cu was the order in sediments. Almost all of the heavy metals in the water and sediment had been found within the established limits, with the exception of Cr and Fe in the river and Cu in the sediment. In the case of water, Cr vs. Zn was found to have the strongest correlation (r = 0.96). Due to the coagulation and adsorption processes, it was shown that Fe and Zn had a substantial correlation of 0.96, Cu and Cd of 0.91, and Cr of 0.78 with Zn. Hazard quotient values of Cd show the not potable nature of Halda river surface water and might give adverse health effects for all age groups except Cu and Zn. Pollution load index values indicated the uncontaminated nature of the river bottom sediments. Natural and human activities were the key factors influencing the accumulation and movement of heavy metals in the water and sediments. Contamination sources are industrial effluents, garbage runoff, farming operations, and oil spills from fishing vessels which are comparable according to multivariate statistical analysis. Ion exchange, absorption, precipitation, complexation, filtration, bio-absorption, redox reaction, and reverse osmosis were considered to be effective for the degradation of metal concentrations. The feasibility of the suggested metal reduction procedures has to be studied to know which is optimally appropriate for this river region. It is expected that this study could provide a useful suggestion to decrease the metal pollution in the river.

Occurrence of Heavy Metals in Groundwater Along the Lithological Interface of K/T Boundary, Peninsular India: A Special Focus on Source, Geochemical Mobility and Health Risk.

Evaluation of the hydrogeochemical processes governing the heavy metal distribution and the associated health risk is important in managing and protecting the health of freshwater resources. This study mainly focused on the health impacts due to the heavy metals pollution in a known Cretaceous-Tertiary (K/T) contact region (Tiruchinopoly, Tamilnadu) of peninsular India, using various pollution indices, statistical, and geochemical analyses. A total of 63 samples were collected from the hard rock aquifers and sedimentary formations during southwest monsoon and analysed for heavy metals, such as Li, Be, Al, Rb, Sr, Cs, Ba, pb, Mn, Fe, Cr, Zn, Ga, Cu, As, Ni, and Co. Ba was the dominant element that ranged from 441 to 42,638 µg/l in hard rock aquifers, whereas Zn was the major element in sedimentary formations, with concentrations that ranged from 44 to 118,281 µg/l. The concentrations of Fe, Ni, Cr, Al, Cr, and Ni fell above the permissible limit in both of the formations. However, the calculated heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and the degree of contamination (Cd) parameters were higher in the sedimentary formation along the contact zone of the K/T boundary. Excessive health risks from consumption of contaminated groundwater were mostly confined to populations in the northern and southwestern regions of the study area. Carcinogenic risk assessment suggests that there are elevated risks of cancer due to prolonged consumption of untreated groundwater. Ba, Sr, and Zn were found to be geochemically highly mobile due to the partitioning between the rock matrix and groundwater, aided by the formation of soluble carbonato-complexes. Factor analysis indicates that the metals are mainly derived from the host rocks and anthropogenic inputs are relatively insignificant. Overall, this study indicated that groundwater in K/T contact zones is vulnerable to contamination because of the favorable geochemical factors. Long-term monitoring of such contact zones is required to avert the potential health hazards associated with consumption of the contaminated groundwater.

ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area.

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular models. ANFIS-MOA models were also useful for resolving the subjectivity of physical and hydrogeological parameters in original DRASTIC method (ODM) and MDM. It is expected that ANFIS-PSO models will produce the excellent results in assessing groundwater contamination vulnerability and that they can greatly contribute to the groundwater security in other areas of the world as well as Miryang City of South Korea.

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.