Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Confined within a sugarcane monoculture: A participatory assessment of water pollution and potential health risks in the community of El Tiple, Colombia.

Communities neighboring monoculture plantations are vulnerable to different forms of pollution associated with agro-industrial operations. Herein, we examine the case of El Tiple, a rural Afro descendant community embedded within one of the largest sugarcane plantations in the Americas. We implemented a participatory approach to assess water pollution, exposure via water ingestion, and non-carcinogenic health risks associated with the use of local water sources available to the community. We conducted household surveys to unveil demographic characteristics and family dynamics linked to water consumption. Additionally, we measured water quality parameters and assessed the concentration glyphosate, its major metabolite (aminomethylphosphonic acid) and metals and metalloids. Drinking water El Tiple households is sourced from three primary sources: the local aqueduct system, water delivery trucks, and private deep wells. Tests on water samples from both the local aqueduct and delivery trucks showed no traces of pesticides, metals, or metalloids surpassing regulatory limits set by Colombian or EPA standards. However, we found concentration of contaminants of primary concern, including mercury (up to 0.0052 ppm) and lead (up to 0.0375 ppm) that exceed the permissible regulatory thresholds in water from groundwater wells. Residents of the peripheric subdivisions of El Tiple are four times more reliant on well water extraction than residents of the central area of the town due to lack of access to public drinking water and sanitation infrastructure. Finally, adult women and school-age children have a higher health risk associated with exposure to local pollutants than adult men due to their constant presence in the town. We conclude that expanding the coverage of clean water and sanitation infrastructure to include all households of the community would be the most recommended measure to minimize exposure and risk via ingestion of water pollutants.

Assessment of drinking water quality using Water Quality Index and synthetic pollution index in urban areas of mega city Lahore: a GIS-based approach.

The aim of the present study was to assess the drinking water quality in the selected urban areas of Lahore and to comprehend the public health status by addressing the basic drinking water quality parameters. Total 50 tap water samples were collected from groundwater in the two selected areas of district Lahore i.e., Gulshan-e-Ravi (site 1) and Samanabad (site 2). Water samples were analyzed in the laboratory to elucidate physico-chemical parameters including pH, turbidity, temperature, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), total hardness, magnesium hardness, and calcium hardness. These physico-chemical parameters were used to examine the Water Quality Index (WQI) and Synthetic Pollution Index (SPI) in order to characterize the water quality. Results of th selected physico-chemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. A GIS-based approach was used for mapping water quality, WQI, and SPI. Results of the present study revealed that the average value of temperature, pH, and DO of both study sites were within the WHO guidelines of 23.5 °C, 7.7, and 6.9 mg/L, respectively. The TDS level of site 1 was 192.56 mg/L (within WHO guidelines) and whereas, in site 2 it was found 612.84 mg/L (higher than WHO guidelines), respectively. Calcium hardness of site 1 and site 2 was observed within the range from 25.04 to 65.732 mg/L but, magnesium hardness values were higher than WHO guidelines. The major reason for poor water quality is old, worn-out water supply pipelines and improper waste disposal in the selected areas. The average WQI was found as 59.66 for site 1 and 77.30 for site 2. Results showed that the quality of the water was classified as "poor" for site 1 and "very poor " for site 2. There is a need to address the problem of poor water quality and also raise the public awareness about the quality of drinking water and its associated health impacts.

Health impact assessment of the surface water pollution in China.

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

Assessment of trace and macroelement accumulation in cyprinid juveniles as bioindicators of aquatic pollution: effects of diets and habitat preferences.

Juveniles of three cyprinids with various diets and habitat preferences were collected from the Szamos River (Hungary) during a period of pollution in November 2013: the herbivorous, benthic nase (Chondrostoma nasus), the benthivorous, benthic barbel (Barbus barbus), and the omnivorous, pelagic chub (Squalius cephalus). Our study aimed to assess the accumulation of these elements across species with varying diets and habitat preferences, as well as their potential role in biomonitoring efforts. The Ca, K, Mg, Na, Cd, Cr, Cu, Fe, Mn, Pb, Sr, and Zn concentration was analyzed in muscle, gills, and liver using MP-AES. The muscle and gill concentrations of Cr, Cu, Fe, and Zn increased with trophic level. At the same time, several differences were found among the trace element patterns related to habitat preferences. The trace elements, including Cd, Pb, and Zn, which exceeded threshold concentrations in the water, exhibited higher accumulations mainly in the muscle and gills of the pelagic chub. Furthermore, the elevated concentrations of trace elements in sediments (Cr, Cu, Mn) demonstrated higher accumulation in the benthic nase and barbel. Our findings show habitat preference as a key factor in juvenile bioindicator capability, advocating for the simultaneous use of pelagic and benthic juveniles to assess water and sediment pollution status.

A water quality assessment model involving novel fluorescence technology.

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Applications of engineered biochar in remediation of heavy metal(loid)s pollution from wastewater: Current perspectives toward sustainable development goals.

Environmental pollution of heavy metal(loid)s (HMs) caused adverse impacts, has become one of the emerging concerns and challenges worldwide. Metal(loid)s can pose significant threats to living organisms even when present in trace levels within environmental matrices. Extended exposure to these substances can lead to adverse health consequences in humans. Removing HM-contaminated water and moving toward sustainable development goals (SDGs) is critical. In this mission, biochar has recently gained attention in the environmental sector as a green and alternative material for wastewater removal. This work provides a comprehensive analysis of the remediation of typical HMs by biochars, associated with an understanding of remediation mechanisms, and gives practical solutions for ecologically sustainable. Applying engineered biochar in various fields, especially with nanoscale biochar-aided wastewater treatment approaches, can eliminate hazardous metal(loid) contaminants, highlighting an environmentally friendly and low-cost method. Surface modification of engineered biochar with nanomaterials is a potential strategy that positively influences its sorption capacity to remove contaminants. The research findings highlighted the biochars' ability to adsorb HM ions based on increased specific surface area (SSA), heightened porosity, and forming inner-sphere complexes with oxygen-rich groups. Utilizing biochar modification emerged as a viable approach for addressing lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), and chromium (Cr) pollution in aqueous environments. Most biochars investigated demonstrated a removal efficiency >90 % (Cd, As, Hg) and can reach an impressive 99 % (Pb and Cr). Furthermore, biochar and advanced engineered applications are also considered alternative solutions based on the circular economy.

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern.

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

A framework model to integrate sources and pathways in the assessment of river water pollution.

Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution are scarce, especially in the case of watersheds impacted by multiple hazardous activities. Therefore, a framework model for investigating the multiple sources of river water pollution was developed. The chosen study area was the Paraopeba River basin located in the Minas Gerais, Brazil. Besides multiple agriculture, industrial, and urban pollution sources, this region was profoundly affected by the rupture of the B1 tailings dam (in January 2019) at the Córrego do Feijão mine, resulting in the release of metal-rich waste. Considering this situation, thirty-nine physicochemical and hydromorphological parameters were examined in the Paraopeba River basin, in the 2019-2023 period. The analysis involved various statistical techniques, including bivariate and multivariate methods such as correlation analysis, principal component analysis, and clustering. The Paraopeba River was mainly impacted by metal contamination resulting from the dam collapse, whereas nutrient contamination, mainly from urban and industrial discharges, predominantly affected its tributaries. Additionally, the elevated concentrations of aluminum, iron, nitrate, and sulfate in both main river and tributaries can be attributed to diffuse and point source pollution. In terms of hydromorphology and soil type, the interaction between woody vegetation and erosion-resistant soils, especially latosols, contributes to the stability of riverbanks in the main river. Meanwhile, in the tributaries, the presence of neosols and sparse vegetation in urbanized areas promoted riverbank erosion potentially amplifying pollution. While the study was conducted in a particular watershed, the findings are based on a methodology that can be applied universally. Hence, the insights on surface water quality from this research can be a valuable resource for researchers studying watersheds with diverse pollution sources.

A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis.

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

Global occurrence characteristics, drivers, and environmental risk assessment of microplastics in lakes: A meta-analysis.

Microplastic (MP) pollution in lakes has received much attention as an increasing amount of plastic waste enters aquatic ecosystems. However, there is still a lack of comprehensive understanding of the global distribution patterns, environmental hazards, factors driving their presence, and the relationships between sources and sinks of MPs. In this study, we conducted a meta-analysis of drivers of lake MP pollution based on 42 articles on MP pollution from three different aspects: geographical distribution, driving factors and environmental risks. The results revealed differences in the MP pollution levels across the different sampling sites in the global lakes. Moreover, there is significant heterogeneity in the abundance of MPs among various lakes, whose distribution pattern is affected by geographical location, sampling method and extraction method. The size of the MPs differed significantly between water and sediment, and the proportion of small (<1 mm) MPs in sediment was significantly greater than that in water (72% > 46%). Environmental risk assessment reveals that the risk level of MP pollution in most lakes worldwide is low, and the environmental risk of pollution in lake water is higher than that in sediment. Based on the risk assessment and geographical location of the lake, the risk of MP pollution is related not only to human activities and economic development but also to local waste management practices, which directly impact the accumulation of MPs. Therefore, we suggest that the production of biodegradable low-risk polymer plastics instead of high-risk materials, and plastic solid waste recycling management should be strengthened to effectively mitigate the presence of MPs in the environment.

Assessment of crAssphage as a human fecal source tracking marker in the lower Great Lakes.

CrAssphage or crAss-like phage ranks as the most abundant phage in the human gut and is present in human feces-contaminated environments. Due to its high human specificity and sensitivity, crAssphage is a potentially robust source tracking indicator that can distinguish human fecal contamination from agricultural or wildlife sources. Its suitability in the Great Lakes area, one of the world's most important water systems, has not been well tested. In this study, we tested a qPCR-based quantification method using two crAssphage marker genes (ORF18-mod and CPQ_064) at Toronto recreational beaches along with their adjacent river mouths. Our results showed a 71.4 % (CPQ_064) and 100 % (ORF18-mod) human sensitivity for CPQ_064 and ORF18-mod, and a 100 % human specificity for both marker genes. CrAssphage was present in 57.7 % or 71.2 % of environmental water samples, with concentrations ranging from 1.45 to 5.14 log10 gene copies per 100 mL water. Though concentrations of the two marker genes were strongly correlated, ORF18-mod features a higher human sensitivity and higher positive detection rates in environmental samples. Quantifiable crAssphage was mostly present in samples collected in June and July 2021 associated with higher rainfall. In addition, rivers had more frequent crAssphage presence and higher concentrations than their associated beaches, indicating more frequent and greater human fecal contamination in the rivers. However, crAssphage was more correlated with E. coli and Enterococcus at the beaches than in the rivers, suggesting human fecal sources may be more predominant in driving the increases in E. coli and Enterococcus at the beaches when impacted by river plumes.

A fuzzy logic-based approach for groundwater vulnerability assessment.

Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating and weighting parameters is the DRASTIC model's major weakness, which increases uncertainty. This study developed a Mamdani fuzzy logic (MFL) in combination with data mining to handle this uncertainty and predict the specific vulnerability. To highlight this approach, the susceptibility of the Qorveh-Dehgolan plain (QDP) and the Ardabil plain aquifers was investigated. The DRASTIC index was calculated between 63 and 160 for the Ardabil plain and between 39 and 146 for the QDP. Despite some similarities between vulnerability maps and nitrate concentration maps, the results of the DRASTIC model based on nitrate concentration cannot be verified according to Heidke skill score (HSS) and total accuracy (TA) criteria. Then the MFL was developed in two scenarios; the first included all seven parameters, whereas the second used only four parameters of the DRASTIC model. The results showed that, in the first scenario of the MFL modeling, TA and HSS values were respectively 0.75 and 0.51 for the Ardabil plain and 0.45 and 0.33 for the QDP. In addition, according to the TA and HSS values, the proposed model was more reliable and practical in groundwater vulnerability assessment than the traditional method, even using four input data.

A critical review on organophosphate esters in drinking water: Analysis, occurrence, sources, and human health risk assessment.

Organophosphate esters (OPEs) are ubiquitous in the environment. Copious studies assessed OPEs in various environmental media. However, there is limited summative information about OPEs in drinking water. This review provides comprehensive data for the analytical methods, occurrence, sources, and risk assessment of OPEs in drinking water. In general, liquid-liquid extraction and solid-phase extraction are the most common methods in the extraction of OPEs from drinking water, while gas chromatography and liquid chromatography are the most commonly used instrumental methods for detecting OPEs in drinking water. On the basis of these techniques, a variety of methods on OPEs pretreatment and determination have been developed to know the pollution situation of OPEs. Studies on the occurrence of OPEs in drinking water show that the total concentrations of OPEs vary seasonally and regionally, with tris(1-chloro-2-isopropyl) phosphate and tris(2-chloroethyl) phosphate dominant among different kinds of drinking water. Source identification studies show that there are three main sources of OPEs in drinking water: 1) source water contamination; 2) residual in drinking water treatment process; 3) leakage from device or pipeline. Besides, risk assessments indicate that individual and total OPEs pose no or negligible health risk to human, but this result may be significantly underestimated. Finally, the current knowledge gaps on the research of OPEs in drinking water are discussed and some suggestions are provided for future environmental research.

A modified DRASTIC model for groundwater vulnerability assessment using connecting path and analytic hierarchy process methods.

Groundwater plays a vital role in supporting water for the different needs of domestic, agricultural, and industrial sectors, and its vulnerability assessment to pollution is a valuable tool for establishing protective and preventive management. DRASTIC is a well-known GIS-based model for assessing groundwater vulnerability to pollution, which uses seven parameters including depth-to-water level, net recharge, aquifer media, soil media, topography, the impact of the vadose zone, and hydraulic conductivity. The predefined weights of DRASTIC parameters have made a barrier to its applicability for different regions with different hydroclimatic conditions. To overcome this problem, it has been suggested to apply analytic hierarchy process (AHP) method for modifying the model by adjusting the weights of the parameters. AHP is a widely used method to elicit experts' judgments about different involving parameters through constructing pairwise comparison matrixes (PCMs). Since AHP calculates the weights by performing pairwise comparisons between the parameters, achieving consistent comparisons is difficult when the number of parameters increases. The objective of this research is to modify the DRASTIC model by integrating the connecting path method (CPM) and AHP. The proposed methodology involves asking experts to perform a number of pairwise comparisons between the parameters and then construct an incomplete PCM using the obtained information. To complete the missing values in the PCM, CPM is employed. The CPM is an effective approach that not only estimates missing judgments but also ensures minimal geometric consistency. The proposed method along with DRASTIC and pesticide DRASTIC models is applied to Khoy County, which is located in the northwest part of Iran. The efficiency of the proposed method was further confirmed through the results of the Pearson coefficient test conducted on nitrate concentrations. The test revealed correlation values of 0.47, 0.27, and 0.57 for DRASTIC, pesticide DRASTIC, and modified DRASTIC, respectively. These results demonstrated that the proposed method provides a more precise evaluation of groundwater vulnerability.

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria.

In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%-45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.

GIS-based multicriteria and artificial neural network (ANN) investigation for the assessment of groundwater vulnerability and pollution hazard in the Braga shallow aquifer (Central Tunisia): A critical review of generic and modified DRASTIC models.

Groundwater vulnerability and pollution hazard in the Braga shallow aquifer were assessed through an integrated GIS-based multicriteria analysis and Artificial Neural Network (ANN) approach, using DRASTIC and DRASTIC-LU models. The DRASTIC model integrates seven geological parameters. The DRASTIC-LU model includes an eighth parameter in addition to the previous ones. This parameter is the land use that represents the human source of groundwater pollution. The DRASTIC map showed four classes: very low (12.06%), low (81.88%), moderate (5.16%) and high (0.9%), where the vulnerability index ranged between 43 and 159. The DRASTIC-LU vulnerability index ranged between 53 and 204 and showed five classes: very low (3.10%), low (14.06%), moderate (17.11%), high (27.08%) and very high (38.65%). The DRASTIC-LU vulnerability map indicated that the high pollution risk is imposed by the intensive vegetable cultivation and the domestic wastewater. The pollution hazard index (PHI) was calculated based on the ANN modelling, using the land-use as an input and the vulnerability as a hidden layer. The DRASTIC model-based PHI map showed six classes: rare hazard (8.6%), very low (30.97%), low (6.18%), moderate (51.45%), high (2.43%) and very high (0.37%). While, The DRASTIC-LU model-based PHI map (PHILU) showed seven classes: rare hazard (2.91%), very low (11.9%), low (12.33%), moderate (13.78%), high (9.23%), very high (15.46%) and extremely hazardous (34.39%). The validation of these maps indicated that the DRSTIC-LU-based PHI is more reliable as it accurately identifies the hazardous zones.

Distribution and pollution assessment of marine debris off-shore Shandong from 2014 to 2022.

This study analysed marine debris monitoring data for Shandong from 2014 to 2022 to obtain a better understanding of marine debris stocking off-shore Shandong in order to reduce marine debris pollution and improving the ecological environment of ocean. The results indicated that the abundance of coastal marine debris was 45,832 items/km2 (1118.5 kg/km2); the abundance of small/medium sized floating marine debris was 8976 items/km2 (1.38 kg/km2); and the abundance of large floating marine debris was 35 items/km2; the abundance of seafloor debris was 104 item/km2 (0.22 kg/km2). Compared with the nationwide abundance of marine debris, the quantity density of floating marine debris in Shandong was higher; the abundance of coastal marine debris and quality density of floating marine debris were lower. The majority of the Shandong marine debris was small/medium plastic, mostly from human activities. And we found no significant correlation between precipitation and the abundance of marine debris by statistical analysis.

A critical application of different methods for the vulnerability assessment of shallow aquifers in Zhengzhou City.

Groundwater vulnerability can partially reflect the possibility of groundwater contamination, which is crucial for ensuring human health and a good ecological environment. The current study seeks to assess the groundwater vulnerability of Zhengzhou City by adopting an amended version of the traditional DRASTIC model, i.e., the DRASTICL model, which incorporates land use type indicators. More specifically, the AHP-DRASTICL, entropy-DRASTICL, and AE-DRASTICL models were established by optimizing weights using the analytic hierarchy process (AHP) and entropy weight method. The evaluation results for these five models were divided into five levels: very low, low, medium, high, and very high. Using Spearman's rank correlation coefficient, the nitrate concentration was used to verify the groundwater vulnerability assessment results. The AE-DRASTICL model was found to perform the best, with a Spearman correlation coefficient of 0.78. However, the AHP and entropy weight method effectively improved the accuracy of vulnerability assessment results, making it more suitable for the study area. This study provides important insights to inform the design of strategies to protect groundwater in Zhengzhou.

Water quality assessment for organic matter load in urban rivers considering land cover dynamics.

The strategy of considering a model that is comparable to the Soil Conservation Service Curve-Number (SCS-CN) method that employs land use maps to estimate the effects of land use on the water quality has considerable potential for application. This paper presents the LUPC (Land Use Pollutant Contribution) Model to estimate water pollution from the watershed land use obtained by satellite image classification (Sentinel-2). It defines that each land use produces a specific pollutant load per unit area, called Pollutant Standard Index (PSI), which undergoes degradation and/or retention until it reaches the river. This decay estimate is based on a Kernel Function. Organic matter (OM) was the pollutant chosen for the definition of the LUPC model and fractions of labile and refractory organic matter (LOM, ROM). The model was applied to the Barigüi River basin, and five samples were collected at 12 points along the river. Water quality parameters such as dissolved organic carbon (DOC) and UV-Visible absorbance in addition to chemical and biological oxygen demand (COD and BOD), dissolved oxygen (DO), and nitrogen and phosphorus fractions were the reference for modeling purposes. The results indicate that organic loads can be estimated from watershed characteristics, despite influence from seasonal influences captured by the PSI values and the basin shape parameter. Considering its versatile response, the LUPC model can be used for integrated water resources and land use planning and management and be indicator of the potential pollution of rivers by OM.