Groundwater quality evaluation of the weathering mantle in crystalline basement aquifer system, southern Brazil.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

Advancing groundwater quality predictions: Machine learning challenges and solutions.

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Space-temporal analysis of groundwater quality in three areas of the state of Yucatán, México, and its relationship with existing anthropogenic activity.

Groundwater in the Yucatan State is the only source of water. The karst aquifer in Yucatan is vulnerable to pollution. Anthropic activities in Yucatan, such as pig farming, are usually related to high wastewater discharges and water pollution. Administrative and logistical issues in developing on-site sampling to evaluate water quality are common in Mexico. The RENAMECA database provides official data related to groundwater quality. However, no analysis based on this database has been reported. A groundwater quality evaluation based on five reference pig farms and the effect of spatial and temporal anthropic activities in the study area was developed. Eighteen wells based on their location concerning the selected pig farms were studied. On-site sampling and laboratory analysis of the supply water and wastewater in the study case farm were done. Fecal coliforms (FC) values (maximum 2850 MPN [100 mL] -1) in most cases for supply water wells exceeded the allowed limit by NOM-127-SAA1-2021. The year of monitoring was significant (P < 0.05) on FC concentrations. Population density and the proximity of wells to population centers affect negatively the presence of total dissolved solids (TDS) and total nitrogen (TN). TDS (maximum value 2620 mg L -1) and phosphorus presence could be related to agricultural activities, human settlements, and local aquifer conditions. A local wastewater treatment issue is evident. Groundwater is not quality for consumption without treatment. Regarding the issues in on-site water monitoring, database analysis provides an approximation of the real situation of groundwater quality.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook.

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

Assessment of Physicochemical Groundwater Quality and Hydrogeochemical Processes in an Area near a Municipal Landfill Site: A Case Study of the Toluca Valley.

Sanitary landfills are considered one of the main sources of contamination of water resources due to the generation of leachate with a high content of dissolved organic matter (DOM), inorganic material, and toxic elements. This study aimed to determine the influence of leachate on the physicochemical quality and hydrogeochemical processes which determine the chemical composition of groundwater in an area near a municipal sanitary landfill site. In situ parameters (pH, temperature, electrical conductivity, dissolved oxygen, ORP), physicochemical parameters (HCO3-, PO43-, Cl-, NO3-, SO42-, NH4+, Ca2+, Mg2+, Na+, K+), and dissolved organic matter were analyzed. The content of dissolved organic matter (DOM) was determined by 3D fluorescence microscopy. The presence of Cl-, NO3-, NH4+, PO43-, BOD, and COD indicated the presence of contamination. The significant correlation between NO3- and PO43- ions (r = 0.940) and DOM of anthropogenic origin in the 3D fluorescence spectra confirm that its presence in the water is associated with the municipal landfill site in question. The type of water in the area is Mg-HCO3, with a tendency to Na-HCO3 and Na-SO+-Cl. The water-rock interaction process predominates in the chemical composition of water; however, significant correlations between Na+ and Ca2+ (r = 0.876), and between K+ and Mg2+ (r = 0.980) showed that an ion exchange process had taken place. Likewise, there is enrichment by HCO3- and SO42- ions due to the mineralization of the organic matter from the leachate. The groundwater quality that supplies the study area is being affected by leachate infiltration from the sanitary landfill.

Sewage static system influence on the concentrations of nitrogen compounds in unconfined aquifers / Influência de sistemas estáticos de esgotamento sanitário nas concentrações de compostos nitrogenados em aquíferos livres

ABSTRACT This research aimed to investigate the relation between sanitary situation and groundwater quality, using the concentration of nitrogenous compounds. The aquifer studied is unconfined and situated in the periurban zone of Fortaleza (NE Brazil). Through the Geographic Information System (GIS), a relational database was created using data from the IBGE demographic census (2011), to analyze numbers of households linked to septic tanks or rudimentary cesspit. The groundwater quality was evaluated based on nitrogen compounds (N-NH3 +; NO2 -; N-NO3 -), pH, and total dissolved solids (TDS). The highest concentrations of nitrates are found in areas with a higher density of septic tanks and rudimentary cesspit. Furthermore, nitrate was more present in water table above 6.6 m, mainly in the interfluvial zones, which have a high oxidation potential. The results contribute to the loss of contamination, based on the number of households with septic tanks and rudimentary cesspit, in unconfined aquifers, which were more vulnerable to contamination, mainly in peripheric expansions areas in the cities, where the deficit in sewage services tends to be high.

RESUMO Esta pesquisa teve como objetivo investigar a relação entre a situação sanitária e a qualidade da água subterrânea, usando concentrações de compostos nitrogenados. O aquífero estudado é do tipo livre e se encontra situado na zona periurbana da cidade de Fortaleza (NE Brasil). Através do Sistema de Informação Geográfica (SIG), foi criado um banco de geodados relacional utilizando dados do censo demográfico do IBGE (2011), analisando o número de domicílios vinculados a fossas sépticas e fossas rudimentares. A qualidade da água subterrânea foi avaliada com base em compostos nitrogenados (N-NH3 +; NO2 -; N-NO3 -), pH e sólidos totais dissolvidos (STD). As altas concentrações de nitrato estão associadas a maior ocorrência de fossas sépticas e rudimentares, principalmente. Além disso, a ocorrência do nitrato ocorre em áreas de níveis estáticos acima de 6,6 m, principalmente nos setores interfluviais, que apresenta alto potencial de oxidação. Os resultados contribuem para predição da contaminação, tendo em vista a quantidade de domicílios com fossas sépticas e rudimentares, sobre aquíferos livres, que são mais vulneráveis à contaminação, sobretudo em áreas de expansão periférica da cidade, onde o déficit de esgotamento sanitário tende a ser maior.

Organochlorine Pesticides and Potentially Toxic Elements in Groundwater from a Protected Reserve in the Maya Region of Hopelchen, Mexico.

Water quality degradation by organochlorine pesticides and potentially toxic elements is of worldwide concern. This research explores groundwater conditions, regarding organochlorine pesticides and potentially toxic elements, in Hopelchen, Campeche, which is located in the buffer zone of the Calakmul Biosphere Reserve. Unfortunately, agriculture is allowed and agrochemical use is not monitored and sanctioned. Results show that Heptachlor, Endosulfan, and Dieldrin, all recognized carcinogens, had concentrations above the Mexican normative recommended values. Conversely, Cd and Ni concentrations were below recommended values. These results demonstrate that government intervention involving immediate control over agrochemical use is mandatory. Also, the results underscore the contamination of groundwater in several of the Calakmul Biosphere Reserve's buffer zones by organochlorine pesticides concentrations, posing a probable threat for local inhabitants who consume this water and use it for recreation.

Optimal design of monitoring networks for multiple groundwater quality parameters using a Kalman filter: application to the Irapuato-Valle aquifer.

A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.