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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Environmental concerns about microplastics (MPs) have motivated research of their sources, occurrence, and fate in aquatic and soil ecosystems. To mitigate the environmental impact of MPs, biodegradable plastics are designed to naturally decompose, thus reducing the amount of environmental plastic contamination. However, the environmental fate of biodegradable plastics and the products of their incomplete biodegradation, especially micro-biodegradable plastics (MBPs), remains largely unexplored. This comprehensive review aims to assess the risks of unintended consequences associated with the introduction of biodegradable plastics into the environment, namely, whether the incomplete mineralization of biodegradable plastics could enhance the risk of MBPs formation and thus, exacerbate the problem of their environmental dispersion, representing a potentially additional environmental hazard due to their presumed ecotoxicity. Initial evidence points towards the potential for incomplete mineralization of biodegradable plastics under both controlled and uncontrolled conditions. Rapid degradation of PLA in thermophilic industrial composting contrasts with the degradation below 50 % of other biodegradables, suggesting MBPs released into the environment through compost. Moreover, degradation rates of <60 % in anaerobic digestion for polymers other than PLA and PHAs suggest a heightened risk of MBPs in digestate, risking their spread into soil and water. This could increase MBPs and adsorbed pollutants' mobilization. The exact behavior and impacts of additive leachates from faster-degrading plastics remain largely unknown. Thus, assessing the environmental fate and impacts of MBPs-laden by-products like compost or digestate is crucial. Moreover, the ecotoxicological consequences of shifting from conventional plastics to biodegradable ones are highly uncertain, as there is insufficient evidence to claim that MBPs have a milder effect on ecosystem health. Indeed, literature shows that the impact may be worse depending on the exposed species, polymer type, and the ecosystem complexity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Antibiotic resistance genes (ARGs) are a major threat to human and environmental health. This study investigated the occurrence and distribution of ARGs in Lake Cajititlán, a hypereutrophic subtropical lake in Mexico contaminated by anthropogenic sources (urban wastewater and runoff from crop and livestock production). ARGs (a total of 475 genes) were detected in 22 bacterial genera, with Pseudomonas (144 genes), Stenotrophomonas (88 genes), Mycobacterium (54 genes), and Rhodococcus (27 genes) displaying the highest frequencies of ARGs. Among these, Pseudomonas aeruginosa and Stenotrophomonas maltophilia showed the highest number of ARGs. The results revealed a diverse array of ARGs, including resistance to macrolides (11.55 %), aminoglycosides (8.22 %), glycopeptides (6.22 %), tetracyclines (4 %), sulfonamides (4 %), carbapenems (1.11 %), phenicols (0.88 %), fluoroquinolones (0.44 %), and lincosamides (0.22 %). The most frequently observed ARGs were associated with multidrug resistance (63.33 %), with MexF (42 genes), MexW (36 genes), smeD (31 genes), mtrA (25 genes), and KHM-1 (22 genes) being the most common. Lake Cajititlán is a recreational area for swimming, fishing, and boating, while also supporting irrigation for agriculture and potentially acting as a drinking water source for some communities. This raises concerns about the potential for exposure to antibiotic-resistant bacteria through these activities. The presence of ARGs in Lake Cajititlán poses a significant threat to both human and environmental health. Developing strategies to mitigate the risks of antibiotic resistance, including improving wastewater treatment, and promoting strategic antibiotic use and disposal, is crucial. This study represents a significant advancement in the understanding of antibiotic resistance dynamics in a hypereutrophic subtropical lake in a developing country, providing valuable insights for the scientific community and policymakers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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 (NO 3 - -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 NO 3 - -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 NO 3 - -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 NO 3 - -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 NO 3 - -N levels in groundwater. Moreover, the NO 3 - -N hotspots align with reported NO 3 - -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 NO 3 - -N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Wastewater-based epidemiology offers a time- and cost-effective way to monitor SARS-CoV-2 spread in communities and therefore represents a complement to clinical testing. WBE applicability has been demonstrated in a number of cases over short-term periods as a method for tracking the prevalence of SARS-CoV-2 and an early-warning tool for predicting outbreaks in the population. This study reports SARS-CoV-2 viral loads from wastewater treatment plants (WWTPs) and hospitals over a 6-month period (June to December 2020). Results show that the overall range of viral load in positive tested samples was between 1.2 × 10 3 and 3.5 × 10 6 gene copies/l, unveiling that secondary-treated wastewaters mirrored the viral load of influents. The interpretation suggests that the viral titers found in three out of four WWTPs were associated to clinical COVID-19 surveillance indicators preceding 2-7 days the rise of reported clinical cases. The median wastewater detection rate of SARS-CoV-2 was one out of 14,300 reported new cases. Preliminary model estimates of prevalence ranged from 0.02 to 4.6% for the studied period. This comprehensive statistical and epidemiological analysis demonstrates that the applied wastewater-based approach to COVID-19 surveillance is in general consistent and feasible, although there is room for improvements., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The Caplina/Concordia transboundary coastal aquifer system, located in the Atacama Desert, is the primary source of water supply for domestic use and irrigation for La Yarada-Los Palos (Peru) and Concordia (Chile) agriculture districts, and to a lesser extent, for Tacna province public supply use (Peru). Despite the scarce amount of rainfall (<20 mm/year) in the area and the limited recharge coming from the Andean highlands, this transboundary aquifer system has been overexploited mainly for agriculture since before the 2000s on the Peruvian side. Consequently, this has caused groundwater depletion and seawater intrusion. In this study, comprehensive hydrogeological information was integrated to understand the aquifer system's behavior and the effects to which it has been subjected to groundwater overexploitation. To that end, a 3D hydrogeological framework was developed using the LEAPFROG software and a constant-density groundwater flow model with equivalent heads was generated in FEFLOW software, which was adjusted with Monte Carlo analysis and conventional automated calibration. Finally, eight scenarios, considering various water resource management options proposed by the authority and potential climatic trends (CMIP6), were simulated from 2020 to 2040. The results showed that between 2002 and 2020, the increase in the seawater wedge and the average groundwater level decline were 216 hm 3 /year and 7 m, respectively. It is expected that the depletion will continue with a groundwater level decline between 5 and 8 m and an increase in the seawater wedge between 1120 hm 3 /year and 1175 hm 3 /year for the forecast period. The study concludes that the aquifer system will remain unsustainable for the next 20 years, regardless of the selected scenarios, and suggests that any mitigation measure requires the participation of stakeholders from Peru, Chile, and Bolivia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

We previously observed that exposure to a complex mixture of high molecular weight polycyclic aromatic hydrocarbons (PAHs) increased sensitivity of rainbow trout (Oncorhynchus mykiss) to subsequent challenge with Aeromonas salmonicida, the causative agent of furunculosis. In this study, we evaluate potential mechanisms associated with disease susceptibility from combined environmental factors of dietary PAH exposure and pathogen challenge. Rainbow trout were fed a mixture of ten high molecular weight PAHs at an environmentally relevant concentration (7.82μg PAH mixture/g fish/day) or control diet for 50 days. After 50 days of PAH exposure, fish were challenged with either Aeromonas salmonicida at a lethal concentration 30 (LC 30 ) or growth media without the pathogen (mock challenge). Head kidneys were collected 2, 4, 10 and 20 days after challenge and gene expression (q<0.05) was evaluated among treatments. In animals fed the PAH contaminated diet, we observed down-regulation of expression for innate immune system genes in pathways (p<0.05) for the terminal steps of the complement cascade (complement component C6) and other bacteriolytic processes (lysozyme type II) potentially underlying increased disease susceptibility after pathogen challenge. Increased expression of genes associated with hemorrhage/tissue remodeling/inflammation pathways (p<0.05) was likely related to more severe head kidney damage due to infection in PAH-fed compared to control-fed fish. This study is the first to evaluate transcriptional signatures associated with the impact of chronic exposure to an environmentally relevant mixture of PAHs in disease susceptibility and immunity., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The transfer of genetic material among bacteria in the environment can occur both in the planktonic and attached state. Given the propensity of organisms to exist in sessile microbial communities in oligotrophic subsurface conditions, and that such conditions typify the subsurface, this study focuses on exploratory modeling of horizontal gene transfer among surface-associated Escherichiacoli in the subsurface. The mathematics so far used to describe the kinetics of conjugation in biofilms are developed largely from experimental observations of planktonic gene transfer, and are absent of lags or plasmid stability that appear experimentally. We develop a model and experimental system to quantify bacterial filtration and gene transfer in the attached state, on granular porous media. We include attachment kinetics described in Nelson et al. (2007) using the filtration theory approach of Nelson and Ginn (2001, 2005) with motility of E. coli described according to Biondi et al. (1998)., (Copyright 2009 Elsevier B.V. All rights reserved.)

Pharmaceutically active compounds (PhACs) in the environment lately have been acknowledged to constitute a health risk for humans and terrestrial and aquatic ecosystems. Human and veterinary applications are the main sources of PhACs in the environment and the major pathways are excretion and discharge to the environment through sewage treatment plants (STPs). In this study, the occurrence and fate of selected human PhACs belonging to different therapeutic classes (non-steroidal anti-inflammatory drugs, lipid regulators, anti-epileptics, antibiotics and beta-blockers) were investigated in a small river in the very south of Sweden. The objectives of the study were to evaluate the impact of a high and rather constant load in sewage influent on downstream concentrations and whether substances that are metabolized to a high degree in humans also show a low persistency in a natural aquatic environment. Water samples were collected from the influent and effluent of the STP, in a series of dammed reservoirs leading to discharge into the Höje River in Sweden, and at several locations in the river downstream of the outfall. After enrichment by solid-phase extraction, the compounds were analyzed using GC-MS (methylated derivatives) or LC-MS/MS. In addition to the targeted pharmaceuticals, GC-MS analysis of the samples revealed the presence of other sewage-related pollutants (triclosan, caffeine, flame-retardants, antioxidants) and these results where included for comparison. Removal efficiencies were calculated in the STP and found to display a wide range with numerous species surviving treatment at greater than half their influent concentrations, including diclofenac, the anti-epileptic carbamazepine, a beta-blocker (propanolol), and antibiotics trimetoprim and sulfamethoxazole. Low removals were also observed for Tris(2-chloroisopropyl)phosphate (flame retardant), BHT-aldehyde (oxidation product of BHT) and synthetic musk (HHCB). The concentrations of chloride (Cl(-)) and boron (B) were used as natural inert tracers to estimate the relative extent of dilution of PhACs measured in the effluent of the STP on concentrations measured further downstream. Based on spatial trends of concentrations (recalculated to reflect a hypothetical scenario with no dilution), ibuprofen, ketoprofen, naproxen and dicofenac were shown to be subject to significant abiotic or biotic transformations or physical sequestration in the river. The beta-blockers atenolol, metoprolol and propanolol, the antibiotics trimetoprim and sulfametoxazole, and carbamazepine demonstrated a high degree of persistence. Fluctuations in the concentration of carbamazepine and gemfibrozil were observed along the series of reservoirs and within the river and are hypothesized to be due to release of parent compound from glucuronides. Several of the investigated substances (metaprolol, propanolol and carbamazepin) that exhibit low excretion rates as parent compounds demonstrate a surprising persistence in the aquatic environment. It is concluded that pharmaceutical substances with a high metabolic rate in humans (low excretion rate) do not necessarily induce a short lifetime in aquatic environments. Results from this study emphasize the need for a broader view on the concept of persistence that accounts for loading rates, in addition to removal mechanisms (e.g., transformation, volatility and physical sequestration by solids), under a variety of spatial and temporal scales.