Unraveling the occurrence of contaminants of emerging concern in groundwater from urban setting: A combined multidisciplinary approach and self-organizing maps

Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions. The analysis reveals a growing trend in publications over the 24-year period, with a sharp expansion since 2020. China, the USA, India, and Iran are identified as the leading contributors to publications and citations, with prominent institutions such as Jilin University, the United States Geological Survey, and the University of Tabriz. Moreover, keyword frequency analysis indicates that principal component analysis (PCA) is the most commonly used method, followed by artificial neural network (ANN) and hierarchical clustering analysis (HCA). The most studied groundwater pollutants include nitrate, arsenic, heavy metals, and fluoride. As machine learning has rapidly advanced, research focuses have evolved from fundamental tasks like hydrochemical evolution analysis, water quality index evaluation, and groundwater vulnerability assessments to more complex issues, such as pollutant concentration prediction, pollution risk assessment, and pollution source identification. Despite these advances, challenges related to data quality, data scarcity, model generalization, and interpretability remain. Future research should prioritize data sharing, improving model interpretability, broadening research horizons and advancing theory-guided machine learning. These will enhance our understanding of groundwater pollution mechanisms, and ultimately facilitate more effective pollution control and remediation strategies. In summary, this review provides valuable insights and suggestions for researchers and policymakers working in this critical field.

Tricks and tracks of prevalence, occurrences, treatment technologies, and challenges of mixtures of emerging contaminants in the environment: With special emphasis on microplastic

This paper aims to emphasize the occurrence of various emerging contaminant (EC) mixtures in natural ecosystems and highlights the primary concern arising from the unregulated release into soil and water, along with their impacts on human health. Emerging contaminant mixtures, including pharmaceuticals, personal care products, dioxins, polychlorinated biphenyls, pesticides, antibiotics, biocides, surfactants, phthalates, enteric viruses, and microplastics (MPs), are considered toxic contaminants with grave implications. MPs play a crucial role in transporting pollutants to aquatic and terrestrial ecosystems as they interact with the various components of the soil and water environments. This review summarizes that major emerging contaminants (ECs), like trimethoprim, diclofenac, sulfamethoxazole, and 17α-Ethinylestradiol, pose serious threats to public health and contribute to antimicrobial resistance. In addressing human health concerns and remediation techniques, this review critically evaluates conventional methods for removing ECs from complex matrices. The diverse physiochemical properties of surrounding environments facilitate the partitioning of ECs into sediments and other organic phases, resulting in carcinogenic, teratogenic, and estrogenic effects through active catalytic interactions and mechanisms mediated by aryl hydrocarbon receptors. The proactive toxicity of ECs mixture complexation and, in part, the yet-to-be-identified environmental mixtures of ECs represent a blind spot in current literature, necessitating conceptual frameworks for assessing the toxicity and risks with individual components and mixtures. Lastly, this review concludes with an in-depth exploration of future scopes, knowledge gaps, and challenges, emphasizing the need for a concerted effort in managing ECs and other organic pollutants.

Assessing health risks in bottled water: chemical compounds and their impact on human health

Bottled mineral and spring water constitute one of the main sources of drinking water. Relevant legal acts in each country individually regulate the highest permitted concentrations of harmful substances in these waters. However, current regulations do not take into account newly emerging contaminants such as BPA. Analysis of the chemical composition of 72 bottled waters from the Polish market showed that undesirable elements occur in quantities that do not exceed the maximum permissible concentrations. Special attention should be paid to bottled therapeutic water, which may contain elevated concentrations of some micronutrients, such as Al, B, Ba, Fe, Mn, or Sr contributing to the pattern of health risk with excessive consumption of this type of water. The presence of BPA was confirmed in 25 tested waters. The calculated hazard index values showed that the most exposed group are children up to 12 years of age. The greatest attention should be paid to waters with high mineralisation, for which the calculated risk values are the highest.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Advanced technologies for the determination of quantitative structure-activity relationships and degradation efficiency of micropollutants and their removal in water - A review

The growing concentrations of micropollutants in aquatic ecosystems are a global water quality issue. Understanding micropollutants varied chemical composition and potency is essential to solving this complex issue. Micropollutants management requires identifying contaminants to reduce, optimal reduction targets, and the best wastewater recycling locations. Management requires appropriate technological measures. Pharmaceuticals, antibiotics, hormones, and other micropollutants can enter the aquatic environment from point and diffuse sources, with wastewater treatment plants (WWTPs) distributing them in urban areas. Micropollutants like pharmaceuticals and hormones may not be removed by conventional WWTPs. Micropollutants affect the EU, especially in densely populated areas where surface water is consumed. This review examines several technological options that can be integrated into existing treatment methods to address this issue. In this work, oxidation, activated carbon, and their combinations as potential solutions, considering their efficacy and cost were evaluated. This study illuminates micropollutants origin and physico-chemical properties, which affect distribution, persistence, and environmental impacts. Understanding these factors helps us develop targeted micropollutant mitigation strategies to protect water quality. This review can inform policy and decision-making to reduce micropollutant impacts on aquatic ecosystems and human health.

Integrating morphological, biochemical, behavioural, and molecular approaches to investigate developmental toxicity triggered by tebuthiuron in zebrafish (Danio rerio)

Tebuthiuron (TBU), a phenylurea herbicide, is widely applied in agricultural and non-agricultural soils. Because TBU resists degradation, it can contaminate water and reach the biota once it is released into the environment. However, the potential toxic effects of TBU on aquatic developing organisms have been poorly studied. By taking advantage of the early-life stages of zebrafish (Danio rerio), we have combined morphological, biochemical, behavioural, and molecular approaches to investigate the developmental toxicity triggered by environmentally relevant concentrations (from 0.1 to 1000 μg/L) of TBU. Exposure to TBU did not elicit morphological abnormalities but it significantly delayed hatching. In addition, TBU altered the frequency of tail coils in one-day post-fertilization (dpf) old embryos. Moreover, TBU exposure during four days significantly inhibited the whole body AChE activity of larvae. At the molecular level, TBU did not significantly affect the mRNA levels of four genes (elavl3, gfap, gap43, and shha) that play key roles during the neurodevelopment of zebrafish. By assessing the motor responses to repeated light-dark stimuli, 6 dpf larvae exposed to TBU displayed hyperactivity, showing greater travelling distance during the dark periods. Our categorization of swimming speed revealed an interesting finding - after the light was turned off, the exposed larvae abandoned the freezing mode (<2 mm/s) and travelled mainly at cruising speed (2-20 mm/s), showing that the larval hyperactivity did not translate into higher swimming velocity. Overall, our results offer new insights into the TBU toxicity to developing organisms, namely effects in AChE activity and hyperactivity, providing support data for future studies considering environmental risk assessment of this herbicide.

Undoped and Fe-Doped Anatase/Brookite TiO2 Mixed Phases, Obtained by a Simple Template-Free Synthesis Method: Physico-Chemical Characterization and Photocatalytic Activity towards Simazine Degradation

For the first time, Fe-doping (0.05, 1.0, and 2.5 wt.% Fe) was performed on a high-surface-area anatase/brookite TiO2 by adopting a simple template-free sol-gel synthesis followed by calcination at a mild temperature. The powders’ textural and surface properties were characterized by following a multi-technique approach. XRD analysis showed that the anatase/brookite ratio slightly varied in the Fe-doped TiO2 (from 76.9/23.1 to 79.3/22.7); Fe doping noticeably affected the cell volume of the brookite phase, which decreased, likely due to Fe3+ ions occupying interstitial positions, and retarded the crystallite growth. N2 sorption at −196 °C showed the occurrence of samples with disordered interparticle mesopores, with an increase in the specific surface area from 236 m2 g−1 (undoped TiO2) to 263 m2 g−1 (2.5 wt.% Fe). Diffuse Reflectance UV-Vis spectroscopy showed a progressive decrease in the bandgap energy from 3.10 eV (undoped TiO2) to 2.85 eV (2.5 wt.% Fe). XPS analysis showed the presence of some surface Fe species only at 2.5 wt.% Fe, and accordingly, the ζ-potential measurements showed small changes in the pH at the isoelectric point. The photocatalytic degradation of simazine (a persistent water contaminant) both under UV and simulated solar light was performed as a probe reaction. Under UV light, Fe-doping improved simazine degradation in the sample at 0.05 wt.% Fe, capable of degrading ca. 77% simazine. Interestingly, the undoped TiO2 was also active both under UV and 1 SUN. This is likely due to the occurrence of anatase/brookite heterojunctions, which help stabilize the photogenerated electrons/holes.

Insights into sorption and molecular transport of atrazine, testosterone, and progesterone onto polyamide microplastics in different aquatic matrices

Microplastics can act as vectors of a wide class of contaminants in aquatic environments. The sorption behavior of two hormones known to cause adverse effects in biota even in low concentrations (testosterone-TTR and progesterone-PGT), and a pesticide with a high environmental persistence known as endocrine disruptor chemical (atrazine-ATZ) onto polyamide (PA) microplastics is investigated under different aquatic matrices using kinetic and isotherm experiments. The sorption equilibrium is achieved in 48 h, and the experimental results are better fitted by pseudo-2nd-order model. Langmuir isotherm better describes the sorption of the contaminants onto PA microplastics. PGT presents the highest sorption efficiency at around 90%, followed by TTR at 70% and ATZ at approximately 20%. Moreover, ATZ is the contaminant with the highest desorption efficiency (∼65%), indicating its preference for staying solubilized in water. Combining classical molecular dynamics and density functional theory calculations, the sorption energies were calculated as 12-15 kcal mol^-1, 13-16 kcal mol^-1, and 19-22 kcal mol^-1 for PGT, TTR, and ATZ, respectively, showing that PGT needs less energy to be transferred from the solvent network to the PA surface, in agreement with experimental results. The sorption mechanism is driven by hydrogen bonds onto PA outer surface, while the electrostatic interactions dominate the PA inner surface sorption. Moreover, the sorption efficiency is statistically different between the investigated matrices, indicating that physicochemical characteristics of water systems could influence the interactions between PA-contaminant. In seawater, the phenomena of salting-out/in and competitive sorption with saline ions are observed for three contaminants. The PA-contaminant complexes are more polar and soluble than the dissociated ones, which increases the contaminant's co-transport by PA in water.

Occurrence, transport, and detection techniques of emerging pollutants in groundwater

Emerging pollutants (EPs) are a group of different contaminants, such as hormones, pesticides, heavy metals, and drugs, usually found in concentrations between the order of ng and µg per liter. The global population's daily city and agro-industrial activities release EPs into the environment.  Due to the chemical nature of EPs and deficient wastewater treatment and management, they are transported to superficial and groundwater through the natural water cycle, where they can potentially cause harmful effects on living organisms. Recent efforts have focused on developing technology that allows EPs quantification and monitoring in real-time and in situ. The newly developed technology aims to provide accessible groundwater management that detects and treats EPs while avoiding their contact with living beings and their toxic effects. This review presents some of the recently reported techniques that have been applied to advance the detection of EPs in groundwater and potential technologies that can be used for EP removal.