Journal of contaminant hydrology最新文献

This study addresses the critical challenge of assessing the quality of groundwater and surface water, which are essential resources for various societal needs. The main contribution of this study is the application of machine learning models for evaluating water quality, using a national database from Mexico that includes groundwater, lotic (flowing), lentic (stagnant), and coastal water quality parameters. Notably, no comparable water quality classification system currently exists. Five advanced machine learning techniques were employed: extreme gradient boosting (XGB), support vector machines, K-nearest neighbors, decision trees, and multinomial logistic regression. The performance of the models was evaluated using the accuracy, precision, and F1 score metrics. The decision tree models emerged as the most effective across all water body types, closely followed by XGB. Therefore, the decision tree models were integrated into the AQuA-P software, which is currently the only software of its kind. It is recommended that these innovative water classification models be used through the AQuA-P software to facilitate informed decision-making in water quality management. This software provides a probability-based classification system that contributes to a deeper understanding of water quality dynamics. Lastly, an open-access repository containing all the datasets and Python notebooks used in our analysis is provided, allowing for easy adaptation and implementation of our methodology for other datasets worldwide.

Livestock manure, a common fertilizer in Chinese agriculture, can lead to environmental contamination and potential health risks due to elevated antibiotic and phosphorus levels. Importantly, the high phosphorus levels initiates transformations of phosphate minerals in soils, especially calcareous soils. These variations in phosphate mineralogy can significantly impact the migration and fate of antibiotics within the soil. However, the impact of the transformation process, particularly involving the metastable phase brushite (DCPD), on the fate of antibiotics remains unclear. In this study, we synthesized DCPD and hydroxylapatite (HAP) and examined their transformation process to assess their removal capacity and investigate the migration and fate of oxytetracycline (OTC). The findings reveal that HAP exhibits a maximum immobilization capacity for OTC of 20.10 mg/g, surpassing that of DCPD by 2.56 times (7.86 mg/g). This disparity in immobilization capacity between DCPD and HAP leads to a redistribution of OTC between the solid and liquid phases during the transformation process. Notably, the introduction of OTC also inhibits the transformation process, potentially impacting the fate of other potentially harmful elements. The study highlights that the transformation process of calcium phosphorus minerals has a significant impact on the mobility and fate of antibiotics in soil, which aids in better management and mitigation of the environmental risks associated with fertilizer application.

Microbially induced calcite precipitation (MICP) while neutralizing soil pH, can lead to pore clogging which in turn may reduce bacteria transport. This study aimed to evaluate the effectiveness of the MICP process for E. coli filtration in two acidic soils. Two soil samples from Amlash (Am) and Lahijan (La) areas with pH values of 5.88 and 3.93, repectively, were collected and poured into plastic columns (14.92 × 2.4 cm). For the MICP reaction, the soil columns were saturated with a solidification solution (1:1 urea: CaCl₂, 1.5 M) and Sporosarcina pasteurii (∼10⁸ cell mL^-1), and incubated at 30 °C for 72 h. Leaching experiments were conducted on both MICP-treated and control soil columns at steady-state, saturated flow condition. A pulse of influent (0.1 PV) containing Escherichia coli (ciprofloxacin-resistant) (∼10⁸ cell mL^-1) and bromide tracer (1000 mg L^-1 KBr) was added at the top of the soil columns, followed by sterile water to collect the effluent. Recovered E. coli, and Br^-, HCO₃^-, NH₄⁺, Ca²⁺ ions were measured in the leachate. The profile of residual E. coli count, urease activity, and bioprecipitated CaCO₃ content were also assessed in the soil. Correlated with bioprecipitated CaCO₃, the hydraulic conductivity coefficients (K_s) was reduced by 4.4 and 5.8 times after MICP treatment in Am and La soils, respectively, thus bacteria leaching was significantly reduced. A higher filtration coefficient (λ_f) and recovery rate of E. coli were calculated in the La soil column, likely due to the lower pH and higher anion exchange capacity, which induced greater bacterial mortality and electrostatic attraction, respectively. MICP treatment reduced the average and cumulative count of E. coli by ∼3.4 times compared to the control column. In conclusion, the application of MICP in acidic soil increased soil pH and reduced the risk of E. coli transport to deeper layers by reducing soil hydraulic conductivity.

Frequent exchange of surface water and groundwater in karst agricultural areas results in soil nutrient loss during rainfall and consequent deterioration of the aquatic environment. To understand nitrogen (N) transformation and leaching processes from karst soil during rainfall events, two typical N fertilizers were added to karst soil and consequently investigated the nitrogenous species using soil column experiments system. The contents of various N forms in the soil and leachate were analyzed, and the net nitrification and the N leaching rates were calculated. The results revealed that NH₄⁺ and NO₂^- accumulation and variation occurred primarily in shallow soils, indicating that shallow soils were the primary sites for ammonification and nitrification processes. The net nitrification rate in the soil was higher with application of urea (12.5 mg N kg^-1d^-1) than with manure (2.4 mg N kg^-1d^-1). Peak N content indicated that it took only 7 days for N to migrate from the topsoil to 90 cm depth of soil after applying urea during successive intense rainfall events. NO₃^- leaching was predominantly composed of N, followed by DON, with leaching ratios of 31.1 % for the urea column and only 1.7 % for the manure column. These results highlight that the rapid nitrification of urea application coupled with intensive rainfall accelerated nitrate leaching into subterraneous streams in karst hydrological system, supporting a significant contribution from agricultural fertilizer loss during rainfall in karst area.

Microplastics (MPs) are ubiquitous and are increasing globally, but there is limited information available on their presence in freshwater ecosystems. This research work aims to investigate the abundance, sinking behavior, and risk assessment of MPs in the freshwater River Basantar, Jammu & Kashmir, India. Microplastic abundance in sediments was recorded in the range of 1-6 items g^-1, with a mean abundance of 3 ± 1.594 item g^-1, whereas MPs in surface water ranged from 200 to 850 items L^-1 with a mean abundance of 530 ± 218.4 items L^-1 among 12 sites for sediments and 10 sites for surface water. Besides, the sinking behavior of MPs was analyzed through portioning coefficients (K_d) at sediments-surface water interface, which ranges from 0.71 to 2.50 L Kg^-1 for River Basantar. The most common shapes identified were fragments, fibres, and films, followed by pellets, foams, and lines. ATR-FTIR polymeric characterization reported polyethylene, polypropylene, polystyrene, polyethylene terephthalate, and polyvinyl chloride, and thus, polymeric risk assessment analysis was also evaluated and normally distributed in the River Basantar. Polymer Hazard Index was calculated across all the sites which observed to be polluted under risk categories "III" and "IV" for both the sediments and surface water samples. Pollution Load Index (PLI) calculated across all the sites was >1 depicting all the sites for both sediments and surface water sampling to be polluted. Pollution Risk Index was assessed and majority of surface water and sediment samples were observed to be under "Very high" risk category. The study, using principal component analysis and heatmap analysis, found that MPs are primarily a result of urbanization and anthropogenic actions, like industrial discharges, household wastes, and agricultural runoffs. This study highlights the significance of more investigation and coordinated efforts to solve the worldwide problem of plastic pollution in freshwater environments. Results data provide insight into the current state of MP contamination and will help government authorities implement strict rules and perform management interventions to reduce and monitor pollution levels in River Basantar. Future studies on the partitioning of MPs in sediments and surface water must be focused on aggregation, biofouling, plastic density & size, salinity, and flow behaviors to understand transport and deposition in rivers.

Microplastics (MPs) in aquatic environments adsorb heavy metals, thereby posing potential environmental risks. However, further research is needed to elucidate the adsorption behavior of different types of MPs for various heavy metals. The aim of this study was to characterize four types of MPs: polypropylene (PP), polyvinyl chloride (PVC), high-density polyethylene (HDPE), and low-density polyethylene (LDPE). Moreover, their Pb²⁺ and Cd²⁺ adsorption properties were determined to investigate the differences in their capacity to function as heavy metal adsorbents. MPs were characterized via scanning electron microscopy (SEM) using energy dispersive X-ray spectrometer (EDS), Brunauer-Emmett-Teller (BET) analysis, and Fourier transform infrared spectroscopy (FTIR). Adsorption experiment data were analyzed using the Langmuir and Freundlich isotherm models to evaluate the adsorption capacity of the MPs. Based on the results of the adsorption isotherm models and 2D-COS FTIR, the presence of oxygen-containing functional groups, including hydroxyl, carbonyl, and carboxyl groups influences the adsorption process of Pb²⁺ and Cd²⁺ onto PP and PVC, with the maximum adsorption capacities (Q_m) being 0.759 mg/g and 0.495 mg/g, respectively. Combination of the adsorption isotherm data and characteristics of MPs revealed that the following order of adsorption efficiencies of MPs for each heavy metal: PP > LDPE > PVC > HDPE for Pb²⁺ and PP > PVC > LDPE > HDPE for Cd²⁺. The results of this study suggest that MPs, particularly PP and PVC, may serve as vectors for heavy metal transport in aquatic environments, highlighting the need for further research to assess their environmental impact.

Microplastics (MPs) and antibiotics can enter groundwater through the interaction of soil and surface water, and MPs as carriers of antibiotics can promote the migration of antibiotics and thus generate more serious ecological risks. Therefore, this paper used experimental and molecular dynamics (MD) simulation methods to investigate the sorption between four common types of MPs in groundwater, namely polyamide (PA), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene (PE), and oxytetracycline (OTC) with high detection rate in groundwater. Additionally, the impact of environmental factors on sorption was examined. The sorption kinetics of the four types of MPs followed the pseudo-second-order kinetics model, and the sorption isotherms of OTC on PA, PE, and PVC were highly linear, suggesting that the electrostatic interaction was the main sorption mechanism. Both experimental and simulation results indicated that PA had the highest affinity for OTC, due to the effect of the formation of hydrogen bonding between the amide groups of PA and OTC. The primary way pH affected sorption was by altering the form in which OTC exists. The effects of the representative substances of protein-like component (bovine serum albumin) and humus-like component (humic acid) in dissolved organic matter varied but were generally inhibitory. Ions could influence the sorption process by competitive sorption or forming complexes with the OTC.

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models. Combined cross-validation (CV) and bootstrapping (B) methods were used to build six predictive models. The entropy-based coastal groundwater quality index (ECWQI) was converted into a normalized index (ECWQIn), which was divided into five classes from very poor to excellent. The self-organizing map (SOM), spatial autocorrelation and fuzzy logic model were used to identify spatial groundwater quality patterns based on 12 physicochemical variables collected from 67 groundwater wells. The SOM analysis identified four distinct spatial patterns, including EC-TDS-Cl^-, MgpH, Ca²⁺K⁺NO₃^-, and HCO₃^-SO₄^2-Na⁺F^-. The results showed that both the ANN (CV) and ANN (B) models performed better than other optuna-based models during the test phase (RMSE = 0.041, MAE = 0.026, R2 = 0.971, RAE = 0.15 = 21 and CC = 0.986) and (RMSE = 0.041, MAE = 0.025, R2 = 0.969, RAE = 0.119 and CC = 0.975), respectively. SO₄^2-, Cl^- and F^- played an important role in the prediction accuracy. F- and SO₄^2- showed higher spatial autocorrelation, which affected groundwater quality degradation. In addition, the ANN (CV) and ANN (B) models showed a Gaussian distribution of model errors (small standard error, <1 %), indicating the stability of the model. These results indicate the efficiency of the ANN model in predicting groundwater quality in coastal areas, which would help regional water managers in real-time monitoring and management of sustainable groundwater resources.

This study investigated the adsorption of 4-Nonylphenol (4-NP) on aged microplastics (MPs) composed of polyethylene terephthalate (PET) and poly(butylene-adipate-co-terephthalate)/polylactic acid (PBAT/PLA). Morphological analysis revealed wear, wrinkles, and increased surface roughness in both aged MPs, with X-ray diffraction showing slight increases in crystallinity. Infrared spectroscopy showed an increase in the carbonyl index from 2.78 to 4.37 for PBAT/PLA and 0.51 to 2.32 for PET after aging. The natural water from the San Pedro River in Chile Atacama region (5.91 mS·cm^-1 conductivity, 3.25 PSU salinity, 2955 mg·L^-1 total dissolved solids, 435 mg·L^-1 CaCO₃ hardness) was used as the environmental medium and compared with a 0.01 mol·L^-1 CaCl₂ as a model solution. Kinetic modeling showed a decrease in 4-NP percentage removal from 90.0 % (2277 μg∙g^-1 adsorption capacity) to 50.2 % (1268 μg∙g^-1) for PET and from 86.8 % (2087 μg∙g^-1) to 70.3 % (1955 μg∙g^-1) for PBAT/PLA when comparing the model solution to natural water, with 30 and 84 h equilibrium times, respectively. Isotherm data showed that 4-NP/PET fits BET n-layer and Temkin models, while 4-NP-PBAT/PLA fits the Toth and Hill models The ionic composition of natural water induces cation attraction to polarized MPs surfaces, intensifying competition for adsorption sites. This involves ion and molecular cooperation, 4-NP reorientation, external diffusion effects, and surface oxidation variations, which are attributed to explaining the bilayer (PET) and monolayer (PBAT/PLA) formation. This work contributes to understanding MP pollution and the importance of considering the bioplastics life cycle, since their waste presents significant potential to resist external factors for transporting contaminants.

The Chili, Vitor and Quilca rivers and their tributaries in Peru serve as a vital water resource for both irrigation and domestic use in the surrounding communities and agricultural areas. The purpose of this study was to establish, for the first time, the presence, abundance, distribution and chemical identity of polymer microparticles in aqueous samples from these river basins. The results showed that, on average, filaments were the most dominant (71.4 %), followed by fragments (17.2 %) and film (6.74 %). Identification of the polymer types revealed that the most abundant type of MPs is polyethylene (40.8 %), followed by polypropylene (23.8 %), synthetic fibres (15.8 %), and other synthetic polymers. All samples showed the occurrence of microplastics, with a mean concentration of 35.34 MPs/m³, a maximum value of 172.70 MPs/m³ and a minimum value of 3.59 MPs/m³. The results reported in this study establish a baseline for the study area for the first time; in addition, the areas were established with a Pollution Indicator, and the Pollutant Load Index (PLI) was calculated, which reinforced the proposed identification, alerting the need to control clandestine urban and rural landfills, as well as the indiscriminate use of PE big bags in the agricultural catchment.