Carbamazepine Levels Related to the Demographic Indicators in Groundwater of Densely Populated Area

Non-Targeted Screening and Identification of the Transformation Pathway of Carbamazepine in the Saemangeum Watershed, Republic of Korea

Carbamazepine (CBZ) is a widely used pharmaceutical for various purposes, including as an anticonvulsant, antibiotic, and antiepileptic agent, and it undergoes diverse metabolic pathways in both the environment and the human body. Therefore, this study aimed to explore the distribution of CBZ, the presence of its transformation products (TPs), and the transformation pathways in the Mangyeong and Dongjin Rivers in the Saemangeum watershed of Korea using non-targeted screening. The concentration distribution results for CBZ and its TPs showed that the average concentrations in the Mangyeong and Dongjin Rivers were 128.8 ng/L and 89.0 ng/L, respectively. The Mangyeong River exhibited a higher CBZ concentration than the Dongjin River, which was similar to those of the reported CBZ concentrations in other major domestic and international rivers. The types and detection frequencies of the identified TPs exhibited similar trends. The detection frequencies of the TPs decreased in the following order: CBZ-EP > DiOH-CBZ > 10OH-CBZ > 2OH-CBZ > 9-carboxyacridine > 9-acridinecarboxaldehyde. The detection frequency of the main TPs was high, and some were believed to be generated during the water treatment process. The presence of additional TPs (CBZ-O-quinone, acridine, and iminostilbene) was confirmed by the generated molecular networks. This study presents the transformation pathway of the CBZ and provides foundational data for understanding the environmental behavior of TPs, improving wastewater treatment plants, managing water quality, and establishing water environmental policies.

First emerging pollutants profile in groundwater of the volcanic active island of El Hierro (Canary Islands)

Emerging pollutants (EPs) are substances present in wastewater that have not been studied, previously, leading to ambiguity in regulations for their presence in water resources. Territories that are highly dependent on groundwater resources are at a high risk of suffering the consequences of EP contamination due to their dependence on good quality groundwater for agriculture, drinking, and other uses. A relevant example is El Hierro (Canary Islands), which was declared a biosphere reserve by the UNESCO in 2000 and is almost completely powered by renewable energies. Using high performance liquid chromatography-mass spectrometry, the concentrations of 70 EPs were assessed at 19 sampling points on El Hierro. The results indicated that no pesticides were present in groundwater; however, varied concentration levels of ultraviolet (UV) filters, UV stabilizers/blockers and pharmaceutically active compounds (PhACs) were found, with La Frontera being the most contaminated municipality. With regard to the different installation types, piezometers and wells were the ones showing the highest concentrations for most EPs. Interestingly, the depth of sampling correlated positively with EP concentration, and four different clusters virtually dividing the island into two areas could be identified based on the presence of each EP. More studies should be performed to ascertain why a few of the EPs showed considerably high concentrations at different depths. The results obtained highlight the need to, not only implement remediation measures once EPs have reached the soil and aquifers, but also to avoid their incorporation into the water cycle via homes, animal husbandry, agriculture, industry, and wastewater treatment plants (WWTPs).

Evaluation of machine learning algorithms for groundwater quality modeling

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

Degradation of Carbamazepine from Aqueous Solutions via TiO2-Assisted Photo Catalyze

Photocatalytic degradation of carbamazepine (CBZ) from spiked aqueous solutions, via a UV/TiO₂ system, was investigated, and the optimum photocatalyst type (P25 Degussa) and dose (500 mg/L), as well as irradiation time (45 min), were established. The degradation process kinetics was studied, and a degradation rate constant of 3.14 × 10⁻⁵ M min⁻¹ was calculated for CBZ, using the Langmuir–Hinshelwood equation. Experiments performed in the presence of scavengers showed that the main reactive species involved in the degradation process are holes and free hydroxyl radicals; superoxide radicals also play a role in CBZ degradation. Eight transformation products of CBZ were identified, and a possible degradation pathway, consisting of four routes, was proposed. Toxicity and genotoxicity tests were also performed for both untreated and treated CBZ solutions, proving that the use of a UV/TiO₂ system represents a suitable treatment approach for aqueous systems with CBZ content.