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The objective of this study is to examine the physical, chemical and biological characteristics of swimming pool water in Mafraq city, north of Jordan and the overall quality of the used water. Three public swimming pools were selected from Mafraq city [Areef Pool (SW1), Teachers Club Pool (SW2) and Anakeel Pool (SW3)] to analyze the physical, chemical and biological properties of their water as well as determine their compliance with the Jordanian Standards for Swimming Pools Water. Sampling was carried out weekly for eight successive weeks between July and August 2019 before bathing (after disinfection) and after bathing and analysed in Al al-Bayt University and Ministry of Environment laboratories. The parameters used to evaluate the quality of water in swimming pools were temperature, pH, electrical conductivity (EC), dissolved oxygen (DO), residual chlorine (Cl2), total organic carbon (TOC), trihalomethanes (THM), major cations and anions, selected heavy metals, and total coliform bacteria, E. coli and Pseudomonas. Most of the physical and chemical parameters analysed were within the recommended limit except for pH and EC. Residual chlorine exceeded the permissible limits in SW3 before and after bathing, recording mean values of pH, EC (4.3 ± 0.25 - 4.33 ± 0.44), (2314 ± 343 - 2453 ± 460), respectively. The dissolved oxygen was less than the recommended limit. Total coliforms, E. coli and Pseudomonas counts were < 1 before and after bathing in all the samples.

Tectonic plate drift, a major force driving Earth’s geological history, governs the opening and closing of ocean basins, the breakup and assembly of supercontinents, and the formation of sedimentary basins. Recent geological, geophysical, and experimental evidence further suggests that plate drift velocity influences the geometry of slabs, the timing of large igneous province volcanism, and the chemical properties of oceanic rocks. While these studies have yielded significant academic insights, the direct impact of plate drift velocity on hydrocarbon resource development remains poorly understood, and relevant research is limited. This study is based on the latest Palaeozoic global plate reconstructions and previously published data on major Palaeozoic hydrocarbon source rocks. By comparing variations in the global plate drift velocity with the quantities and total organic carbon (TOC) contents of different types of hydrocarbon source rocks, we demonstrate the significant impact of plate drift velocity on hydrocarbon source rock development. Our results indicate that low plate drift velocities (0.97–5.00 cm/yr) provided the most favourable conditions for hydrocarbon source rock formation. Medium velocities (5.00–10.00 cm/yr) were moderately favourable, whereas high velocities (10.00–12.76 cm/yr) were relatively unfavourable. Furthermore, under low drift velocities, when plates were located at palaeolatitudes of 15–30°N/S and exhibited high stability, conditions were most conducive to high-abundance hydrocarbon source rock formation. These findings underscore the critical role of plate drift velocity in controlling hydrocarbon source rock development and provide a new perspective and approach for global hydrocarbon exploration and resource assessment.

Some natural organic compounds (NOC) such as aromatic compounds can trigger the formation of disinfection by-products (DBPs). In chlorination (disinfectant) process resultant water quality depletes. Some safe alternative oxidants are needed for cleaning water pollutants. KMnO4 had shown better oxidation results, especially for reducing aromatic and non-aromatic organic compounds present in water. The aim of this study was to analyze the effect of KMnO4 and Ca(OCl)2 oxidants on the concentration of high and low molecular weight organic matter including aromatic compounds in the water sample. In this experiment, artificial organic compounds, namely sinapic acid (high molecular weight aromatic compound) and resorcinol (low molecular weight aromatic compound) were used to identify the characteristic of organic matter under different molecular weights. Sinapic acid and resorcinol were oxidized by using KMnO4 and Ca(OCl)2 with a minimum contact time of 60 minutes. Samples were analyzed for aromatic contents and total organic carbon (TOC) before and after completion of the experiment by using UV-Vis spectrophotometer at 254 nm wavelength (UV254). It has been observed that both oxidants increased TOC concentration. Ca(OCl)2 produces a higher percentage of organic matter degradation by-products (DBPs) such as chloroform (CHCl3) a highly toxic compound than KMnO4. Since Ca(OCl)2 has a higher oxidation potential than KMnO4. It has been observed that KMnO4 is a safer oxidant than Ca(OCl)2 as potassium permanganate produces less amount of DBPs.

The field of environmental and water remediation faces a significant challenge in removing organic dyes from wastewater, particularly Rhodamine B (RhB), a stubborn dye used in various industries. Traditional treatment methods struggle with its resistance to decomposition, posing risks to water quality, human health, and aquatic life. This study demonstrates a novel approach to enhance photocatalytic efficiency for RhB degradation by constructing a MOF-5/g-C 3 N 4 composite through a facile mechanical grinding method, which is unprecedented. The composite addresses the limitations of g-C 3 N 4 , such as rapid recombination of electron–hole pairs, low electron transfer rates, and small surface area, by forming a heterojunction with MOF-5. The composite exhibits enhanced photocatalytic efficiency for the degradation of RhB under sunlight, with a degradation of 91.5% achieved within 90 min. Optimization studies highlight the importance of pH and catalyst dosage in the degradation process. The reusability test shows consistent performance over five successive cycles, maintaining a degradation efficiency of over 90%. Total organic carbon (TOC) analyses confirm the mineralization of the dye solution to 82.05% after 90 min of irradiation, demonstrating the environmental benignity of the composite. Trapping experiments suggest the involvement of superoxide radicals, electrons, and holes in the photocatalytic mechanism. This study introduces a promising strategy for addressing challenges in dye degradation through innovative composite materials.

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R2) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy.

In this paper, the removal of total organic carbon (TOC) from a primary treated municipal wastewater using a new electrode configuration in electrocoagulation was evaluated. The used electrode configuration induces a dielectrophoretic (DEP) force by using an asymmetrical aluminum electrode with an alternating current power supply. The impact of applied current, electrolysis time, and interelectrode distance on the removal efficiency of TOC were evaluated. The experimental results showed that the maximum removal efficiency of TOC was obtained at 30 min electrolysis time, 600 mA applied current, and 0.5 cm interelectrode distance. Under these operating conditions, the TOC removal was 87.7% compared to 80.5% using symmetrical aluminum electrodes with no DEP effect. The energy consumption at the selected operating conditions was 3.92 kWh/m 3 . The experimental results were comparable with the simulation results done by COMSOL Multiphysics software.

Understanding sediment sources and budgeting is crucial for effective watershed management and soil conservation. This study employs n-alkanes as biomarkers to trace sediment origins in a small watershed in Northern Iran, comparing the contribution of degraded forests, coniferous afforestation, and natural forests. Soil and vegetation samples were collected from different land use/land covers, while bed sediment samples were obtained from multiple points along the main stream. The distribution patterns of n-alkanes in sediment samples were analyzed in relation to those found in soil and vegetation samples. Additionally, several key indices including the carbon preference index (CPI), aquatic plant proxy (Paq), Hydrocarbon vegetation index (HVI), total organic carbon (TOC), total nitrogen (TN), and soil particle size distribution were measured to further refine source attribution. Using principal component analysis (PCA) and the FingerPro package in R, the contribution of different sediment sources was pinpointed. According to the PCA, the three sediment sources were well separated from each other. The results were striking: degraded forests accounted for the largest contribution at 45.10 %, followed by planted forests at 28.12 % and natural forests at 26.78 %. Considering the area of each land cover, the specific contribution of degraded forest and planted forest to sediment yield were 10.87 % and 7.14 % per hectare, nearly three times and two times that of natural forests (3.78 % per hectare), respectively. Our analysis, validated with a 70 % accuracy rate through the GOF index as well as field evidences, demonstrates that sediment fingerprinting with n-alkanes can effectively reveal erosion patterns and sediment yield rates between different types of forest land use. This insight is crucial for future soil conservation using appropriate afforestation species, ensuring that land management practices are aligned with long-term sustainability goals. •Reforestation using conifer species was less conservative than native broadleaf forest.•Natural-, planted- and degraded forest contribute 26.78 %, 28.12 %, 45.10 % in bed sediments.•The contribution of degraded forests to sediment yield was about three times that of natural forests.•The contribution of planted forests to sediment yield was about twice that of natural forests.

To assess the effects of long-term intensive management on soil carbon cycle and microbial functional diversity, we sampled soil in Chinese hickory (Carya cathayensis Sarg.) stands managed intensively for 5, 10, 15, and 20 years, and in reference Chinese hickory–broad-leaved mixed forest (NMF) stands. We analyzed soil total organic carbon (TOC), microbial biomass carbon (MBC), and water-soluble organic carbon (WSOC) contents, applied 13C-nuclear magnetic resonance analysis for structural analysis, and determined microbial carbon source usage. TOC, MBC, and WSOC contents and the MBC to TOC ratios were lower in the intensively managed stands than in the NMF stands. The organic carbon pool in the stands managed intensively for twenty years was more stable, indicating that the easily degraded compounds had been decomposed. Diversity and evenness in carbon source usage by the microbial communities were lower in the stands managed intensively for 15 and 20 years. Based on carbon source usage, the longer the management time, the less similar the samples from the monospecific Chinese hickory stands were with the NMF samples, indicating that the microbial community compositions became more different with increased management time. The results call for changes in the management of the hickory stands to increase the soil carbon content and restore microbial diversity.

The stable carbon isotope composition of total organic matter (δ13Corg) has been utilized in aeolian sediments, serving as an indicator for reconstructing terrestrial paleoenvironments. The Qinghai Lake (QHL) Basin is a climate-sensitive region of significant importance in paleoclimatic reconstruction. However, the reconstructed climatic variations based on δ13Corg in aeolian sediments in the QHL Basin in the northeastern Qinghai-Tibet Plateau (QTP) are lacking, and their paleoclimatic significance remains poorly understood. By conducting δ13Corg measurements on the Niaodao (ND) aeolian profile near QHL, we reconstructed the paleoclimate changes of 11 ka–present. The variation range of the δ13Corg values in the ND profile indicated the terrestrial ecosystems were not the sole contributor to lacustrine organic matter. The δ13Corg values are an indicator of historical temperature changes in the study area, exhibiting similar trends with the reconstruction of Chinese summer temperatures, East Asian air temperature, global temperature, and Northern Hemisphere summer insolation at 37° N. The temperature increased with high frequency and amplitude oscillations, with strong aeolian activity and low total organic carbon accumulation during the Early Holocene. The temperature was maintained at a high and stable level, with the weakest aeolian activity and intensified pedogenesis during the Middle Holocene. The temperature decreased at a high rate, with renewed aeolian activity and weak pedogenesis during the Late Holocene.

Total organic carbon (TOC) is a critical parameter for evaluating shale source rock quality and hydrocarbon generation potential. However, accurate TOC estimation from conventional well logs remains challenging, especially in data-limited geological settings. This study proposes an optimized XGBoost model for TOC prediction using conventional logging data from the Shahejie Formation in the Dongying Depression, Bohai Bay Basin, China. We systematically transform four standard logs—resistivity, acoustic transit time, density, and neutron porosity—into 165 candidate features through multi-scale smoothing, statistical derivation, interaction term creation, and spectral transformation. A two-stage feature selection process, combining univariate filtering and recursive feature elimination and further refined by principal component analysis, identifies ten optimal predictors. The model hyperparameters are optimized via Bayesian search within the Optuna framework to minimize cross-validation error. The optimized model achieves an R2 of 0.9395, with a Mean Absolute Error (MAE) of 0.3392, a Root Mean Squared Error (RMSE) of 0.4259, and a Normalized Root Mean Squared Error (NRMSE) of 0.0604 on the test set, demonstrating excellent predictive accuracy and generalization capability. This study provides a reliable and interpretable methodology for TOC characterization, offering a valuable reference for source rock evaluation in analogous shale formations and sedimentary basins.