Journal of Environmental Science and Health Part A-toxic\/hazardous Substances & Environmental Engineering最新文献

GenX (HFPO-DA), a short-chain per- and polyfluoroalkyl substance (PFAS) substitute, is implicated in testicular toxicity. GenX-related genes were intersected with aging-associated genes to construct a GenX-Aging gene set. Single-cell RNA sequencing (scRNA-seq) data from human testicular aging (GSE254315) were analyzed to evaluate cell-type-specific aging sensitivity and intercellular communication dynamics. Male infertility transcriptomic datasets (GSE45885/GSE45887) were integrated, and least absolute shrinkage and selection operator (LASSO) regression combined with support vector machine recursive feature elimination (SVM-RFE) were applied to identify hub genes, which were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in GenX-exposed rat testicular tissues. Spermatids exhibited the highest aging sensitivity, with progressive decline in intercellular communication. Four hub genes-SOD1, XRCC5, FOXO3, and POLB-demonstrated diagnostic value for male infertility. RT-qPCR confirmed computational predictions: SOD1, XRCC5, and FOXO3 were upregulated, while POLB was downregulated. Functional enrichment implicated FoxO signaling, cellular senescence, and DNA repair pathways. Molecular docking confirmed favorable GenX-protein binding interactions. SOD1, XRCC5, FOXO3, and POLB are candidate biomarkers for GenX-induced reproductive toxicity, with oxidative stress and genome maintenance as key pathological mechanisms.

Microplastic (MP) pollution is a growing global concern with serious implications for ecosystems and human health. The present review provides a comprehensive analysis of the occurrence, mobility, and toxicological impacts of MPs, focusing on their role as vectors for heavy metals and persistent organic pollutants. The review explores their subsequent translocation into the human food web. This review summarizes and evaluates microbial and enzymatic degradation. The review framework integrates traditional environmental assessment with emerging technologies, specifically evaluating the efficacy of microbial enzymes (such as PETase and laccase) and the potential of artificial intelligence (AI)-enabled predictive modeling for pollution hotspot identification. This holistic approach bridges the gap between field-based quantification and advanced bioremediation strategies. The findings are contextualized within the UN Sustainable Development Goals (SDGs). Using the PRISMA 2020 guidelines, a systematic review of 666 studies published between 2010 and 2025 in the Scopus database was conducted to synthesize insights across multiple levels of MPs and bioremediation using published articles in India. The novelty lies in combining India-specific field data, cross-trophic toxicological insights, and AI to address existing research gaps. The framework aligns with multiple SDGs, including SDG 3, 6, 9, 12, and 14.

The study examined how abattoir effluent discharge affects groundwater quality and the environment in Umuahia South and Aba South, Nigeria. Twenty-seven groundwater samples were taken upstream, midstream, and downstream for duration of six months. Standard techniques and influential statistics in SPSS were used to analyze parameters (pH, Cl^-, TSS, TDS, NH₄N, NO₃^-, EC, PO₄, and SO₄^2-). Hence, in Umuahia South, mean values of pH (9.72 ± 0.08), Cl^- (279.06 ± 76.63 to 329.58 ± 77.39), TSS (915.84 ± 12.13 to 1178.33 ± 85.70), TDS (604.62 ± 46.16 to 707.46 ± 17.21), NH₄N (13.75 ± 1.07 to 16.93 ± 1.38), and PO₄ (33.55 ± 1.06 to 36.47 ± 0.66) exceeded WHO limits. While in Aba South, mean values of pH (9.44 ± 0.05), Cl^- (345.43 ± 76.75 to 495.96 ± 9.60), TSS (1488.28 ± 36.65 to 1673.26 ± 83.00), TDS (766.89 ± 39.40 to 981.66 ± 50.89), NH₄N (11.72 ± 0.55 to 13.63 ± 0.54), PO₄ (27.64 ± 4.08 to 39.14 ± 1.18) were above recommended standard. There was a significant difference between Cl^-, TSS, TDS, NH₄N, NO₃^-, EC, and SO₄² at P-value <0.05 across the study area. There is a substantial positive association between pH (TDS, NH₄N, PO₄, Cl^-), Cl^- (EC, PO₄), TSS (NH₄N, NO₃^-), TDS and NH₄N (EC, PO₄, Cl^-), and NO₃^- and EC (PO₄ pH (TDS, NO₃^-), Cl^- (EC, PO₄), TSS (SO₄²), TDS, NH4N (NO₃^-), and NO₃^- (PO₄) in Umuahia South and Aba South. Finally, abattoir water should be sanitized before use. Therefore, the state environmental protection agency should actively supervise slaughterhouses and assure health and safety compliance.

Soil moisture is a critical variable in environmental monitoring, water resource management and agricultural production. However, accurate soil moisture forecasting remains challenging due to complex spatial-temporal interactions and the large volume of remote sensing data. Traditional prediction methods often struggle to effectively capture these nonlinear relationships. To address these limitations, this study proposes an Attention-Based CNN-LSTM model optimised using the Tabu Search Algorithm for enhanced soil moisture forecasting. The model integrates Convolutional Neural Networks (CNN) to extract spatial features and Long Short-Term Memory (LSTM) networks to model temporal dependencies. An attention mechanism is incorporated to emphasise the most relevant spatial and temporal information, thereby improving predictive performance. Furthermore, the Tabu Search Algorithm is employed to optimise model hyperparameters, reducing forecasting errors and improving efficiency. The proposed approach is evaluated against conventional methods, including standard LSTM and XGBR-GA models, using performance metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and the coefficient of determination (R²). Experimental results demonstrate that the attention-based CNN-LSTM model achieves superior accuracy, characterised by lower error values and higher R² scores. These findings highlight the effectiveness and scalability of the proposed framework for large-scale soil moisture forecasting using remote sensing data.

Bacterial transport is the critical initial step for biofilm formation and microbial functions in porous media. Various physicochemical properties determine the interaction between bacterial cells and matrix. There is, hence, interest to evaluate the Hamaker constant as a comprehensive indicator to quickly predict bacterial deposition in porous media. In this study, percolation column experiments were conducted using four model bacterial strains with distinct hydrophobicity and surface charge properties. Deposition efficiencies were quantified using clean-bed filtration theory and interpreted based on the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) interaction energy. Research found that hydrophobicity determines the surface energy and, hence, varies the Hamaker constant, posing significant effects on the XDLVO energy to dominant deposition efficiency. The positive correlation between the Hamaker constant and deposition efficiency was mechanistically explained by variations of the XDLVO interaction energy at the secondary minimum distance, which governs reversible deposition. This correlation was validated at both the initial and final stages of bacterial deposition in porous media. These findings indicate that the Hamaker constant provides a simplified yet effective theoretical tool for the prediction of bacterial transport.

Potentially Toxic Elements (PTEs) are hazardous for human and ecosystem health due to their non-biodegradable nature. In this study we investigated the concentrations of PTEs, including As, Co, Cr, Cu, Mn, Mo, Ni, Pb and V in sediments of Lake Tuz around the salt pans for possible contamination. Lake Tuz is a shallow saline lake where halite (table salt) production is carried out in the salt pans and has significant geo and eco-tourism potential due to its unique ecosystem and natural beauty. The extent of pollution level and ecological risk were evaluated by geochemical indices and guideline values. According to the Geoaccumulation Index (I_geo), Enrichment Factor (EF) and Contamination Factor (Cf) indices Cr, Mo, As and occasionally Ni accumulated in moderate to strong levels. Intensity maps of Pollution Load Index (PLI) and Modified Degree of Contamination (mCdeg) indicated pollution hotspots in the neck region and in the eastern shore of the lake respectively. The Potential Ecological Risk Index (PERI) values indicated low and moderate levels of ecological risk. Statistical analyses including Pearson Correlation Coefficient (PCC), Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) suggested that Co, Cr, Cu, Mn, Mo, Ni and V are of geogenic origin and As and Pb are of anthropogenic origin. Provenance analysis suggested that host rocks for geogenic PTEs were granodiorites and ophiolites situated in the catchment area of the lake. Anthropogenic PTEs were most likely related to agrochemicals used in surrounding farmlands.

Monitoring nitrate and fluoride levels in drinking water is essential due to their potential adverse health effects. While studies have assessed these contaminants across Iran, comprehensive analyses of their spatial-temporal distribution and probabilistic health risks remain scarce for Maragheh County. This study addresses this gap by applying Monte Carlo simulation (MCS) and principal component analysis (PCA) to 132 drinking water samples collected from 2018 to 2023. This novel framework identifies contamination sources and quantifies risks across demographic groups. Results revealed that 97% of nitrate and 96% of fluoride concentrations met World Health Organization (WHO) guideline limits. PCA explained 76.5% of total variance, with EC, TH, TDS, and Ca²⁺ as dominant factors. The water quality index (WQI) rated over 88% of samples as excellent and less than 1.5% as poor. Fluoride posed negligible health risks (HQ < 1), but nitrate exposure yielded elevated hazard indices (HI > 1) for children, signaling potential non-carcinogenic effects. Overall, findings underscore the need for ongoing monitoring, better wastewater and fertilizer management, and targeted protections for vulnerable groups in agricultural regions.

Core-shell Fe₃O₄@poly(acrylic acid)/chitosan (Fe₃O₄@PAA/CS) submicrospheres were synthesized through the polymerization of acrylic acid in CS solution, using uniformly sized magnetite colloid nanocrystal clusters (MCNCs) as the core materials. The obtained submicrospheres were characterized using scanning electron microscopy, transmission electron microscopy, dynamic light scattering, Fourier-transform infrared, thermo-gravimetric, vibrating sample magnetometer, and X-ray diffraction analyses. The results confirmed that the submicrospheres with the Fe₃O₄ nano-core located in the central region and encapsulated by a CS shell exhibited superparamagnetic behavior. The removal efficiency of Congo red (CR) dye by magnetic submicrospheres was determined by investigating several factors, including pH, adsorbent dose, contact time, and dye concentrations. Over 97.4% of CR (90 mg L^-1) was removed at a dosage above 1.2 g L^-1. The maximum adsorption capacity obtained from the Langmuir isotherm model for CR was 143 mg g^-1 at 290 K. Adsorption kinetics and isotherm data were well described by the pseudo‑second‑order and Langmuir models, respectively. Furthermore, the submicrospheres were successfully regenerated and, subsequently, reused for four adsorption-desorption cycles without any noticeable loss of stability. The exceptional removal performance of magnetic submicrospheres on CR renders it a highly appealing adsorbent for the treatment of dye-containing wastewaters.

Near-road particulate matter poses significant risks to public health and the ecological environment, and its levels are affected by the meteorological and traffic factors significantly. However, the contributions of these factors to particulate matter concentrations and the interactions among these factors were not well studied. In this study, the causal relationships among traffic flow (TF), near-road PM_2.5 levels, and meteorological factors were elucidated based on the long-term real-time data on near-road PM_2.5 concentrations alongside concurrent meteorological and traffic data. A predictive modeling framework was developed to predict near-road PM_2.5 concentrations using traffic and meteorological data as input. The results indicate that the correlation between TF and near-road PM_2.5 concentrations is significant (P < 0.05). Furthermore, robust causal relationships were identified between TF and meteorological parameters such as temperature and atmospheric pressure. It is suggested that TF could indirectly influence the level of near-road PM_2.5 by altering meteorological factors. By comparing the prediction performance among Long Short-Term Memory (LSTM), Backpropagation (BP) and Extreme Learning Machine (ELM) models for near-road PM_2.5 concentrations, combined with Shapley Additive exPlanations (SHAP) for feature importance analysis, it revealed that the inclusion of TF data markedly improves model accuracy in near-road PM_2.5 concentrations prediction.