Environmental Science and Pollution Research最新文献

A qualitative and quantitative analysis of 18 elements was conducted on Procambarus clarkii and its environmental samples to evaluate its potential as a bioindicator species. Analysis of biological samples was carried out to both assess the safety of the use of raw materials and, together with environmental samples, to assess the contamination status of the sampled sites. Significant differences highlighted by the PERMANOVA, HCA and PCA analyses confirmed the valid use of P. clarkii as a bioindicator of the health status of the studied ecosystem. The bioaccumulation factor (BAF) and the biotic sediment accumulation factor (BSAF), except in a few cases, reported values below the bioaccumulative criterion and showed the highest BAF values for manganese, iron and barium in the exoskeleton while the highest BSAF values concerned mercury, copper and zinc in the muscle and barium in the exoskeleton. These findings indicate that, for most of the trace elements (TEs), the extent of pollution at these sites is such that it does not result in significant bioaccumulation in the muscle and exoskeleton of P. clarkii. The TE concentration levels signalled mild contamination of the sampling sites, proving a good health status of the studied aquatic ecosystem. Finally, the results obtained in P. clarkii muscle were below the threshold limits of EU Regulation 2023/915 suggesting that these edible parts are safe for human consumption.

Polycyclic aromatic hydrocarbon (PAH) contamination in industrial soils poses significant environmental challenges, necessitating cost-effective bioremediation approaches like tree-based phytoremediation. However, the defence mechanisms and adaptability of trees to PAH exposure remain poorly understood, while the identification of molecular markers could help in the detection of toxicity symptoms. This study explores the molecular response of Populus canadensis to a phenanthrene (PHE) contamination gradient (from 100 to 2000 mg kg^-1) using RNA-seq analysis of roots and leaves after 4 weeks of exposure. Both differentially expressed genes (DEGs) and DRomics, a dose-response tool, identified transcriptomic changes, with about 50% of deregulated genes responding significantly at a benchmark dose (i.e. minimal dose that produces a significant effect) below 400 mg PHE kg^-1. The highest number of DEGs was found both at a low concentration (200 and 700 mg kg^-1) and at the highest concentrations (1500-2000 mg kg^-1) for both roots and leaves. Ethylene signalling genes were activated via ABA-independent pathways at low concentrations and ABA-dependent pathways at high concentrations. Across the gradient, responses to oxidative stress were triggered, including reactive oxygen species scavenging and phenylpropanoid biosynthesis, specifically at 1500-2000 mg kg^-1. Additionally, PHE disrupted pathways related to plant responses to biotic stress. These findings revealed unexpected dose-dependent transcriptomic shifts, demonstrating poplar's adaptive defence mechanisms against PHE toxicity.

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022). The parameters include water temperature, pH, specific conductivity (Sp_Con), dissolved oxygen (DO), nitrate-N, total phosphate (TP), turbidity, biochemical oxygen demand (BOD), silicate, total dissolved solids (TDS), and rainfall. The present study evaluated the predictive performance of five models-Multiple Polynomial Regression (MPR), Generalized Additive Models (GAMs), Decision Tree Regression, Random Forest (RF), and XGBoost (Extreme Gradient Boosting)-using RMSE, MAE, MAPE, NSE and R² metrics. XGBoost emerged as the top performer, with an RMSE of 0.024, MAE of 0.018, MAPE of 51.805, NSE of 0.855 and R² of 0.85, explaining 85% of the variance in nitrate-N concentrations. Random Forest also demonstrated strong predictive capability, with an RMSE of 0.028, MAE of 0.021, MAPE of 57.272, NSE of 0.804 and R² of 0.80. MPR effectively modelled non-linear relationships, explaining 75% of the variance, while Decision Tree Regression and GAMs were less effective, with R² values of 0.60 and 0.48, respectively. Variables (BOD, pH, Rainfall, water temperature, and total phosphate) were the best predictors of nitrate-N dynamics. Comparative analysis with previous studies confirmed the robustness of XGBoost and Random Forest in environmental data modelling. The findings highlight the importance of advanced machine learning models in accurately predicting water quality parameters and facilitating proactive management strategies.

Antibiotics and pharmaceuticals exert significant environmental risks to aquatic ecosystems and human health. Many effective remedies to this problem have been developed through research. Metal-organic frameworks (MOFs) are potential constituents, for drug and antibiotic removal. This article explores the potential of MOFs like UiO-66 (University of Oslo-66) to remove pharmaceutical and antibiotic contaminants from water. Zr-based MOF UiO-66 is used in water treatment due to its well-known chemical, thermal, and mechanical stability. The review covers several modifications, including metal doping, organic-group functionalization, and composite construction, to increase the UiO-66 selectivity and adsorption capacity for various pollutants. Recent studies have shown that UiO-66 is an effective material for pharmaceutical pollutants such as ciprofloxacin, tetracycline, and sulfamethoxazole removal. Practical application, photostability, and large-scale synthesis remain challenges in water treatment methods. Moreover, recent studies indicate the recycling potential of UiO-66 that validates its capability to retain its efficiency over multiple cycles, indicating its cost-effectiveness and sustainability. Besides, the toxicity of UiO-66 and its derivatives, which occur during water treatment, has also been highlighted, addressing the health and environmental risks. Prospective research directions include designing flaws, producing stable analogs of UiO-66, and transforming powdered UiO-66 into other forms that might be utilized, including films and membranes. This review is crucial as no comprehensive literature is currently available that thoroughly discusses the design techniques and applications of UiO-66 and its composites for drug and antibiotic removal. Our study specifically concentrates on the latest developments, emphasizing particular alterations that improve performance in water treatment.

In recent decades, freshwater bodies have experienced significant stress due to the excessive disposal of dyes from textile industries and waste antibiotic discharges from pharmaceutical industries. The continuous disposal of these substances may harm the natural ecosystem and generate antibiotic resistance in living organisms. Conventional treatment facilities are inadequate in treating these contaminants effectively, leading to a focused interest in advanced technologies, such as electrooxidation. This study aimed to assess graphite sheet electrode's efficacy in removing methylene blue (MB) dye and antibiotic ciprofloxacin (CIP) under different operating conditions, such as voltage (2.5, 5, and 7.5 V), initial concentration (5, 10, 25, and 50 ppm), pH (3, 6, and 9), and electrolyte (Na₂SO₄ and NaCl). The results indicated that 10 ppm MB and CIP could be removed by more than 99%, with pseudo-first-order reaction kinetics in 2 h. The degradation was more effective in the NaCl medium than in Na₂SO₄ due to the presence of highly active chlorine species_. The degradation by-products revealed successful degradation of MB and CIP molecules in both electrolytes yielding low m/z value by-products and the toxicity analysis via ECOSAR V2.2 reveals that the daughter products are not harmful. The operating cost of the system was between 0.05 and 0.07 $ m^-3 for degradation in both electrolyte systems. These findings suggest that electrooxidation systems utilizing thin graphite sheet electrodes may be promising for dye and pharmaceutical wastewater treatment due to their effectiveness, versatility, and relatively low environmental impact.

This study investigates the concentration of heavy metals lead (Pb), cadmium (Cd), and zinc (Zn) in the blood of house sparrows (Passer domesticus) across various urban habitats in Meknes, Morocco. Fifty adult sparrows were captured from five distinct sites, including industrial, high-traffic, and rural areas. Blood samples were specifically analyzed for Pb, Cd, and Zn using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES). Significant variations in metal concentrations were observed across the different sites, with the highest levels found in the industrial zone: lead (Pb) at 336.02 µg/L, cadmium (Cd) at 12.28 µg/L, and zinc (Zn) at 1736.09 µg/L. Principal component analysis (PCA) and K-means clustering identified three distinct pollution clusters: Cluster 0 (high Zn, low Pb and Cd), Cluster 1 (moderate levels of all metals), and Cluster 2 (high levels of all metals). These findings emphasize the ecological and health risks posed by urban pollution, and demonstrate the value of house sparrows as effective bioindicators.

This study employs mechanically synthesized nano-scrap carbon iron filings (nSCIF) as a cost-effective and sustainable catalyst in heterogeneous electro-Fenton process. The catalytic behaviour of nSCIF was studied for the oxidation of cytarabine (CBN) under the influence of various experimental parameters such as pH, catalyst dose and applied current density. The highest removal efficiency (~ 99%) was achieved in 90 min of reaction at pH 3, 0.4 g L^-1 of nSCIF dose and applied current density of 40 mA cm^-2. Being a solid catalyst, nSCIF enhances the production of •OH radicals and promotes the cathodic regeneration of iron species (Fe³⁺ to Fe²⁺). The mineralization efficiency reached 78% within 3 h of reaction time. The daughter products generated during the reaction were identified through mass spectrometry analysis where eight major transformation productions were identified. The degradation of CBN was mainly contributed by the oxidation of aromatic ring. These findings corroborate the potential of utilizing industrial waste in the electrocatalytic oxidation of persistent pollutant.

Seaweeds contribute to the energy input in marine communities and affect the chemical makeup, species composition, nutrient availability, pH, and seawater oxygen levels. However, the annual introduction of 28.5 million tons of plastic waste into oceans makes up 85% of marine litter, which is expected to grow fourfold in the next 25 years, causing a rise in concern for human health and the environment. Microplastics are small plastic particles of 1-5 mm that are either manufactured or formed due to the degradation of large plastic materials. This study analyzes the prevalence of microplastics in marine environments, their interaction with marine macro- and microalgae, environmental implications, genetic responses to microplastic exposure, and potential strategies for mitigating microplastic pollution. The leading causes identified were high plastic production rate (390 million tons annually), increased usage, inefficient waste management, meager recycling (9% is recycled), slow degradation (up to 1200 years), easy distribution via oceanic currents, and industrialization that has led to the accumulation of microplastics in the marine ecosystems. Therefore, it is recommended that the waste management system be strengthened, focusing on recycling, repurposing, reducing single-use plastics, and redirecting plastic waste away from water bodies. Developing reliable detection technologies, studying the long-term effects of microplastics in marine ecosystems, and collaborating with the public and private sectors may be encouraged. Further investigations on microplastic-seaweed interaction, the bioremediation potential of various species, and the involved molecular mechanisms may lead to new strategies for reducing microplastic loads in marine ecosystems.

Forests play a vital role in environmental balance, supporting biodiversity and contributing to atmospheric purification. However, forest fires threaten this balance, making the identification of forest fire probability (FFP) areas crucial for effective mitigation. This study assesses forest fire trends and susceptibility in the Similipal Biosphere Reserve (SBR) from 2012 to 2023 using four machine learning models-extreme gradient boosting tree (XGBTree), AdaBag, random forest (RF), and gradient boosting machine (GBM). A forest fire inventory was created using the delta normalized burn ratio (dNBR) index, and 19 conditioning factors were incorporated after rigorous collinearity testing. FFP maps were generated and evaluated using ROC-AUC, MAE, MSE, and RMSE metrics. The frequency ratio (FR) model was also applied to assess the importance of variables. The results show that approximately 40.85% of the study area is high to very high susceptible to forest fires, with the RF model achieving the highest accuracy (AUC = 0.965). An average analysis across all models revealed that high susceptibility areas accounted for 23.08% of the study area, the largest among all classes. Moderate susceptibility zones covered 16.19%, while very high susceptibility areas comprised 18.23%. Interestingly, very low and low susceptibility zones together represented 42.50%, indicating a large portion of the area is at relatively low fire risk. Temporal analysis identified 2021 as the peak year for fire incidents, with 94.72% of the fires occurring during March and April. The buffer zone experienced the highest number of incidents, with a significant anthropogenic influence. Using the FR model, variable importance analysis showed that land use and land cover (LULC), NDVI, and NDMI were the most influential factors in fire susceptibility. This study contributes to forest fire management by integrating the dNBR index with machine learning models and FR analysis to generate precise FFP maps. These findings provide valuable insights for policymakers and conservationists, enabling targeted interventions in high-risk zones and enhancing fire management strategies to reduce the impact of forest fires.