Environmental Research最新文献

Air pollution is one of the major environmental threats contributing to the global burden of disease. Among diverse air pollutants, fine particulate matter (PM_2.5) poses a significant adverse health impact and causes multi-system damage. As a highly dynamic organelle, mitochondria are essential for cellular energy metabolism and vital for cellular homeostasis and body fitness. Moreover, mitochondria are vulnerable to external insults and common targets for PM_2.5-induced cellular damage. The resultant impairment of mitochondrial structure and function initiates the pathogenesis of diverse human diseases. This review mainly summarizes the in vivo and in vitro findings of PM_2.5-induced mitochondrial dysfunction and its implication in PM_2.5-induced health effects. Furthermore, recent advances toward the underlying mechanisms of PM_2.5 and its components-induced mitochondrial dysfunction are also discussed, with an attempt to provide insights into the toxicity of PM_2.5 and basic information for devising appropriate intervention strategies.

This is the first successful report on selenium bio-attenuation to satisfy drinking water regulations as per Indian Standards (10 μg/L) in the presence of concomitant nitrate and sulfate from water sources utilizing a fixed bed bioreactor. The bioreactor was immunized with blended microbial culture and worked in downflow mode under anoxic conditions at 30 ± 2 °C for around 190 days under varying influent selenate (100–500 μg/L as selenium), nitrate (50 mg/L), sulfate concentrations (as per selenium removal) and necessary dose of acetic acid (as COD, a carbon source) in synthetic groundwater, operated at an empty bed contact time (EBCT) of 45–120 min. After supplying an adequate dosage of sulfate and alteration of EBCT, selenium was found to comply with drinking water regulations and nitrate was completely removed. X-ray diffraction and transmission electron microscopy analyses depicted nanocrystalline selenium sulfides (SeS and SeS₂) formation as the possible mechanisms of selenium removal. Extended toxicity characteristic leaching procedure (TCLP) extractions confirmed a maximum selenium leaching of 52 and 282 μg/L during anoxic and oxic extractions, respectively. Long-term column leaching (>3-month equilibration) under aerobic conditions at pH 7 confirmed the produced precipitate to be essentially stable (∼0.14% Se leaching). This work exhibits the synchronous bioremoval of selenium and its co-anions from contaminated water complying with drinking water standards, and leaving a stable and non-hazardous selenium-laden biosludge.

Food systems can negatively impact health outcomes through unhealthy diets and indirectly through ammonia emissions originating from agricultural production, which contribute to air pollution and consequently cardiovascular and respiratory health outcomes. In the UK, ammonia emissions from agriculture have not declined in the same way as other air pollutants in recent years. We applied a novel integrated modelling framework to assess the health impacts from six ammonia reduction scenarios to 2030: two agriculture scenarios – a “Current trends” scenario projecting current mitigation measures to reflect a low ambition future, and “High ambition mitigation” based on measures included in the Climate Change Committee's Balanced Pathway to Net Zero; three dietary scenarios – a “Business as usual” based on past trajectories, “Fiscal” applying 20% tax on meat and dairy and 20% subsidy on fruit and vegetables, and “Innovation” applying a 30% switch to plant-based alternatives; one combination of “High ambition mitigation” and “Innovation”. Compared to “Current trends”, the “High ambition mitigation” scenario would result in a reduction in premature mortality of 13,000, increase life years by 90,000 and reduce incidence of respiratory diseases by 270,000 cases over a 30 year period. Compared to Business as Usual, the dietary scenarios would reduce the number of premature deaths by 65,000 and 550,000–600,000 life years gained over 30 years, with most of the benefits gained by reducing ischemic heart disease (incidence reduction: 190,000). The “High ambition combination” would lead to 67,000 deaths averted, 536,000 incidence reductions and 650,000 life-years gained. For all scenarios, older age groups and those living in lower income households would experience the greatest benefits, because of higher underlying mortality rates or higher levels of risk factors. Our study shows that combining mitigation policies targeting agricultural production systems with diet-related policies would lead to significant reductions in emissions and improvement in health outcomes.

Environmental occurrence and risks of novel synthetic phenolic antioxidants (SPAs) remain largely unclear. By using a typical algae (Chlorella pyrenoidosa) as model organism, we evaluated the ecological risks of both traditional and novel SPAs, based on their concentrations in water, sediment, and soil collected from the Yangtze River Delta, China. Detection frequencies (DFs) of 10 novel SPAs were 25–100% in water, 3–100% in sediment, and 0–100% in soil, with geometric means (GMs) of 2700 ng/L, 1270 ng/g, and 2440 ng/g, respectively. For 8 traditional SPAs, DFs were 50–100% (GM: 680 ng/L), 3–100% (534 ng/g), and 47–100% (2240 ng/g) in water, sediment, and soil, respectively. AO3114 was the main pollutant in water, while AO1010 dominated in sediment and soil. Notably, low-molecular-weight SPAs showed migration behavior from sediment to water. Four SPAs (AO626, AO1035, AO1098, and AO1076) showed dose- and time-dependent toxicity on Chlorella pyrenoidosa. As time progressed, sediment-released SPAs became more toxic than those in water. Two SPAs (AO1135 and BHT-Q) posed high risks (RQ_W > 1) to green algae, daphnia, and fish. The SPA mixture exhibited high risks (RQ_mix > 1) to these organisms, increasing with the trophic level. This research holds valuable guidance for further SPA risk assessments.

In karst areas, the dissolved inorganic carbon (DIC) concentrations in aquatic systems are typically higher than that in non-karst areas due to intensive carbonate rock weathering. Understanding the sources and input fluxes of DIC in karst reservoirs is crucial for regional carbon cycle studies. This study utilized dual carbon isotopes (δ¹³C_DIC and Δ¹⁴C_DIC) to estimate the contribution rates and input fluxes of DIC from various sources in Aha Reservoir (AHR), located in southwestern China. Our results indicated that the DIC concentrations (22.33–32.79 mg L⁻¹) and δ¹³C_DIC values (−10.02‰ to −8.55‰) were nearly homogeneous both vertically and laterally in the reservoir (p > 0.05). The Δ¹⁴C_DIC values (−246.31‰ to −137.86‰) were homogeneous along the vertical profile (p > 0.05), but showed significant horizontal variation (p < 0.05), with values decreasing from −149.57 ± 10.27‰ to −232.85 ± 2.37‰ at the mouths of the inflowing rivers. We found that the inflowing rivers were the primary DIC sources to AHR, contributing 70% of the total input, while groundwater and atmospheric CO₂ contributions were relatively minor, at 18% and 12%, respectively. The Jinzhong River (JZR), influenced by industrial and domestic wastewater discharge, contributed the largest DIC input flux at 2.01 t/(km²·mon). In contrast, the Youyu River (YYR), influenced by acidic mine drainage, and the Baiyan River (BYR), influenced by agricultural activities, contributed relatively smaller DIC input fluxes of 1.29 t/(km²·mon) and 1.03 t/(km²·mon), respectively. This study highlights the significant impact of anthropogenic activities on DIC input in AHR, with industrial and domestic wastewater discharges having a greater influence than agricultural activities and acidic mine wastewater inputs. These findings underscore the critical need to manage and mitigate the impacts of human activities on karst reservoir ecosystems.

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

Covalent organic framework (COF) catalytic photocatalysts mediating Fenton-like reactions have been applied to the treatment of organic dyes in printing and dyeing wastewater. However, the photocatalytic performance of original COF is often unsatisfactory. This study investigated the impact of porosity modification strategies on the performance of COF photocatalysts in mediating the removal of organic dyes via Fenton-like reaction. Porosity modification was achieved by increasing the concentration of acetic acid (HAc) catalyst during COF preparation. The modified TAPB-DMTA COF (12M COF) exhibited excellent adsorption and photocatalytic properties. The Fenton-like reaction mediated by 12M COF photocatalysis removed nearly 96% of malachite green (MG) within 20 min, with a rate constant of 0.091 min⁻¹, which was 2.9 and 6.5 times higher than that of g-C₃N₄ and original COF under the same reaction conditions, respectively. Additionally, the modulation mechanism of porosity modification on COF photocatalysis was explored. The conduction band (CB) of COF was reduced from −0.14 eV to −0.38 eV after porosity modification, facilitating the generation of longer-lived O₂^•- in the reaction system, which was conducive to efficient MG removal. Anti-interference experiments showed that the photocatalytic Fenton-like reaction system based on 12 M COF was less affected by common anions, cations and dissolved organics, while maintaining a high MG removal rate in tap water, mid-water, secondary clarifier effluent and river water. In summary, porosity modification was an effective strategy to improve the catalytic performance of original COFs. This study presented an efficient metal-free photocatalyst modification strategy for the Fenton-like reaction while avoiding the production of toxic by-products during dye degradation.

Riparian zones play a vital role in the river ecosystem. Solutes in vertical riparian zones are transported being by alternating hydraulic gradients between river water and groundwater, due to natural or human activities. This study investigates the impacts of porous sediments and alternating rate of surface water-groundwater on nitrogen removal in the riparian zone through experiments based on the field sampled. The experimental results, combined with dimensionless numbers (Péclet and Damköhler) and Partial Least Squares-Path Modeling, analyze the nitrogen fate responding to hydrodynamics changes. The results show that increased sediment porosity contributes to the ammonium removal, particularly when the oxygen content of river water is low, with the removal rate up to 72.57%. High ammonium content and dissolved organic carbon (DOC) in rural rivers lead to a constant low-oxygen condition (4 mg/L) during surface water-groundwater alternation, and promote denitrification. This threatens groundwater with ammonium pollution and causes accumulation at the top of vertical riparian zones during upwelling, potentially causing secondary river pollution. However, increasing the alternating rate hinders the nitrate denitrification and drastically changes in the redox environment of the riparian zone, despite contributing to ammonium removal. Rapid oxygen consumption during aerobic metabolism and nitrification in groundwater-surface water exchange created favorable conditions for denitrification. Floodplains sediment porosity is unfavorable for nitrification. This study improves understanding of coupled hydrologic and solute processes in vertical riparian zones, informing strategies for optimizing nitrogen attenuation and riparian zone construction.

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R² by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

Recalcitrant chemicals in the environment not only present obstacles to living organisms but also contribute to the degradation of natural resources. One contribution to environmental pollution is the discharge of synthetic dyes from the textile sector. This study investigates the combined effect of microbial cells and biochar on eliminating methyl orange (MO) dye. The immobilization of Aeromonas veronii on peanut shell biochar (APSB) was conducted to investigate its efficacy in removing MO dye from water. PSB synthesized by pyrolysis at 300 °C for 120 min showed maximum bacterial immobilization potential. The highest degradation rate of 96.19 % was achieved in APSB within 96 h using MO dye concentration of 100 mg L⁻¹, incubation temperature of 37 °C, pH 7, and biocatalyst dosage of 1g L⁻¹. In comparison, free cells achieved degradation rates of 72.53 % and 61.56 % for PSB. Moreover, the adsorption process was primarily controlled by PSB, with subsequent dye mineralization by A. veronii, as supported by FTIR and LC-MS studies. Moreover, this innovative approach exhibited the reusability of the biocatalyst, giving 76.23 % removal after fifth cycle, suggesting sustainable alternative in dye remediation and potential option for real-time applications.