Environmental Research最新文献

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.

Microeukaryotic plankton are essential to marine food webs and biogeochemical cycles, with coastal seas playing a critical role in aquatic ecosystems. Understanding the diversity of microeukaryotic plankton, deciphering their community structure and succession patterns, and identifying the key factors influencing these dynamics remain central challenges in coastal ecology. In this study, we examine patterns of biodiversity, community structure, and co-occurrence using environmental DNA (eDNA)-based methods. Our results show a linear correlation between α-diversity and distance from the shore, with nutrient-related factors, especially inorganic nitrogen, being the primary determinants of the spatial distribution of plankton communities. Alternation of coastal habitat have shifted the succession patterns of coastal eukaryotic plankton communities from stochastic to deterministic processes. Additionally, our observations indicate that the topology and structure of eukaryotic plankton symbiotic patterns and networks are significantly influenced by environmental heterogeneity such as nutrients, which increase the vulnerability and decrease the stability of offshore ecological networks. Overall, our study demonstrates that the distribution of microeukaryotic plankton communities is influenced by factors related to environmental heterogeneity.

Background

Organophosphate esters (OPEs), flame retardants and plasticizers found widely in consumer products, may impact vascularization processes in pregnancy. Yet, the association between maternal exposure to OPEs and both preeclampsia and blood pressure during pregnancy remains understudied.

Methods

Within the LIFECODES Fetal Growth Study (N = 900), we quantified 8 OPE metabolites from maternal urine collected at up to 3 time points during pregnancy and created within-subject geometric means. Outcomes included diagnosis of preeclampsia and longitudinal systolic (SBP) and diastolic (DBP) blood pressure measurements (mean = 14 per participant). Cox proportional hazards models were used to estimate associations between OPE metabolites and preeclampsia. Associations between average OPE metabolite concentrations and repeated blood pressure measurements were estimated using generalized estimating equations.

Results

Five OPE metabolites were detected in at least 60% of samples; 3 metabolites detected less frequently (5–39%) were examined in an exploratory analysis as ever vs. never detectable in pregnancy. There were 46 cases of preeclampsia in our study population. Associations between OPE metabolites and preeclampsia were null. We noted several divergent associations between OPE metabolites and longitudinal blood pressure measurements. An interquartile range (IQR) difference in average bis(2-chloroethyl) phosphate concentrations was associated with a decrease in SBP (−0.81 mmHg, 95% confidence interval [CI]: −1.62, 0.00), and, conversely, bis(1-chloro-2-propyl) phosphate was associated with a slight increase in SBP (0.94 mmHg, 95% CI: 0.28, 1.61). We also noted a decrease in SBP in association with several metabolites with low detection frequency.

Conclusions

We observed null associations between OPE metabolites and preeclampsia, but some positive and some inverse associations with blood pressure in pregnancy. While our study was well-designed to assess associations with blood pressure, future studies with a larger number of preeclampsia cases may be better poised to investigate the association between OPE metabolites and phenotypes of this heterogenous hypertensive disorder of pregnancy.

This study investigates the enhancement of ozone adsorption on diverse TiO₂ crystal interfaces through an innovative electrochemical modulation approach. The research focuses on the effects of applied electric field strength and reaction sites on ozone interfacial adsorption energies for Ti/Anatase TiO₂ (0 0 1) and Ti/Rutile TiO₂ (1 1 0) interfaces. The findings reveal that positive electric fields significantly enhance ozone adsorption on both interfaces, with adsorption energies increasing by up to 18% for Ti/Anatase TiO₂ (0 0 1) and 15% for Ti/Rutile TiO₂ (1 1 0). Notably, double water molecule sites (≡(H₂O)₂) play a crucial role in this enhancement process. The study demonstrates that the applied electric field alters the charge distribution at the TiO₂ catalytic interface, thereby increasing interfacial charge density and promoting charge migration to ozone. Furthermore, this process leads to enhanced overlap and hybridization between ≡(H₂O)₂ sites and the s and p orbitals of ozone molecules, resulting in the formation of chemical bonds with lower Fermi levels. These comprehensive results demonstrate the broad applicability of the electrochemical interfacial ozone adsorption enhancement method across different crystal types and surfaces. Consequently, this study provides essential data to support the advancement of greener and more energy-efficient heterogeneous catalytic ozonation processes, potentially contributing to significant improvements in ozone-based water treatment technologies.

The presence of organic matter in sludge plays a significant role in sludge dewatering, anaerobic sludge digestion, resource (i.e., protein) recovery and pollutants removal (i.e., heavy metals) from sludge, as well as post-application of sludge liquid and solid digestate. This study summarized the current knowledge on using liquid chromatography organic carbon detection and organic nitrogen detection (LC-OCD-OND) for characterization and quantification of organic matter in sludge samples related with sludge treatment processes by fractionating organic matter into biopolymers, building blocks, humic substances, low molecular weight (LMW) acids, low LMW neutrals, and inorganic colloids. In addition, the fate, interaction, removal, and degradation of these fractions in different sludge treatment processes were summarized. A standardized extraction procedure for organic components in different extracellular polymeric substances (EPS) layers prior to the LC-OCD-OND analysis is highly recommended for future studies. The analysis of humic substances using the LC-OCD-OND analysis in sludge samples should be carefully conducted. In conclusion, this study not only provides a theoretical foundation and technical guidance for future experiments and practices in characterizing sludge organic matter using LC-OCD-OND, but also serves as a valuable resource for consulting engineers and other professionals involved in sludge treatment.

Bixafen (BIX), a member of the succinate dehydrogenase inhibitor (SDHI) class of fungicides, has seen a surge in interest due to its expanding market presence and positive development outlook. However, there is a growing concern about its potential harm to aquatic life, largely due to its resistance to breaking down in the environment. In this study, we thoroughly examined the toxicological impact of BIX on zebrafish as a model organism. Our results revealed that BIX significantly hindered the development of zebrafish embryos, leading to increased mortality, hatching failures, and oxidative stress. Additionally, we observed cardiovascular abnormalities, including dilated cardiac chambers, reduced heart rate, sluggish blood circulation, and impaired vascular function. Notably, BIX also altered the expression of key genes involved in cardiovascular development, such as myl7, vmhc, nkx2.5, tbx5, and flt1. In summary, BIX was found to induce developmental and cardiovascular toxicity in zebrafish, underscoring the risks associated with SDHI pesticides and emphasizing the need for a reassessment of their impact on human health. These findings are crucial for the responsible use of BIX.