Environmental Research最新文献

This work presents a novel approach for univariate anomaly detection in membrane pressure of an aerobic membrane bioreactor (aMBR) using Long Short-Term Memory (LSTM) autoencoders. Membrane fouling, manifested by an increase in applied or transmembrane pressure, is one of the most critical challenges in the operation of aMBRs, as it directly affects effluent quality, energy consumption, and operating costs. Early and reliable fouling detection is therefore essential for maintaining process stability and reducing downtime. The study utilizes experimental data from an aMBR pilot plant treating Pulp and Paper (P&P) effluents, where applied pressure is a key indicator of membrane fouling and operational performance. Aiming to enhance real-time monitoring and increasing operational efficiency, the proposed methodology involves training an LSTM autoencoder to capture the normal temporal patterns of applied pressure and detect deviations that indicate anomalies such as fouling development, or other operational issues. The model exhibited high performance with R² = 0.996 in reconstructing previously unseen normal data. Furthermore, the model's efficacy was tested using case studies simulating sensor malfunctions and irreversible membrane fouling, where the model successfully identified these anomalies in real time. In contrast to prior studies that primarily relied on synthetic or laboratory datasets, this work uses real pilot-scale operational data, thereby providing insights that are more representative of system behavior. The performance of the proposed model suggests that LSTM autoencoders can be an efficient tool for predictive maintenance and anomaly detection in aMBR systems, paving the way for better management and optimization of wastewater treatment processes.

Anaerobic ammonium oxidation coupled with Fe(III) reduction (Feammox) plays a critical role in the natural nitrogen cycle, but the differential regulatory mechanisms of various iron sources that influence Feammox sludge acclimation remain systematically unexplored in wastewater treatment. In this study, four iron compounds (FeCl₃, Fe₂O₃, Fe₃O₄, and Fe(OH)₃) were individually introduced into anaerobic sequencing batch reactors to initiate Feammox processes. After 126 days of enrichment, 48.18%, 29.38%, 27.56%, and 32.18% of NH₄⁺-N removal efficiencies were achieved, respectively. Notably, the FeCl₃ reactor exhibited stronger dynamic interactions between Feammox and nitrate-dependent ferrous oxidation (NDFO), achieving superior NH₄⁺-N and total nitrogen removal efficiencies. Additionally, prolonged acclimation led to a reduction in floc size and an improvement in the settling performance of Feammox sludge, while a large quantity of iron sediments accumulated on the surface of the sludge. High Fe³⁺ concentrations promoted extracellular polymeric substances transformation toward fulvic acid- and humic acid-like components, facilitating Feammox through electron shuttle-mediated indirect electron transfer. Additionally, the FeCl₃ reactor effectively mediated the transformation of iron crystal into FeO(OH) and achieved more efficient reuse of iron sediments. Microbial analysis revealed the enrichment of iron-reducing bacteria (Ferruginibacter) and autotrophic denitrifier (Blastocatellaceae), with intensified microbial interactions driving effective nitrogen-iron cycling process. Furthermore, the FeCl₃-enriched community exhibited higher potential for nitrogen and iron related metabolic functions related compared to the other reactors, enabling more efficient enrichment of Feammox microorganisms and improved nitrogen conversion rates. These findings provide critical insights for optimizing iron sources in Feammox-based wastewater treatment.

Arsenic(As) exposure is a global health issue. Prior research has demonstrated that As exposure is associated with various adverse birth outcomes. Nevertheless, a systematic assessment of the health risks linked to adverse birth outcomes, especially congenital defects, caused by As exposure is still pending. In addition, it is also necessary to propose a clear safety threshold in terms of the prevention of As exposure in public policies. We searched five databases (PubMed、Web of Science、Wanfang, CNKI, and Weipu) to obtain studies published before June 2025. The meta-analysis comprised 76 studies (n = 4,164,491). As exposure was associated with adverse pregnancy outcomes (spontaneous abortion: OR = 1.50, 95% CI: 1.14, 1.99; stillbirth: OR = 2.10, 95% CI: 1.75, 2.52; preterm birth: OR = 1.19, 95% CI: 1.05, 1.33; neonatal mortality: OR = 1.38, 95% CI: 1.11, 1.70 and infant mortality: OR = 1.50, 95% CI: 1.22, 1.85), an increased incidence of congenital cardiac disease was linked to As exposure (OR = 1.92, 95% CI: 1.07, 3.52). Birth weight was lowered after As exposure (β = -12.67 g, 95% CI: -19.93 -5.40). Our research findings suggest that As exposure is closely associated with various adverse birth outcomes. Using the Bayesian Benchmark Dose (BBMD) analysis system, we identified a dose-response relationship between As exposure from drinking water and adverse pregnancy outcomes. Neonatal mortality emerged as the most sensitive endpoint, with the lowest benchmark concentration lower limit (bmcl₁₀) found to be 27.73 μg/L.

The environmental pollution caused by NO has gained global visibility. Currently, photocatalytic removal of ppb-level NO is one of the relatively economical and effective methods, with minimal impact on the environment. Numerous studies have proved that defect engineering can change the surface physical/chemical properties of photocatalysts and become a common approach to improve the catalytic performance in the application of NO removal. However, there is currently a lack of corresponding reviews to understand the latest developments in this field. This paper reviews the reaction mechanism of defects enhancement performance in the photocatalytic removal of NO, as well as the contributions of different types of defects in this process, and further discusses the effect of defects from the aspects such as quantification, localization and stability. Finally, the difficulties and challenges of defects for NO removal are debated, which is expected to inspire rational engineering of defects in photocatalysts toward NO removal.

While epidemiological studies have examined the effects of exposure to particulate matter with diameter ≤2.5 μm (PM2.5) on heart rate variability (HRV) on a daily basis, there is a need for more granular data to better understand the ultrashort-term effects of PM2.5 on HRV. This study aimed to investigate the minute-level association between PM2.5 exposure and HRV using real-time personal monitoring data, focusing on time-lagged effects in vulnerable populations. We collected minute-by-minute data of PM2.5 and HRV using a wearable device from 73 individuals , with 3 days of continuous observations per participant (total 315,360 observations). PM2.5 and electrocardiogram (ECG)-derived HRV indices, including the standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD), were continuously measured for five days using wearable devices. Distributed lag non-linear and linear mixed-effects models were used to evaluate exposure-response relationships. PM2.5 exposure was significantly associated with acute HRV reductions within 180 min. SDNN decreased by -8.04% (95% CI: -11.10% to -4.88%) at the initial lag following exposure, peaking at approximately 49 minutes and gradually attenuating thereafter, and RMSSD decreased by -4.17% (95% CI: -7.23% to -1.00%) at the initial lag, with a similar attenuation pattern beyond 38 minutes. More pronounced effects occurred during nighttime (18:00-06:00), with SDNN decreasing by -13.9% (95% CI: -17.81% to -9.80%) and RMSSD by -7.43% (95% CI: -11.13% to -3.57%). Females exhibited a more immediate HRV decline at the initial lag (SDNN: -10.24%, RMSSD: -6.39%) compared to males (SDNN: -4.44%, RMSSD: -8.61%). Patients with arrhythmia showed the greatest reductions at the initial lag (SDNN: -11.42%, RMSSD: -15.95%). This study highlights the immediate autonomic impact of PM2.5 exposure, emphasizing its differential effects by time of day, sex, and health status. Findings underscore the importance of personal air pollution monitoring and targeted interventions for high-risk populations.

Electromagnetic wave pollution has always been a problem that affects human health and the operation of precision instruments. The recent research focus has been on the preparation of effective electromagnetic wave absorbers using biomass materials. In this study, natural bamboo was utilized as the raw material, and an innovative strategy was established by combining delignification pretreatment with iron salt impregnation, followed by in-situ pyrolysis. Through this process, high-performance magnetic bamboo-based composites (BBCs) were successfully fabricated. The delignification process played a crucial role in modifying the hierarchical pore architecture of bamboo, thereby promoting more uniform iron salt infiltration and enhanced dispersion of magnetic nanoparticles during carbonization. Owing to these structural improvements, the BBC-1000 sample demonstrated outstanding electromagnetic wave absorption capability: at a thickness of just 2.1 mm, it achieved a minimum reflection loss of -57.6 dB and a broad effective absorption bandwidth (RL ≤ -10 dB) reaching 7.8 GHz. This study highlights that delignification not only improves impedance matching but also facilitates the formation of a three-dimensional conductive framework with well-distributed magnetic loss sites, resulting in a synergistic enhancement of both dielectric and magnetic losses. Moreover, CST simulations confirmed that BBC-1000 offers substantial radar wave attenuation across different incident angles when used as a coating material. At 5V, the temperature of BBC-1000 rapidly increases to 166°C.

Inter-basin water diversion projects are crucial for mitigating the spatial imbalance between water supply and demand. However, their operation is challenged by complex uncertainties arising from climate variability and hydrological fluctuations. To address these challenges, this study develops a multi-objective risk operation model and decision-making model for inter-basin water diversion projects. At the operational level, a multi-objective risk operation model is formulated to minimize both the total water shortage in water-receiving regions and the total transfer cost at pumping stations, while explicitly accounting for forecast uncertainties in source water availability. The model is solved by the Multi-Objective Mantis Search Algorithm (MOMSA) to generate a set of non-dominated solutions. At the decision level, a comprehensive risk decision-making model is established by integrating the Kemeny Median Indicator Ranks Accordance (KEMIRA) framework with the Best-Worst Method (BWM) for expert input and Data Envelopment Analysis (DEA) for dynamic weighting. The proposed framework is applied to the "One Gate Three Lines " project, a major water diversion project in China. The results show that non-dominated solutions under uncertainties effectively encompass the deterministic solution space. Compared with the deterministic case, the average water shortage rate reduces by 1.92 %, while the average transfer cost increased by CNY 63,300 due to uncertainty considerations. The risk decision-making model effectively captures multiscale risks and supports adaptive, risk-informed decision making for inter-basin water diversion operations under climate variability.

While extreme temperatures have been linked to adverse mental health outcomes, the impact of prenatal heat stress on postpartum depression remains understudied. We evaluated associations between prenatal heat stress and depressive symptoms one year postpartum, identified susceptible gestational windows, and assessed whether neighborhood climate vulnerability modifies maternal susceptibility to heat-related postpartum depression. We included 275 predominantly low-income Hispanic/Latina participants in the MADRES cohort. Residential wet bulb globe temperature (WBGT), is more reflective of physiological responses to heat stress compared to temperature alone, and was estimated throughout pregnancy at residential addresses. Depressive symptoms at 12 months postpartum were assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale. The Climate Vulnerability Index (CVI) was linked at census tract level. We used multivariable linear regression to estimate associations between trimester-specific heat stress and log-transformed CES-D scores and examined effect modification by CVI (≥75th vs. <75th percentile). Distributed lag models (DLM) were used to identify susceptible exposure windows to WBGT, and Bayesian distributed lag interaction models (BDLIM) were used to assess differences in associations by CVI. We found that each interquartile range increase (IQR) in first-trimester WBGT was associated with a 18.93 % (95 % CI: 0.03 %-41.75 %) increase in postpartum CES-D scores. Neighborhood-level CVI modified second-trimester effects of heat stress on postpartum depressive symptoms, with participants in high-CVI neighborhoods experiencing stronger positive associations (31.21 %, 95 % CI: -11.80 %-95.21 %) compared to those in low-CVI neighborhoods (-16.70 %, 95 % CI: -32.29 %-3.55 %) for WBGT. These findings provide novel evidence linking early-pregnancy heat exposure to postpartum depression risk, with effects amplified in climate-vulnerable neighborhoods during mid-pregnancy. Our results suggest that heat mitigation strategies, including providing heat-health education during prenatal visits, may be important for postpartum mental health.

Per- and poly-fluoroalkyl substances (PFAS) can cause detrimental ecological and health impacts in water. Exploring cutting-edge water purification strategies to address this concern is essential for fostering sustainable global progress and ensuring a future for humanity. This work examines the potential of MXene-based membranes for removing PFAS in water. It discusses the unique properties of MXene materials, and key removal mechanisms, such as size exclusion, electrostatic interactions, adsorption, and nanoconfinement effect, which contribute to effective PFAS removal, are also investigated. These findings demonstrate the potential of MXene-based membranes for practical applications in addressing environmental issues related to emerging pollutant contamination. The MXene-carbon nanotube (CNT) membrane achieved over 91% elimination of perfluorooctanoic acid (PFOA), demonstrating its potential applicability for effective PFAS removal. MXenes present an innovative alternative to traditional materials, creating novel opportunities for leading-edge and environmentally sustainable water solutions. With the incorporation of MXenes in commercial membrane applications, these materials offer advanced separation performance, increased durability, and boosted fouling resistance. Challenges, including membrane stability, fouling, and scalability, are highlighted alongside potential solutions that could contribute to developing MXene-based membrane technologies. Finally, prospects focus on advancements in MXene modifications and real-world applications for sustainable water treatment in support of the Sustainable Development Goals (SDGs). By supporting SDG 6 ("Clean water and sanitation") and SDG 14 ("Life below water") and impacting SDG 3 ("Good health and well-being") and SDG 12 ("Responsible consumption and production"), membrane-based solutions address the gap between research and industry in MXene-powered water purification. The outcomes of this research are meaningful for policymakers, environmental specialists, and educators as we collaboratively pursue a more responsible future.

This study explores a sustainable route to synthesize a novel supplementary cementitious material (SCM) through co-calcination of recycled concrete fines (RCF)-a secondary powder waste from recycled aggregate production-with kaolin clay. The co-calcination process effectively utilizes the overlapping temperature ranges of cement hydrate decomposition and kaolinite dehydroxylation, enabling the formation of a limestone calcined clay (LC₂)-type SCM. Co-calcination promotes the formation of α- and β-C₂S phases from RCF decomposition, along with free lime and Ca-rich aluminosilicate amorphous phase arising from calcite decomposition and chemical interactions between RCF and kaolinite. Reactivity results confirm synergistic effects between the two precursors, achieving reactivity levels comparable to ground granulated blast furnace slag even at RCF contents up to 60 %. This new SCM not only enhances early-stage cement hydration, but also enriches the Ca²⁺ and CO₃^2- concentrations, fostering the formation of AFm-CO₃ phases and improving the Ca/Si and Ca/Al ratios in C-A-S-H gel. The gradual hydration of C₂S phases contributes to extended pozzolanic activity and late-age strength development. Microstructural analysis reveals a significant pore structure refinement and reduction in total pore volume. Overall, this work highlights the co-calcination of RCF with kaolin clay as a viable and sustainable strategy for valorizing construction and demolition waste into a high-performance SCM, contributing to circular economy practices and low-carbon cement technologies.