Environmental Research最新文献

The airborne microbiome significantly influences human health and atmospheric processes within Earth's troposphere and is a crucial focus for scientific research. This study aimed to analyze the composition, diversity, distribution, and spatiotemporal characteristics of airborne microbes in Qatar's ambient air. Air samples were collected using a sampler from ten geographically or functionally distinct locations during a period of one year. Spatial and seasonal variations significantly impacted microbial concentrations, with the highest average concentrations observed at 514 ± 77 CFU/m³ for bacteria over the dry-hot summer season and 134 ± 31 CFU/m³ for fungi over the mild winter season. Bacterial concentrations were notably high in 80% of the locations during the dry-hot summer sampling period, while fungal concentrations peaked in 70% of the locations during winter. The microbial diversity analysis revealed several health-significant bacteria including the genera Chryseobacterium, Pseudomonas, Pantoea, Proteus, Myroides, Yersinia, Pasteurella, Ochrobactrum, Vibrio, and fungal strains relating to the genera Aspergillus, Rhizopus Fusarium, and Penicillium. Detailed biochemical and microscopic analyses were employed to identify culturable species. The strongest antibiotic resistance (ABR) was observed during the humid-hot summer season, with widespread resistance to Metronidazole. Health risk assessments based on these findings indicated potential risks associated with exposure to high concentrations of specific bioaerosols. This study provides essential baseline data on the natural background concentrations of bioaerosols in Qatar, offering insights for air quality assessments and forming a basis for public health policy recommendations, particularly in arid regions.

The contamination of trace elements and heavy metal(loid)s in water bodies has emerged as a global environmental concern due to their high toxicity at low concentrations to both biota and humans. This study aimed to evaluate the ecological risk associated with the occurrence and spatial distribution of Mn, Fe, Co, Cd, Ni, Zn, Sb, As, Tl, Cu, Pb, U, and V in the heavily polluted waters of an important river-reservoir system (Atoyac River Basin) in central Mexico, using two-level tired probabilistic approaches: Risk Quotient based on Species Sensitivity Distribution (RQ_SSD) and Joint Probability Curves (JPCs). The concentrations of these elements varied widely, ranging from 0.055 μg L⁻¹ to 9200 μg L⁻¹ and from 0.056 μg L⁻¹ to 660 μg L⁻¹, in both total and dissolved fractions, respectively. Although geogenic and anthropogenic sources contribute to the presence of these elements in waters, the discharge of untreated or poorly treated industrial wastewater is the main source of contamination. In this regard, the RQ_SSD results indicated high ecological risk for Mn, Fe, Co, Ni, Zn, and Sb, and medium or low ecological risk for As, Tl, U, and V at almost all sampling sites. The highest RQ_SSD values were found downstream of a large industrial corridor for Co, Zn, Tl, Pb, and V, with Tl, Pb, and V escalating to higher risk levels, highlighting the negative impact of industrial contamination on biota. The JPC results for these elements are consistent with the RQ_SSD approach, indicating an ecological risk to species from Mn, Fe, Co, Ni, Zn, and Sb in waters of the Atoyac River Basin. Therefore, the results of this study offer a thorough assessment of pollution risk, providing valuable insights for legislators on managing and mitigating exposure.

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques—Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)—it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

Objective

To assess the association of dinitroaniline herbicides as well as their interactions with genetic susceptibility and lifestyle with glucose dysregulation.

Methods

A total of 4310 Chinese urban adults from the baseline of the Wuhan-Zhuhai Cohort were included in the cross-sectional study. A follow-up panel from the cohort was included in the longitudinal study, including 158 participants with 432 observations. Glucose dysregulation, including fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (HOMA-IR), type 2 diabetes mellitus (T2DM), and impaired fasting glucose (IFG) were assessed. Serum dinitroaniline herbicides including benfluralin, trifluralin, and pendimethalin were measured. T2DM-related polygenic risk score (PRS) and healthy life scores were constructed.

Results

Cross-sectionally, each 2-fold increase in serum benfluralin was associated with a 1.12%, 2.03%, and 9% increase in FPG, HOMA-IR, and IFG risk, respectively. Each 2-fold increase in serum trifluralin was associated with a 0.70% increase in FPG. Each 2-fold increase in serum pendimethalin was associated with a 2.53% and 24% increase in FPG and IFG risk, respectively (all P < 0.05). Positive associations were found between the dinitroaniline herbicide mixture and glucose dysregulation. Longitudinally, serum benfluralin and pendimethalin were associated with the annual increases in FPG and HOMA-IR (P < 0.05). Joint and interaction effect analysis showed that compared with participants with high benfluralin/trifluralin/pendimethalin, high PRS, and unhealthy lifestyle, those with low benfluralin/trifluralin/pendimethalin, low PRS, and healthy lifestyle showed the greatest declines in FPG, i.e., −15.46%, −13.58%, and −10.51% changes, respectively; and the greatest reductions in IFG risks, i.e., 75%, 61%, and 73% reductions, respectively (all P < 0.05).

Conclusions

This study highlighted the importance of controlling dinitroaniline herbicide exposure and following healthy lifestyles in glucose dysregulation prevention, especially among individuals with high genetic risk of T2DM.

China's groundwater is facing a significant threat from nitrate pollution. Here we analyzed 2348 regional surveys of groundwater nitrate levels in China from 1990 to 2020, examining distribution, trends, and drivers. This study uncovers a concerning rise in nitrate pollution, with estimated median nitrate levels climbing from 3.84 mg/L in 1990 to 6.94 mg/L in 2020. A stark contrast is observed between regions: the northern areas have a median nitrate concentration of 8.54 mg/L, significantly higher than the southern regions, where the median is just 7.15 mg/L. From 1990 to 2020, agricultural activity consistently emerges as the dominant driver of changes in groundwater nitrate concentrations, while groundwater exploitation, domestic pollution, and industrial production also contribute to varying degrees. This analysis highlights the urgency for region-specific policies and interventions to address the escalating nitrate pollution in China's groundwater.

The study focusses on risk related generalization beliefs, i.e., the belief that the risk of a specific agent can be generalized across various conditions. These conditions are: G1: across the frequency of usage (from often to rare); G2: across exposure modalities (hot to cold); G3: across exposure routes (oral to dermal), and G4: across detrimental outcomes (specific detrimental endpoint to various detrimental endpoints). We examined how different risk descriptions impact those generalization beliefs using the risks of bamboo tableware for consumers as an example. The research followed a 2x2 between-subjects design with repeated measurements, and the test subjects were non-experts. The first factor, disclosure format, refers to the disclosure (yes/no) of risk generalization limitation. Half of the study participants were informed that bamboo tableware only poses a health risk if it is frequently used for hot beverages or foods. In contrast, the other half received no information about the risk restrictions regarding bamboo tableware use. The second factor referred to the agent description, either described by a particular unfamiliar term (formaldehyde) or a generic, more familiar term (plastics). Furthermore, we tested whether subjects who were initially not informed about the limits of risk generalizations altered their risk generalization beliefs G1 - G4 when they were informed that only frequent hot food and beverage consumption in bamboo tableware causes risks. It was found that respondents' four risk generalization beliefs G1 - G4 were statistically significantly lower for those who were informed about the risk generalization limitations. Additionally, the generalization beliefs G1 - G3 of subjects who were initially not informed, but received the information about the restrictions later, were statistically significantly lower than their initial beliefs, except for generalization across endpoints (G4). We discussed the findings in terms of their implications for risk communication.

Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

In this study, a peracetic acid (PAA) alone process was systematically demonstrated to give a high efficiency in the selective degradation of sulfonamide antibiotics (SAs). The employment of scavengers and probe compounds in this process demonstrates the predominant role of PAA in direct oxidation, and the limited role of carbon-centered radicals (R-O^•) in the degradation of representative SA, sulfamethazine (SMT). The process also exhibits high tolerance towards solution pH and competing anions in wastewater, indicating its applicability in enhancing the biodegradation of SAs in wastewater. Furthermore, the relationships between the observed rate constants (k_obs) and the molecule descriptors for ten SA compounds are demonstrated through the assessment of structure−activity relationships, calculated from density functional theory (DFT). This study gives new insights into the selectivity, performance and mechanism of PAA direct-oxidation in SA degradation.

Background

Per- and polyfluoroalkyl substances (PFAS) is perceived as an emerging environmental endocrine disruptor, which have been linked to children neurodevelopment. However, the potential mechanisms are not clear. Brain-derived neurotrophic factor (BDNF) is a vital protein in neurodevelopment, and the associations between PFAS exposure and BDNF require exploration.

Objective

We aimed to explore the relationships between PFAS exposure and the levels of BDNF in cord serum.

Methods

A total of 1,189 mother-infant dyads from the Sheyang Mini Birth Cohort Study (SMBCS) were enrolled. The levels of 12 PFAS and BDNF were measured in cord serum. We utilized generalized linear models (GLMs), quantile-based g-computation (QGC) models, and Bayesian Kernel Machine Regression (BKMR) models to explore the relationships between single and mixed PFAS exposure and BDNF concentration. Additionally, the potential sex differences were explored by sex-stratified analysis.

Results

Median concentrations of the included 10 PFAS ranged from 0.04 to 3.97 μg/L. In the single chemical models, four PFAS congeners, namely perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), were negatively associated with BDNF levels in cord serum among females only (β: −0.116 to −0.062, p < 0.05). In the BKMR models of total mother-infant dyads and female fetuses, the significant negative relationships between PFAS mixtures and BDNF were observed, and PFUnDA was identified as an important contributor (Posterior inclusion probability, PIP = 0.8584 for the total subjects; PIP = 0.8488 for the females). PFOS was another important driver based on the mixture approaches.

Conclusions

We found that PFNA, PFOS, PFDA, and PFUnDA were associated with decreased BDNF concentration in the females, although the causal inference might be limited. PFAS mixtures were also negatively linked with BDNF levels in the total mother-infant pairs and female fetuses. The adverse effect of PFAS exposure on fetal BDNF levels might be sex-specific.