Environmental Research最新文献

Background: With the accelerating population ageing globally, disability has become a major public concern. Residential greenness may be one of the influencing factors of disability, but epidemiological evidence in the associations of residential greenness exposures with disability is limited. We aimed to investigate the associations of residential greenness exposures with the risk of disability in the elderly.

Methods: Data of 8408 residents were obtained from the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE) implemented in China during 2007-2018. Participants were matched to the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at their residential address. Disability was measured by the 12-item Chinese version of the World Health Organization Disability Assessment Schedule (WHODAS 2.0). The associations were examined using a generalized linear mixed model with stratified analyses by the covariates.

Results: We observed significantly negative associations of greenness exposures with the summary WHODAS score [NDVI_500m: -0.290, 95% Confidence Intervals (95%CI): -0.510, -0.070; EVI_500m: -0.453, 95%CI: -0.757, -0.149], and with the score of cognition (EVI_500m: -0.472, 95%CI: -0.881, -0.063), mobility (NDVI_500m: -0.632, 95%CI: -0.965, -0.299; EVI_500m: -0.739, 95%CI: -1.199, -0.280), and participation (NDVI_500m: -0.388, 95%CI: -0.651, -0.125; EVI_500m: -0.530, 95%CI: -0.893, -0.166). People living alone had a more pronounced association in cognition (NDVI_500m: -1.546, 95%CI: -2.471, -0.621). The associations with summary WHODAS score were stronger among participants living in rural areas (NDVI_500m: -0.420, 95%CI: -0.683, -0.157), having less education level (NDVI_500m: -0.618, 95%CI: -0.982, -0.253), and living in northern China (NDVI_500m: -0.381, 95%CI: -0.776, 0.013).

Conclusions: Residential greenness may reduce the onset and worsening of disability, particularly for domains of cognition, mobility, and social participation. Because of its stronger influence among people with low socioeconomic status, increasing greenness levels in areas with lower socioeconomic status may promote health equity.

Efficient removal of ammonia nitrogen and sulfamethoxazole (SMX) from wastewater has become increasingly critical due to their detrimental effects on aquatic ecosystems and public health. This study aimed to investigate the nitrogen transformation and SMX removal in a membrane aerated biofilm reactor (MABR) under different SMX concentrations (0 ∼ 200 μg·L^-1) with a nitrifying membrane bioreactor (MBR) as a control. Results suggested that SMX removal in MABR was better than that of MBR with SMX addition (50 ∼ 200 μg·L^-1). Membrane aerated biofilms tended to secrete more extracellular polymeric substances (EPS) and generate less antioxidant enzymes in response to SMX stress when compared with nitrifying sludge in MBR. Metagenomic analysis indicated that distinct succession of microbial community was observed in both systems after SMX addition, and the relative abundance of nitrifying bacteria (Nitrosomonas, Nitrospira, and Nitrobacter) evidently decreased under SMX concentration of 200 μg·L^-1. The proliferation of predominant antibiotic resistance gene (ARG) sul2 was suppressed more obviously in MABR than that in MBR. Thus, this study provided extensive insights into the advantages of nitrifying MABR in simultaneous removal of ammonium and antibiotics with less risk of associated ARGs spread.

Fine particulate matter (PM_2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM_2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM_2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24h PM_2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m³) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m³) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m³), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m³ vs. -1.5 μg/m³ for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m³, S-MESH outperforms the reanalysis (MB of -7.3 μg/m³ and -10.3 μg/m³ respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM_2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.

The increasing role of environmental pollutants and nutrients in the development of myopia. Further longitudinal evidence is needed to elucidate the effects of early-life environmental pollutants on myopia and nutrients to protect against pollutant-induced myopia. We used mother-child dyads from the Ma'anshan Birth Cohort (n = 2028) to explore the modifying effects of maternal vitamin D status of prenatal and childhood arsenic exposure with refractive parameters and myopia. We measured serum or plasma arsenic concentrations during three trimesters, in cord blood, in childhood (5-year-old), and performed cycloplegic refraction in children aged 7 to 9 years (n = 1616). Primary outcomes were myopia and refractive parameters including axial length (AL), corneal radius of curvature, and spherical equivalent refraction error. Linear regression, logistic regression, and multiple informant models were performed for the association of prenatal and childhood arsenic exposure with vision health. Sex- and vitamin D status stratification analyses were also conducted. Cord serum arsenic was positively associated with AL (β = 0.15, 95%CI: 0.01, 0.29) and the risk of developing myopia (OR = 1.72, 95%CI: 1.07, 2.75). Among boys, a 1 ng/L increase in cord serum log₁₀-transformed arsenic resulted in larger AL and a higher risk of myopia. In the vitamin D deficient group (<20 ng/mL), cord serum arsenic showed positive associations with AL (0.26 [0.06, 0.46]) and the risk of myopia (1.99 [1.01, 3.90]). Late pregnancy arsenic exposure likely affects offspring development of myopia, especially in boys. Maternal sufficient vitamin D may confer protective effects against the development of myopia.

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

A notable greening and warming of the Arctic and Subarctic due to climate change has uncertain implications for the global cycling of mercury (Hg). Snowpacks are dynamic reservoirs for Hg susceptible to solar radiation and wind pumping, with vegetative cover potentially altering Hg photochemistry. However, the impact of northern greening on the transformation of major Hg species and on Hg stocks remain poorly understood. Temporal surface snow and snowpit sampling was conducted under tree canopies and open tundra sites at the boreal-tundra ecotone in Nunavik, Canada. Maximum (mean) concentrations of 69.1 ng/L (8.8 ng/L) total mercury (HgT) and 46.9 ng/L (5.5 ng/L) reactive mercury (HgR) were measured in forest surface snow, with maximums attributed to rapid atmospheric oxidation events. Significant post-depositional reductions were recorded in the bay, tundra, and forest (67-99% HgR) and suggested greater Hg sequestration may occur under tree canopies. Increasing methylmercury (MeHg), HgT, and dissolved organic carbon (DOC) concentrations were detected across a vegetation gradient shifting towards humic-like organic matter. Notably, springtime depth profiles presented an approximate 12-fold greater accumulation of HgT under tree canopies compared to open tundra (p<0.01), with up to 16-times higher stocks (HgT, MeHg, DOC) at elevated vegetation density (p<0.05). In the North, increasing vegetation cover and surface warming may favor Hg accumulation and methylation in snowpacks, facilitated by interactions with organic matter, and further enriched by the reduced wind and solar exposure experienced under forest canopies.

The growing demand for sustainable sewage treatment requires technologies that overcome the limitations of energy-intensive and chemical-dependent systems. This study presents an innovative solution addressing both environmental and operational challenges with the design and development of an Up-flow Compact Constructed Wetland (UCCW) based Sewage Treatment Plant (STP). This system integrates preliminary, primary, secondary, and tertiary treatment units into a single setup. The performance of UCCW based STP was evaluated over 720 days under different Hydraulic Retention Times (HRTs), considering seasonal variations in both rectangular and circular configurations. The system achieved significant pollutant removal as Total Suspended Solids (96%), Chemical Oxygen Demand (86%), Biochemical Oxygen Demand (90%), Total Nitrogen (70%), Total Phosphorus (65%), and Fecal Coliforms (99%) at a 36-h HRT. These parameters meet discharge standards, except FC, which requires disinfection for safe reuse and recycling. Further, Response Surface Methodology (RSM) and Monte Carle Simulation of UCCW based STP confirmed optimal and reliable performance at a 36-h HRT. Compared to conventional treatment technologies, the UCCW based STP demonstrated higher efficiency, a smaller footprint (1m²/KLD), better operational flexibility, cost-effectiveness, and minimal operation & maintenance to make it sustainable for decentralised treatment.