Environmental Research最新文献

There is a growing interest in how exposure to biodiversity influences mental health and wellbeing; however, few studies have focused on children. The aim of this review was to identify studies that used components of biodiversity and children's health outcomes to assess if there were any themes that could be used to inform urban design and understand the mechanisms behind associations. We used a PROSPERO registered protocol to identify eligible studies following pre-defined inclusion criteria. After searching five databases, 25 studies were included in the review. From these articles we extracted data on the biodiversity exposure and mental health and wellbeing outcomes. Five categories of biodiversity exposure were identified, including species diversity (n = 1; 4%), functional diversity (n = 6; 26%), ecological community (n = 9; 36%), green space metrics (n = 4; 16%), and high-level classifications (n = 6; 24%). Children's health and wellbeing were tabulated into seven categories: play (n = 10; 40%), wellbeing (n = 6; 24%), mental health and cognitive functioning (n = 5; 20%), attention deficit hyperactivity disorder (ADHD)-related behaviours (n = 4; 16%), preferences for nature (n = 3; 12%), academic achievement (n = 2; 8%), and restoration (n = 2; 8%). The high heterogeneity of biodiversity and health measures reduced our ability to identify relationships across studies and formally test for an exposure-dose response. Future research that uses standardised and transferable biodiversity measurements at multiple scales, has stronger reporting rigour, greater consideration of potential modifiers, and increased representation of studies from the Majority World are essential for building a stronger evidence base to deliver child-centred biodiverse landscapes.

Background: Higher greenspace exposure has been associated with lower risk of certain cancers. However, few studies have evaluated potential benefits of increasing population-level exposure to greenspace on cancer disparities. We estimated the impact of a hypothetical intervention to increase residential greenspace cover on neighborhood socioeconomic disparities in total, breast, colorectal, lung, and prostate cancer incidence.

Methods: Our study included 411,787 cancer-free UK Biobank participants. Percentage of greenspace around baseline residential addresses (300m, 1000m distance buffers) was derived by combining domestic gardens and greenspace cover from the Generalized Land Use Database. We categorized neighborhood socioeconomic deprivation using the Index of Multiple Deprivation (2010). We estimated hazard ratios (HR) and 95% confidence intervals (CI) of each cancer associated with greenspace, adjusting for sociodemographic and lifestyle factors. We additionally adjusted for air pollution in supplementary analyses as we a-priori hypothesized that it was on the causal pathway between greenspace and cancer. Further, we used parametric g-computation to calculate the standardized 10-year risk of each cancer, comparing the least to most socioeconomically disadvantaged participants, both without any hypothetical greenspace intervention and under a hypothetical intervention to increase residential greenspace cover to a favorable threshold (75th percentile amongst the least socioeconomically deprived participants).

Results: We documented 40,519 incident cases of cancer over 4,210,008 person-years follow-up. An interquartile range increase in greenspace cover within 300m was associated with lower incidence of total (HR 0.98; 95% CI 0.97, 1.00) and lung (HR 0.96; 95% CI 0.92, 0.99) cancer, and was suggestively associated with lower prostate and breast cancer incidence, but not colorectal cancer. Additional adjustment for fine particulate matter air pollution (PM_2.5) weakened lung cancer associations but strengthened breast and prostate cancer associations (e.g., greenspace 1000m breast cancer HR 0.94; 95% CI 0.89 0.99; 1000m prostate cancer HR 0.91; 95% CI 0.86, 0.95). The hypothetical intervention to increase greenspace (300m) resulted in 1.3 fewer total cancer cases per 1000 (95% CI 1.0, 1.6) in the most compared to least deprived group, a 23% reduction in the socioeconomic disparity gap.

Discussion: Higher residential greenspace cover was associated with lower total and lung cancer incidence, and suggestively associated with lower breast and prostate cancer incidence. Policies to increase residential greenspace cover may reduce the risk of certain cancers, particularly among socioeconomically disadvantaged groups.

Ammonia oxidation, the first and rate-limiting step of nitrification, is a crucial step in nitrogen cycling. The distribution patterns of key ammonia oxidizers, including ammonia-oxidizing archaea (AOA) and bacteria (AOB), and comammox (complete ammonia oxidation) Nitrospira, provide vital insights for nitrogen cycling in natural ecosystems. Currently, the distribution and contribution of AOA, AOB and comammox Nitrospira in freshwater ecosystems remain largely underexplored. This study explored the abundances, diversity, phylogenetic characteristics, and community structures of AOA, AOB and comammox Nitrospira in the Yellow River sediments using high-throughput sequencing and qPCR. Comammox Nitrospira displayed the highest amoA gene abundance in sediments from all sampling sites compared to that of AOA and AOB. The diversity of AOA shown no significant correlations with physicochemical properties, while the diversity of AOB negatively correlated with pH (p < 0.05), and the diversity of comammox Nitrospira positively correlated with NH₄⁺ content and TC content (p < 0.05), respectively. Phylogenetic analysis identified Nitrososphaera, Nitrosospira, and cladeA1 as the most dominant clusters of AOA, AOB and comammox Nitrospira, respectively. The community composition of AOA, AOB, and comammox Nitrospira exhibited distinct spatial patterns, varying across the upper, middle and lower reaches. pH was the key factor shaping the community structure of AOB and comammox Nitrospira (p < 0.05), while organic carbon was the key determinant of the AOA community structure (p < 0.05). The results of this study advance our understanding of N cycling in freshwater ecosystems.

Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD₅) is crucial for optimizing wastewater treatment processes, enhancing efficiency, and reducing costs. Traditional prediction methods struggle to capture the dynamic variations of influent parameters. Mechanistic biochemical models are unable to predict these parameters, and conventional machine learning methods show limited accuracy in forecasting key water quality indicators such as COD and BOD₅. This study proposes a hybrid model that combines signal decomposition and deep learning to improve the accuracy of COD and BOD₅ predictions. Additionally, a new dynamic feature selection (DFS) mechanism is introduced to optimize feature selection in real-time, reducing model redundancy and enhancing prediction stability. The model achieved R² values of 0.88 and 0.96 for COD, and 0.75 and 0.93 for BOD₅ across two wastewater treatment plants. RMSE and MAE values were significantly reduced, with decreases of 14.93% and 12.55% for COD at WWTP No. 5, and 20.89% and 20.40% for COD at WWTP No. 7. For BOD₅, RMSE and MAE decreased by 3.56% and 5.28% at WWTP No. 5, and by 10.06% and 10.20% at WWTP No. 7. These results highlight the effectiveness of the proposed model and DFS mechanism in improving prediction accuracy and model performance. This approach provides valuable insights for wastewater treatment optimization and broader time series forecasting applications.

The well-designed bamboo charcoal (BC) composite Fe-g-C₃N₄/BC with multi-active sites of FeO_x, FeN_x, and g-C₃N₄, was fabricated in-situ by calcining Fe-melamine loaded bamboo charcoal (Fe-Me-BC) under nitrogen atmosphere. The as-synthesized Fe-g-C₃N₄/BC(550) exhibited a mesoporous structure with a large specific surface area of 108.23 m²/g. The adsorption of tetracycline (TCL) on Fe-g-C₃N₄/BC(550) was calculated following the Langmuir isotherm model, and showed a maximum adsorption capacity of 19.92 mg/g. Furthermore, the pseudo-second-order kinetic model showed a good fit for the TCL adsorption process on Fe-g-C₃N₄/BC(550). The Fe-g-C₃N₄/BC(550)/H₂O₂ system exhibited excellent photo-Fenton catalytic performance in degrading TCL with a degradation efficiency reaching up to 98.9% within 5 min under visible-light. The effects of initial pH value and coexisting anions on TCL degradation were determined. As narrow band gap semiconductors, g-C₃N₄, Fe₃O₄, and Fe₂O₃ in the Fe-g-C₃N₄/BC exhibited good visible-light-driven photocatalytic activity. Moreover, photogenerated electrons could further activate H₂O₂ to produce high concentrations of ∙OH radicals. This outstanding photo-Fenton catalytic performance can be ascribed to the synergistic effect of g-C₃N₄/Fe₃O₄-Fe₂O₃/Fe_xN multi-active sites as well as the excellent adsorption ability and conductivity provided by bamboo charcoal. This work presents a convenient approach for constructing economical catalysts for environmental remediation through g-C₃N₄ and Fe-N codoped BC.

Peroxymonosulfate (PMS) activation renders a promising way for in-situ regeneration of carbon-based adsorbents towards sustainable water decontamination, but the effects of structure and composition of carbon adsorbent on its adsorption and catalytic regeneration performances remains unclear. Herein, the nitrogen-doped carbon aerogels (NCAs) were prepared to couple adsorption and PMS activation in a continuous fixed-bed reactor for effective bisphenol A (BPA) removal. The nitrogen species and carbon structure of NCAs were varied by changing carbonization temperature (700 °C, 800 °C, 900 °C and 1000 °C) to investigate their correlation with the adsorption and catalytic regeneration abilities of NCAs. Results showed the PMS activation significantly boosted the adsorption capacity of NCAs and extended the breakthrough time of BPA. The optimal NCA-800/PMS system showed 1.8 times higher adsorption capacity and 37.5 times longer breakthrough time that those of NCA-800 alone. Moreover, the NCA-800/PMS system also demonstrated good adaptability across a broad pH range (3.0-12.0) and maintained high performance in real surface water matrices. Experimental and characteristic results collectively confirmed the critical roles of carbon structure and N species of NCA in adsorption and catalytic regeneration: On one hand, the intrinsic carbon defects served as the main adsorption site for BPA; on the other hand, the pyrrolic N and graphitic N promoted PMS adsorption and surface-mediated electron transfer process, while the electron-deficient C atoms adjacent to N species induced PMS oxidation into ¹O₂, which jointly contributed to efficient BPA degradation for in-situ regeneration of NCA.

In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) is commonly used as a proxy for algal biomass. However, current methods for measuring Chl-a concentration face challenges in accurately quantifying algae by categories and effectively adapting to natural aquatic environments. This study combined convolutional neural networks (CNNs) and three-dimensional fluorescence data matrices to address these challenges. The algal classification model achieved over 99.5% accuracy in identifying thirteen types of algal samples, with class activation maps showing that the model primarily focused on algal pigment regions. In determining Chl-a concentrations of each algal species in mixed algae solutions (Microcystis aeruginosa, Cyclotella, and Chlorella), the Chl-a models demonstrated Mean Absolute Percentage Errors (MAPEs) ranging from 6.55% to 10.56% in the ultrapure water background, 11.57%-14.12% in the Qingcaosha Reservoir raw water background, and 21.46%-123.37% in the Lake Taihu raw water background. After calibration, the models were significantly improved, achieving MAPEs ranging from 11.86% to 14.18% in the Lake Taihu raw water background. Discrepancies in determination performance indicated that the intensity and locations of characteristic algal pigment fluorescence peaks greatly influenced the Chl-a models' accuracy. This research introduces a novel approach for algal classification and Chl-a concentration determination in water bodies, with significant potential for practical applications.

Ocean acidification, a major consequence of climate change, poses significant threats to marine organisms, particularly when combined with other environmental stressors such as chemical pollution. This study investigated the physiological responses of Trochus niloticus to a 28-day exposure of ocean acidification and/or sulfamethoxazole, a commonly detected antibiotic in the South China Sea. Exposure to either acidification or sulfamethoxazole individually triggered adaptive responses through immune activation, antioxidant reactions, and metabolic adjustments. However, concurrent exposure resulted in significant adverse effects, including compromised immunity, oxidative damage, and disrupted energy budget. These findings provide new insights into how ocean acidification interacts with antibiotic pollution to synergistically impact marine gastropods, suggesting that multiple stressors may pose greater threats to T. niloticus populations than single stressors alone.

Background: PAH exposure is associated with adverse health outcomes, but exposure sources in pregnancy are not well-understood.

Objectives: We examined associations between urinary OH-PAHs during pregnancy and environmental tobacco smoke (ETS) and short-term ambient air pollution exposure. Participants included 1603 pregnant non-smokers in three cohorts from 7 sites across the USA. We also examined associations with intake of foods typically high in PAHs in one cohort with dietary assessment data (n = 801).

Methods: Urinary OH-PAHs were measured using LC-MS/MS; urinary cotinine was measured using SPE/UPLC-MS/MS. To accommodate different detection limits by cohort, ETS exposure was represented by modified cotinine quartiles; these combined concentrations below the highest detection limit in the first category (0-0.017 ng/mL), with the rest divided evenly into three categories (0.0171-0.2 ng/mL, 0.21-1.191 ng/mL, 1.192-1465 ng/mL). Air pollution exposure was represented by quartiles of same-day ambient PM_2.5 in residential census tracts estimated from EPA's Downscaler Model. We fitted separate Tobit regression models for log-OH-PAH concentrations in association with cotinine or ambient PM_2.5 quartile adjusted for specific gravity, site, batch, household income, education, employment status, neighborhood deprivation index, season, and year. For the food model, PAH dietary intakes were estimated using food frequency questionnaire data and standard portion weights from a national database.

Results: In adjusted models, the highest modified cotinine quartile vs. the lowest was associated with 48% (95% CI: 13%, 94%) higher urinary 1-hydroxynaphthalene, 36% (15%, 61%) higher 2-hydroxynaphthalene, 41% (23%, 63%) higher 3-hydroxyphenanthrene, and 70% (28%, 127%) higher 1-hydroxypyrene. Second and third quartile cotinine concentrations were associated with higher OH-PAHs, although not consistently. Same-day ambient PM_2.5 was not associated with any OH-PAH, nor was self-reported dietary intake.

Conclusions: ETS is a major source of PAH exposure for pregnant people in the USA while ambient PM_2.5 and diet measured via usual intakes appear less influential. Our findings underscore the importance of policies/actions to reduce environmental tobacco smoke exposure among pregnant people.