Microplastics promote N2O emissions by enhancing nitrification via ammonia-oxidizing bacteria in estuarine and coastal sediments.

The asymmetric impact of air pollution on livestream E-commerce sales

Air-liquid interface (ALI) systems have emerged as a physiologically relevant in vitro platform for evaluating the toxicological impact and potential health effects of airborne pollutants. When utilizing collected ultrafine particles (UFPs), application volume and liquid type are critical parameters. Using a smaller volume of liquid for the cell exposure results in a heterogeneous distribution of UFPs across the cell monolayer, whereas application of a sufficient volume optimises even UFP distribution. A buffered solution for UFP administration minimises potential side effects and unravels dose-dependent effects in toxicological endpoints. However, standardised exposure methodologies limit reproducibility and comparability across studies. Therefore, we propose a refined manual exposure technique of suspended airborne pollutants in an adequate exposure volume that bridges the gap between conventional submerged cultures and ALI systems. Our model uses cell culture inserts with A549 epithelial cells, THP-1 macrophages, and EA.hy926 endothelial cells to mimic the in vivo alveolar barrier within the lungs. This approach offers a balance of experimental reproducibility whilst addressing the current challenges of standardisation and feasibility in exposure studies with manual UFP exposure. In conjunction with existing aerosol ALI continuous flow exposure systems, our studies are advancing translational in vitro evaluations, aligning with the 3R principle.

Machine learning-enabled meta-analysis reveals the effect of microplastics on nitrogen removal performance in constructed wetlands and its potential mechanisms.

Impact of multiple air pollutants on probable sarcopenia in the UK population: the mediating role of physical activity and biological aging.

ABSTRACT This study examines ageing population trends and the increasing urgency of cognitive health issues in Taiwan. Given the dual impact of air pollution and cognitive decline, this study explores the spatial relationship between the neighbourhood environment and the cognitive function of older adults. Through questionnaire surveys and spatial analysis, including GIS-based walkability indices and Kriging interpolation, this study assesses cognitive status among older adults, quantifies built-environment characteristics, and estimates air pollution exposure in Tainan City. The findings reveal a spatial distribution pattern linking environmental factors and air pollution exposure to cognitive health.

Increasing public concern regarding health issues caused by air quality has intensified the search for effective solutions to air pollution. This paper collects annual data from 31 regions in China from 2013 to 2018, applying a First-Difference model, a Difference-in-Differences (DID) model, and a Two-Stage Least Squares (2SLS) model to analyze the impact of air pollutants on health levels. Furthermore, it examines the effects of two new environmental protection laws—the Environmental Protection Law (2015) and the Law on the Prevention and Control of Atmospheric Pollution (2016)—on air quality and health. The research results indicate that the growth of PM2.5 has a significant positive impact on the first difference of Death Total Population (DTP). The implementation of the new laws significantly reduces the concentration of air pollutants; in areas with severe air pollution, the decline in PM2.5 concentration is even more pronounced. However, no significant correlation was found between the two new laws and health levels. This paper provides a theoretical basis for governing air pollution and improving public health.

Environmental health challenges in the twenty-first century increasingly arise from the interaction between atmospheric pollution, plastic contamination, and climate change. This study examines the impacts of air pollution and microplastic exposure on environmental health using a quantitative descriptive approach combined with a systematic literature review based on secondary data. Scientific articles were retrieved from PubMed, Scopus, and ScienceDirect, resulting in 156 initial publications, of which 38 studies met the inclusion criteria and were analyzed. The synthesis integrates global datasets from the World Health Organization, Institute for Health Metrics and Evaluation, OECD reports, and national environmental statistics. Findings indicate that ambient air pollution contributes to approximately 6.67 million premature deaths annually, with average PM₂.₅ exposure reaching 29 µg/m³, substantially exceeding the WHO recommended limit of 5 µg/m³. Global plastic production increased dramatically from 2 million tons in 1950 to 460 million tons in 2021, while only 9% of plastic waste is recycled, facilitating the formation of microplastics detected in human blood, lungs, and gastrointestinal samples. Plastic production also generates approximately 1.8 gigatons of CO₂ emissions annually, strengthening the linkage between pollution and climate change. The results highlight synergistic environmental health risks arising from combined exposure to particulate pollution and microplastics, emphasizing the importance of integrated environmental health policies and climate mitigation strategies..

Agricultural transboundary water pollution induced by inter-regional trade poses a complex and pressing challenge for environmental governance. This study integrates an agricultural water pollutant emission inventory, multi-regional input–output model, responsibility-sharing framework, and ecological compensation scheme to establish the collaborative control of agriculture water pollution embodied in China’s inter-provincial trade. The findings reveal, firstly, that inter-provincial agricultural trade led to significant transfers of agricultural water pollution, predominantly flowing from economically developed provinces to less developed provinces, reflecting a mismatch between economic gains and environmental costs. Specifically, Gansu and Qinghai bear the largest agricultural water pollution impact (2.15 Kt and 3.25 Kt, respectively), while it is still a loss in terms of economic net benefits (0.21 trillion and 0.06 trillion yuan, respectively). Secondly, the economic benefit responsibility-sharing shows that for most provinces, responsibility lies between production- and consumption-based accounting and provides a feasible pathway for responsibility sharing. Third, economically developed provinces like Beijing, Jiangsu, and Zhejiang bear the largest compensation liabilities to others, with 1.60 Kt, 0.73 Kt, and 0.54 Kt, respectively. Conversely, provinces including Qinghai, Gansu, and Jiangxi require the greatest compensation inflows, at 2.55 Kt, 0.62 Kt, and 0.34 Kt, respectively. Finally, the maximum acceptable payment value for compensating provinces and the minimum acceptable compensation value for recipient provinces are identified. Our study elucidates the inter-provincial disparities in agricultural water pollution burdens and economic benefits, establishing a quantitative foundation for optimizing responsibility-sharing and compensation strategies in China, which is crucial for fostering regional cooperation in water pollution control.

ABSTRACT This study investigates the political consequences of environmental degradation, specifically river pollution from illegal gold mining (‘Galamsey’), on electoral behaviour in Ghana’s 2024 general elections. Utilising constituency-level data, the research combines propensity score matching with difference-in-differences estimation to assess the political impact. Findings reveal a consistent and statistically significant decline in voter turnout in polluted constituencies, suggesting environmental harm fostered political disengagement. However, effects on party vote share were mixed; while support for the ruling New Patriotic Party (NPP) decreased in some affected regions, others saw increases or no significant change, highlighting regional variations. These results imply that environmental grievances did not uniformly translate into partisan backlash but rather into voter withdrawal. The study contributes to environmental politics by quantifying ecological degradation’s effect on political participation, demonstrating the mediation of voting patterns by local political economies, and emphasising ecological issues beyond traditional ethnic and elite political frameworks in African politics.

Background:Growing epidemiological evidence links air pollution to male reproductive health through oxidative stress, endocrine disruption, and direct cellular damage. However, the magnitude and consistency of effects on semen quality parameters across different pollutant types and exposure durations remain uncertain.Methods:We systematically searched PubMed, Web of Science, and Scopus through January 2025 for meta-analytic studies evaluating the impact of air pollution on semen parameters. Four eligible studies were assessed for methodological quality using AMSTAR 2. Random-effects meta-analyses yielded standardized mean differences (SMDs) for sperm concentration, count, total motility, progressive motility, and morphology.Results:We observed statistically significant negative associations between higher air pollution exposure and semen quality outcomes. Specifically, greater pollutant exposure was associated with lower total motility (SMD ‐0.24; 95% CI ‐0.48 to ‐0.01; P = .04), progressive motility (SMD ‐0.06; 95% CI ‐0.10 to ‐0.01; P < .001), and morphology (SMD ‐0.09; 95% CI ‐0.13 to ‐0.04; P < .001). Concentration was inversely related to pollution levels (SMD ‐0.14; 95% CI ‐0.21 to ‐0.06; P < .001), while sperm count showed a trend toward reduction but did not reach statistical significance (SMD ‐0.12; 95% CI ‐0.26 to 0.01; P = .06).Conclusion:This meta-umbrella analysis confirms consistent, modest negative associations between ambient air pollution and key semen quality parameters, underscoring air pollution as a modifiable risk factor for male infertility. Future studies should define threshold exposures and evaluate protective interventions.

Air pollution is a major threat to respiratory health. We review the current literature on accessible interventions that could help patients build resilience against this threat. We explore the potential benefits of antioxidant-rich diets, including supplements such as fish oil, vitamins C, D, E and prebiotics, which have shown promise in reducing inflammation and oxidative stress, thereby mitigating air pollution-related respiratory decline. Nasal washes are commonly used to clear nasal passages, which could support may aid to clearing pollutants from the nasal passages and improve mucociliary clearance. Furthermore, medications such as nonsteroidal anti-inflammatory drugs and intranasal corticosteroids may have been reported to reduce pollutant- related airway inflammation and lung function deterioration triggered by pollutant exposure. Given the generally favourable safety profile of these interventions, they are reasonable to consider in consultation with a care provider. Further research is needed to establish optimal dosing, safety and long-term efficacy, particularly for those exposed to chronic air pollution. Healthcare professionals should work together to further identify and implement effective interventions to mitigate the impact of air pollution on respiratory health.

Photocatalytic processes have been studied as promising solutions to mitigate the impact of pollutants on aquatic environments. Here, the enhancement of photocatalytic performance and stability of titanate nanostructures (TNS), a well-established photocatalyst, were investigated through Sr modification. Structural characterization confirmed Sr in-corporation in the crystalline structure, mainly in the interlayers. The sample Sr(5%)TNS, synthesized with 5% (wt.), exhibited fine lamellar morphology, different from the elongated nanowires of pristine TNS. The photocatalytic performance of the Sr-modified sample was studied for the removal of a model pollutant, caffeine, under UV-Vis and visible irradiation. A clear enhancement in the caffeine removal rate was observed using Sr(5%)TNS as a photocatalyst, when compared with the pristine material. Further improvement in the photocatalytic performance was obtained by combining Sr(5%)TNS with graphitic-like carbon nitride (g-C3N4) as a novel composite film. This proved to be a promising strategy for enhancing both the visible-light photocatalytic efficiency and the stability of the films, while also facilitating their reuse. Various configurations of the hybrid system were tested, and the best results for caffeine degradation and catalyst robustness were achieved with a 4:1 ratio of Sr(5%)TNS to g-C3N4. Mechanisms for charge transfer in irradiated Sr(5%)TNS particles, and in Sr(5%)TNS/g-C3N4 composite films are proposed and discussed.

Accurate prediction of PM2.5 concentration is crucial for air quality management and public health protection. However, existing methods often struggle to capture and interpret the nonlinear relationships among multiple atmospheric variables. This study proposes GT-iFormer, a novel interpretable deep learning model that integrates graph convolutional networks (GCNs), Temporal Convolutional Networks (TCNs), and inverted Transformer (iTransformer) for PM2.5 concentration prediction. The model features a GTCN-Block that encapsulates GCN and TCN with residual-style fusion, preserving feature-level dependencies alongside temporal patterns to prevent information degradation. The Pearson correlation coefficients and KNN algorithm are innovatively integrated to build a data-driven graph structure, which allows GCNs to flexibly model the nonlinear relationships between pollutants and meteorological factors based on observed data. TCNs obtain multi-scale temporal patterns via causal dilated convolutions. Subsequently, the concatenated representations of GTCN-Block are input into iTransformer to model global inter-variable interactions using attention mechanisms along the axis of the variable. We incorporated SHAP (SHapley Additive exPlanations) analysis to expose feature importance and nonlinear relationships with PM2.5 predictions. Results on the hour-level data of Beijing (2020–2021) and Shenzhen (2021) show that our proposed GT-iFormer surpasses all baseline models, with an RMSE of 8.781 μg/m3 and R2 of 0.978 for Beijing, and an RMSE of 3.871 μg/m3 and R2 of 0.957 for Shenzhen on single-step prediction, equating to RMSE reductions of 15.75% and 17.92%, respectively, over the best baseline model. The SHAP analysis shows clearly distinct regional patterns, with combustion sources dominant in Beijing (represented by CO at 28.231%), and traffic emissions dominant in Shenzhen (represented by NO2 at 25.908%). Crucial threshold effects are established for all variables, with significant cross-city differences that can serve as general forecasts and guidance for city-specific air quality management policies.

The US Southwest is one of the driest and hottest regions, with a recent upsurge in land surface temperature (LST). Further, with land-use changes and global warming, anthropogenic pollution also significantly contributes to the rise in surface temperatures. While the impact of pollution on LST has been studied only in specific urban regions, insights from a broader, more diverse topography remain limited. This research incorporates LST with land cover parameters (NDBI, MNDWI, NDBSI, SAVI, WET), surface albedo, air pollutants (NO2, SO2, O3, CO), aerosol particles, urban nighttime light, and digital elevation model to evaluate the non-linear spatial dependence of these variables for the summer (from June to August 2025) and winter (from December 2024 to February 2025) seasons in the US southwest. All multi-resolution inputs were harmonized by projecting to WGS84 and applying a ~11 km fishnet sampling grid commensurate with the coarsest-resolution dataset (Sentinel-5P), ensuring each sample captures a unique pixel value across all layers. AutoML was applied to benchmark learning algorithms, and we found that CatBoost, Extra Trees, LightGBM, HistGradientBoosting, and Random Forest were among the optimal models for predicting LST. After tuning these models using Bayesian optimization, we achieved a mean R2 of 0.86 during summer and 0.84 during winter. After developing the hyperparameter-optimized model, explainable AI, e.g., SHAP, was employed to understand the complex nonlinear dynamics and top contributing features. Landcover variables had a more dominant impact on the spatial distribution of summer LST, while winter LST was more influenced by pollutant parameters. Partial Dependency Plot and Accumulated Local Effect were further incorporated to examine the marginal effects of the top-contributing features on spatial LST prediction. By extending the study area to the entire US Southwest, this study effectively captures urban–rural contrasts, climate- and land-cover–dependent pollutant responses, and regional climatic influences. It presents explicit spatial dependencies among LST, pollutants, land cover, topography, and nighttime activity that will aid future researchers and policymakers in effectively developing sustainable thermal planning for urban activities.

While air pollution is an established risk factor for ischemic stroke (IS), evidence on differential susceptibility across population subgroups remains limited. We investigated how associations between long-term air pollution exposure and IS risk varied across socioeconomic groups and urbanicity levels. This population based cohort study included 11.9 million adults ≥18 years with linked residential exposure data for PM2.5, PM10, and NO2. Socioeconomic position was assessed using individual-level and area-level indicators including welfare, education, employment history, and composite scores. Time-varying Cox proportional hazards models examined associations between annual average air pollution at home addresses and incident IS risk across socioeconomic and urbanicity groups. During 2014-2019 follow-up, 152,868 incident IS cases were identified. Air pollution exposure was associated with increased IS risk for all pollutants: hazard ratios (HR) 1.07 [95% CI: 1.06; 1.08] per 5.0μg/m³ PM2.5, 1.06 [1.05; 1.07] per 5.0μg/m3 PM10, and 1.08 [1.08; 1.09] per 10 μg/m³ NO2. Associations were stronger in rural to moderately urbanised versus highly urbanised areas. Socioeconomic differences were most pronounced in rural to moderately urbanised areas, with consistently higher HRs among lower socioeconomic groups (e.g., lowest area-level education: HR 1.16 [1.08; 1.25] per 5.0 μg/m³ PM2.5), while disparities were minimal in highly urbanised areas. Air pollution's impact on IS risk varies significantly by socioeconomic position and urbanicity, with lower socioeconomic groups in rural to moderately urbanised areas showing increased vulnerability. These findings emphasize considering geographic and socioeconomic contexts in air pollution health assessments.

The cumulative impact of air pollution on dry eye disease: Evidence from the Korea national health and nutrition examination survey (2017-2020).

Grazing the plastisphere: Trophic transfer of the bioaccumulated metals to Artemia salina.

Effects of microsized and nanosized polystyrene on detrital processing and nutrient dynamics in streams.

The most significant negative impact of environmental mercury pollution occurs in aquatic ecosystems, where mercury accumulates in organisms over time, reaching increasingly higher concentrations. The neurotoxic effects of mercury accumulation in the human body have been well documented in poisoning incidents worldwide. Consequently, the development of rapid and straightforward techniques for mercury detection in water is essential. In this study, we present a spectrophotometric method for the determination of Hg2+ in water, based on its interaction with silver nanoparticles functionalized with sodium 2-mercaptoethanesulfonate (Ag@MESNA). These nanoparticles possess free electrons in their conduction band, resulting in a strong interaction with incident radiation at 392nm. Upon contact with Hg2+, a complex is formed with the mercaptoethanesulfonate ligand, leading to a decrease in the absorbance signal of the functionalized nanoparticles. This decrease is proportional to the mercury concentration in the sample. Measurements were performed using a nanodrop spectrophotometer, which requires only a few microliters of sample. The proposed method is simple, rapid, and portable, enabling in-situ quantification of ionic mercury in water. Moreover, it has been successfully applied to real water samples from various sources.