Search Results (5,330)

The environmental effects of green finance policies involve complex systemic interactions across multiple levels, yet existing studies often adopt fragmented analytical approaches. Drawing on the multi-level perspective (MLP), this study conceptualizes the environmental impacts of Green Finance Reform and Innovation Pilot Zones (GFRIPZs) as a process of systemic green transformation involving interactions among landscape, regime, and niche levels. Using panel data of 287 prefecture-level and above cities in China from 2012 to 2022, this study applies a staggered difference-in-differences (DID) model to evaluate the environmental impacts of GFRIPZs. The results show that GFRIPZs significantly reduce both PM_2.5 concentrations and CO₂ emissions. Mechanism analyses based on multiple mediation models and GSEM reveal pollutant-specific differences in underlying channels. Green technological innovation (GTI) constitutes one observable pathway for PM_2.5, whereas the policy effect is more closely associated with energy structure adjustment for CO₂. Heterogeneity analysis further shows that PM_2.5 mitigation is stronger in colder cities, while CO₂ reduction is more pronounced in developed cities. These findings reveal pollutant-specific mechanisms of green finance and offer policy implications for developing countries seeking to promote systemic green transformation. Full article

Despite notable improvements in China’s ambient air quality, local implementation outcomes remain uneven, with some cities continuing to show gaps between officially assigned PM2.5 targets and observed annual PM2.5 concentrations. This study examines target-based PM2.5 implementation deviation under China’s air-pollution target responsibility system. Drawing on an Ambiguity–Pressure–Adaptation framework, it analyzes how policy ambiguity, implementation pressure, and local adaptation are statistically associated with target-based PM2.5 implementation deviation, and whether these associations vary across policy stages. Using panel data from 293 prefecture-level cities from 2013 to 2022, this study applies two-way fixed-effects models, sub-dimension models, stage-heterogeneity interaction models, and robustness checks. The results show that policy ambiguity is positively associated with target-based PM2.5 implementation deviation, whereas implementation pressure and implementation adaptation are negatively associated with it. Goal ambiguity, government pressure, and resource adaptation show relatively stronger associations within their respective dimensions. The stage-heterogeneity analysis indicates that ambiguity is more strongly associated with deviation during 2013–2017, pressure shows a stronger negative association during 2018–2020, and adaptation shows a stronger negative association during 2021–2022. These findings provide association-based evidence suggesting that clearer policy design, stable supervision, and stronger local adaptive capacity are linked to smaller implementation gaps and support sustained air-quality improvement. Full article

Sperm cryopreservation is important for livestock breeding and germplasm conservation, but freeze–thaw injury can impair ram sperm quality through oxidative stress, membrane damage, and metabolic disturbance. This study evaluated the concentration-dependent effects of kaempferol supplementation on the cryopreservation quality of semen from Yuansheng Aite dairy rams. Qualified ejaculates were pooled and randomly allocated to five equally spaced kaempferol treatment groups: 0, 25, 50, 75, and 100 μg/mL. Post-thaw sperm motility, oxidative stress status, ATP-related energy metabolism, acrosome integrity, and multi-omics profiles were evaluated. Data were analyzed using appropriate parametric or non-parametric tests after assessment of normality and homogeneity of variance. Orthogonal polynomial analysis was performed to evaluate linear and nonlinear dose–response patterns across the tested kaempferol concentrations. Kaempferol supplementation significantly affected PM, VCL, and VAP, while RPM, LIN, WOB, and VSL were not significantly affected. No significant linear effect was observed for the motility parameters, whereas VCL exhibited a significant quadratic response to kaempferol concentration. Based on the observed overall responses of sperm motility, antioxidant capacity, oxidative stress markers, ATP content, and acrosome integrity, 25 μg/mL kaempferol showed the most favorable overall profile among the tested concentrations and was selected for subsequent mechanistic analyses. Proteomic and metabolomic analyses suggested that the protective effects of kaempferol may be associated with pathways related to focal adhesion, cytoskeletal organization, oxidative phosphorylation-related energy metabolism, and central carbon metabolism. These findings indicate that moderate kaempferol supplementation may improve the post-thaw quality of Yuansheng Aite dairy ram semen, although further fertility-oriented studies are needed to confirm its practical reproductive benefits. Full article

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005–2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM_2.5 as the key drivers and revealed pronounced nonlinear response patterns and threshold effects. In particular, the promoting effect of PM_2.5 on TB incidence increased sharply at medium-to-high concentration levels. To further investigate spatial and temporal non-stationarity, Geographically and Temporally Weighted Regression (GTWR) was applied. The results demonstrated strong spatiotemporal heterogeneity in driver effects across provinces. The influence of PM_2.5 showed a consistently positive association with TB incidence and exhibited a distinct temporal evolution characterized by an initial strengthening before 2015 followed by a weakening thereafter, closely aligning with China’s air pollution control process. These findings provide new insights into the nonlinear and spatiotemporally heterogeneous effects of meteorological and environmental factors on TB incidence and support the development of more targeted, region-specific TB prevention strategies. Full article

Fine particulate matter (PM_2.5) exposure increases the risk of neurodevelopmental abnormalities by disrupting neural stem cell (NSC) proliferation and cell cycle homeostasis, which are critical for normal neurodevelopment. This study investigated the impact of fine particulate matter (PM_2.5) on NSC proliferation and cell cycle using a three-dimensional (3D) graphene oxide (GO) scaffold that mimics the NSC microenvironment. PM_2.5 exposure led to concentration-dependent decreases in NSC viability and induced G0/G1 phase arrest via the marked downregulation of Cyclin D1-Cdk4 and Cyclin E-Cdk2, which critically impact G1/S transition. NSCs in 3D GO scaffolds maintained higher expression of key cell cycle regulators (Cyclin A, Cdk1/Cdk2, APC, and Cdc20) and superior cell viability when suffering PM_2.5 exposure, demonstrating the 3D culture environment was beneficial for NSC proliferation. We speculate that the 3D culture environment is more favorable and protective for cell proliferation. Therefore, these findings highlight the utility of the 3D GO scaffold for studying PM_2.5 effects on growing neural stem cells. This work provides a physiologically relevant in vitro platform that captures microenvironment-dependent neurotoxic responses, consequently offering valuable mechanistic insights into PM_2.5-induced developmental neurotoxicity. Full article

Health impact assessments of ambient particulate matter remain far less developed in sub-Saharan African cities, despite fine particulate matter (PM_2.5) being a significant contributor to premature mortality globally. This study quantified the public health burdens of adult mortality associated with long-term PM_2.5 exposure in Addis Ababa, Ethiopia, under different counterfactual air quality scenarios. Hourly PM_2.5 data were collected across nine monitoring stations from 2022 to 2023. AirQ+ tool was utilized to estimate attributable natural-cause and cardiovascular disease (CVD) mortality among adults aged ≥ 30 years. Spatial analysis showed mean concentrations ranging from 15 µg/m³ to 33 µg/m³, with an overall mean of 26.74 µg/m³, exceeding the WHO annual guideline by more than fivefold. Seasonal peaks occurred from June to August and diurnal maxima at 7:00 AM. In 2022, attributable natural-cause deaths ranged from 1489 (6.16%) at the less stringent WHO Interim Target 3 (15 µg/m³) to 3169 (13.11%) at the WHO Air Quality Guidelines (5 µg/m³). In 2023, the range was 1544 (6.40%) to 3218 (13.33%). For specific chronic endpoints, PM_2.5 concentration level was responsible for between 509 and 1071 CVD deaths in 2022, and between 535 and 1126 CVD deaths in 2023 across the counterfactual scenario. These results highlight the substantial health burden posed by ambient PM₂.₅ in Addis Ababa and emphasize the urgent need for targeted interventions. Full article

Accurate air pollutant concentration prediction is crucial for public health and sustainable urban development. Existing methods predominantly rely on single-modal data, resulting in inadequate representation of pollutant spatiotemporal evolution, poor prediction accuracy, and limited generalization capabilities. To address these challenges, this research proposes a novel PM_2.5 prediction framework termed MFD-DF that integrates ground-station time series and satellite remote sensing images. In feature extraction, learnable decomposition and deformable convolution are introduced, and a Cross-Modal Slot Attention module explicitly decomposes features to resolve information blurring. Subsequently, a dynamic cross-modal alignment mechanism is designed alongside a learnable Time-Expansion Network (TEN) to ensure fine-grained interaction. Furthermore, a local-global attention feature fusion mechanism is proposed to optimize data integration efficacy. Experimental results demonstrate that in single-step PM_2.5 prediction tasks, the proposed MFD-DF achieves significant improvements of approximately 10–20% in MAE, RMSE, and MAPE compared to state-of-the-art baselines. In multi-step PM_2.5 prediction, it effectively alleviates the error accumulation problem in long-sequence forecasting, demonstrating superior robustness and accuracy. Full article

The environmental impacts of mine dust in mining operations can be mitigated through improved prediction of its spatial distribution using dispersion models, particularly Gaussian-based air quality models. However, Gaussian-based models often predict concentrations that differ substantially from observed mine dust behavior, because dust properties and transport mechanisms vary markedly with particle size. In this study, particle-size-related mechanisms for dust dispersion behaviors were classified as dry/wet deposition, turbulent diffusivity, erosion, hygroscopicity, or agglomeration, and their effects on dust dispersion behaviors and effective simulation methods were reviewed. Currently, the most clearly established particle size influence is on deposition, especially for coarse dust emitted from mechanical mining processes. Other mechanisms, including erosion, hygroscopicity, and agglomeration, are more relevant to finer dust below 2.5 µm or in the submicron range. This study proposes that wind erosion, mainly saltation flux, can also be integrated into Gaussian dispersion models as near-ground boundary flux terms. Hygroscopic and agglomeration effects can be assessed using relative humidity and simplified particle size redistribution assumptions near dust emission sources. In particular, incorporation of agglomeration mechanisms may begin with a simple bimodal assumption: the agglomeration of PM_2.5 into PM₁₀. This can be incorporated into a modified Gaussian deposition equation. Finally, the size dependence of the turbulent diffusivity coefficient is relatively insignificant, so the diffusivity values can be regarded as constants. These findings provide a mechanistic basis for improving mine dust prediction and environmental management in open-pit mines, haul roads, tailings areas, and stockpile environments. Full article

Urban Park vegetation plays a crucial role in mitigating air pollution by serving as a natural sink for gaseous and particulate pollutants, thereby enhancing the ecological sustainability of cities. Identifying tree species with high tolerance to air pollution is therefore essential for effective urban park planning and management in highly polluted urban environments. This study evaluated the air pollution tolerance of selected tree species commonly found in urban parks of Kandy City, Sri Lanka, using the Air Pollution Tolerance Index (APTI). Five tree species—Terminalia catappa (Indian almond), Cassia fistula (golden shower tree), Pongamia pinnata (Indian beech), Madhuca longifolia (butter tree), and Tabebuia rosea (pink poui)—were assessed at two urban park locations representing contrasting pollution levels, identified based on ambient SO₂, NO₂, and PM_2.5 concentrations. APTI was calculated using four leaf biochemical parameters: pH, ascorbic acid content, relative water content, and total chlorophyll content. Leaf samples were collected from ten replicates of each species at both sites. Madhuca longifolia exhibited the highest APTI values (17.06 at the HP site and 25.17 at the LP site), followed by Cassia fistula, Terminalia catappa, Tabebuia rosea, and Pongamia pinnata. These findings suggest that the identified species, particularly Madhuca longifolia and Cassia fistula, are well-suited for urban greening and can contribute to mitigating air pollution impacts. However, these findings are constrained by a single cross-sectional sampling term, limited species screening, sequential data collection variances, and fixed mathematical equations. Consequently, future research should implement continuous multi-station monitoring arrays, expand species diversity, establish localized biochemical weightings, and initiate long-term multi-seasonal tracking to resolve temporal dynamics in tropical urban ecosystems. Full article

Source apportionment and the elucidation of driving mechanisms are essential for targeted soil pollution management. This study investigated surface soils across six towns in southern Shimen County, northwestern Hunan Province, where 662 samples were collected to determine the concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn. Multivariate statistics and the APCS-MLR receptor model were integrated to quantify pollution sources, while three machine learning models (RF, XGBoost, and LightGBM) were applied to identify key drivers of the spatial enrichment of Cd. Results showed that Cd was significantly enriched, with a mean concentration of 0.43 mg/kg (3.41 times the provincial background value). The mean concentrations of As, Cr, Cu, Ni, Pb and Zn were 11.97 mg/kg, 81.01 mg/kg, 24.15 mg/kg, 49.25 mg/kg, 29.56 mg/kg and 76.77 mg/kg, respectively, and these PTEs remained at normal background levels. Significant inter-element correlations indicated common sources. Three primary sources were quantified—natural parent material (43.83%), mining activities (30.99%), and mixed sources of coal mining and agricultural inputs (7.84%), with 17.34% attributed to unidentified mixed sources. Natural sources dominated the geogenic enrichment of Cd, Cu, Ni, Pb, and Zn; mining activities governed the accumulation of As, Cr, Cu, and Pb; a mixed source of coal mining and agricultural practices contributed substantially to Cd enrichment. Machine learning identified PM10, topography, strata, and soil type as dominant drivers, with their total feature importance reaching 70.05%. Among these factors, natural factors and anthropogenic factors accounted for 44.23% and 55.77% of the total feature importance, in turn revealing coupled natural–anthropogenic controls. This study establishes an integrated framework linking source apportionment and driver identification, providing scientific insights for potentially toxic elements (PTEs) control in analogous mining–agricultural regions. Full article

This experiment aimed to explore the effects of different doses of compound probiotics (a 1:1:1 mixture of Saccharomyces cerevisiae, Lactobacillus acidophilus, and Bacillus subtilis) added to the diet on pregnant sows and weaned piglets. The experiment was carried out in two stages. Experiment with pregnant sows: thirty-six second-parity Large White sows at 80 d of late gestation were randomly divided into a control group, experimental group I, and experimental group II. The control group was fed a basal diet, while experimental groups I and II were fed the basal diet supplemented with 2 g/kg and 3 g/kg of compound probiotics, respectively. The pre-experiment lasted 7 d, and the formal experiment continued until the end of lactation. The results showed that the numbers of live piglets per litter, healthy piglets per litter, litter birth weight and litter weaning weight in the experimental groups were significantly higher than those in the control group (p < 0.05). Colostrum IgG concentration in experimental group I was significantly higher than that in the control group and experimental group II (p < 0.05). Compound probiotics significantly increased colostrum immunoglobulin levels (p < 0.05). The concentrations of ammonia, carbon dioxide and PM2.5 in the barns of the experimental groups all showed a decreasing trend. Experiment with weaned piglets: a total of 160 Landrace × Yorkshire crossbred weaned piglets at 30 d of age with an initial body weight of (8.01 ± 0.13) kg were randomly assigned to four groups. The control group was fed a basal diet, while the treatment groups were supplemented with 2, 3, and 4 g/kg of compound probiotics, respectively. The results indicated that average daily gain and average daily feed intake in experimental group III were significantly higher than those in the control group, while the feed-to-gain ratio and diarrhea rate were significantly lower (p < 0.05). The apparent digestibility of crude fiber was significantly higher than that in the control group (p < 0.05), and serum IgA was significantly higher than in the other groups (p < 0.05). In conclusion, dietary supplementation with 2 g/kg compound probiotics for sows in late gestation showed the optimal effect, improving reproductive performance, colostrum immune indices and reducing harmful gases in the barn. For weaned piglets, supplementation with 4 g/kg compound probiotics improved growth performance, nutrient digestibility and serum immune indices. Full article

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in the central region of Türkiye. Therefore, this study focused on the association between the daily variations in air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) and hospital admissions due to respiratory, cardiovascular, and total (non-accidental) causes in the Sivas province. Daily average concentrations of air pollutants were obtained from two air quality (AQ) monitoring stations, and daily meteorological (air temperature and relative humidity) data were obtained from one meteorological station in Sivas province to determine the effects of air pollution on hospital admissions. It was found to be a significant relationship between air pollution and respiratory hospital admissions in the province. The results of the study showed the relative magnitudes of the risks of cardiovascular diseases and hospital admissions related to air pollutants were as follows: The highest association of each pollutant with cardiovascular diseases was observed for PM₁₀ at lag 4 (ER = 1.74%; 95% CI = 0.95–3.19%), PM_2.5 at lag 2 (ER = 5.12%; 95% CI = 1.39–19.0%), NO₂ at lag 8 (ER = 4.89%; 95% CI = 0.08–288.8%) and SO₂ at lag 5 (ER = 1.21%; 95% CI = 1.10–1.32%). It was seen that short-term exposure to air pollution in Sivas between 2016 and 2019 was positively associated with increasing respiratory hospital admissions. As the first air pollution study to use the generalized linear model (GLM) method in hospital admissions in Sivas, these findings may have implications for local environmental policies and help to combat air pollution. Full article

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article

The aim of this study was to determine the effects of PM10 and PM2.5 particulate matter pollution from the coal mining sector in the three municipalities of Cesar, La Guajira, and El Magdalena on respiratory morbidity in children under 5 years of age and adults over 60 years of age residing in these municipalities. This descriptive time series study included three municipalities in three departments: Algarrobo, Albania, and La Jagua de Ibirico. The SEVCA (Seasonal Environmental Monitoring System) was used to collect PM10 and PM2.5 pollutants. Data on secondary source air quality (RIPS) were collected from the public health services (ESE) in each municipality. The daily average concentration of μg/m³ was used for the statistical analysis of the pollutants. A time series statistical model was applied to compare the temporal variations in exposure levels and the event itself. The air quality data databases were analyzed using descriptive statistics. A logistic regression model was used to assess the association between pollutants and air quality. To account for the effects of time lags in air quality data, moving averages with lags of 0 to 3 days were used. Statistical analyses were performed using R version 4.5.1. We found daily averages of ARI in children under 5 years of age and adults over 60 years of age in the three municipalities of (1.35) admissions per day. The average daily concentrations of μg/m3 for Algarrobo were (29.79 μg/m³) for PM10 and (12.68 μg/m³) for PM2.5, for Albania (33.49 μg/m³) for PM10 and (13.23 μg/m³) for PM2.5, and for La Jagua (41.42 μg/m³) for PM10 and (15.18 μg/m³) for PM2.5. Significant positive associations greater than 1 were obtained between ARI admissions and PM10 and PM2.5 pollutants, with an RR of 1.105, 1.106, 1.125, 1.124, 1.157, and 1.155 95% CI, when PM10 and PM2.5 increase by 10 μg/m³ and for delays of 1 and 1–3 days. In conclusion, we observed significant positive associations between hospital admissions for ARI in children under 5 years of age and adults over 60 years of age for the three municipalities and the pollutants PM10 and PM2.5, which leads us to conclude that there is an epidemiological association and that the change in μg/m³ levels represents a change in the risk of hospital admission for ARI for children under 5 years of age and older adults in this coal corridor of the Colombian Caribbean. Full article

Sustainable urban air quality in tropical cities is threatened by interactions between climate change, extreme drought, and long-range wildfire smoke transport. This study investigated the causes of PM_2.5 pollution in Manaus, Brazil, under El Niño conditions during the 2023 drought, focusing on long-range wildfire smoke transport. The links among hydroclimatic drying, wildfire activity, and urban air quality were examined using hourly PM_2.5 observations, meteorological data, long-term climate records, MODIS hotspot and fire radiative power (FRP) data, and air-mass trajectory analyses. Significant long-term warming, decreasing precipitation, and a declining standardized precipitation evapotranspiration index were observed around Manaus during 1981–2024, indicating persistent drying. In 2023, severe drought and increased wildfire activity caused an annual mean PM_2.5 concentration of 15.09 µg m⁻³. Directional analyses, upwind FRP, potential source contribution function, and backward trajectories consistently highlighted the eastern and southeastern source regions approximately 500–2200 km from Manaus. These results indicated that PM_2.5 levels were more sensitive to spatial alignment between upwind fires and prevailing winds than to total fire activity alone. In conclusion, the 2023 PM_2.5 surge was driven by long-range wildfire smoke transport under intensified drying and drought, with implications for urban sustainability, public health, and climate-resilient early warning systems. Full article