Search Results (2,499)

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

Microplastics (MPs; <5 mm) represent a growing environmental concern that increasingly challenges environmental monitoring, governance, and evidence-based decision-making. This review critically examines how current scientific understanding of microplastic sources, classification, occurrence, and environmental behavior can support environmental governance. MPs are classified as primary and secondary particles; however, persistent inconsistencies in size definitions, shape descriptors, and polymer identification limit the comparability of monitoring data and constrain the development of coherent regulatory frameworks. Evidence on the occurrence of MPs in surface waters and sediments highlights widespread contamination and pronounced spatial variability, raising challenges for risk assessment and policy harmonization across regions. Key transport pathways, including atmospheric deposition, terrestrial runoff, and riverine fluxes, are analyzed to illustrate how local emissions translate into large-scale environmental impacts. Rivers emerge as key components linking sources to receptors, offering relevant points for policy intervention and management measures. The review evaluates current policy responses to microplastic pollution, identifying significant gaps in standardized monitoring, data integration, and risk assessment approaches. It emphasizes the need for stronger alignment between scientific outputs and policy requirements, including the co-production of knowledge involving scientists, regulators, and stakeholders. By outlining pathways through which scientific evidence can inform regulatory design and environmental management, this study provides actionable insights for improving policy effectiveness. Advancing harmonized methodologies and integrating science into decision-making processes are essential steps toward mitigating microplastic pollution and supporting sustainable environmental governance. Full article

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

Hydrogen (H₂) has emerged as a promising next-generation energy carrier with significant potential to mitigate climate change and environmental pollution. The hydrogen evolution reaction (HER) is the critical half-reaction directly responsible for hydrogen production. Efficient HER electrocatalysts must exhibit low overpotential values and fast reaction kinetics to achieve high catalytic performance. While platinum (Pt) remains the benchmark catalyst due to its ideal hydrogen adsorption energy, high electrical conductivity, and superior chemical stability, further innovations are essential. This review summarizes recent advances in Pt-based HER catalysts, focusing on two primary design strategies: metal-level engineering and support-level engineering. These approaches allow for precise control over electronic structures, active site distributions, and interfacial properties, paving the way for next-generation HER electrocatalysts. Full article

Introduction: Temperature is a key environmental factor influencing the physiological and biochemical processes of aquatic organisms, including xenobiotic metabolism. Understanding how temperature modulates the toxicological effects of pollutants such as polycyclic aromatic hydrocarbons (PAHs) is crucial in the context of climate change. Among these compounds, benzo[a]pyrene (BaP) and benzo[a]anthracene (BaA) are priority pollutants in aquatic environments, resulting from incomplete combustion. Their relevance is attributed to persistence and metabolic bioactivation potential. Fish primary hepatocyte cultures represent a relevant in vitro model for studying combined effects of thermal stress and chemical exposures, while supporting the 3Rs principles (Replacement, Reduction, and Refinement). Objective: This study aims to assess temperature-dependent effects of BaP and BaA, and their mixtures in brown trout hepatocytes using cytochrome P450 1A (CYP1A) immunohistochemistry as an indicator of xenobiotic metabolism. Methodology: Primary hepatocytes were isolated using a two-step collagenase perfusion method and cultured in 24-well plates at 18 °C and 22 °C. Cells were exposed for 72 h to supplemented L-15 medium (control) or to 0.1% dimethyl sulfoxide in supplemented L-15 medium (solvent control), as well as to single exposures of 1 and 10 µM of BaP and BaA and to equimolar mixtures of both compounds (1 and 10 µM). Viability was assessed using the lactate dehydrogenase (LDH) assay. CYP1A immunostaining was quantified based on cytoplasmic staining intensity relative to background area. Results: No significant effects on cell viability were observed under any condition. Temperature significantly reduced CYP1A expression in single exposures at 22 °C compared to 18 °C. BaP induced a significant dose-dependent increase, while BaA differed from controls only at 10 µM. In mixtures, only treatment- and dose-dependent effects were observed, with no temperature influence detected. Conclusions: Overall, the data highlight temperature as a key modulator of biochemical responses to PAHs, with single and mixed exposures eliciting distinct effects and suggesting potential synergism in mixtures. Full article

Introduction: The temperature of rivers in the Iberian Peninsula has increased due to global warming. In addition, these rivers are polluted by contaminants of emerging concern, such as polycyclic aromatic hydrocarbons (PAHs). Higher temperatures and pollution concurrently impose threats to the Iberian Peninsula’s endemic species, including the brown trout (Salmo trutta), a cold-water species widely used in ecotoxicological studies. Because the liver is the main biotransformation organ, and is particularly sensitive to both chemical and temperature changes, in vitro liver models may represent valuable alternatives for assessing combined stressor effects, complying with the 3Rs principle. Objective: In line with the above, the present study aimed to evaluate the combined effects of a 4 °C temperature increase and the model PAH benzo[k]fluoranthene (B[k]F) on fish liver cells using a primary brown trout hepatocyte culture as a model. Methodology: Primary hepatocytes were seeded in 6-well plates at a density of 1.0 × 10⁶ cells/mL and exposed for 48 h to 1, 10, and 20 µM B[k]F at 18 °C (normothermia) and 22 °C (warming scenario). Cell viability was assessed using trypan blue, alamarBlue, and lactate dehydrogenase (LDH) assays. Cytochrome P450 (CYP)1A was evaluated in terms of its gene expression by RT-qPCR and its protein expression through immunocytochemistry (ICC). The immunostaining was quantified using a score system which considered five intensity staining levels. Results: Exposure to B[k]F and to the higher temperature increased LDH leakage without interaction effects. In contrast, the other viability assays did not show significant differences across conditions. Regarding CYP1A, both gene and protein expression increased with all B[k]F concentrations in relation to the controls, but were not influenced by temperature. Notably, the lowest B[k]F concentration (1 µM) elicited the highest CYP1A gene expression, suggesting a non-monotonic response. Conclusions: Overall, the model was responsive to both temperature (4 °C) increase and to B[k]F, validating its usefulness for assessing liver pollutant effects in the context of global warming. These findings support the application of fish primary hepatocyte models as relevant tools in ecotoxicology under environmentally realistic multi-stressor scenarios. Full article

Backfilling the industrial solid waste coal gangue into deep coal mine goafs for CO₂ geological sequestration is a crucial pathway to achieve the synergistic effect of pollution reduction and carbon mitigation. However, in complex deep geological environments, the chemical evolution of multiple mineral phases of coal gangue under gas–water–rock coupling effects and the carbon-controlling mechanism of residual total organic carbon (TOC) remain unclear. In this study, coal gangue from the goaf of the Xiaobaodang Coal Mine was used as the research object. Relying on a customized high-temperature and high-pressure reaction system to simulate the deep in situ environment (45 °C, 10 MPa), and combined with X-ray diffraction (XRD), total organic carbon determination, and isothermal CO₂ adsorption experiments, the geochemical mechanism by which inorganic minerals and organic residual carbon synergistically control the ultimate CO₂ adsorption potential was systematically revealed. The results show that the modification of the CO₂ adsorption potential of coal gangue by gas–water–rock reactions exhibits strong mineral phase differentiation. Systems rich in active silicates generate a large amount of secondary clay minerals through intense carbonation alteration, achieving a significant increase in micro–nano pores and absolute adsorption capacity. Systems rich in carbonates steadily release deep primary adsorption potential by widening mass transfer channels through mineral dissolution. In contrast, systems rich in primary clay minerals face an irreversible attenuation of adsorption space due to physical clogging of pore throats caused by fluid migration. Furthermore, the initial organic carbon content exerts a significant non-linear regulatory effect on the development of the micropore network. The physical adsorption sites provided by the high relative content of layered clay minerals (>41%), coupled with the interfacial enhancement effect exerted by a moderate organic carbon content (0.12~0.16%), constitute an optimal physicochemical synergistic enhancement network, which is the core geological reason for stimulating the ultimate carbon sequestration capacity of coal gangue. The results of this study not only enrich the multiphase interfacial thermodynamic theory of complex heterogeneous geological bodies but also provide solid theoretical support for the precise optimization of target areas and the long-term evaluation of carbon sinks in goaf CO₂ sequestration engineering. 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

Microplastic (MP) contamination is an emerging threat to aquatic ecosystems. However, species-specific bioaccumulation patterns across trophic guilds in tropical river ecosystems remain scarcely understood. This study assessed the occurrence, organ-level distribution, polymer composition, and ecological risk of MPs in 220 fish representing 12 species, spanning across multiple trophic guilds, sampled from four sites along a pollution gradient of the river Yamuna, India. MPs were detected in all examined species, confirming extensive distribution across the river ecosystem. A total 1678 MPs were recovered, with significantly higher abundance in fish from the highly urban Delhi stretch than in those from upstream regions (Kruskal–Wallis, H = 11.03, p = 0.011). The highest species-specific MP load was recorded in omnivorous Oreochromis niloticus from Sonia Vihar (436 MPs), whereas the carnivorous species Xenentodon cancila exhibited the lowest accumulation (37 MPs). Surface- and mid-water herbivores and omnivores accumulated more MPs than benthic carnivores and detritivores. Nonetheless, spatial pollution gradients exerted a stronger influence on MP accumulation, compared to trophic guilds. The gastrointestinal tract exhibited the highest MP abundance (751 MP particles), followed by gills (605) and muscle tissues (322), confirming ingestion as primary uptake route, and suggesting possible tissue translocation. Fibers dominated in the assemblage (77.8%), while transparent (44%) and blue (19.5%) were most abundant colors. ATR–FTIR analysis confirmed 10 diverse polymers, with polyethylene (≈24%) and polypropylene (≈21%) together accounting for nearly half of the identified particles. The Polymer Hazard Index analysis classified the recovered MP mix as Category IV (high ecological hazard). These findings identify the Delhi stretch of the Yamuna as a high MP contamination zone and highlight the combined influence of urban pollution and fish ecology on MP bioaccumulation. Full article

Engineered nanomaterials are widely used in environmental remediation, agriculture, and industrial applications owing to their large specific surface area, high reactivity, and tunable physicochemical properties. However, their release into aquatic environments has raised increasing concerns regarding potential risks to primary producers. Microalgae are highly sensitive to environmental stressors and play essential roles in photosynthesis, nutrient cycling, carbon fixation, and aquatic food-web stability, making them important model organisms for assessing the toxicity of engineered nanomaterials. This review summarizes the toxic effects and mechanisms of representative engineered nanomaterials, including metal and metal oxide nanoparticles, nanoplastics, and carbon-based nanomaterials, on microalgae. Major toxic pathways include nanoparticle attachment and aggregation on algal surfaces, shading effects, membrane damage, altered permeability, cellular internalization, toxic ion release, reactive oxygen species overproduction, photosynthetic inhibition, and metabolic disturbance. The review further discusses how particle size, morphology, surface coating, dissolution, aging, light, pH, and natural organic matter regulate nanomaterial bioavailability and toxicity. Combined toxicity caused by coexisting nanoparticles or emerging pollutants is also considered, with emphasis on synergistic, antagonistic, and concentration-dependent effects. Finally, recent methodological advances, such as near-native imaging, Raman-based spectroscopy, particle-specific elemental analysis, and multi-omics approaches, are highlighted. This review provides an integrated perspective for understanding nanomaterial toxicity to microalgae and supports future ecological risk assessment in aquatic environments. Full article

Potentially toxic element (PTE) contamination in rice poses significant food safety risks, particularly in regions with intensive agriculture, industry, and traffic. This study provides a systematic assessment of the accumulation, translocation, sources, and health risks of PTEs (As, Cd, Cr, Cu, Ni, Pb, Zn) in the atmospheric deposition–soil–rice system across four distinct anthropogenic source areas (industrial, peri-urban, rural, and roadside areas) in Hunan Province, China. The rural area was categorized as clean. Industrial areas had the highest soil pollution index, while roadside areas recorded the highest atmospheric deposition flux of Pb (19.95 μg/m²/day) and As (1.93 μg/m²/day). Correspondingly, industrial areas exhibited the highest Cd (0.38 mg/kg) and Pb (0.94 mg/kg) in rice grains, whereas roadside areas showed the highest Pb (1.40 mg/kg) and As (2.99 mg/kg) in leaves. The findings indicated that rice in roadside areas primarily accumulate PTEs through foliar absorption of atmospheric deposition, whereas in industrial and peri-urban areas it was primarily through root uptake and translocation of PTEs to rice grains, particularly for Cd and Pb. Source apportionment identified natural, industrial, and traffic as the three primary sources. The Bayesian mixing model revealed that the natural source contributed the highest proportion to rice grains (48.3–70.6%) across all four source areas. Except for natural sources, industrial sources dominated in industrial areas (29.1%), traffic emissions prevailed in roadside areas (19.4%), while mixed sources had the highest proportion in peri-urban areas (28.4%). Health risk assessment revealed that the total hazard index followed the order of peri-urban > industrial > roadside > rural areas, with rice ingestion being the dominant exposure pathway, accounting for over 90% of the total risk. The primary contributors to health risks were identified as As, Cd, and Pb, particularly in industrial and peri-urban areas. These findings provide a scientific basis for developing region-specific mitigation strategies tailored to the dominant contamination pathways in each area. Full article

This study evaluates the demand dynamics of the 20 leading strategic destinations in the global tourism market by modeling the interactions between traditional macroeconomic determinants and climate-linked environmental sustainability indicators. The primary objective is to assess the predictive capacity of physical and structural environmental factors—including water stress, air pollution, renewable energy adoption, and sanitation infrastructure—relative to established economic metrics like GDP per capita. Employing non-parametric predictive frameworks on a panel dataset of 400 observations (2000–2019), the empirical analysis suggests that tree-based ensemble models, notably Extra Trees (90.54%) and CatBoost (84.75%), yield higher predictive accuracy than conventional multiple linear regression (73.97%). Interpretations derived from cooperative game theory via SHAP analysis suggest that environmental determinants may serve as important predictive drivers of tourism demand. Specifically, variables such as water stress (28.20%), renewable energy share (27.12%), and sanitation infrastructure carry substantial predictive weight, whereas the benchmark macroeconomic indicator (2.30%) exerts a relatively marginal influence within the model architecture. These findings imply that environmental sustainability metrics may capture international tourism demand variations more effectively than traditional economic variables. The results suggest that acute environmental vulnerabilities may be associated with reduced tourism inflows, potentially reflecting limitations in destination sustainability thresholds. Broadly, the evidence is consistent with the notion that contemporary global tourism demand may be increasingly interdependent with ecological resilience and low-carbon transition policies. It is important to note that the findings reported here reflect predictive associations derived from machine learning models and should not be interpreted as evidence of causal relationships. Full article

Industrial settlements of the East Kazakhstan Region face a persistent technogenic burden driven by the dense concentration of non-ferrous metallurgy and heat-and-power enterprises, further compounded by unfavorable pollutant dispersion conditions inherent to the region’s mountain–basin topography. This study evaluated spatiotemporal associations between annual mean concentrations of NO₂, SO₂, H₂S, and CO, the integrated air pollution index (API₅), and primary neoplasm morbidity across five settlements over the period 2014–2024. A retrospective ecological analysis was carried out for Ust-Kamenogorsk, Ridder, Altai, Shemonaikha, and the settlement of Glubokoe, incorporating Spearman’s rank correlation, lag analysis (1–3 years), and the Mann–Kendall trend test. Throughout the study period, neoplasm morbidity in the region consistently exceeded the national average by a factor of 1.3 to 2.0. In Ust-Kamenogorsk—where metallurgical SO₂ and NO₂ emissions are most heavily concentrated—strong positive associations were found in children for SO₂ (ρ = 0.791, p < 0.05) and in adolescents for NO₂ and CO, reflecting elevated inhalation exposure under conditions of chronic pollution. The negative associations with API₅ observed in Ridder and Altai, where the index showed a statistically significant downward trend, are interpreted as evidence of the inertial character of oncological processes and the lasting influence of cumulative past exposure. Across all studied settlements, SO₂ emerged as the most consistent predictor of morbidity variation. These findings support prioritizing stricter emission controls for SO₂ and NO₂ from metallurgical and energy facilities, establishing oncological screening programs for children and adolescents in chronically polluted areas, and strengthening ambient air monitoring—measures whose effective implementation will require coordinated action between public health authorities and environmental regulators. Full article

Freshwater pollution by nutrients is a global concern. While agriculture is the largest contributor globally, domestic and industrial emissions are responsible for substantial emission hotspots worldwide. To this end, this paper presents the global grey water footprint (GWF) of nitrogen (N) and phosphorus (P) from domestic and industrial sources as a water pollution indicator. GWFs are displayed as gridded datasets with 5 × 5 arc minute resolution annually from 1990 to 2019, extending previous time series. Methodologically, the domestic GWF calculations were refined but were largely based on previous GWF studies. For industrial GWFs, this study presents a novel approach to estimating emissions based on country-specific industrial-to-domestic load ratios instead of the uniform ratios used in earlier studies. The global N-related GWF rose from 2.6 × 10¹² m³/yr to 6.3 × 10¹² m³/yr between 1990 and 2019. During the same period, the P-related GWF increased from 75.2 × 10¹² m³/yr to 194.5 × 10¹² m³/yr. Domestic wastewater is the dominant contributor, with hotspots in densely populated regions, such as East China, North India, and parts of Africa. Industrial contributions show relevance in heavily industrialized areas with limited wastewater treatment infrastructure. Population growth was the primary driver of increased GWFs, particularly in regions with limited sanitation and wastewater treatment. This reflects the need to improve these to mitigate nutrient pollution. Full article

Baiyangdian Lake, the largest freshwater lake in North China, plays a critical role in the ecological security of the Beijing–Tianjin–Hebei urban agglomeration. This study conducted systematic monitoring of Baiyangdian Lake from April 2023 to November 2024. Utilizing the Trophic State Index (TSI) and principal component analysis (PCA), we elucidated the impact mechanisms of extreme precipitation events on the water quality of shallow lakes. The results indicate that: (1) During the study period, Baiyangdian Lake exhibited moderate to severe eutrophication. The average total nitrogen (TN) concentration was 2.13 mg/L, exceeding the Class V threshold of the national surface water quality standard. The average total phosphorus (TP) concentration was 0.05 mg/L, far surpassing the recognized eutrophication threshold for freshwater lakes. (2) The average TSI was 49.6 ± 4.0, indicating the lake is in a transitional state from mesotrophy to eutrophy, with 64% of sampling sites classified as eutrophic. Nitrogen was identified as the primary limiting nutrient. (3) The 2023 extreme precipitation event exerted a significant three-phase impact on water quality: “dilution–legacy–restoration”. A clear dilution effect was observed from the pre-flood to the flood period (TN decreased from 1.52 to 1.04 mg/L). A pronounced legacy effect emerged post-flood, with the TN concentration sharply increasing to 4.22 mg/L in September 2023, the highest value recorded during the study. (4) PCA identified two major pollution sources: agricultural non-point source pollution (PC2, contribution: 25.4%) and domestic sewage/livestock farming (PC1, contribution: 27.6%). Correlation analysis further revealed that the flood event significantly altered the intrinsic relationships among parameters like nitrogen and phosphorus, reinforcing the dominance of agricultural non-point source pollution. (5) Source analysis suggests that external inputs are the primary contributors, while the internal loading from sediments is relatively limited. This study enhances the understanding of how shallow lakes respond to extreme climatic events and provides a scientific basis for lake management in the Beijing–Tianjin–Hebei region. Full article