Search Results (340)

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

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

The sustainability, crop production, and food safety of agriculture are increasingly challenged by microplastic pollution, as agricultural soils are the largest reservoirs and may serve as points of contact for plastic particles in the food chain. This review provides a comprehensive overview of plant materials, fate and uptake pathways, detection techniques, and the possible risks of microplastics in agriculture. Agroecosystems are also a source of microplastics, such as plastic mulch films, sewage sludge, compost and manure additives, wastewater irrigation, polymer-coated fertilizers, greenhouse materials, atmospheric deposition, and decomposition of discarded agricultural plastics. Their distribution and mobility in soil are controlled by polymer composition, particle size, morphology, density, surface ageing, soil texture, organic matter content, tillage practices, runoff, leaching, and soil biota. Recent data show that microplastics, especially smaller microplastics and nanoplastics, can attach to root surfaces, penetrate plants via cracks in roots, areas of lateral root development, and apoplastic pathways, and eventually move to tissues aboveground. Plant tissue detection is often accomplished by digestion of the sample, density separation, visual and fluorescence microscopy, Fourier-transform infrared spectroscopy, Raman spectroscopy, pyrolysis–gas chromatography mass spectrometry, and electron microscopy, but standardization of these methods remains a significant challenge. Microplastics can disrupt seed germination, root structure, nutrient absorption, photosynthesis, oxidative homeostasis, biomass buildup, yield development, and quality. Further, their capacity to transport additives, plasticizers, heavy metals, and persistent organic pollutants raises concerns about the transfer of contaminants to edible plant parts and their potential transfer to human diets. Further studies are needed focusing on field-realistic exposure conditions, long-term crop–soil interactions, nanoplastics behaviour, standardised analysis procedures, uptake and translocation pathways, edible crop risk assessments, and sustainable mitigation approaches to reduce microplastics in agroecosystems. Full article

The research aims to develop methodologies for the detailed characterization and spatial zoning of landfills as a means of assessing their environmental impact. The principal objective is to establish an integrated framework for evaluating landfill conditions through multisource data analysis, encompassing remote sensing, field investigations, and geochemical analyses. The proposed framework incorporates several critical components: satellite and UAV-based remote sensing, multispectral vegetation assessment, geochemical soil profiling, temporal and functional zoning, and morphodynamic evaluation. Research findings indicate substantial environmental pollution in the vicinity of landfill sites, at levels that exceed the natural self-purification capacity of surrounding ecosystems. This encompasses the contamination of all principal environmental components, including groundwater, surface water, soil, vegetation, and atmosphere. The key findings demonstrate that only a comprehensive environmental impact analysis, conducted in conjunction with detailed landfill zoning, yields a thorough understanding of the associated adverse effects. Remote sensing methodologies are shown to play a pivotal role in data acquisition and ongoing monitoring. The practical contribution of this study lies in the development of methodological frameworks for detailed landfill zoning, environmental impact assessment, monitoring, damage mitigation measures, and waste management optimisation. The results obtained have the potential to improve waste management systems, inform the development of effective monitoring protocols, and underpin strategies aimed at reducing the environmental footprint of landfills. Overall, this research advances scientific and technical knowledge in the field of waste management and contributes towards efforts to mitigate environmental impact—a matter of persistent concern given rising rates of waste generation and the increasingly constrained availability of suitable landfill capacity. Full article

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM_2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min. Full article

Reducing carbon emissions while improving air quality is a central challenge for rapidly urbanizing regions. Focusing on 31 prefecture-level cities in the Middle Reaches of the Yangtze River Urban Agglomeration, this study examines carbon–pollution synergy (CPS), spatial dynamics, and the driving factors of CO₂ and representative air pollutants from 2013 to 2023. Spatial autocorrelation analysis, a revised four-factor Logarithmic Mean Divisia Index (LMDI) decomposition, and a factor-based CPS assessment were used to identify spatial clustering, compare driver heterogeneity, and evaluate coordination between CO₂ and primary pollutants. To improve methodological consistency, the LMDI decomposition and CPS assessment focus on the primary pollutants SO₂, CO, and NO₂, whereas PM_2.5 and O₃ are retained in the spatial analysis and discussion because they are strongly affected by secondary formation, atmospheric transport, and meteorological conditions. The results show that CO₂ and the selected pollutants exhibit significant but pollutant-specific spatial clustering. High CO₂ values remain concentrated in the core cities of Wuhan, Changsha, and Nanchang, PM_2.5 shows a persistent north–south gradient, and SO₂ hotspots shift from traditional industrial cores toward peripheral areas receiving industrial relocation. The revised LMDI results show that economic development is the most stable positive driver of CO₂ and the primary pollutants, whereas the energy-consumption factor generally suppresses emissions. The recalculated population-scale factor fluctuates around 1, indicating a comparatively limited and stage-dependent contribution once the other factors are controlled for. CPS analysis further indicates that coordinated reduction is most robust under the energy-consumption factor and, for conventional combustion-related pollutants, also under the energy-structure factor. Overall, the region has a clear basis for CPS governance, but effective implementation requires pollutant-specific and region-specific control strategies rather than a uniform co-mitigation pathway. Full article

Micro(nano)plastics (MNPs) represent a pervasive and escalating threat to global ecosystems and human health. This review provides a critical synthesis of MNPs’ exposure risks across marine, atmospheric, and terrestrial compartments, with a distinct emphasis on identifying cross-media linkages and methodological inconsistencies that limit current risk assessments. Within marine environments, pollution hazard indices reveal significant spatial heterogeneity, yet their utility is constrained by the absence of toxicity weighting and particle characteristic integration. Atmospheric exposure profiles show variable risks, and the MNPs’ concentration in indoor air (up to 15.8 particles/m³) is significantly higher than in outdoor environments, posing a greater inhalation risk to infants and children who spend more time indoors. A marked increase in MNPs’ concentrations within agricultural soils is identified, where the MNP content in mulched soils (average: 570.2 particles/kg) is more than twice that of non-mulched soils (259.6 particles/kg). Critically, studies have now detected MNPs within human tissues, including the blood, intestines, liver, kidneys, tonsils, and brain, highlighting an urgent need to elucidate their multi-organ toxicity mechanisms, with a novel synthesis of gut–brain axis disruption and transgenerational effects. By integrating exposure dynamics with mechanistic toxicity data, this review advances a cross-system framework that identifies priority research directions, namely standardized detection methodologies, combined pollutant toxicity, and cross-system toxicity mechanisms, which are essential for informing mitigation strategies amid this escalating public health crisis. Full article

Image-based PM2.5 estimation has emerged as a promising complementary approach to traditional physicochemical monitoring. However, achieving accurate predictions in severely polluted environments remains a critical challenge, as existing deep learning models tend to prioritize luminance variations induced by PM2.5 while neglecting the impact of complex atmospheric light interference, leading to substantial estimation errors. To address this issue, this paper proposes a novel luminance–spatial decoupling (LSD) module constructed based on L2–Lp Retinex theory and integrated into a VGG16 backbone. By establishing a prior knowledge module linking luminance to PM2.5, the proposed method achieves high-fidelity separation of atmospheric luminance (AL) and target luminance (TL) during feature extraction. TL represents the luminance variation induced by PM2.5 concentrations, whereas AL characterizes the luminance contribution arising from atmospheric light. Simulation experiments validate the reliability of the L2–Lp Retinex-based decomposition. Ablation studies reveal that the LSD module effectively mitigates haze interference in high-pollution conditions while minimizing influence on the backbone network in clear weather, thereby resolving the conflict between dehazing and feature extraction. Comparative experiments demonstrate that LSD-VGG16 significantly outperforms traditional methods and standard convolutional neural networks, achieving a minimum prediction error of 12.42 while exhibiting stronger stability against temporal variations. Furthermore, evaluation on the unseen RHID-AQI dataset without retraining confirms the model’s robust generalization capability under abrupt illumination fluctuations and diverse weather conditions. Full article

Air pollution remains a major challenge for sustainable development because of its impacts on human health, ecosystems, and climate. At the same time, the rapid growth of environmental data and advances in artificial intelligence (AI) have created new opportunities for atmospheric composition research and air-quality management. This review examines AI applications in atmospheric composition studies, focusing on two related but distinct tasks: (i) spatiotemporal forecasting of pollutant concentrations and (ii) emission inference from mobile and point sources. It emphasizes the fundamental differences between these tasks in terms of data requirements, model design, and physical interpretability. A synthesis of representative studies published between 2018 and 2025 is provided, covering machine learning and deep learning approaches for air-quality prediction and emission characterization. Recent foundation-style architectures and global AI weather models introduced in late 2025 and early 2026 further demonstrate the growing role of large-scale spatiotemporal learning in atmospheric and environmental prediction. Particular attention is given to hybrid and physics-informed models that aim to connect data-driven methods with atmospheric processes. The review also discusses major methodological challenges, including data representativeness, sensor uncertainty, spatial transferability, and model generalization under nonstationary conditions. It highlights the importance of leakage-resistant evaluation, appropriate temporal and spatial splitting strategies, and the roles of interpretability and uncertainty quantification in physically meaningful atmospheric modelling. From a sustainability perspective, these AI approaches can support more reliable monitoring, improved emission assessment, and better-informed strategies for air-pollution mitigation. Full article

Based on a bimodal normal distribution for dust size distribution, a quantitative method for estimating dust input was established in this study, and then the transport, microphysics, and environmental effects of a dust event from 26 to 28 November 2025 were investigated based on a multi-source vertical remote sensing system in Zhejiang. The results indicate that the net PM₁₀ input was approximately 7760 tons, exhibiting a spatial distribution that decreased from northeast to southwest. The net input per unit area ranged from 0.001 to 0.293 t/km². The dust was coarse-dominated, initially lowering the PM_2.5/PM₁₀ ratio, which later recovered due to gravitational settling and aging. A distinct “upper-small, lower-large” depolarization ratio profile, caused by gravitational settling and hygroscopic absorption, signaled dust intrusion into the breathing zone and an imminent rise in surface PM₁₀, thereby providing a potential early-warning indicator. Dust influx first elevated the relative humidity below the dust layer via radiative cooling but later reduced the near-surface humidity through hygroscopic absorption after settlement. Additionally, decreases in SO₂ and NO₂ suggested a potential mitigation of atmospheric acidity by the dust. The O₃ response showed spatial heterogeneity: in most areas, it was negatively correlated with NO₂, reflecting NO₂ titration effects under a VOC-controlled regime, while, in a few areas, both decreased synchronously. These findings underscore the dual physical–chemical impacts of dust on regional air quality and support the development of dust-related pollution early-warning systems. Full article

Fugitive greenhouse gas (GHG) emissions in industrial parks are characterized by high opacity and spatial dispersion. Existing localization and quantification methods often rely on idealized meteorological assumptions and low-precision mobile monitoring data, making it difficult to achieve accurate source characterization. This study focuses on the Nansha Economic and Technological Development Zone in Guangzhou—one of the first pilot zones for synergistic pollution and carbon reduction in China—to develop an atmospheric inversion model based on multi-site fixed monitoring. By integrating GHG concentrations with multi-dimensional meteorological parameters, the model couples an atmospheric dispersion framework with a Bayesian inversion algorithm. Specifically, site-specific conditions and high-frequency meteorological data are utilized to constrain dispersion parameters, effectively reducing model uncertainty driven by meteorological variability. Within the Bayesian framework, the model enables the simultaneous inversion of both the locations and emission strengths of multiple sources. Results identified three distinct fugitive emission sources: one primary source in the International Auto Industrial Park with a CO₂ emission intensity of 103.15 g/s and two sources in the Western Industrial Park with intensities of 0.051 g/s and 0.26 g/s, respectively. Overall, this research framework significantly enhances the accuracy and spatial resolution of emission inversion, providing robust technical support for precision carbon management and the development of targeted mitigation strategies for key industrial processes. Full article

The release of heavy metals into the environment due to human activities is increasing, and this has led to concern about heavy-metal contamination on farmland. Prior studies have primarily focused on short-term investigations or specific pollution sources, lacking systematic monitoring of cadmium’s long-term input-output fluxes and their mass balance at the scale of a complete farmland ecosystem. This study clarified the cadmium (Cd) pollution trends for a typical paddy system in southern China. A six-year long-term monitoring study (2019–2024 inclusive) of a Cd-contaminated paddy system in Ningxiang City, Hunan Province, China, was conducted. The Cd flux dynamics for three input pathways (atmospheric deposition, irrigation water, and fertilizer) and three output pathways (crop harvesting, surface runoff, and subsurface infiltration) were investigated. The results showed that atmospheric deposition is the primary source of Cd input, accounting for 76% of total inputs, and leads to persistent net accumulation of soil Cd. Straw removal serves as the dominant output mechanism, facilitating substantial Cd removal, representing 77% of total Cd exports, while straw retention significantly reduces export fluxes. The study found that the net Cd fluxes from 2019 to 2024 were 1.994, 2.624, 8.984, 11.299, 9.944, and 20.162 g·(hm²·a)⁻¹, straw removal was primarily adopted during the period. A net flux analysis showed that progressive soil Cd accumulation had occurred over the study period. The results suggest that science-based straw management is critical when attempting to mitigate soil Cd pollution and enhance safe land utilization. These findings can be used to improve region-specific pollutant source control strategies and soil management policies. Full article

Terrestrial environments function as major sinks and dynamic sources of microplastics. Land use strongly influences inputs, accumulation, and transport pathways of these contaminants in the environment. Despite the extensive literature, few reviews have compared contamination levels and the potential impacting factors across land uses. To fill this gap, this review synthesizes current knowledge on the origins, occurrence, pathways, and ecological effects of microplastics across diverse land uses. The review revealed multiple interconnected pathways that drive microplastic contamination in terrestrial systems. Abundances are consistently higher in intensively managed croplands, urban areas and industrial vicinities. However, their detection in remote environments underscores the critical role of diffuse inputs and long-range atmospheric transport. Vertically, microplastics are enriched in topsoils, and their concentrations declines with depth. Horizontally, concentration declines with increasing distance from major hotspots like agricultural fields, industrial facilities, and road networks. Ecologically, microplastics alter soil physical properties, modify chemical conditions, and shift microbial community composition and enzyme activities. Furthermore, they stress soil fauna and plants through ingestion, toxicity, and physical blockage, with impacts contingent on polymer type, particle morphology, and concentration. Collectively, this review reveals consistent spatial patterns and widespread adverse ecological impacts, highlighting the clear need for integrated management strategies to mitigate terrestrial microplastic pollution. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

(1) Research Highlights: This study provides the first integrated assessment of Scots pine (Pinus sylvestris L.) growing in the urban forests of Karaganda, Kazakhstan, a city consistently ranked among the most air-polluted cities globally. We examined the adaptive phyto-chemical response of needles to extreme technogenic stress and evaluated their dual potential as biological filters and renewable sources of bioactive compounds. (2) Background and Objectives: Urban forests are critical for mitigating air pollution; however, the biochemical responses of trees in heavily industrialized environments remain poorly understood. Karaganda faces severe atmospheric pollution from mining, metallurgy, and energy sectors, with particulate matter (PM) levels exceeding permissible limits by up to 20-fold. This study aimed to evaluate the state of Pinus sylvestris, a key component of local protective plantations, by studying heavy metal accumulation, anatomical localization of secondary metabolites, and the phytochemical profile and biological activity of needle extracts obtained using different extraction techniques. (3) Materials and Methods: Needles were collected from 15 trees across three sites in Karaganda’s industrial green zones. Heavy metal content (Pb, Cd, As, and Hg) was determined using atomic absorption spectroscopy and voltammetry. Anatomical–histochemical analysis localizes major metabolite classes. Liquid extracts were prepared using four methods, percolation (PER), vortex-assisted (VAE), microwave-assisted (MAE), and ultrasound-assisted (UAE) extraction, and analyzed by GC-MS. Antimicrobial activity was tested against S. aureus, B. subtilis, E. coli, and C. albicans using the disk diffusion method. The antioxidant capacity (water- and fat-soluble) was measured amperometrically. Statistical analysis was performed using one-way ANOVA with Tukey’s HSD test (p < 0.05). Results: Despite extreme ambient pollution, heavy metal concentrations remained below pharmacopoeial limits (Pb < 0.1, Cd < 0.05, As < 0.01, Hg < 0.001 mg/kg), indicating effective biofiltration without toxic accumulation. Histochemistry confirmed the active synthesis of protective phenolics, flavonoids, and essential oils in the mesophyll, epidermis, and schizogenic cavities. GC-MS identified 72 compounds in the PER extract, 70 (the VAE), 72 in (MAE), and 46 in (UAE). The PER extract exhibited the highest relative abundance of bioactive terpenoids: α-cadinol (5.24%), α-muurolene (4.32%), and caryo-phyllene (2.20%). UAE extracts exhibited elevated 5-hydroxymethylfurfural (6.90%), indicating degradation. Antimicrobial testing revealed that PER produced the largest inhibition zone against S. aureus (15.0 ± 1.0 mm), significantly exceeding that of the other methods (p < 0.001). PER extract also demonstrated the highest water-soluble antioxidant capacity (3600 ± 0.40 mg quercetin equiv./dm³) and substantial fat-soluble activity (1633 ± 0.23 mg gallic acid equiv./dm³). (4) Conclusions: Pinus sylvestris in Karaganda exhibits remarkable adaptive resilience, maintaining safe heavy metal levels while accumulating a rich repertoire of stress-induced secondary metabolites. Classical percolation optimally preserves this native phytocomplex, yielding extracts with superior antimicrobial and antioxidant properties. These findings support a dual-use model wherein urban pine plantations simultaneously serve as living biofilters and renewable sources of standardized bioactive extracts, a concept with direct implications for circular bioeconomy strategies in industrial regions worldwide. This supports the strategic importance of coniferous plantations for bioremediation and sustainable resource use in industrial regions. Full article