Search Results (247)

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

Trajectory prediction plays a crucial role in autonomous driving by forecasting the future trajectories of agents to support safe and efficient decision-making. Most existing methods that adopt an encoder–decoder architecture have achieved remarkable success, where the scene encoder extracts contextual representations from agents’ history trajectories and lane segments. However, this architecture remains fundamentally constrained by the blind encoder. Specifically, the scene encoder of models extracts contextual information without foresight, leading to significant semantic pollution from proposal-irrelevant context, thereby degrading the prediction performance. To rectify this model deficiency, we propose ProFocus, a proactive encoding framework that reformulates the trajectory prediction model architecture via an anticipatory feedback loop. ProFocus generates the potential proposals in the nascent stage layers, utilizing them as attentional priors to dynamically modulate the scene encoding process. In addition, to optimize the information flow within the attention mechanism and reduce irrelevant context interference in attention distributions, we introduce spatio-temporal focal attention (STFA). By implementing a relation-conditioned sharpening operator through a spatio-temporal relation-controlled softmax, STFA adaptively recalibrates the attention distribution according to related dependencies. Comprehensive evaluations on the Argoverse 1 dataset and INTERACTION dataset validate that ProFocus attains competitive performance across miss rate (MR), minimum average displacement error (minADE) and minimum final displacement error (minFDE), while maintaining a real-time inference speed of 16 ms on an RTX 3090. The results from our ablation studies demonstrate that ProFocus reduces MR, minFDE, and minADE by 2.80%, 2.52%, and 1.41% relative to the baseline, respectively. Furthermore, qualitative visualizations also corroborate that ProFocus exhibits robust performance in diverse traffic scenarios. Full article

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article

The relevance of this study is determined by the need for a scientifically grounded assessment of environmental risks associated with rocket launches and by the necessity of ensuring environmental safety in areas potentially affected by space activities. Comprehensive monitoring of rocket-stage impact zones and adjacent populated areas is especially important because pollutant distribution depends on natural, climatic, and spatial factors. This study assesses the environmental impact of the “Soyuz-2.1a” launch with the “Progress MS-29” cargo spacecraft in Kazakhstan using integrated field monitoring, laboratory analysis, and geoinformation methods. The work should be interpreted as a single-event environmental monitoring assessment, while historical monitoring data from 2020–2023 were used only as a retrospective comparative background for the U-25 impact area and were not included in the main BACI statistical analysis. The study covered the launch site, adjacent populated areas, and the U-25 stage impact zone. A before–after control-impact (BACI) design with distance stratification and consideration of wind direction was applied to identify post-launch changes. Measurements below the limit of detection and limit of quantification were processed using censored-data methods, including Regression on Order Statistics (ROS) and the Kaplan–Meier estimator. Spatial analysis was used to generate concentration fields, contour maps, and risk zones, revealing an anisotropic distribution of environmental stress in the downwind sector. An integrated hazard quotient (HQ) metric was applied to compare air, water, and soil conditions on a unified scale. The results indicate that the post-launch impact was localized and time-limited, with the greatest sensitivity observed in the soil component of the U-25 zone during the early post-launch period. Atmospheric air and water indicators remained within regulatory limits in populated areas. The proposed approach combines BACI monitoring, censored-data analysis, spatial modeling, and GIS-based visualization, providing a reproducible framework for the environmental assessment of rocket-stage impact areas. The practical recommendations include staged post-launch monitoring, temporary restriction of access to high-stress zones, primary reclamation of contaminated soil, and the use of WebGIS tools to support environmental decision-making. Full article

Biomass burning is a major source of atmospheric pollution. However, rapid and quantitative assessment of particulate matter in smoke plumes remains challenging, owing to the physical uncertainties, limited coverage, and labor-intensive quality control of conventional monitoring approaches. Existing image-based deep learning methods typically address either smoke detection or air quality assessment separately. To address this gap, we develop a Unified Smoke Detection and Aerosol Estimation Framework (SDAF), a three-stage deep learning approach evaluated using a smoke-rich airborne dataset. The framework integrates smoke localization with PM₁ estimation by combining a YOLOv11-based detector with an optimized convolutional neural network. The model achieves high accuracy under in-plume conditions (R² of 0.985). However, its performance degrades under out-of-plume conditions due to substantial differences in visual features between the two domains. Consequently, direct across-domain transfer performs poorly, whereas region of interest (ROI)-level fine-tuning substantially improves performance for out-of-plume images (R² of 0.621). Despite these promising results, fundamental limitations remain. Image-based PM₁ estimation is intrinsically ill-posed due to the non-unique mapping between visual observations and particle mass. Overall, the framework enables an integrated workflow from smoke localization to quantitative PM₁ estimation using image data alone, offering a scalable solution for biomass burning monitoring and air quality assessment while highlighting the fundamentally indirect nature of image-based PM₁ inference relative to spatially resolved retrievals. Full article

The common root characteristic of CO₂ and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO₂ and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O₂/CO₂ cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO₂ and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO₂ emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement. Full article

The durability of building façades is critical to sustainable construction because it affects maintenance demand, safety, and long-term service performance. As building stocks age, especially in rapidly urbanizing countries such as China, reliable prediction of façade degradation becomes increasingly important for service-life planning and maintenance decision-making. However, conventional service-life prediction methods are commonly based on ordinary least squares (OLS) regression, which mainly estimates the conditional mean and may therefore fail to represent the heterogeneity of degradation processes. Using visual inspection data from 375 painted façade samples in Ningbo, China, this study applies quantile regression (QR) to model façade degradation and predict service life. Degradation was quantified using an overall degradation level (ODL) index that integrates defects related to aesthetic deterioration, loss of integrity, and loss of adhesion. The results show that façade degradation follows heterogeneous rather than uniform trajectories, and that the effects of key variables vary across degradation levels. In particular, pollution exposure and water ingress become markedly more influential at higher quantiles, while the effect of routine maintenance weakens in severely degraded façades. After 5-fold cross-validation, the median quantile model reduced MAE by approximately 5.3% relative to the OLS benchmark (0.0537 vs. 0.0567), and the fitted quantiles showed good calibration, with empirical coverage deviations not exceeding 0.007. The QR framework predicted a service-life range of 4.3–31.8 years, substantially wider than the 8.8–20.2 years obtained from the MLR model, indicating a stronger ability to represent uncertainty and high-risk degradation paths. These results show that QR provides a more informative basis for risk-based inspection planning and façade service-life assessment in existing buildings. Full article

Tunnels, soundproof screens and other vertical roadside traffic facilities play an important role in isolating the driving environment, maintaining driving safety, and reducing driving noise. As the usage time increases, these facade traffic buildings become polluted and cause traffic safety problems. Obstacles on three-dimensional walls of different shapes, colors, and sizes are the most challenging problem in intelligent cleaning environment perception. This paper proposes an obstacle segmentation method based on a visual language model to overcome these problems. Firstly, in the constructed experimental environment, a visual–language obstacle dataset is collected, named the Road-side General Obstacles Dataset (RGOD), and the collected dataset is labeled with both a segmentation mask and a language description. These preprocessing results are used as the training input of the perception model to obtain the foreground and background separation results. Secondly, a VLM-GOS model was proposed to segmentation special-shaped obstacles, which emphasizes the distinction between background and foreground targets. Finally, the general obstacle is segmented by a vision–language model with a similar loss function, and evaluated with different metrics. Experimental results show that compared with models such as MaskFormer, SegFormer, and ASD-Net, this method improves the model’s perceptual ability and increases accuracy by 3%. More importantly, the model is more interpretable. Full article

Visual pollution in urban environments has a significant impact on aesthetic quality, level of environmental complexity, coherence, and emotional well-being. Due to that, it needs to be analysed considering not only physical environment features and indicators but also aspects of environmental psychology and human emotional needs towards the urban environment. Taking into account this approach, in this research, it is studied applying a genotype-based framework using space syntax analysis and emotional mapping. Spatial analysis tools, such as space syntax and visibility graph analysis (VGA) provide reliable tools for statistically analysing this phenomenon. This method evaluates visual exposure and connectedness to polluting components across the map, resulting in locations with the most obvious pollution (The research examines spatial metrics such as integration, connectivity, and visibility, as well as emotional responses, to reveal significant links between urban spatial configurations and the visual pollution index (VPI). Zones with great accessibility and reachable by people, such as parks and public spaces, have positive emotional responses and low VPI scores, suggesting accessibility and visual harmony. On the contrary, low-integrated and fragmented areas have high VPI ratings, suggesting visual clutter, poor maintenance, and user dissatisfaction. Visual pollution affects the quality of urban surroundings by filling the visual space with contrasting and varied elements, resulting in visual dissonance. Common sources of visual pollution include architectural forms, billboards, advertising boards, signage, and poorly maintained building façades, particularly in modernist neighbourhoods. The Dainava neighbourhood in Kaunas city is used as a case study to apply this integrated methodology, revealing spatial and emotional aspects of the neighbourhood relevant to the VPI assessment. The findings highlight the relevance of a complex methodological approach that integrates spatial and emotional qualities of the environment and the importance of targeted actions, such as improving visibility, creating visual relations, and reducing visual clutter, in establishing inclusive, legible, and visually harmonious urban spaces. This methodological framework provides urban planners with a practical tool for the evaluation of visual pollution that integrates egzogenous (physical) and endogenous (emotional) factors and has predictive capacities to indicate the environment that is the most sensitive to visual pollution. Full article

This study systematically analyzed research trends in aquaculture wastewater treatment from 2000 to 2024 using bibliometric methods. Through knowledge mapping and keyword co-occurrence analysis conducted with Citespace6.1 software on the Web of Science Core Collection and the CNKI (China National Knowledge Infrastructure) Core Journal Database, we aimed to elucidate the distribution characteristics, evolution of research hotspots, and differences in technological pathways within the existing research landscape, while identifying gaps in integrated knowledge synthesis and cross-regional comparative analysis. The results indicate: (1) China’s publication output in this field over the past five years has significantly surpassed international levels, reflecting an imbalance in regional research activity; (2) antibiotics, nitrogen and phosphorus, organic pollutants, and heavy metals constitute the primary pollutant categories, with increasing attention focused on antibiotic and heavy metal pollution in recent years; (3) domestic research demonstrates a preference for natural ecological treatment technologies, whereas international research is predominantly oriented toward biological treatment technologies. By integrating Chinese- and English-language literature data with visual analytics, this study addresses the existing gap in systematic knowledge mapping and comparative analysis of regional technological pathways, and highlights the ongoing paradigm shift from pollution elimination toward resource recovery. The findings provide an empirical basis for formulating differentiated regional governance policies and guiding investments in low-carbon and environmentally friendly technology research and development, thereby promoting the transition of the aquaculture industry toward green and sustainable development. Full article

Investigating multiple source apportionment methods and quantitatively characterizing heavy metal contamination in soils are of critical importance for effective pollution control and prevention. This study systematically investigates multiple source apportionment methods for soil heavy metals, with quantitative characterization of contamination features crucial for effective pollution control. Taking Jingxi City in Guangxi, China, as a case study, we conducted a comprehensive analysis of 8816 soil samples using multi-source big data integration. By synergistically applying machine learning algorithms, the potential ecological risk index, and bivariate local Moran’s index, we achieved dual objectives: quantitative inversion of eight heavy metal concentrations and simultaneous ecological risk assessment with pollution source identification. Through comparative model evaluation, the XGBoost algorithm demonstrated optimal predictive performance. Contribution analyses revealed that soil properties (Fe₂O₃, Al₂O₃, and phosphorus content), road distribution, and elevation significantly regulate heavy metal accumulation. Spatial risk mapping identified cadmium, mercury, and arsenic contamination hotspots as critical environmental threat zones. The bivariate local Moran’s index model elucidated spatial coupling characteristics between ecological risks and environmental drivers, providing spatially explicit decision-making support for precision environmental management. Our multidimensional analytical framework incorporates spatial visualization of heavy metal distribution, hierarchical ecological risk assessment, and pollution source contribution analysis, ultimately establishing a scientific decision-making system for land safety utilization and pollution risk management. This integrated approach offers methodological references for regional heavy metal pollution control in karst environments. Full article

Due to the weak electrical response characteristics of groundwater nitrate contamination, traditional monitoring and remediation assessment methods are limited by low spatiotemporal resolution, high cost, and strong subjectivity. To address this issue, this study proposed an integrated technical framework combining field detection, laboratory-controlled experiments, and remediation process monitoring, aiming to explore the application potential of Electrical Resistivity Tomography (ERT) in nitrate pollution monitoring and remediation evaluation. First, ERT survey lines (L1 and L2) were deployed at a chemical-contaminated site in Luzhou, Sichuan Province, and groundwater samples were collected. Coupled with hydrochemical analysis, the feasibility of ERT for identifying nitrate plumes was verified. Second, a quantitative response model between nitrate concentration and resistivity was established through Miller box experiments, and a multi-line layout was optimized via sand tank experiments to mitigate boundary effects and improve monitoring accuracy. Finally, grouped sand tank experiments involving electroactive bacteria (EAB) and magnetite were conducted. Combined with 16S rRNA sequencing, the coupling mechanism between ERT electrical responses and biogeochemical processes was elucidated. The results showed that the low-resistivity anomaly zones identified by field ERT were accurately consistent with the high-nitrate contamination zones, and Piper diagrams confirmed that nitrate-related ions were the primary cause of the low-resistivity anomalies. The power function quantitative model established by the Miller box experiment (y = 1021.97x^−0.74, R² = 0.9589) enabled the indirect inversion of nitrate concentrations, with a small deviation between theoretical and measured values in the deep layer (16–18 m). The optimized layout of one main and three auxiliary survey lines effectively characterized the spatiotemporal migration of the contamination plume. Under high-water level conditions, the ternary system of nitrate–magnetite–EAB exhibited the strongest low-resistivity response. Microbial analysis indicated that electroactive groups (e.g., Pseudomonas and Flavobacterium) enriched in the EAB group were the core drivers of enhanced electrical conductivity. The integrated ERT monitoring technology system constructed in this study realizes the visual identification of nitrate plumes and dynamic tracking of remediation processes, providing technical support for the precise monitoring and in situ remediation of nitrate contamination in agricultural non-point sources and industrial sites. Full article

In fire and emergency rescue operations, visibility is often severely degraded by smoke, airborne debris, or atmospheric pollutants including smog and yellow dust. Several image restoration techniques, including Dark Channel Prior (DCP), Color Attribution Prior (CAP), Peplography, and Adaptive Removal via Mask for Scatter (ARMS), have been proposed to recover clear images under such conditions. However, these methods exhibit significant limitations in heavy scattering environments. This paper proposes a novel visibility restoration method for disaster situations, building upon the state-of-the-art ARMS method. To maximize the suppression of scattering effects, the Scattering Media Model is refined through Gaussian estimation. Additionally, an overlapping matrix is introduced to effectively handle non-uniformly distributed scattering conditions. The proposed method is evaluated using a real rescue operation image dataset provided by the Fire and Disaster Management Agency of Japan. Qualitative visual assessments and quantitative performance metrics demonstrate that the proposed approach significantly outperforms conventional methods under severe scattering conditions. Full article

Duckweed/algal bloom-type black–odorous waters (DAWs) exhibit composite optical properties of vegetation and pollution, posing intractable remote sensing identification challenges in complex environments. Current methods suffer from three critical limitations: a misclassification rate exceeding 25% due to spectral confusion with artificial green covers, an 18.7% false-negative rate for small patches (stemming from the imbalance between CNNs and Transformers), and insufficient feature dimensionality to characterize the dual properties of DAWs. To address these gaps, this study proposes a novel method that integrates the ASGICTVS feature set with a customized SwinTf-Unet model. The ASGICTVS feature set combines vegetation-sensitive metrics, optical water quality indicators, and visual features. The SwinTf-Unet model utilizes an optimized 4 × 4 window, an embedded feature fusion module, and an adaptive shifted window stride to balance global context capture and local detail reconstruction. Experiments on 21,104 GF-2 satellite samples demonstrate that the method achieves 87.50% precision, 88.41% recall, an 85.32% F1-score, and an 83.46% Intersection over Union (IoU), outperforming DeepLabV3+ by 14.56 percentage points in the IoU. With an inference time of 0.87 s per 512 × 512-pixel image and a stable performance across cross-regional datasets (IoU: 82.1–85.3%), it exhibits strong efficiency and generalization. This study resolves DAW spectral confusion, enables high-precision segmentation, and establishes a standardized feature threshold system, providing reliable technical support for large-scale automated DAW monitoring and regional water environment management. Full article

Advances in plant nutrition have driven substantial progress in modern fertilization technologies. Nevertheless, excessive chemical fertilizer application, low nutrient-use efficiency, and the resulting environmental pollution remain widespread. We have reviewed the research progress and existing limitations in the field of plant nutrition and fertilization technology. Based on the traditional plant nutrition diagnosis and integrating visual diagnosis methods, this study explores the intrinsic relationship between plant growth status, nutrient supply conditions, and crop yield and proposed the concept of “status nutrition”. Variations in environmental nutrient conditions lead plants to exhibit distinct growth status in terms of vigor and phenotype. We define the plant nutritional status reflected by this growth status as “status nutrition”. Based on growth characteristics, plant growth status can be classified as weak, normal, or vigorous, corresponding to deficient, appropriate, and excessive environmental nutrient supply, respectively. Guided by this concept, an innovative rice “extremely dense planting” technology is integrated by increasing planting density, eliminating tiller-stage fertilization, and optimizing nitrogen management. The technology adapts to growth status with low nutrient demand, coordinates population growth and main-stem panicle formation, and achieves high yield with reduced fertilizer inputs. Further research is needed on the nutrient metabolism mechanisms of plants under different growth statuses and the growth status grading system. The promotion of “extremely dense planting” is constrained by crop variety traits and soil fertility, and its parameters urgently need to be optimized. Overall, the framework of “status nutrition” provides important theoretical support for the development and application of crop high-yield cultivation technologies. Full article