Search Results (342)

To clarify the pollution characteristics and source composition of heavy metals in the paddy soils of the Yellow River irrigation district of Ningxia, a total of 515 surface soil samples were collected, and the concentrations of As, Hg, Pb, Cd, and Cr were measured. Regional-scale pollution assessment and source apportionment were conducted using the geo-accumulation index, spatial interpolation analysis, and the Positive matrix factorization (PMF) model. Based on the regional pollution assessment and spatial distribution patterns, a representative area with relatively elevated Cd accumulation and more pronounced anthropogenic influence was selected for local-scale isotope investigation. Eight paddy soil samples and potential end-member samples were collected, and, combined with literature-based end-member data, Pb and Cd isotopes were analyzed using the MixSIAR Bayesian (version 3.1.12) mixing model to further constrain the sources of Pb and Cd in village soils. The results showed that some sampling points in the study area exceeded the soil background values, but none of the points surpassed the screening values for agricultural soil pollution risk, indicating that the overall risk of paddy soils in the study area remained low. Geo-accumulation index results indicated that As, Pb, and Cr were predominantly classified as unpolluted, whereas Hg and Cd showed more pronounced accumulation, with most sampling points reaching unpolluted to moderately polluted or higher. PMF results revealed that heavy metals in the study area primarily originated from natural sources, agricultural activities, coal combustion-related sources, and industrial–traffic mixed sources. Cd was mainly influenced by agricultural sources, Hg was primarily affected by coal combustion and related industrial activities, and Pb exhibited a mixture of multiple sources. Local isotope analysis in the representative area further indicated that industrial and agricultural sources were the main contributors to soil Cd, accounting for 34.3% and 33.1%, respectively. Pb was primarily derived from agricultural activities (38.1%), while coal emissions, industrial sources, natural sources, and traffic contributed 19.2%, 18.3%, 16.9%, and 7.5%, respectively, indicating a complex mixture of agricultural, industrial, coal-combustion, natural, and traffic-related inputs. The combined application of PMF and Pb/Cd isotopes allowed for constraints on heavy metal sources at both regional and local scales, providing a scientific basis for pollution control and agricultural safety management in paddy soils of the Yellow River irrigation district. Full article

Fresh agricultural product live-streaming involves multi-objective operational trade-offs rather than a simple channel choice problem. Farmers, influencers, and platforms must balance demand expansion and freshness assurance against pricing pressure, selling effort cost, preservation cost, revenue sharing, and platform fees. We examine a fresh agricultural product live-streaming supply chain involving a farmer, an influencer, and a platform under three modes: farmer self-streaming (FS), influencer streaming (IS), and farmer–influencer hybrid streaming (FIS). We develop game-theoretic models to jointly analyze pricing, selling effort, preservation effort, traffic spillover, and payoff allocation, and derive equilibrium decisions through analytical optimization and backward induction. The results show that the FS and IS modes follow different value-conversion logics: the FS mode relies more on farmer-channel information value, while the IS mode relies more on influencer-channel traffic value. The FIS mode combines these two value sources, but its advantage is not merely due to channel addition. A no-spillover comparison shows that the FIS mode creates value through channel addition and traffic-spillover-based synergy. The results also reveal threshold-like amplification in freshness-value realization within the modeled parameter environment: preservation investment becomes more valuable when preservation efficiency, consumer freshness sensitivity, demand scale, and channel complementarity are relatively strong. These findings guide live-streaming mode selection, traffic collaboration, and preservation investment. Full article

The present study investigates the presence, sources, and impacts of microplastics (MPs) in different soil types, including agricultural, urban, and forest areas, through a synthesis of results of published scientific papers. MPs originate from a variety of human activities, such as the widespread use of plastic mulch in agriculture and the application of organic fertilizers and treated sewage sludge, as well as from vehicle tire wear, industrial processes, and the gradual degradation of plastic products in the environment. In urban soils, the main sources of MPs are related to road traffic, industrial activity, and landfills, while in forest soils, concentrations are generally lower. However, MPs in forest areas are thought to be carried there by the air, by runoff, or from nearby areas with human activity. Available data show that larger MP particles tend to remain in the surface layers of the soil, while smaller particles can penetrate deeper soil layers, increasing their bioavailability and the likelihood of interaction with microorganisms and plant root systems. In terms of their chemical composition, polyethylene (PE) and polypropylene (PP) polymers dominate in agricultural soils, which is directly linked to agricultural practices, while polystyrene (PS) and polyvinyl chloride (PVC) are more frequently detected in urban soils. The morphological types of MPs include fragments, fibers, and films, while their color characteristics provide clues to possible sources of origin, such as plastic ground covers, tire wear, and packaging materials. Overall, the study’s results underscore the growing environmental significance of MP soil pollution and highlight the need for more effective management and recycling of plastic materials, as well as for further interdisciplinary research aimed at understanding the mechanisms of transport, accumulation, and long-term ecological effects of microplastics in terrestrial ecosystems. Full article

Polymetallic mining severely disrupts farmland soil ecosystems, yet the vertical migration of heavy metals, interlayer pollution disparities between topsoil and deep soil, and quantitative source apportionment of composite pollutants remain poorly understood in mining–agricultural overlapping zones. Two core hypotheses were accordingly proposed: mining-derived heavy metals can migrate downward and accumulate in deep soil layers, and the coupling of geostatistical analysis and receptor modeling enables reliable differentiation between geogenic and anthropogenic pollution sources. To test these hypotheses, 512 topsoil and 148 deep soil samples were collected from the Fenghuang Mining Area for quantification of eight metals and metalloids (including As). Geostatistical approaches, the single pollution index (Pi), and Nemerow comprehensive pollution index (P_N) were utilized to characterize spatial heterogeneity and evaluate pollution severity, while a coupled PCA–PMF receptor model was adopted for quantitative source identification; vertical comparisons of element concentrations across soil profiles further validated the robustness of source apportionment outputs. The results revealed extensive heavy metal enrichment in both soil layers, with only topsoil Cd exceeding China’s risk screening value for agricultural land. Hg exhibited pronounced spatial variability and prominent anthropogenic fingerprints, and all target metals displayed consistent spatial distribution patterns along vertical soil profiles. Four distinct pollution sources were discriminated: geogenic sources dominating Cu, Zn, Cr, and Ni accumulation, mining-industrial emissions as the major contributor to Hg pollution, mixed industrial–agricultural inputs governing As and Pb enrichment, and traffic activities serving as the primary Cd source. Cd was identified as the priority pollutant threatening local farmland security. Confirmed downward percolation of anthropogenic metals creates persistent latent ecological risks across the study area, where mining and industrial discharges represent the dominant anthropogenic pollution inputs. This work systematically elucidates the geochemical signatures, vertical migration pathways, and quantitative source contributions of heavy metals in mining-disturbed farmlands, delivering solid scientific support for targeted source control, tiered risk management, and soil ecological remediation within the Fenghuang Mining Area. Moreover, the multi-method integrated analytical framework developed herein provides transferable guidance for heavy metal pollution mitigation in global polymetallic mining–agricultural regions with analogous geological and industrial backgrounds. Full article

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

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

Polycyclic aromatic hydrocarbons (PAHs) in farmland soils pose potential ecological and human health risks, yet their contamination characteristics and source-related risks in farmland soils across different river basins in China remain insufficiently understood. This present study analyzed 84 farmland soil samples from northeast (primarily the middle and lower reaches of the Songhua River and Liao River basin), central (primarily the middle reaches of the Yellow River basin and Dongting Lake system), northwest (primarily the middle and upper reaches of the Yellow River and Yarlung Zangbo River basin), and southern (primarily the upper reaches of the Pearl River and Yangtze River basin) China in order to assess the contamination characteristics, sources, ecological risks, and human health risks associated with 16 US EPA priority PAHs in the samples. The findings suggest that the 16 aggregate PAHs’ concentrations in Chinese farmland soils varied from 63.9 to 9637.7 μg/kg, with an average of 1919.3 μg/kg. A gradual decline was observed from north to south, with dibenz[a,h]anthracene (DahA) accounting for the highest proportion at 14.3%. Correlation analysis, principal component analysis, and positive matrix factorization jointly indicated that fossil fuel combustion, high-temperature combustion, and traffic-related emissions were the main PAH inputs to farmland soils. The results of the ecological risk assessment indicated that the northeastern region exhibited the highest PAH ecological risk, with 41.2% of sample plots demonstrating severe PAH contamination. Conversely, the southern region exhibited the lowest PAH ecological risk, with 73.9% of the sample plots demonstrating no ecological risk. The human health risk assessment found that non-carcinogenic risks for both children and adults were within safe limits, while carcinogenic risks for both groups were relatively high. DahA was identified as the primary carcinogen, accounting for 45.9% and 70.3% of the total carcinogenic risk for children and adults, respectively. Oral ingestion was the primary route of exposure. This study provides an integrated basin-scale assessment of PAH contamination and source-related risks in Chinese farmland soils, supporting targeted management of PAH inputs in agricultural 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

The quantitative identification of heavy metal sources is essential to clarify their relationships with ecological and health risks. This study focused on the Manghe Watershed in Jiyuan City, Henan Province, China, integrating the Positive Matrix Factorization (PMF) model, ecological risk index (RI), and health risk assessment (HRA) to construct a coupled PMF-RI/PMF-HRA framework to quantify source-specific risk contributions and propose targeted mitigation strategies. Key findings included: (1) Among the 121 surface soil samples, Cr and Ni showed natural background levels, while Cd, Pb, Hg, Zn, As, and Cu exceeded regional backgrounds by 1.63–33.65 times with anthropogenic-driven spatial heterogeneity. (2) The PMF identified four sources: natural–agriculture mixed (42.65%), the main contributor to Cr, Ni, As, and Cu; industrial activity (24.99%), the primary source of Cd and Zn; traffic–agriculture mixed (20.99%), primarily emitting Pb and As; and coal combustion (11.36%), dominating Hg emissions. (3) Ecological and health risks were governed by heavy metal toxicity and exposure pathways rather than mere concentration levels. Specifically, industrial sources (Cd, Zn) should be prioritized for ecological risk control, whereas natural–agricultural mixed sources (As, Pb, Cr) should be prioritized for health risk control. Oral ingestion was the dominant exposure pathway for both non-carcinogenic risk and carcinogenic risk in children, with the natural–agricultural mixed source contributing the most to this pathway. (4) The total carcinogenic risk (TCR) for children was 1.17 × 10⁻⁴, which exceeds the commonly accepted unacceptable threshold of 1 × 10⁻⁴, indicating a potential carcinogenic concern. (5) The PMF-RI and PMF-HRA frameworks quantitatively proved that the main sources of ecological risks and health risks may be completely different, and this phenomenon was jointly regulated by the toxicity response coefficient and exposure pathways. A “source–risk-pathway” quantitative attribution was achieved and provides clear support for targeted interventions, emphasizing source control for industrial emissions (Cd-Zn), traffic–agriculture inputs (Pb-As), and coal-derived Hg, alongside optimized agricultural practices. Full article

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions. Full article

Shallow groundwater in the Dongting Lake area is an important resource for domestic, agricultural, and industrial use, and its quality is essential for regional sustainable development and public health. Therefore, effective protection of this resource is urgently needed. In this paper, we integrate Positive Matrix Factorization (PMF) and Self-Organizing Map (SOM) machine-learning algorithms to conduct an in-depth analysis of the distribution, sources, and risks of toxic elements in shallow groundwater along the southern shore of Dongting Lake. The results indicate that Fe and Mn in the groundwater of the study area are at a severe pollution level, while As is at a light pollution level. The model analysis identified four pollution sources: natural sources (Fe, Mn) accounting for 31.33%, agricultural production (Zn) for 18.96%, traffic-mining mixed source (Pb, Cu, Cd) for 32.67%, and mineral dissolution-redox driven (As) for 17.04%. The average concentrations of Fe and Mn exceeded the standard limits. Although the carcinogenic metal Cd did not pose a health risk, the health risk value of As exceeded the maximum acceptable level, which requires serious attention. The PMF model quantified four potential sources of toxic elements, while SOM was used as a complementary nonlinear clustering tool to examine the consistency of the PMF-derived source contribution patterns. The integrated PMF–SOM framework, together with spatial distribution and geochemical evidence, improved the interpretability and robustness of source identification. Full article

Unmanned Aerial Vehicle (UAV) clusters have broad applications in agricultural detection, traffic control, and disaster rescue, where navigation and positioning serve as the core technology. However, satellite navigation fails to meet the requirements of region-wide navigation due to the urban canyon effect. Although the China Satellite Network (CSN) boasts advantages such as high landing power and low latency, it can only achieve single-link communication. Consequently, exploring how to realize cooperative positioning via UAV clusters has become an urgent research need. In this paper, an Unmanned Aerial Vehicle Cluster Communication–Navigation Integrated Cooperative Positioning (UCNCP) algorithm is proposed. This algorithm combines the communication and navigation characteristics of the CSN, establishes a single pseudorange measurement model and cluster geometric topology, and constructs an architecture for cooperative positioning based on UAV cluster pseudorange measurements and inter-UAV ranging data, thereby achieving reliable navigation and positioning of UAV clusters. Comparative experiments between the proposed method and other low-orbit satellite positioning methods demonstrate that the UCNCP algorithm exhibits higher positioning stability. When abrupt changes occur in navigation information, it can effectively mitigate the impact of abrupt change errors on positioning accuracy, improving the positioning stability of UAV clusters by more than 30%. Full article

Change detection is a fundamental task in remote sensing with broad applications in urban monitoring, agriculture, watershed management, and land use and land cover analysis. In urban environments, accurate change detection is particularly critical for resource management, urban planning, and smart city development. Rapid urbanization has led to frequent and complex changes in buildings, which constitute key structural components of cities. Consequently, continuous and precise monitoring of building dynamics is essential for informed decision-making related to urban growth, environmental assessment, traffic management, and sustainable development. This paper presents a comprehensive review of two-dimensional (2D) and three-dimensional (3D) change detection methods applied to urban areas. Conventional and advanced approaches are systematically analyzed, and their strengths and limitations are critically discussed from a holistic perspective. Special emphasis is placed on recent learning-based techniques, which demonstrate enhanced robustness and accuracy in complex urban environments. Finally, current challenges and future research directions are identified to support the further development of effective 2D and 3D urban change detection methods. Full article

The study investigates the spatial variability of the cone index (CI) and its implications for vehicle mobility across two contrasting regions in Poland: the Suwalki Gap and Garwolin County. Background motivation stems from the need to assess off-road trafficability for agricultural, forestry, and other vehicles operating on soils whose strength varies seasonally and spatially. Using 230 penetrometric measurements collected with an electronic Penetrologger equipped with a soil moisture sensor, CI values were recorded to a depth of 80 cm and supported with soil–agricultural maps and Sentinel-2 land-cover data. Results demonstrate clear relationships between CI, soil moisture, land cover, soil type, and depth. Wetlands exhibited consistently low CI (<1 MPa), while agricultural, artificial, and forested areas showed increasing resistance with depth, surpassing 2 MPa in deeper layers. Seasonal differences were pronounced: summer drying increased surface CI, whereas autumn profiles were generally softer but more uniform. Regression analysis confirmed a strong negative correlation between soil moisture and CI, particularly below 20 cm. Comparative assessment with vehicle cone index thresholds indicates that most terrains are suitable for heavy vehicles, except saturated wetlands, which pose significant trafficability constraints. The findings emphasize the importance of depth-specific CI assessment, the strong influence of local soil disturbances, and the need for high-density measurements to support real-time mobility modelling for agricultural and crisis-management applications. Full article

Environmental contamination with potentially toxic elements is a growing concern for ecosystem quality and food safety. This study evaluated the relationships between environmental conditions, anthropogenic activities, and the elemental composition of Prunus spinosa fruits collected from western and central Romania along a pollution gradient. Eighty samples from ten sites representing non-polluted, agricultural, traffic-exposed, and mining-affected areas were analyzed by ICP-MS after microwave digestion. Fruits from impacted areas showed compositional differences, including lower concentrations of some essential macroelements and higher levels of several trace elements potentially associated with anthropogenic pressure. Increased sodium, aluminum, and silicon contents were consistent with environmental stress and enhanced environmental exposure and possible soil-derived particulate influence, while boron and molybdenum declined with pollution intensity. Elemental patterns were mainly associated with local environmental conditions and appeared consistent with site-specific environmental influences. Food safety assessment indicated generally low to moderate risk depending on sampling origin. Overall, Prunus spinosa fruits showed potential as a bioindicator of environmental quality and a useful tool for monitoring anthropogenic contamination. Full article