Top Read Articles

Published in last 1 year |

In last 2 years |

In last 3 years |

All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Time series uncertainty forecasting based on graph augmentation and attention mechanism Chaojie MEN, Jing ZHAO, Nan ZHANG J* E* C* N* U* N* S*    2025, 2025 (1): 82-96.   DOI: 10.3969/j.issn.1000-5641.2025.01.007 Abstract （813）   HTML （20）    PDF（pc） （1026KB）（1722）       Knowledge map    Save To improve the ability to predict future events and effectively address uncertainty, we propose a network architecture based on graph augmentation and attention mechanisms for uncertainty forecasting in multivariate time series. By introducing an implicit graph structure and integrating graph neural network techniques, we capture the mutual dependencies among sequences to model the interactions between time series. We utilize attention mechanisms to capture temporal patterns within the same sequence for modeling the dynamic evolution patterns of time series. We utilize the Monte Carlo dropout method to approximate model parameters and model the predicted sequences as a stochastic distribution, thus achieving accurate uncertainty forecasting in time series. The experimental results indicate that this approach maintains a high level of prediction precision while providing reliable uncertainty estimation, thus providing confidence for use in decision-making tasks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Knowledge-distillation-based lightweight crop-disease-recognition algorithm Wenjing HU, Longquan JIANG, Junlong YU, Yiqian XU, Qipeng LIU, Lei LIANG, Jiahao LI J* E* C* N* U* N* S*    2025, 2025 (1): 59-71.   DOI: 10.3969/j.issn.1000-5641.2025.01.005 Abstract （804）   HTML （23）    PDF（pc） （3454KB）（685）       Knowledge map    Save Crop diseases are one of the main factors threatening crop growth. In this regard, machine-learning algorithms can efficiently detect large-scale crop diseases and are beneficial for timely processing and improving crop yield and quality. In large-scale agricultural scenarios, owing to limitations in power supply and other conditions, the power-supply requirements of high-computing-power devices such as servers cannot be fulfilled. Most existing deep-network models require high computing power and cannot be deployed easily on low-power embedded devices, thus hindering the accurate identification and application of large-scale crop diseases. Hence, this paper proposes a lightweight crop-disease-recognition algorithm based on knowledge distillation. A student model based on a residual structure and the attention mechanism is designed and knowledge distillation is applied to complete transfer learning from the ConvNeXt model, thus achieving the lightweight model while maintaining high-precision recognition. The experimental results show that the accuracy of image classification for 39 types of crop diseases is 98.72% under a model size of 2.28 MB, which satisfies the requirement for deployment in embedded devices and indicates a practical and efficient solution for crop-disease recognition. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Knowledge graph completion by integrating textual information and graph structure information Houlong FAN, Ailian FANG, Xin LIN J* E* C* N* U* N* S*    2025, 2025 (1): 111-123.   DOI: 10.3969/j.issn.1000-5641.2025.01.009 Abstract （758）   HTML （16）    PDF（pc） （1436KB）（167）       Knowledge map    Save Based upon path query information, we propose a graph attention model that effectively integrates textual and graph structure information in knowledge graphs, thereby enhancing knowledge graph completion. For textual information, a dual-encoder based on pre-trained language models is utilized to separately obtain embedding representations of entities and path query information. Additionally, an attention mechanism is employed to aggregate path query information, which is used to capture graph structural information and update entity embeddings. The model was trained using contrastive learning and experiments were conducted on multiple knowledge graph datasets, with good results achieved in both transductive and inductive settings. These results demonstrate the advantage of combining pre-trained language models with graph neural networks to effectively capture both textual and graph structural information, thereby enhancing knowledge graph completion. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research progress on the ecological restoration and protection of the water-soil interface by periphyton bioregulation techniques Wenliang JIANG, Baicheng HU, Dan LI, Yonghong WU, Hongmei YE J* E* C* N* U* N* S*    2025, 2025 (2): 1-11.   DOI: 10.3969/j.issn.1000-5641.2025.02.001 Abstract （708）   HTML （20）    PDF（pc） （1239KB）（1902）       Knowledge map    Save The study summarized the characteristics of periphyton biology, investigating of periphyton bioregulation technology in water and soil ecological restoration and greenhouse gas regulation. The application scenarios of periphyton biocapture and bioregulation technology that utilizes the enrichment and removal characteristics of pollutants by periphyton and their mediating carbon cycle functions were expounded. The effect and function of applying this technology in enriching and removing novel environmental pollutants and resource recovery under environmental stress were discussed in depth. Its potential advantages for ecological community restoration and ecosystem regulation were clarified. Finally, constructing a systematic and integrated periphyton bioregulation technology based on spatial optimization of water-soil interface, coupling with the integration of new technologies for the enhancement of periphyton functions, was proposed, which will provide a perspective for the application of periphyton bioregulation technology in the ecological restoration of the water-soil interface. The prospects provide technical support for green and low-carbon secondary resource recycling technology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research on software classification based on the fusion of code and descriptive text Yuhang CHEN, Shizhou WANG, Zhengting TANG, Liangyu CHEN, Ningkang JIANG J* E* C* N* U* N* S*    2025, 2025 (1): 46-58.   DOI: 10.3969/j.issn.1000-5641.2025.01.004 Abstract （703）   HTML （23）    PDF（pc） （2128KB）（649）       Knowledge map    Save Third-party software systems play a significant role in modern software development. Software developers build software based on requirements by retrieving appropriate dependency libraries from third-party software repositories, effectively avoiding repetitive wheel-building operations and thus speeding up the development process. However, retrieving third-party dependency libraries can be challenging. Typically, third-party software repositories provide preset tags (categories) for software developers to search. However, when a software’s preset tags are incorrectly labeled, software developers are unable to find the libraries required, and this inevitably affects the development process. This study proposes a software clustering model to address the aforementioned challenges. The model combines method vectors, method importance, and text vectors to categorize unknown categories of software into known categories. In addition, because no publicly available dataset exists for this problem, we built a dataset and made it publicly available. This clustering model was tested on a self-built dataset comprising 30 categories and software systems from the Maven repository. The accuracy of the prediction category was 70% for one candidate (top-1) and 90% for three candidates (top-3). The experimental results show that our model can help software developers find suitable software, can be useful for classifying software systems in open-source repositories, and can assist software developers in quickly locating third-party libraries. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Purging diffusion models through CLIP based fine-tuning Ping WU, Xin LIN J* E* C* N* U* N* S*    2025, 2025 (1): 138-150.   DOI: 10.3969/j.issn.1000-5641.2025.01.011 Abstract （695）   HTML （12）    PDF（pc） （1531KB）（124）       Knowledge map    Save Diffusion models have revolutionized text-to-image synthesis, enabling users to generate high-quality and imaginative artworks from simple natural-language text prompts. Unfortunately, due to the large and unfiltered training dataset, inappropriate content such as nudity and violence can be generated from them. To deploy such models at a higher level of safety, we propose a novel method, directional contrastive language-image pre-training (CLIP) loss-based fine-tuning, dubbed as CLIF. This method utilizes directional CLIP loss to suppress the model’s inappropriate generation ability. CLIF is lightweight and immune to circumvention. To demonstrate the effectiveness of CLIF, we proposed a benchmark called categorized toxic prompts (CTP) to evaluate the ability to generate inappropriate content for text-to-image diffusion models. As shown by our experiments on CTP and common objects in context (COCO) datasets, CLIF is capable of significantly suppressing inappropriate generation while preserving the model’s ability to produce general content. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Label-perception augmented causal analysis of mental health over social media Yiping LIANG, Luwei XIAO, Linlin WANG J* E* C* N* U* N* S*    2025, 2025 (1): 124-137.   DOI: 10.3969/j.issn.1000-5641.2025.01.010 Abstract （637）   HTML （6）    PDF（pc） （1605KB）（150）       Knowledge map    Save Online social media are frequently used by people as a way of expressing their thoughts and feelings. Among the vast amounts of online posts, there may be more concerning ones expressing potential grievances and mental illnesses. Identifying these along with potential causes of mental health problems is an important task. Observing these posts, it is found that there is a label co-occurrence phenomenon in contexts, i.e., the semantics of multiple candidate labels appear in the context of one sample, which interferes with the modeling and prediction of label patterns. To mitigate the impact of this phenomenon, we propose a label-aware data augmentation method, which leverages large-scale pre-trained language models with excellent text comprehension capability to identify potential candidate labels, abates the noise from irrelevant co-occurring labels by estimating sample-independent label semantic strengths, and constructs well-performing classifiers with pre-trained language models. Extensive experiments validate the effectiveness of our model on the recent datasets Intent_SDCNL and SAD. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Water quality evaluation and spatial-temporal variation characteristics of the Taihu Lake Basin based on PCA and CCME-WQI Yaoyi LIU, Lingge ZHAI, Peng ZENG, Yifan WANG, Tian TIAN, Yue CHE J* E* C* N* U* N* S*    2025, 2025 (2): 20-33.   DOI: 10.3969/j.issn.1000-5641.2025.02.003 Abstract （630）   HTML （14）    PDF（pc） （3953KB）（968）       Knowledge map    Save Considering the advantage of the Canadian council of ministers of the environment water quality index (CCME-WQI), this study evaluated the rivers and lakes water quality in the Taihu Lake Basin from 2013 to 2017. This study aimed to devise an efficient approach for water quality assessment, providing a scientific and straightforward management tool for watershed agencies in China. Results showed that BOD5, CODCr, CODMn, and phosphorus were widespread and persistent pollution sources within the Taihu Lake Basin during the study period. Additionally, ammonia-nitrogen nutrients emerged as a potentially contributorto pollution, while heavy metals were identified as episodic pollutants. Based on the CCME-WQI scoring system, indicated “moderate” water quality for the basin. Spatial heterogeneity was observed in water quality, with “good” water quality around the South River systems and the Camps Creek. Temporal variation of water qualitywas also observed, the CCME-WQI values increases continuously over the study period. Seasonally, the highest CCME-WQI values was observed in autumn, followed by summer and winter, with the spring lowest. This study accurately evaluated the water quality of the Taihu Lake Basin using CCME-WQI method, providing a suitable and reliable tool for water quality evaluation in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Surface-height- and uncertainty-based depth estimation for Mono3D Yinshuai JI, Jinhua XU J* E* C* N* U* N* S*    2025, 2025 (1): 72-81.   DOI: 10.3969/j.issn.1000-5641.2025.01.006 Abstract （621）   HTML （17）    PDF（pc） （1215KB）（397）       Knowledge map    Save Monocular three-dimensional (3D) object detection is a fundamental but challenging task in autonomous driving and robotic navigation. Directly predicting object depth from a single image is essentially an ill-posed problem. Geometry projection is a powerful depth estimation method that infers an object’s depth from its physical and projected heights in the image plane. However, height estimation errors are amplified by the depth error. In this study, the physical and projected heights of object surface points (rather than the height of the object itself) were estimated to obtain several depth candidates. In addition, the uncertainties in the heights were estimated and the final object depth was obtained by assembling the depth predictions according to the uncertainties. Experiments demonstrated the effectiveness of the depth estimation method, which achieved state-of-the-art (SOTA) results on a monocular 3D object detection task of the KITTI dataset. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Infrared small target detection algorithm deployed on HiSilicon Hi3531 Xiaoxue FU, Chang HUANG J* E* C* N* U* N* S*    2025, 2025 (1): 151-164.   DOI: 10.3969/j.issn.1000-5641.2025.01.012 Abstract （607）   HTML （8）    PDF（pc） （1719KB）（98）       Knowledge map    Save In response to the existing shortcomings of large computational complexity, poor real-time performance, and deployment difficulties in current algorithms, and to meet the high requirements of real-time performance and accuracy for infrared detection systems, proposes a lightweight algorithm deployed on domestically produced embedded chips, termed YOLOv5-TinyHisi. The YOLOv5-TinyHisi algorithm undertakes lightweight modifications to the backbone network structure based on the characteristics of infrared small targets. Additionally, it utilizes SIoU optimized loss function for boundary error, thereby enhancing the accuracy of infrared small target localization. The YOLOv5-TinyHisi algorithm model is deployed on Hi3531DV200, utilizing the chip-integrated neural network inference engine (NNIE) to accelerate network inference. Experimental results on public datasets demonstrate that the algorithm achieves a 1.52% improvement in average precision (mAP) compared to YOLOv5, while significantly reducing parameter count and model size. On the Hi3531DV200, the inference speed for a single image with a resolution of (1280 × 512) pixels reaches 35 frames per second (FPS), with a recall rate of 95%, meeting the real-time and accuracy requirements of the infrared detection system. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Rule extraction and reasoning for fusing relation and structure encoding Jimi HU, Weibing WAN, Feng CHENG, Yuming ZHAO J* E* C* N* U* N* S*    2025, 2025 (1): 97-110.   DOI: 10.3969/j.issn.1000-5641.2025.01.008 Abstract （591）   HTML （14）    PDF（pc） （2201KB）（641）       Knowledge map    Save The domain knowledge graph exhibits characteristics of incompleteness and semantic complexity, which lead to shortcomings in the extraction and selection of rules, thereby affecting its inferential capabilities. A rule extraction model that integrates relationship and structural encoding is proposed to address this issue. A multidimensional embedding approach is achieved by extracting relational and structural information from the target subgraph and conducting feature encoding. A self-attention mechanism is designed to integrate relational and structural information, enabling the model to capture dependency relationships and local structural information in the input sequence better. This enhancement improves the understanding and expressive capabilities of context of the model, thus addressing the challenges of rule extraction and selection in the complex semantic situations. The experimental results for actual industrial datasets of automotive component failures and public datasets demonstrate improvements in the proposed model for link prediction and rule quality evaluation tasks. When the rule length is 3, an average increase of 7.1 percentage points in the mean reciprocal rank (MRR) and an average increase of 8.6 percentage points in Hits@10 are observed. For a rule length of 2, an average increase of 7.4 percentage points in MRR and an average increase of 3.9 percentage points in Hits@10 are observed. This confirms the effectiveness of relational and structural information in rule extraction and inference. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research progress on the impact of connectivity restoration and ecological water replenishment on river-lake water ecosystem Haiyao QIU, Yan HE, Yehong FAN, Minsheng HUANG, Chengjin CAO, Peimin HE, Bingbing XU, Wenhui HE J* E* C* N* U* N* S*    2025, 2025 (2): 12-19.   DOI: 10.3969/j.issn.1000-5641.2025.02.002 Abstract （579）   HTML （23）    PDF（pc） （800KB）（1123）       Knowledge map    Save Connectivity restoration and ecological water replenishment aim to enhance hydrological connectivity, thereby strengthening water exchange and purifying water quality through increased water mobility and water reoxygenation capacity. This study introduces the impact of connectivity restoration and ecological water replenishment on the ecosystem of various river-lake water systems, examining four aspects, including water quality, water dynamics, plant life, and animal population. The study identifies several challenges that require resolution in the connectivity of river-lake systems and proposes recommendations for future research to address these issues effectively. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Fa-Weyl’s theorem and a-Weyl’s theorem for bounded linear operators Simeng LI, Ye ZHANG, Xiaohong CAO J* E* C* N* U* N* S*    2025, 2025 (1): 13-27.   DOI: 10.3969/j.issn.1000-5641.2025.01.002 Abstract （572）   HTML （15）    PDF（pc） （925KB）（241）       Knowledge map    Save Both Fa-Weyl’s theorem and a-Weyl’s theorem are the variants of Weyl’s theorem. The study of Weyl’s type theorems is very important for spectral theory. By defining a new spectral set in this paper, sufficient and necessary conditions for a bounded linear operator $T $ definded on a Hilbert space to satisfy the Fa-Weyl’s theorem and the a-Weyl’s theorem are established. In addition, we discuss the Fa-Weyl’s theorem and the a-Weyl’s theorem of bounded linear operator $T $ under a finite rank perturbation. Reference | Related Articles | Metrics | Comments（0）

Select

Pullback attractors for the classical reaction-diffusion equation with time-dependent memory kernel Yuna LI, Xuan WANG J* E* C* N* U* N* S*    2025, 2025 (1): 28-45.   DOI: 10.3969/j.issn.1000-5641.2025.01.003 Abstract （550）   HTML （30）    PDF（pc） （871KB）（386）       Knowledge map    Save This paper presents a discussion on the long-time dynamical behavior of solutions for the classical reaction-diffusion equation with time-dependent memory kernel when nonlinear term adheres to subcritical growth and the external force term $g(x,t) $ belongs to the space $ L^{2}_{{\mathrm{loc}}}(\mathbb{R};L^{2}(\varOmega)) $ in the time-dependent space $ L^2(\varOmega)\times L_{\mu_{t}}^2(\mathbb{R}_{+}; H_{0}^1(\varOmega)) $. Within the new theorical framework, the well-posedness and the regularity of the solution, as well as the existence of the time-dependent pullback attractors are established. This is achieved by applying the delicate integral estimation method and decomposition techniques. Reference | Related Articles | Metrics | Comments（0）

Select

Dynamic characteristics and degradation transformation mechanism of dissolved organic matter in estuarine wetlands: A case study of Chongming Dongtan salt marsh wetland in Shanghai Run LI, Jienan CHEN, Hao CHEN, Fang CAO J* E* C* N* U* N* S*    2025, 2025 (2): 55-67.   DOI: 10.3969/j.issn.1000-5641.2025.02.006 Abstract （544）   HTML （15）    PDF（pc） （1507KB）（1215）       Knowledge map    Save Estuarine wetlands are important components of coastal ecosystems that contribute tremendously to global blue carbon sinks. The amount and quality of dissolved organic matter (DOM) vary significantly with tidal exchange and seasonal cycles. Focusing on the Chongming Dongtan salt marshes in Shanghai under the influence of the Yangtze River, we conducted high-frequency sampling across a complete tidal cycle in each season and characterized the dynamics of the quantity (expressed by the C content in DOM, i.e., the concentration of dissolved organic carbon (DOC)) and spectral characteristics (expressed by the light absorption characteristics of chromophoric DOM (CDOM)) of DOM across tidal and seasonal scales. The results indicated that on the tidal scale, waters leaving the marshes during the ebbing tide were rich in DOC, strong in optical absorbance (${a_{{\rm{CDOM}}}} $(350) and ${a_{{\rm{CDOM}}}^*} $(350)), with high aromaticity (specific ultraviolet absorbance, SUVA254) and low spectral slope (S275-295), compared to water entering the marshes during the flooding tide. On a seasonal scale, waters in the ebbing tide during summer and fall had elevated DOC concentrations, high absorption and aromaticity, and correspondingly lower spectral slopes relative to waters collected in winter and spring. The results of on-site incubation experiments demonstrated that photochemical degradation was the major process that removed the colored fraction from the DOM pool, whereas microbial processing played an important role in affecting the bulk DOM. This study helps improve our understanding of the dynamics of marsh DOM and the mechanisms of its degradation processes associated with lateral transport from estuarine marshes to adjacent estuaries. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Response characteristics of the evolution of Chongming Dongtan wetland in the Yangtze River Estuary to sea level rise Yating HOU, Weiming XIE, Jiaheng YUAN, Xianye WANG J* E* C* N* U* N* S*    2025, 2025 (2): 42-54.   DOI: 10.3969/j.issn.1000-5641.2025.02.005 Abstract （529）   HTML （11）    PDF（pc） （2974KB）（465）       Knowledge map    Save The SLAMM (Sea Level Affecting Marshes Model) is employed to simulate a wetland’s evolution by considering scenarios of sea level rise and whether the wetland is protected by seawalls. This study investigates the effects of tidal range, slope, and land subsidence on wetland stability. The findings are as follows: ① Chongming Dongtan wetland will be reduced in size in future under the influence of sea level rise. From 2020 to 2050, its wetland area retention rate will be 0.732 ~ 0.763. In addition, the Chongming Dongtan wetland is expected to begin shrinking as early as 2039. ② The seawall in the restored area of Chongming Dongtan wetland can prevent reverse succession and transitional salt marsh during the response to sea level rise; ③ The changes in tidal range, slope, and land subsidence can affect the stability of the Chongming Dongtan wetland. If the goal is to stabilize Chongming Dongtan wetland by 2050, the tidal range must reach at least 3.0 meters or the slope of the profile must be above 2.06‰. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Exploring initial rainwater collection methods in a chemical industrial park based on water quality objectives Zhaokang WU, Qinglai SUN, Yuji JI, Ziling DAI, Qixin XU, Sheng XIE, Kai YANG J* E* C* N* U* N* S*    2025, 2025 (2): 121-131.   DOI: 10.3969/j.issn.1000-5641.2025.02.012 Abstract （512）   HTML （4）    PDF（pc） （1568KB）（35）       Knowledge map    Save Currently, industrial enterprises primarily utilize two initial rainwater collection methods based on time (first 15 ~ 30 min) and depth (first 20 ~ 30 mm) according to the relevant specifications and standards. However, online monitoring and control of rainwater discharge water quality in chemical industrial parks is relatively weak and there is limited research on the pollution control effects of different initial rainwater collection methods. This study utilized monitoring data from rainwater outlets in a Shanghai chemical industrial park to investigate the pollution risks associated with different initial rainwater collection methods and align them with the requirements for pollution emission load control and water quality compliance. The cumulative distribution curve of the runoff pollution load was used to examine the response of key water quality indicators to rainfall. This study discusses variations in different initial rainwater collection and management methods in chemical industrial parks from the perspective of pollution reduction. Chemical companies generally adopt time-based initial rainwater collection for operational convenience during continuous heavy rainfall, with its rainwater pollution control effect being relatively limited. Therefore, it is recommended that chemical industrial parks adopt depth-based collection methods tailored to rainfall characteristics to effectively reduce rainwater pollution loads and mitigate pollution risks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Performance evaluation system of urban wastewater treatment plants based on ESG + E Chenxi YU, Xiangyu ZHANG, Zheng WEI, Jianbo LIN, Yan HE J* E* C* N* U* N* S*    2025, 2025 (2): 132-140.   DOI: 10.3969/j.issn.1000-5641.2025.02.013 Abstract （511）   HTML （4）    PDF（pc） （879KB）（39）       Knowledge map    Save This study developed an evaluation system for urban wastewater treatment plants (WWTPs) based on Environment, Social, Governance, and Economy (ESG + E), encompassing four dimensions: environmental impact, social responsibility, corporate governance, and economic benefits. Seven detailed criteria and twenty evaluation indicators were developed accordingly. The analytic hierarchy process method and fuzzy comprehensive evaluation method were used to create the evaluation model, which was then used to evaluate the overall performance of three urban WWTPs in Shanghai. The results showed that WWTP1 and WWTP3 performed exceptionally well, while WWTP2 performed admirably. Recommendations for WWTP2 include improving resource utilization, diversifying employee training, and strengthening disclosure systems. The evaluation results were consistent with the WWTPs’ actual operational status, demonstrating the accuracy and scientificity of the evaluation system. This study provides a useful reference for the performance evaluation of urban WWTPs. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Ergodicity for population dynamics driven by a class of $\alpha $ -stable process with negative jumps Jinying TONG, Ziyi LIANG, Wenze CHEN, Zhenzhong ZHANG, Xin ZHAO J* E* C* N* U* N* S*    2025, 2025 (1): 1-12.   DOI: 10.3969/j.issn.1000-5641.2025.01.001 Abstract （495）   HTML （25）    PDF（pc） （632KB）（339）       Knowledge map    Save In order to characterize that stochastic environment, we consider a class facultative population systems driven by Markov chains and pure-jump stable processes with negative jumps. To begin with, the existence and uniqueness for global positive solution is proved for our model. Then, some sufficient conditions for stationary distribution are provided. Reference | Related Articles | Metrics | Comments（0）

Select

Factors influencing heavy metal pollution in open-pit coal mine dumps Hai NIU, Yupeng HUA, Lijie MA, Shuo ZHANG, Ruiyang GUAN, Min SHENG, Wenyan DUAN J* E* C* N* U* N* S*    2025, 2025 (2): 106-120.   DOI: 10.3969/j.issn.1000-5641.2025.02.011 Abstract （471）   HTML （6）    PDF（pc） （1844KB）（289）       Knowledge map    Save In this study, two open-pit coal mine dumps in Ordos City, Inner Mongolia, namely, Shigetai and Heidaigou, were selected as research subjects. Soil physicochemical properties, structure, heavy metal pollution (assessed using a single-factor evaluation index), and their interrelationships were analyzed at each dumping site along eight directions: east, west, south, north, northeast, northwest, southeast, and southwest. The aim of this study was to elucidate the factors influencing heavy metal contamination in open-pit coal mine dumps. The results of this study can guide the selection of ecological remediation measures for mining areas based on local conditions. The results showed that: (1) There were considerable differences in soil physicochemical properties, structure, and heavy metal contamination between the Shigetai and Heidaigou mining areas. (2) Soil total phosphorus varied considerably among the different orientations in Shigetai, whereas there was a considerable difference in soil total potassium among the different orientations in Heidaigou. (3) Heavy Cd pollution was observed, and there was slight Mn pollution in Shigetai. In Heidaigou, there was heavy Cd pollution and slight Cu and Ni pollution. (4) While there was a considerable difference in the level of Cd pollution along the different directions in Shigetai, the level of Mn pollution showed no correlation with direction. There was a considerable difference in Ni pollution levels among the different orientations in Heidaigou, whereas Cd and Cu pollution levels were not related to orientation. (5) The trends in the Cd pollution levels in Shigetai and Ni in Heidaigou along the different directions were generally consistent with the changes in their topography. In other words, areas with lower topography experienced more severe heavy metal pollution. (6) Correlation analysis revealed that the level of heavy metal contamination was strongly associated with soil pH, total K content, and the proportion of large-sized soil particles. In conclusion, varying levels of heavy metal pollution were observed in the different mining dumps. Significant correlations were observed between three soil environmental factors, namely, the topography of the dump, soil fertility, soil structure, and the level of soil heavy metal pollution. Table and Figures | Reference | Related Articles | Metrics | Comments（0）