Exploring soil heavy metal sources is of great significance for ensuring the safety of ecological environments and agricultural product safety, as well as for guiding pollution control and management policies. This paper retrieved 452 research papers on soil heavy metal source analysis published over the 2004–2024 period from the Web of Science database. The collected literature was subjected to multidimensional bibliometric analysis using the CiteSpace 6.3.R1. The results showed significantly increasing trends in the scientific outputs and the number of papers on heavy metal source analysis in soils and water over the study period. In addition, related research topics have expanded from single to multiple heavy metal elements in environmental media and have increasingly recognized the impact of water pollution on soil contamination. Research methods have also evolved from basic statistical analysis to complex spatial analysis techniques, covering agricultural and urban soils. Previous related studies have focused on heavy metal pollution in different areas, and related research on heavy metal source analysis has now extended from ecological environments to associated human health risks. The present study provides directions for future related research and guidance for ensuring effective source control of heavy metal pollution and safe utilization of land and water resources.

The potentially toxic element pollution of agricultural soils has become a significant environmental threat to food safety and human health. Accurately identifying sources of potentially toxic element pollution is key to developing effective pollution prevention and control measures. In this study, regional potentially toxic element pollution of the soils in the Nanliujiang River Basin was analyzed using the positive matrix factorization (PMF) model and the geo-detector model. First, topsoil samples from the study area were collected to analyze eight potentially toxic elements in the soil, including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The PMF model was used to conduct source apportionment of the potentially toxic element data and identify the primary pollution sources and their contribution rates. Then, the geo-detector model was used to analyze the key factors affecting the spatial distribution of the potentially toxic elements and the influence of natural and human factors on the distribution of the potentially toxic elements. There are four potentially toxic element pollution sources of the agricultural soil in the study area: geological background, agricultural activities, industrial discharge, and river irrigation. The geological background contributed the most. The main factors affecting the spatial distribution of potentially toxic elements included agricultural activities, industrial discharge, and river irrigation. This integrated method can analyze the formation of potentially toxic element pollution in depth from the perspectives of source apportionment and spatial differentiation and provide a scientific basis and decision support for preventing and controlling potentially toxic element pollution in agricultural soils. This study provides a new method and scientific basis for identifying and preventing potentially toxic element pollution sources in agricultural soil and can guide the formulation of targeted soil pollution control measures.

This study conducts a comprehensive bibliometric analysis of literature from 2000 to 2025, aiming to map the intellectual landscape and evolving trends in research on heavy metal transport within watershed ecosystems. By leveraging the Citespace literature visualization tool, we analyzed publication trends, intellectual bases, and, most importantly, the dynamic shifts in research fronts through keyword co-occurrence and clustering analysis. The findings reveal a clear trajectory from basic geochemical theories to specific applications, characterized by three prominent themes: (1) the evolution of pollution source tracking from single-method tracing to coupled multi-method quantitative modeling; (2) the establishment of a comprehensive risk evaluation chain spanning regional assessments to targeted analyses of sensitive receptors; and (3) the analysis indicates that the current research on heavy metal transport in watershed environments remains somewhat fragmented, with limited cross-comparative synthesis across different metal species and watershed contexts, and uneven progress in applying advanced data-driven and multi-model approaches. Addressing these issues is crucial for enhancing the predictive power of models and formulating effective strategies. This study thus provides a detailed overview of the field’s development while highlighting critical pathways for future research to strengthen the scientific foundation for preventing and controlling heavy metal pollution in watershed ecosystems.

Agricultural carbon emissions represent a major source of greenhouse gases and play a critical role in achieving global climate mitigation and sustainable agricultural development targets. In China, the rapid transformation of agricultural production systems has led to substantial spatial heterogeneity in emission patterns and driving mechanisms of agricultural carbon emissions, while the underlying processes at the county scale remain insufficiently understood. This study investigated the spatiotemporal evolution and potential influencing factors of agricultural carbon emissions at the county level from 2000 to 2022 in Guangdong Province, China. First, agricultural carbon emissions were estimated based on a multi-source accounting framework covering land management, crop cultivation, animal production, and straw burning based on internationally recognized emission accounting methods and IPCC global warming potentials. Then, spatial clustering characteristics were analyzed using local spatial autocorrelation (LISA) to identify heterogeneous emission patterns. Finally, an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) was employed to quantify the nonlinear effects and relative contributions of multiple socioeconomic and agricultural drivers. The results showed that agricultural carbon emissions in Guangdong Province exhibited a fluctuating but overall decreasing trend, declining from 50.89 Mt CO₂-eq in 2000 to 39.24 Mt CO₂-eq in 2022, with an overall reduction of 22.9%. High-emission clusters were primarily concentrated in western and northern Guangdong, while low-emission areas were mainly located in the Pearl River Delta (PRD). The RF models demonstrated satisfactory predictive performance, with spatial cross-validated R² values ranging from 0.75 to 0.91 across different years. SHAP analysis suggested that ploughing area, fertilizer and pesticide usage, agricultural machinery power, and primary industry GDP were the dominant factors associated with agricultural carbon emissions, whereas urbanization consistently showed a negative association. Furthermore, these drivers exhibited pronounced nonlinear responses and distinct regional heterogeneity, particularly between the PRD and the western and northern parts of Guangdong Province. These findings suggested that agricultural carbon emissions are jointly influenced by agricultural production intensity, mechanization, and socioeconomic transition and can provide a scientific basis for developing region-specific low-carbon agricultural policies and promoting the sustainable transformation of agricultural systems.

Potentially toxic element pollution caused by coal mining activities, especially the accumulation of cadmium, has become a major threat to the global environment and health. Long-term mining activities in China, a major coal consumer, caused a large accumulation of coal gangue. Gangue weathering and leaching release Cd, which threatens the ecological safety of the surrounding soil and water bodies. Although the government has implemented ecological restoration projects in the mining areas, there is still a lack of systematic evaluation of pollution control of downstream farmlands. For this study, remote sensing analyses of fractional vegetation coverage (FVC), geo-accumulation index (I_geo), and potential ecological risk index (EI) data, as well as the pollution characteristics and ecological risks of Cd, were evaluated for a coal mining area in Jiangxi Province. Coal gangue, restoration cover soil, downstream farmland soil, irrigation water, and sediment samples were used in the analyses. After restoration, the Cd concentration in the mining cover soil (0.23 mg/kg) was significantly lower than that of the coal gangue (1.18 mg/kg), while the Cd concentration in the downstream farmland soil (0.44 mg/kg) was roughly an average of the two. The geo-accumulation index indicates that the farmland soil is mainly unpolluted (with an average I_geo of −0.25). However, some points have reached the level of no pollution to moderate pollution. Coal gangue poses a relatively high ecological risk (with an average EI of 118), while cover soil and farmland soil pose low risks (with an average EI of 22.5 and 39.86, respectively). The restoration project significantly reduced the Cd input in the downstream farmlands. The study revealed the effective blocking of external soil cover on Cd migration, providing a key scientific basis for the optimization of ecological restoration strategy and risk prevention and control in similar mining areas worldwide.

Watershed-scale simulation of heavy metal transport and transformation is an important tool for pollution source identification, environmental risk assessment, and watershed management decision-making. Existing hydrological and water quality models provide essential process-based foundations for heavy metal transport simulation, including runoff generation, soil erosion, sediment transport, and channel routing. However, limitations remain in the coupling of geochemical reactions, representation of metal speciation, response to extreme events, and separation of multiple uncertainty sources. Based on literature records from the Web of Science Core Collection, this study integrates a PRISMA-based screening procedure, CiteSpace bibliometric analysis, comparative assessment of representative hydrological and water quality models, and an AHP—MCDA semi-quantitative evaluation method to systematically review modeling frameworks for watershed-scale heavy metal transport and transformation. The SWAT model and its extended module SWAT-HM are further used as representative cases to analyze the coupling mechanisms among hydrological processes, sediment processes, and heavy metal transport and transformation. The results indicate that the SWAT model has relatively high overall applicability in terms of hydrological process representation, sediment/erosion process representation, and heavy metal coupling basis, making it suitable for long-term pollution load assessment in medium- and large-scale watersheds. However, for urban storm runoff, high-frequency water quality responses, surface water–groundwater exchange, and detailed reactive transport processes, other models or coupled modeling frameworks are still required. The SWAT-HM model links dissolved, particulate, and sediment-associated heavy metal transport with runoff, erosion, and sediment transport processes, but its representation of complex speciation transformation, valence state changes in redox-sensitive elements such as As and Cr, and colloid-facilitated transport remains simplified. Future research should move toward an integrated modeling system that coordinates mechanisms, data, and uncertainty, with particular emphasis on breakthroughs in multi-scale dynamic coupling, element-specific parameter systems, dynamic pH–Eh modules, and standardized uncertainty decomposition workflows, thereby improving model interpretability, predictive reliability, and management relevance.