The rapid development of industrialization and urbanization has posed serious challenges for coastal farmland ecosystems. Source apportionment of soil heavy metals is an effective way for the detection of non-point source pollution in farmland to help support the high-quality development of coastal agriculture. To this end, 113 surface soil samples were collected in the coastal delta of China, and the contents of As, Cd, Cr, Cu, Ni, Pb, and Zn were determined. A variety of models were integrated to apportion the source of soil heavy metals, including positive matrix factorization (PMF), geographical detector (GD), eXtreme gradient boosting (XGBoost), and structural equation modeling (SEM). The result of PMF models revealed that there was collinearity between various heavy metals, and the same heavy metal may have a mixed source. The XGBoost model analysis indicated that there were significant non-linear relationships between soil heavy metals and source factors. A synergy between air quality and human activity factors was the key source of heavy metal that entered the study area, based on the results of the GD. Furthermore, the input path effect of heavy metals in the soil of the study area was quantified by SEM. The balance of evidence from the above models showed that air quality (SO₂ and NO₂) and factories in the study area had the greatest impacts on Cd, Cr, and Zn. Natural sources were dominant for Pb, while As, Cu, and Ni were contributed by soil parent material and factories. The above results led to the conclusion that there was a cycle path in the study area that continuously promoted the migration and accumulation of heavy metals in farmland soil; that is, the heavy metals discharged during oil exploitation and smelting entered the atmosphere and then accumulated in the farmland soil through precipitation, atmospheric deposition, and other paths. In this study, it is shown that a variety of models can be used to more comprehensively assess the sources of soil heavy metals. This approach can provide effective support for the rapid prevention and decision-making management of soil heavy metal pollution in coastal areas.

Cultivated land fragmentation (CLF) is a key obstacle to agricultural development and has a strong relationship with regional food security and global sustainable development. However, few studies have analyzed the spatio-temporal distribution pattern and evolution characteristics of CLF and the complex interactions among their influencing factors in rapidly developing regions. In this study, first, the GlobeLand30 datasets were used to obtain characteristic parameters of cultivated land in counties in Guangdong Province in 2000, 2010, and 2020. Then, the linear weighted comprehensive evaluation model based on the principal component analysis (PCA) was used to measure the extent of CLF. Finally, the exploratory spatial data analysis (ESDA) was used to analyze the spatio-temporal distribution pattern and evolution characteristics of CLF, and geodetector (GD) and random forest (RF) models were used to explore the factors influencing the spatial difference in CLF. The results showed that the spatial differences in the distribution of cultivated land resources in Guangdong Province are relatively large and the extent of agglomeration is generally low. The extent of CLF on the county scale is mainly medium and higher. The overall spatial distribution shows an increasing trend from the south to the north and from the west to the east, and the spatial distribution pattern with agglomeration and randomness remains relatively stable. From 2000 to 2020, the overall CLF continued to intensify and the evolution of CLF on the county scale mainly increased. The spatial difference in CLF is the result of that based on the natural environment and influenced by factors such as social, economic, and agricultural development. The interaction between influencing factors is very strong, dominated by nonlinear enhancement. The results are of great significance for promoting the intensive and efficient utilization of cultivated land resources and sustainable regional development.

Urban flood susceptibility has emerged as a critical challenge for cities worldwide, exacerbated by rapid urbanization. This study evaluates urban flood susceptibility under different Shared Socioeconomic Pathways (SSPs) in the context of urbanization. A coupled modeling approach integrating the System Dynamics (SD) model and the Future Land Use Simulation (FLUS) model was employed to project future land use changes under sustainable development, moderate development, and conventional development scenarios. Additionally, an XGBoost model was developed to assess urban flood susceptibility. The results indicate that urban construction land will continue to increase over the next 30 years, with the extent of growth varying across different scenarios. Notably, under the conventional development scenario, rapid economic growth leads to a significant expansion of built-up land and a sharp decline in ecological land, which in turn exacerbates the urban flood susceptibility. Consequently, urban flood susceptibility is projected to increase across all three scenarios, albeit at varying rates. Specifically, under the sustainable development scenario, 27% of Guangzhou is projected to face high flood risk. In the moderate development scenario, the area classified as high-risk increased by 868.73 km². Under the conventional development scenario, the high-risk area expanded from 1282.9 km² in 2020 to 2761.33 km², representing a 16% increase. These differences are primarily attributed to changes in land use, which alter surface runoff and subsequently enhance the city’s vulnerability to waterlogging. This study provides a comprehensive framework for assessing urban flood susceptibility in the context of urbanization, offering valuable insights for formulating targeted flood prevention and mitigation strategies.

Trace elements in farmland soil are important indicators of soil quality and farmland health, and also maintain the nutrient balance and promote the healthy growth of plants. In this study, taking Conghua District of Guangzhou city as the study area, the effects of topography, soil, land use, and other factors on trace elements in soil were investigated, and the spatial variability of boron (B), manganese (Mn), molybdenum (Mo), copper (Cu), and zinc (Zn) in farmland soil in a typical red soil region were mapped using a geographically weighted regression (GWR) method. The pH and land economic index (LEI) were important factors affecting the changes in trace element concentrations in the five soils, and the Cu and Zn concentrations were clearly affected by human factors. In the study area, 86.99% of B measurements were classified as low and very low levels, 50.61% and 49.20% of Mo measurements were also low and very low, 71.79% of Mn measurements were classified as moderate, while 91.02% of Cu and 52.95% of Zn measurements were classified as high. After a cross validation, the GWR Kriging (GWRK) model results of each element were relatively stable, and the order of the fitting coefficient (R²) was Cu > Zn > B> Mn > Mo. This study clarifies the spatial distribution and influencing factors of soil microelements in the studied region. This information can be used to improve the nutrient imbalance, further guide agricultural production, strengthen the management of farmland, and improve the healthy productivity of cultivated land.

Toxic trace elements in farmland soils are potential threats to human health. In this study, we collected soil samples from the farmlands of southern Guangzhou. We used a sequential indicator simulation (SIS) to deal with the problem of skewed distribution in the sample data. We assessed the human health risks, as well as the uncertainties, of five toxic trace elements: arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg). The results were as follows: (1) The risk indices of two trace elements (Cd and Hg) were less than the standard threshold, which means that there was no human health risk due to Cd and Hg in the study area. However, the maximum risk indices of As, Cr, and Pb exceeded the standard threshold. In particular, the maximum risk index of Pb was twice the standard threshold; (2) The risk probabilities of As and Cr were less than 25% in most areas, and only a few parcels of farmland have a 100% risk probability. The risk map of Pb was used to identify contiguous areas of high-risk probability (i.e., 75%–100%) in the center of the study area. (3) E-type estimation by the SIS method overestimates the risk when the number of samples with concentrations above the threshold have a large proportion of total samples. Our conclusions are as follows: (1) The simulation results show that areas with high-risk indices were concentrated in the Panyu District, which is close to the Pearl River and the core urban area of Guangzhou; (2) Except for Pb, these trace elements are not likely to pose health risks in southern Guangzhou; (3) This study considers the risk probability found with the SIS method to be more reliable for visualizing regional risk.

Cropland is a comprehensive system influenced by the natural environment and human activities. This article collects the data of cropland use, soil, and other geographic, social and economic factors in the study area and then uses the methods of system analysis, induction and deduction to propose a new research perspective for establishing a cognitive framework and analyzing cropland resources and their functions. The framework is used to assess the rapidly urbanizing region of Guangzhou and investigate the production, ecological, and living functions provided by cropland resources. Synergistic relationships between functions are analyzed using the hot and cold spot methods. The results indicate that the production function of cropland resources in Guangzhou is good, the ecological function is favorable, and the living function is relatively low. A synergistic relationship between the three functions is observed in 91% of areas of Guangzhou, whereas a balanced relationship occurs in some areas of the southern part of Zengcheng, the northwestern and northeastern parts of Conghua, and the western part of Nansha. This research provides guidance for managing cropland resources and ensuring their sustainable utilization.