In the last 30 years, severe soil acidification has been found in China due to acid deposition and nitrogen fertilizer overuse. Understanding the spatial pattern and vertical variations in base saturation percentage (BSP) and exchangeable cations (Ca²⁺, Mg²⁺, K⁺, Na⁺, H⁺ and Al³⁺) can directly benefit fertilization management and ecological protection. Here, 1253 soil profiles were surveyed in tropical and subtropical regions in China to investigate the spatial variations in BSP and exchangeable cations at three soil depths of 0–20 cm, 20–50 cm and 50–100 cm. The spatial distributions were interpolated by using advanced machine learning techniques. We found that the exchangeable Ca²⁺ (Exch. Ca), Mg²⁺ (Exch. Mg) and BSP were significantly higher in paddy fields and uplands than in forests and gardens, regardless of soil depth, while the exchangeable K (Exch. K) did not significantly differ between various land-use types. The Exch. Ca and BSP in Anthrosols were significantly higher than those in Ferrosols, Argosols and Cambosols in the three soil layers. The spatial prediction results indicated that exchangeable cations and BSP were generally characterized by strong heterogeneity, and the Exch. Ca, Exch. K and exchangeable H⁺ (Exch. H) contents and BSP declined with increasing soil depth. This study helps us understand the spatial variation in BSP and exchangeable cations in the study area and benefits fertilization management and environmental protection.

Accurately identifying pollution risks and sources is crucial for regional land resource management. This study takes a certain coastal county in eastern China as the object to explore the spatial distribution, pollution risk, and source apportionment of heavy metals in topsoil. A total of 633 samples were collected from the topsoil with a depth ranging from 0 to 20 cm, which came from different topographical and land use types (e.g., farmland, industrial areas, and mining areas), and the concentrations of HMs and As were measured by using atomic fluorescence spectrometry and inductively coupled plasma mass spectrometry. Firstly, the spatial distribution of soil HMs (Cd, Cr, Hg, Ni, and Pb) and arsenic (As) was predicted by incorporating environmental variables strongly affecting soil formation into geostatistical methods and machine learning approaches. Then, various pollution indicators were employed to conduct pollution evaluations, and potential ecological risk assessments were implemented based on the generated soil map. Finally, source apportionment was conducted using random forest (RF), absolute principal component score–multiple linear regression (APCS-MLR), correlation analysis, and spatial distribution of soil HMs and As. Findings in this research reveal that the RF approach yielded the best spatial prediction performance (0.59 ≤ R² ≤ 0.73). The Nemerow and geoaccumulation indices suggest that various pollution levels exist in this area. The average concentrations of As, Hg, and Ni are 7.233 mg/kg, 0.051 mg/kg, and 27.43 mg/kg respectively, being 1.14 times, 1.27 times, and 1.15 times higher than the background levels, respectively. The central–northern region presented a slight potential ecological risk, with Hg and Cd being identified as the primary risk factors. Natural, agricultural, transportation, and industrial and mining activities were identified as the main HMs and As sources. These findings will assist in the design of targeted policies to reduce the risks of HMs and As in urban soil and offer useful guidelines for soil pollution research in similar regions.

Quantifying changes in soil properties greatly benefits our understanding of soil management and sustainable land use, especially in the context of strong anthropogenic activities and climate change. This study investigated the effects of long-term reclamation on soil properties in an artificial oasis region with a cultivation history of more than 50 years. Critical soil properties were measured at 77 sites, and a total of 462 soil samples were collected down to a depth of 1 m, which captures both surface and subsurface processes that are critical for long-term cultivation effects. Thirteen critical soil properties were analyzed, among which four properties—soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)—were selected for detailed analysis due to their ecological significance and low intercorrelation. By comparing cultivated soils with nearby desert soils, this study found that semicentennial cultivation led to significant improvements in soil properties, including increased concentrations of SOC, NH₄⁺, and TP, as well as reduced pH throughout the soil profile, indicating improved fertility and reduced alkalinity. Further analysis suggested that environmental factors—including temperature, clay content, evaporation differences between surface and subsurface layers, sparse vegetation cover, cotton root distribution, as well as prolonged irrigation and fertilization—collectively contributed to the enhancement of SOC decomposition and the reduction of soil alkalinity. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function, and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree, and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R² = 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10), and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security.

Quantifying the rates of soil evolution greatly benefits our understanding of soil formation and management, especially in the context of strong anthropogenic activities and climate change. This study investigated soil evolution in an artificial oasis region with a reclamation history of more than 50 years, and critical soil properties were measured at 77 sites. A total of 462 soil samples were collected down to a depth of 1 m. A total of seven critical soil properties were analysed, and four (i.e., soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)), which were not closely correlated with each other, were selected for further investigation. Through comparison with desert soils, this investigation found that semicentennial cultivation resulted in significant changes in soil properties, with strong vertical variations, including increases in the C, N and P contents and decreases in pH throughout the whole profile. The temperature, clay content, evaporation rate between the topsoil and subsoil, low vegetation cover, cotton lateral roots, irrigation and fertilization played crucial roles in promoting SOC decomposition and reducing soil alkalinity, thereby contributing to rapid soil evolution. Thus, reclaimed desert soil was scientifically confirmed to be suitable for agricultural use, which will ease the food production crisis, protect the environment, and promote soil evolution. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R²= 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10) and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security.