With the rapid development of industry and agriculture, soil contamination has become a significant environmental issue, and the heavy metal contamination of soils is an important part of it. The main methods for the remediation of heavy metal-contaminated soils include physical methods, chemical methods, biological methods, and combined remediation methods have been proposed as research deepens. However, the standards and evaluation methods for the remediation of heavy metal-contaminated soils are still not well-established. This article discusses the sources and contamination status of heavy metals in soils, the advantages and disadvantages of remediation technology for heavy metal-contaminated soils, remediation standards, and post-remediation evaluation methods. It also proposes scientific issues to be addressed in future research and provides an outlook on future development, hoping to assist in subsequent remediation studies of heavy metal-contaminated soils.

Forest functionality is generally considered a byproduct of forest diversity. Perhaps unsurprisingly, many researchers associate increasing multi-functionality with increasing diversity. Diversity, however, is an often-overused word that may describe a host of features, including the diversity of species, functional trait and structure. Furthermore, variable environmental features (such as topography) influence the interaction between forest plants and their function. Incorporating complex topography (like that associated with tropical and subtropical forests) into estimates of forest functionality is challenging and highly uncertain. In this paper, we applied structural equation models to disentangle the relative importance of topography and different components of what might be considered “plant diversity” to forest multifunctionality using repeated census of a 20-ha subtropical forest plot. We found that multifunctionality was principally influenced by structural diversity more so than either species composition or functional trait diversity. In our SEM model approach, we observed variations in topography could account for about 30% of variation in multifunctionality. Furthermore, variations in topography could indirectly influence forest multifunctionality by changing species composition, functional trait diversity, and structural diversity. Our work highlights the importance of topography and forest structure in regulating subtropical forest multifunctionality on the local scale. This suggests future subtropical forest management should focus on regulating forest structure. Namely, our results suggest land managers must take topography (and the complex interaction between topography and plant diversity) into account in order to build robust and multifunctional forests.

Vegetation restoration and farmland irrigation are important environmental factors affecting the water cycle process in basins. Analyzing the impact of vegetation restoration and farmland irrigation on runoff is an international frontier and hot topic in current research, which is crucial for the management and protection of water resources, especially for the ecological protection and high-quality development of basins. Based on the normalized difference vegetation index (NDVI), effective irrigated area (EIA), and meteorology and hydrology data for Wujiang River (WJR), this research aims to quantitatively calculate the influence degree of vegetation recovery, agricultural irrigation, and climatic variability on discharge alteration in WJR. First, Mann–Kendall and Pettitt approaches were used for recognizing the mutation year of streamflow data at Wulong station from 1982 to 2015. Then, a corrective Budyko model was built by constructing multiple linear regression equations for the NDVI, climate factors, EIA, and Budyko parameters. Finally, the corrective Budyko model was adopted to reveal how vegetation restoration, agri-cultural irrigation, and climate variation influence discharge alteration in WJR. The results showed the following: (1) Both runoff depth (R) and rainfall (Pr) exhibited a non-significant de-clining tendency, while potential evapotranspiration (ET0) demonstrated a non-significant in-creasing tendency. The NDVI and EIA both demonstrated a notable upward tendency (p < 0.01). (2) The mutation year of discharge in WJR was 2004. (3) The underlying surface parameters ω have a strong correlation with vegetation, agricultural irrigation, and climate factors. (4) The contribution of rainfall (Pr), potential evapotranspiration (ET0), NDVI, EIA, and human activities to runoff depth in WJR were 57.34%, 24.67%, -11.75%, 11.71%, and 18.02%, respectively. This re-search is helpful for elucidating the effects of ecological construction measures and agricultural irrigation on streamflow in WJR, and offers great scientific significance and practical value for understanding the evolution mechanism of water circulation and for managing regional water resources.

This study investigates the relative contributions of climate change and human activities to future seasonal runoff changes in the Jinsha River (JSR) Basin (western China), with particular emphasis on quantifying the influence of multiple sources of uncertainty on attribution results. An integrated framework combining global climate models (GCMs), shared socioeconomic pathways (SSPs), hydrological models (HMs), seasonal-scale Budyko models, and variance analysis (VAAN) is developed. The conclusions were as follows: (1) The mutation year of runoff in JSR was 1984. (2) Both the ABCD and dynamic water balance model with variable time scales (DWBM) hydrological models performed excellently in simulating historical runoff, with Nash–Sutcliffe efficiency (NSE) values exceeding 0.85 and relative errors within 10%. (3) Under the SSP119 scenario, human activities dominate runoff changes in spring, summer, and autumn, contributing 13.5 mm, 48.1 mm, and 30.5 mm, respectively, while climate change dominates in winter with a contribution of −10.7 mm. (4) Under the SSP245 and SSP585 scenarios, human activities remain the dominant factor for summer (46.5 mm and −47.4 mm) and autumn (29.1 mm and −30.8 mm), whereas climate change dominates in spring (−14.3 mm and −14.5 mm) and winter (−13.3 mm and −14.2 mm). (5) The interactions among the HMs, GCMs, and SSPs are the primary source of uncertainty, contributing 44.45% to 82.03% of the total variance in attribution results across different seasons.

Predicting future land-use patterns and carbon storage is essential for understanding regional terrestrial ecosystems, as regional land-use change plays a crucial role in ecosystem carbon storage variations. Using the Patch-generating Land Use Simulation Model (PLUS), we simulated the 2020 land-use types in the Yellow River Basin (YRB) based on the 2010 data. Subsequently, we predicted YRB land-use types for 2030 to 2100. Finally, based on these simulated land-use patterns, we calculated the carbon storage in the YRB from 1980 to 2100 using the InVEST model. The results showed that: (1) From 1980 to 2020, the InVEST model showed that carbon storage in the Yellow River Basin (YRB) exhibited an increasing trend of 12.10%. Rapid carbon storage increases can be observed in 2000–2020 (16.9 million tons). The largest carbon storage was found in Grassland (2487.24 million tons), which accounts for 51.03% of the total carbon storage in YRB. (2) During 2030 to 2100, the grassland area showed a decrease trend in SSP1-2.6 (−12.22%). The forest area showed an increase trend in SSP1-2.6 (3.49%). (3) Among the different scenarios, SSP1-2.6 (103.99 million tons) and current scenarios (23.07 million tons) showed the largest carbon storage gains from 2030 to 2100, primarily attributed to the cultivated land and forest, despite a major loss from grassland. SSP2-4.5 showed a carbon storage loss of 23.48 million tons, while a slight gain of 6.49 million tons was observed under SSP5-8.5. (4) Carbon storage losses were primarily observed in the grassland-dominated northern regions of the YRB. In contrast, the southernmost and eastern regions showed an increasing trend. This research provides essential scientific support for optimizing land-use structure and enhancing land management strategies across the YRB basin.