Vegetation restoration is a widely used, effective, and sustainable method to improve soil quality in post-mining lands. Here we aimed to assess global patterns and driving factors of potential vegetation restoration effects on soil carbon, nutrients, and enzymatic activities. We synthesized 4838 paired observations extracted from 175 publications to evaluate the effects that vegetation restoration might have on the concentrations of soil carbon, nitrogen, and phosphorus, as well as enzymatic activities. We found that (1) vegetation restoration had consistent positive effects on the concentrations of soil organic carbon, total nitrogen, available nitrogen, ammonia, nitrate, total phosphorus, and available phosphorus on average by 85.4, 70.3, 75.7, 54.6, 58.6, 34.7, and 60.4 %, respectively. Restoration also increased the activities of catalase, alkaline phosphatase, sucrase, and urease by 63.3, 104.8, 125.5, and 124.6 %, respectively; (2) restoration effects did not vary among different vegetation types (i.e., grass, tree, shrub and their combinations) or leaf type (broadleaved, coniferous, and mixed), but were affected by mine type; and (3) latitude, climate, vegetation species richness, restoration year, and initial soil properties are important moderator variables, but their effects varied among different soil variables. Our global scale study shows how vegetation restoration can improve soil quality in post-mining lands by increasing soil carbon, nutrients, and enzymatic activities. This information is crucial to better understand the role of vegetation cover in promoting the ecological restoration of degraded mining lands.
Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.