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.
To determine the influence of nutrients on the rate of biodegradation, a five-level, three-factor central composite design (CCD) was employed for bioremediation of seawater artificially contaminated with crude oil. Removal of total petroleum hydrocarbons (TPH) was the dependent variable. Samples were extracted and analyzed according to US-EPA protocols. A significant (R(2)=0.9645, P<0.0001) quadratic polynomial mathematical model was generated. Removal from samples not subjected to optimization and removal by natural attenuation were 53.3% and 22.6%, respectively. Numerical optimization was carried out based on desirability functions for maximum TPH removal. For an initial crude oil concentration of 1g/L supplemented with 190.21 mg/L nitrogen and 12.71 mg/L phosphorus, the Design-Expert software predicted 60.9% hydrocarbon removal; 58.6% removal was observed in a 28-day experiment.