Smelter-impacted soils often result in soil degradation and the destruction of the soil structure. Although soil aggregate typically plays a crucial role in soil structure, the influence of phytoremediation on soil aggregate structure stability and stoichiometric characteristics remains unclear. To study the influence of phytoremediation on soil aggregate structure, stability and stoichiometric characteristics, a 3-year in situ experiment was conducted. After hydroxyapatite was applied, Elsholtzia splendens, Sedum plumbizincicola, and Pennisetum sp. were planted in a smelter-impacted soil. After 3 years, the soil aggregate structure, stability, and stoichiometric of chemical elements were analyzed. The results showed that the three phytoremediation treatments increased the content of >0.25 mm mechanically-stable (DR0.25) and water-stable (WR0.25) aggregates by 6.6%–10.4% and 13.3%–17.5%, respectively. Aggregate mean weight diameter (MWD), geometric mean diameter, and aggregate stability rate (AR, %) were significantly increased, and the soil mechanically stable aggregate fractal dimension (D) was significantly reduced after the 3-year remediation. Soil total nitrogen and phosphorus in aggregates with different particle sizes were significantly increased by 11.4%–46.4% and 107%–236% after different plant treatments. For the stoichiometric characteristics of the aggregates, the combined remediation only significantly reduced the value of N:P and C:P in different particle size aggregates and had no significant effect on the C:N in all particle size aggregates. Meanwhile, the combined remediation of hydroxyapatite and Elsholtzia splendens, Sedum plumbizincicola, and Pennisetum sp. in heavy metal heavily contaminated soil could reduce the availability of Cu and Cd by 54.1%–72.3% and 20.3%–47.2% during the 3 years, respectively. In summary, this combined remediation method can be used for the remediation of farmland that is contaminated by heavy metals.

The water-soluble heavy metal ions in contaminated soil may enter aquatic ecosystem through runoff, thus causing negative impact on the water environment. In this study, a two-year in situ experiment was carried out to explore an effective way to reduce the runoff erosion and water-soluble copper (Cu) and cadmium (Cd) in a contaminated soil (Cu: 1,148 mg kg−1, Cd: 1.31 mg kg−1) near a large Cu smelter. We evaluated the ability to influence soil properties by four Cu-tolerance plant species (Pennisetum sp., Elsholtzia splendens, Vetiveria zizanioides, Setaria pumila) grown in a contaminated acidic soil amended with lime. The results show that the addition of lime can significantly reduce the exchangeable fraction (EXC) of Cu and Cd in soil (81.1–85.6% and 46.3–55.9%, respectively). Plant species cannot change the fraction distributions of Cu and Cd in the lime-amended soils, but they can reduce the runoff generation by 8.39–77.0%. Although water-soluble Cu concentrations in the runoff were not significantly differed and water-soluble Cd cannot be detected among the four plant species, the combined remediation can significantly reduce 35.9–63.4% of Cu erosion to aquatic ecosystem, following the order: Pennisetum sp. > Elsholtzia splendens > Vetiveria zizanioides > Setaria pumila. The implication of this study would provide valuable insights for contaminated soil management and risk reduction in the Cu and Cd contaminated regions.

No abstract

Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the ''standard deviation'' operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination(R 2 ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.INDEX TERMS Variation predictor, spatial downscaling, disaggregation, unmixing, scaling, geographically weighted regression (GWR), tropical rainfall measuring mission (TRMM), precipitation, north-south transitional zone of China.The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .