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  • Title: Temporal changes of metal bioavailability and extracellular enzyme activities in relation to afforestation of highly contaminated calcareous soil.
    Author: Hu Y, Huang Y, Su J, Gao Z, Li S, Nan Z.
    Journal: Sci Total Environ; 2018 May 01; 622-623():1056-1066. PubMed ID: 29890575.
    Abstract:
    Metal bioavailability and extracellular enzyme activity are two important indicators of soil quality in metal-contaminated soil. However, it is unclear how the chronosequence effect modifies these two factors in highly contaminated calcareous soils undergoing afforestation. We used Populus simonii Carr. and the calciphilous Ulmus macrocarpa Hance as contrasting tree species to study the chronosequence effect. We found that afforestation significantly increased soil total nitrogen (N) content as well as soil carbon (C)/phosphorus (P) and N/P ratios, but decreased soil total P content and soil C/N ratio, regardless of the tree species and stand age, suggesting strong P limitation. However, available P did not change significantly with stand age. In both tree species, P mobilization depleted soil organic matter through the priming effect of dissolved organic carbon, whereas the decrease in soil pH in the U. macrocarpa stands enhanced CaCO3 dissolution, collectively reducing the capacity of the soil to immobilize metals, resulting in increased metal bioavailability with stand age. The activity of oxidase (dehydrogenase) was positively correlated with bioavailable zinc concentration, soil electrical conductivity, and soil total N content. Hydrolase activities (alkaline phosphatase, β-glucosidase, and urease) were significantly positively correlated with the ratios of soil C/N and C/P, soil pH, and CaCO3, but negatively correlated with soil N/P ratio and bioavailable cadmium concentration. Increasing stand age was associated with the gradual recovery of oxidase activity and remarkable inhibition of hydrolase activity. Our results suggest that the combination of soil hydrolase activity and metal bioavailability can predict soil quality in the afforestation of highly contaminated soils.
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