Soil ecological criteria for nickel as a function of soil properties.

Environ Sci Pollut Res Int

CSIRO Land and Water Waite Road, Centre for Environmental Contaminant Research, Urrbrae, SA, 5064, Australia.

Published: January 2018

Scientific information for the chemistry and ecotoxicology of metals in soils has been obtained in the research conducted in recent years. However, the latest "science" obtained from this research has not yet been translated into "regulations" in China. In the present study, the predicted no effect concentrations (PNECs) for Ni which denoted as soil ecological criteria for Ni were derived based on the effects of soil properties on bioavailability/ecotoxicity of Ni, incorporating the differences in species sensitivity as well as in laboratory and field conditions. First, collected all ecotoxicity data of Ni from bioassays in Chinese soils and filtered with given criteria to obtain reliable data. Second, corrected the compiled data with either aging factor or leaching and aging factors simultaneously to eliminate the discrepancy caused by difference between laboratory and realistic field conditions. Prior to being put into a species sensitivity distribution (SSD) method to fit SSD curves, the corrected Ni ecotoxicity data were normalized with Ni ecotoxicity predictive models to modify the variation in Ni ecotoxicity caused by different soil properties. Then, the hazardous concentration for x% of the species (HCx) was figured out from the Ni SSD curve and the ecological criterion of Ni (PNEC) was set equal to HCx. Finally, predictive models for HCx were developed by quantifying the relationship between the Ni HCx and soil properties. Soil pH was the major factor controlling the values of HCx for Ni, with HC5 varying from 6.5 mg/kg in an acidic soil (pH 5.0) to 218.8 mg/kg in an alkaline soil (pH 8.5). The predictive models with parameters of soil pH and organic carbon (%) could calculate HCx with determination coefficients (R) of 0.90-0.95. When soil cation exchange capacity was taken into account the third parameter, the predictive models could predict HCx more accurate with R values of 0.94-0.99.

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http://dx.doi.org/10.1007/s11356-017-0456-6DOI Listing

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