Environmental quality standards for agricultural land in China: What should be improved on derivation methodology?

Soil pollution has caused increasingly widespread attention in China. The environmental risk threshold of pollutants is a yardstick to measure soil environmental quality. For decades, plenty of research on soil environmental quality standards (SEQSs) has been carried out, providing scientific basis for the investigation and supervision of soil environmental quality. This paper summaries the development of SEQSs in China, the corresponding influencing factors and methodology of SEQSs derivation. In the current version of SEQSs (GB156182018), the thresholds of soil pollutants are derived by the methods of environmental risk assessment, which are more methodologically scientific than geochemical method and ecological effect method used in the previous version (GB15618-1995). Abundant toxicology data on related species is required for risk assessment of soil pollution using extrapolation; however, basic toxicological data is insufficient and few valid data is available at present. Besides, the inadequate consideration on influencing factors for the derivation of soil pollutant threshold would affect the scientificity and rationality of SEQSs, such as biotic factors (species type, test endpoint etc.) and abiotic factors (aging effect, leaching effect, synergistic or antagonistic effects of elements etc.). These problems should be paid close attention in future research on soil environmental quality standards. The contents summarized in this review may provide reference for decision-making on supervision of soil environmental quality and point out important directions for future studies.

Automated summary

This paper summarizes the development, influencing factors, and derivation methods of soil environmental quality standards (SEQSs) in China. The current version of SEQSs is more methodologically scientific, but there is a lack of toxicology data on soil pollution and inadequate consideration of influencing factors.