Estimating the pollution risk of cadmium in soil using a composite soil environmental quality standard.

Mingkai Qu,Biao Huang,Yongcun Zhao,Weidong Li,Chuanrong Zhang

doi:10.1155/2014/750879

Abstract

Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km2 area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs.

Highlights

With increasing industrialization and rapid urbanization in many regions of the world, contamination by trace metals in the terrestrial environment has become widespread in a global context [1]
The objective of this study is to suggest a method for estimating the standard-exceeding probability map of soil Cd using a composite soil environmental quality standards (SEQSs), that is, a method which incorporates both the spatial variability and uncertainties of soil Cd and pH and the site-specific land use type information
A method for estimating the standard-exceeding probability map of soil Cd based on a composite SEQS for Cd in soil was presented

Summary

Introduction

With increasing industrialization and rapid urbanization in many regions of the world, contamination by trace metals in the terrestrial environment has become widespread in a global context [1]. There is an increasing awareness that an estimate is more valuable in the presence of a measure of the associated uncertainty, and this is the case in prediction of environmental variables where the prediction uncertainty is required to support decision-making about further management [8]. Sequential simulation methods, such as sequential Gaussian simulation (SGS), provide a useful solution for this problem, because simulated realizations overcome the smoothing effect and spatial uncertainty measures such as threshold-exceeding probabilities can be estimated from a number of simulated realizations [5, 9].

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