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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Ecological risk assessment of combined polluted soil has been conducted mostly on the basis of the risk screening value (RSV) of a single pollutant. However, due to its defects, this method is not accurate enough. Not only were the effects of soil properties neglected, but the interactions among different pollutants were also overlooked. In this study, the ecological risks of 22 soils collected from four smelting sites were assessed by toxicity tests using soil invertebrates (Eisenia fetida, Folsomia candida, Caenorhabditis elegans) as subjects. Besides a risk assessment based on RSVs, a new method was developed and applied. A toxicity effect index (EI) was introduced to normalize the toxicity effects of different toxicity endpoints, rendering assessments comparable based on different toxicity endpoints. Additionally, an assessment method of ecological risk probability (RP), based on the cumulative probability distribution of EI, was established. Significant correlation was found between EI−based RP and the RSV−based Nemerow ecological risk index (NRI) (p < 0.05). In addition, the new method can visually present the probability distribution of different toxicity endpoints, which is conducive to aiding risk managers in establishing more reasonable risk management plans to protect key species. The new method is expected to be combined with a complex dose–effect relationship prediction model constructed by machine learning algorithm, providing a new method and idea for the ecological risk assessment of combined contaminated soil.

Details

Title
A New Method for Ecological Risk Assessment of Combined Contaminated Soil
Author
Wang, Qiaoping 1   VIAFID ORCID Logo  ; Wang, Junhuan 1   VIAFID ORCID Logo  ; Cheng, Jiaqi 2 ; Zhu, Yingying 3 ; Geng, Jian 4 ; Wang, Xin 4 ; Feng, Xianjie 5 ; Hou, Hong 1 

 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] (Q.W.); 
 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] (Q.W.); ; School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China 
 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] (Q.W.); ; College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, China 
 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] (Q.W.); ; College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China 
 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; [email protected] (Q.W.); ; School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China 
First page
411
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
23056304
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2819456883
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.