Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

Peng Huang; Shu-Shen Liu; Ze-Jun Wang; Ting-Ting Ding; Ya-Qian Xu

doi:10.1016/j.chemosphere.2022.134303

Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

Chemosphere. 2022 Jul:298:134303. doi: 10.1016/j.chemosphere.2022.134303. Epub 2022 Mar 11.

Authors

Peng Huang¹, Shu-Shen Liu², Ze-Jun Wang³, Ting-Ting Ding¹, Ya-Qian Xu³

Affiliations

¹ Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
² Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China. Electronic address: ssliuhl@263.net.
³ Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China.

PMID: 35288184
DOI: 10.1016/j.chemosphere.2022.134303

Abstract

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC₅ and HC₁₀, respectively, were calculated by the optimal SSD model. The assessment factor used to determine the PNEC of the chemical based on the HC₁₀ was derived from the quantitative correlation between HC₁₀ and HC₅ of pesticides found in this study. When the toxicity data are insufficient, it may be more appropriate to calculate the PNECs of chemicals using HC₁₀ than using HC₅.

Keywords: Dragon 7.0; Environmental risk assessment; Model optimization; Nonlinear curve fitting; Quantitative structure-activity relationships.

MeSH terms

Humans
Pesticide Residues*
Pesticides* / toxicity
Quantitative Structure-Activity Relationship
Risk Assessment
Water Pollutants, Chemical* / analysis

Substances

Pesticide Residues
Pesticides
Water Pollutants, Chemical