摘要
地表水中的除草剂混合物可能威胁水生生物安全.本研究基于危害商法初步评估我国太湖、辽河地表水中除草剂对初级生产者藻类的单一危害;使用基于Morris采样的全局敏感性分析方法,研究环境相关浓度下除草剂对斜生栅藻(Scenedesmus obliquus)的复合效应.结果表明:太湖、辽河流域检出的39种除草剂中,共有14种除草剂的危害商值≥1,莠灭净和吡氟酰草胺排序最高;14种除草剂复合暴露的混合物总浓度不超过79μg/L,而引起斜生栅藻的最强生长抑制效应超过50%,混合毒性不容忽视;混合毒性效应是非线性的,去草净、苄嘧磺隆、阿特拉津、莠灭净是效应的重要贡献因子,灭草松对整体混合效应的贡献最小.基于Morris采样的全局敏感性分析可在有限样品中识别混合毒性效应的重要因子,为混合物的毒性效应纳入污染物风险评价提供技术支撑.
The use of herbicides in crop production continues to increase globally.By 2015,China had become one of the most important producers and consumers of herbicides in the world.Many herbicides are persistent organic pollutants and are used continuously for long periods of time,causing the high residual concentrations in the water surrounding farmland and negative effects on aquatic life.The concentrations of herbicides in the environmental water are variable and the types of herbicides are diverse.Herbicide mixtures in surface water may threaten aquatic life.However,in most areas of China,the hazard of herbicide mixtures associated with environmental exposure levels has not been assessed.This paper establishes a hazard identification assessment method for mixtures with environment-related concentrations.Firstly,the hazard of individual pollutant was assessed by hazard quotient(HQ),and the pollutants with high hazards(HQ≥1)to target organisms were further studied.Secondly,the global sensitivity analysis(GSA)based on Morris method was used to study combined effect,and identify the important or hidden drivers in chemical mixtures.In brief,the GSA experimental design consists of four steps:Input factors(target pollutants),Morris sampling,high-throughput screening,and the sensitivity analysis.Thirty-nine herbicides detected in the surface water of Tai Lake and Liao River were screened by HQ with Scenedesmus obliquus(S.obliquus)individually.The herbicides with high hazard were the input factors of GSA.Then,the Morris method was used to generate 150 samples of mixtures.The S.obliquus was exposed to 150 samples for 24 h and subjected to high-throughput screening.Finally,the sensitivity analysis was used to identify the important or hidden drivers of the herbicide mixtures.The results showed that 14 herbicides had high hazards(HQ≥1)among 39 herbicides detected in Tai Lake and Liao River basin.Ametryn and diflufenican had higher hazards to algae.At the individual herbicide exposure of 500μg/L,most herbicides,except C5 and C10,significantly inhibited the growth of S.obliquus(P<0.05).And the strongest growth inhibition effect caused by individual herbicide was about 40%.The results of 150 samples of mixtures with environment-related concentration showed that most of the mixtures had growth inhibition effects on S.obliquus.The total concentrations of the mixtures that consist of 14 herbicides are not greater than 79μg/L.And the strongest growth inhibition effect caused by mixture samples was over 50%.The toxicity of herbicide mixtures cannot be ignored.The growth inhibition effect of the mixtures that consist of 14 herbicides on S.obliquus was nonlinear,and there are strong interactions between herbicides.It is difficult to predict the toxic effects of the mixture system from the exposure concentration of individual herbicides.According to theμ*value,the most important factors in the combined pollution were terbutryne,bensulfuron methyl,atrazine,and ametryn.Bentazone had the lowestμ*and contributed little to the overall mixture system.And bensulfuron methyl was the hidden driver of the mixture system.The important drivers in the herbicide mixtures need to be focused,and their concentrations should be monitored regularly to control emissions and minimize the negative effect on aquatic life.Global sensitivity analysis based on Morris sampling can identify the important factors in cocktail effects of mixtures in limited samples,providing technical support for incorporating the actual toxic effects of mixtures into the risk assessment of pollutants.
作者
张晓婷
宋静文
刘红玲
Xiaoting Zhang;Jingwen Song;Hongling Liu(State Key Laboratory of Pollution Control and Resource Reuse,School of Environment,Nanjing University,Nanjing 210023,China)
出处
《科学通报》
EI
CAS
CSCD
北大核心
2022年第23期2802-2810,共9页
Chinese Science Bulletin
基金
国家自然科学基金(22176095,21677073)
国家重点研发项目(2018YFC1801505)资助。
关键词
混合物
风险评价
高通量筛选
隐藏驱动因子
mixtures
risk assessment
high-throughput screening
hidden drivers