Balancing chemical function with reduced environmental health hazards: A joint probability approach to examine antimicrobial product efficacy and mammalian toxicity

JOURNAL OF CLEANER PRODUCTION(2020)

引用 3|浏览21
暂无评分
摘要
The fourth principle of Green Chemistry, a critically important area of sustainability science, identifies that substances should maintain intended functions while possessing limited intrinsic hazards to public health and the environment. In the present study, we empirically determined efficacy of 20 cleaning products against E. coli attachment to surfaces. Subsequently, probabilistic assessments using novel chemical efficacy distributions (CEDs) were conducted. Results for most chemicals indicated bacterial detachment with increasing concentration. Using a threshold concentration of 5th centiles, these cleaning product ingredients were predicted to detach E. coli (Lowest Observable Effect Concentration) at or below 0.7 (0.2, 2.2) mg L-1 for 5% of ingredients. We then employed chemical toxicity distributions (CTDs) to examine acute toxicity, based on currently available information, for common mammalian models. Results demonstrate that probabilities of associated hazards for rat (oral exposure route), mouse (oral exposure route), rabbit (dermal exposure route), and rat (inhalation exposure route) were 43.2%, 36.7%, 38.6% and 75.5%, respectively. A novel joint probability distribution analysis approach was then developed and applied to identify substances with various efficacy (CED) and hazard (CTD) characteristics. Our observations indicate that combining efficacy and toxicity information using joint probability curves may be useful for identifying classes of antimicrobial products, and other chemical classes, to optimize efficacy while minimizing environment and health hazards. (c) 2020 Elsevier Ltd. All rights reserved.
更多
查看译文
关键词
Cleaning product ingredients,Antimicrobial efficacy,Mammalian toxicity,Chemical toxicity distributions,Thresholds of toxicological concern,Joint probability distribution
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要