Ecological effect assessment of typical green alternative process characteristic pollutants based on QSAR-ICE-SSD model

CAI Yuchen; ZHANG Jiawei; LI Bin; GE Hui; ZHANG Mengtao; YANG Yewen; XIE Rong; NI Jiahua; SHI Jianghong

doi:10.11949/0438-1157.20260438

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Ecological effect assessment of typical green alternative process characteristic pollutants based on QSAR-ICE-SSD model

更新时间：2026-07-16

- Ecological effect assessment of typical green alternative process characteristic pollutants based on QSAR-ICE-SSD model
- CIESC Journal (2026)
- 作者机构：
  
  1.土壤污染防治与安全全国重点实验室，南方科技大学环境科学与工程学院，广东深圳 518055
  2.生态环境部环境规划院战略规划研究所，北京 100041
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20260438
  CLC： X 826;X 52
- Received：30 March 2026，
  
  Revised：2026-07-15，
  
  Accepted：16 July 2026，
- 稿件说明：
移动端阅览
CAI Yuchen, ZHANG Jiawei, LI Bin, et al. Ecological effect assessment of typical green alternative process characteristic pollutants based on QSAR-ICE-SSD model[J/OL]. CIESC Journal, 2026.
DOI：

CAI Yuchen, ZHANG Jiawei, LI Bin, et al. Ecological effect assessment of typical green alternative process characteristic pollutants based on QSAR-ICE-SSD model[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260438.

摘要

面向过程工业绿色转型需求，本研究针对甲基丙烯酸甲酯（MMA）和环氧丁烷（BO）生产涉及的26种特征污染物，提出基于QSAR-ICE-SSD的多情境环境风险评估框架。依据毒性数据丰度，构建差异化评估策略：数据充足情境采用直接物种敏感性分布（SSD）法；数据不足情境采用ICE-SSD种间外推法；数据缺失情境采用QSAR-ICE-SSD预测法与毒性试验-ICE-SSD实验法。通过整合QSAR模型、Web-ICE平台及标准化毒性测试，系统完善了覆盖藻类、溞类、鱼类的300余条急性毒性数据。基于多函数拟合SSD模型推导预测无效应浓度（PNEC），26种物质急性PNEC介于0.0027 mg/L（丙烯醛）~1030 mg/L（丙酮）。经急慢性比（ACR）换算获得慢性PNEC，进一步确定最大允许排放浓度（C

allow

）。结果表明，低C

allow

物质需在化工废水处理中重点监控并确保出水严格达标。该框架突破了数据稀缺性制约，为特征污染物的源头削减与风险预警提供了定量化技术支撑。

Abstract

In order to meet the needs of green transformation of process industry

this study proposes a multi-context environmental risk assessment framework based on QSAR-ICE-SSD for 26 characteristic pollutants involved in the production of methyl methacrylate (MMA) and epoxybutane (BO). According to the abundance of toxicity data

a differentiated evaluation strategy was constructed: the direct species sensitivity distribution (SSD) method was used in the data-rich situation; the ICE-SSD interspecific extrapolation method was used in the situation of insufficient data. QSAR-IC

E-SSD prediction method and toxicity test-ICE-SSD experiment method were used in data missing situation. By integrating QSAR model

Web-ICE platform and standardized toxicity test

more than 300 acute toxicity data covering algae

daphnia and fish were systematically improved. Based on the multi-function fitting SSD model

the predicted no effect concentration (PNEC) was derived

and the acute PNEC of 26 substances was between 0.0027 mg / L (acrolein) and 1030 mg / L (acetone). Chronic PNEC was obtained by acute to chronic ratio (ACR) conversion to further determine the maximum allowable emission concentration (C

allow

). The results show that low C

allow

substances need to be monitored in chemical wastewater treatment and ensure that the effluent is strictly up to standard. The framework breaks through the constraints of data scarcity and provides quantitative technical support for source reduction and risk warning of characteristic pollutants.

关键词

Keywords

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