基于SWAT-KM暴露模拟的环境暴露与环境风险分析方法——以壬基酚为例
Environmental Risk Analysis for Chemicals with Exposure Model of SWAT-KM: A Demonstrative Study with Nonylphenol
-
摘要: 水文过程是环境系统中化学物质迁移的关键过程，也是化学物质环境暴露浓度不确定性的主要来源之一。利用半分布式水文模型SWAT对水文以及植被生命史过程的高精度模拟能力，通过增设表层大气模块、扩容植被模块等设计，研发了SWAT-KM模型，实现流域尺度化学物质在“土壤、地表水及沉积物、表层大气、植被、浅层地下水”等环境多介质系统中的日精度迁移转化模拟，并开展江苏省中部地区某小流域壬基酚环境暴露浓度的逐日浓度模拟应用。结果表明，SWAT-KM模拟的壬基酚暴露浓度具有时空分异、多介质浓度协同变化的特征，暴露浓度与降水等气象水文过程有较强关联。使用模拟浓度的75%分位点值作为预测环境浓度(PEC)，发现研究区壬基酚造成了不合理水生生态风险，且在春季达到最高；考虑到大多数鱼类春季繁殖的习性，应高度关注壬基酚的水生生态风险。SWAT-KM可为化学物质环境暴露时空高精度模拟和风险评估及区域化学物质环境风险防范提供技术支持。Abstract: Hydrological process is one of the governing processes for chemical transport in an environmental system, contributing most of uncertainties thereof. The Soil and Water Assessment Tool (SWAT) is a renowned semi-distributed hydrological model on watershed-scale; its capacity to simulate many physical processes involving soil, water/vapor, vegetation, etc. with explicitly high spatiotemporal resolution was made use of to develop a novel chemical environmental exposure model, namely SWAT-KM. By differentiating the dissolved species from the sorbed, and the vapor counterpart of a chemical, its intra- and intermedia transport are delineated along with various processes in SWAT-KM, which is enabled after integrating a simplified atmospheric module into, and overhauling the vegetation module of the SWAT model. Hence, the SWAT-KM model will concertedly simulate the fate of a chemical and present the series of its daily concentrations in the environmental multi-media system including soil, surface water and its sediment, atmosphere, vegetation and shallow aquifer for each subbasin of the watershed. This paper reports a demonstrative simulation of nonylphenol (NP) by SWAT-KM for a small watershed in Central Jiangsu Province of China. The simulation results revealed the spatiotemporal differentiation and coordinated variation of the multi-media NP concentrations, and notified the strong association between these concentrations and meteorological and hydrological events. Using the 75 percentile of the simulated concentrations as the predicted environmental concentration (PEC) of NP, an aquatic ecological risk assessment suggested an unreasonable risk in the study region, and a particularly high risk in spring. In light of the breeding of most fishes in spring and the endocrine disruptive nature of NP, this simulation suggests a greater concern of the NP risks in this area. This demonstrative case study corroborates the competence of SWAT-KM to technically support environmental exposure assessment, and consequently to assist the risk assessment and risk mitigation for a targeted chemical.
-
-
Jack D B, Gaarn H B, Johansson S, et al. Technical guidance document on risk assessment. Part 1. Part 2[R]. Brussels: European Commission, 2002 贺莹莹, 李雪花, 陈景文. 多介质环境模型在化学品暴露评估中的应用与展望[J]. 科学通报, 2014, 59(32): 3130-3143 He Y Y, Li X H, Chen J W. Use of multimedia environmental models in chemical exposure assessments[J]. Chinese Science Bulletin, 2014, 59(32): 3130-3143(in Chinese)
MacKay D, MacLeod M. Multimedia environmental models[J]. Practice Periodical of Hazardous, Toxic, and Radioactive Waste Management, 2002, 6(2): 63-69 MacLeod M, Woodfine D G, MacKay D, et al. BETR North America: A regionally segmented multimedia contaminant fate model for North America[J]. Environmental Science and Pollution Research International, 2001, 8(3): 156-163 MacKay D, Reid L. Local and distant residence times of contaminants in multi-compartment models. Part I: A review of the theoretical basis[J]. Environmental Pollution, 2008, 156(3): 1196-1203 Toose L, Woodfine D G, MacLeod M, et al. BETR-World: A geographically explicit model of chemical fate: Application to transport of alpha-HCH to the Arctic[J]. Environmental Pollution, 2004, 128(1-2): 223-240 张少轩, 张冰, 张芊芊, 等. 化学品环境归趋模型及应用[J]. 环境化学, 2019, 38(8): 1684-1707 Zhang S X, Zhang B, Zhang Q Q, et al. Chemical environmental fate models and their applications[J]. Environmental Chemistry, 2019, 38(8): 1684-1707(in Chinese)
Suzuki N, Murasawa K, Sakurai T, et al. Geo-referenced multimedia environmental fate model (G-CIEMS): Model formulation and comparison to the generic model and monitoring approaches[J]. Environmental Science & Technology, 2004, 38(21): 5682-5693 Feijtel T, Boeije G, Matthies M, et al. Development of a geography-referenced regional exposure assessment tool for European Rivers—GREAT-ER[J]. Journal of Hazardous Materials, 1998, 61(1-3): 59-65 Feijtel T, Boeije G, Matthies M, et al. Development of a geography-referenced regional exposure assessment tool for European Rivers—GREAT-ER[J]. Journal of Hazardous Materials, 1998, 61(1-3): 59-65 Koormann F, Rominger J, Schowanek D, et al. Modeling the fate of down-the-drain chemicals in rivers: An improved software for GREAT-ER[J]. Environmental Modelling & Software, 2006, 21(7): 925-936 Kehrein N, Berlekamp J, Klasmeier J. Modeling the fate of down-the-drain chemicals in whole watersheds: New version of the GREAT-ER software[J]. Environmental Modelling & Software, 2015, 64: 1-8 Webster E, MacKay D, Di Guardo A, et al. Regional differences in chemical fate model outcome[J]. Chemosphere, 2004, 55(10): 1361-1376 青达罕, 许宜平, 王子健. 基于环境逸度模型的化学物质暴露与风险评估研究进展[J]. 生态毒理学报, 2018, 13(6): 13-29 Qing D H, Xu Y P, Wang Z J. The evolution of environmental fugacity models on chemical exposure and risk assessment[J]. Asian Journal of Ecotoxicology, 2018, 13(6): 13-29(in Chinese)
Grill G, Khan U, Lehner B, et al. Risk assessment of down-the-drain chemicals at large spatial scales: Model development and application to contaminants originating from urban areas in the Saint Lawrence River Basin[J]. The Science of the Total Environment, 2016, 541: 825-838 Xu L Y, Song H M, Wang Y, et al. Assessment of industry-induced urban human health risks related to benzo[a]pyrene based on a multimedia fugacity model: Case study of Nanjing, China[J]. International Journal of Environmental Research and Public Health, 2015, 12(6): 6162-6178 傅明珠, 李正炎, 石金辉, 等. 壬基酚的内分泌干扰作用和环境分布特征[J]. 海洋湖沼通报, 2005(4): 45-52 Fu M Z, Li Z Y, Shi J H, et al. Endocrine-disrupting properties and environmental distribution characteristics of nonylphenols[J]. Transaction of Oceanology and Limnology, 2005 (4): 45-52(in Chinese)
Arslan O C, Parlak H. Embryotoxic effects of nonylphenol and octylphenol in sea urchin Arbacia lixula[J]. Ecotoxicology, 2007, 16(6): 439-444 Kuo C C, Kuo D H, Huang C J, et al. Nonylphenol-induced apoptotic pathways in SCM1 human gastric cancer cells[J]. Drug Development Research, 2010, 71(2): 139-148 Ren L, Marquardt M A, Lech J J. Estrogenic effects of nonylphenol on pS2, ER and MUC1 gene expression in human breast cancer cells-MCF-7[J]. Chemico-Biological Interactions, 1997, 104(1): 55-64 Ardeshir R A, Rastgar S, Salati A P, et al. The effect of nonylphenol exposure on the stimulation of melanomacrophage centers, estrogen and testosterone level, and ERα gene expression in goldfish[J]. Comparative Biochemistry and Physiology Toxicology & Pharmacology, 2022, 254: 109270 孟耀斌, 李想, 宋昊政, 等. 环境系统模拟中化学物质的土壤行为模拟方法: CN113654943A[P]. 2022-04-22 孟耀斌, 李想, 龙清风. 化学物质环境暴露模拟模型SWAT-KM技术文档及使用手册[R]. 北京: 北京师范大学, 2022: 24-186 Meng Y B, Li X, Long Q F. SWAT-KM Technical Document and User Manual of Chemical Environmental Exposure Simulation Model[M]. Beijing: Beijing Normal University, 2022: 24 -186(in Chinese)
Neitsch S L, Arbold J G, Kinry J R, et al. Soil and water assessment tool theoretical documentation[R]. Temple: Texas Water Resources Institute, 2011 Harkey G A, van Hoof P L, Landrum P F. Bioavailability of polycyclic aromatic hydrocarbons from a historically contaminated sediment core[J]. Environmental Toxicology and Chemistry, 1995, 14(9): 1551-1560 陈凯麟, 江春波. 地表水环境影响评价数值模拟方法及应用[M]. 北京: 中国环境出版集团, 2018: 48-135 Brusseau M L, Reid M E. Nonequilibrium sorption of organic chemicals by low organic-carbon aquifer materials[J]. Chemosphere, 1991, 22(3-4): 341-350 Birdwell J, Cook R L, Thibodeaux L J. Desorption kinetics of hydrophobic organic chemicals from sediment to water: A review of data and models[J]. Environmental Toxicology and Chemistry, 2007, 26(3): 424-434 孟耀斌, 李想, 宋昊政. 化学物质环境系统行为模拟中大气行为的简化模拟方法: CN113704954A[P]. 2022-08-26 Seibert P, Beyrich F, Gryning S E, et al. Review and intercomparison of operational methods for the determination of the mixing height[J]. Atmospheric Environment, 2000, 34(7): 1001-1027 Laakso L, Grönholm T, Rannik V, et al. Ultrafine particle scavenging coefficients calculated from 6 years field measurements[J]. Atmospheric Environment, 2003, 37(25): 3605-3613 Zhang L M, Gong S L, Padro J, et al. A size-segregated particle dry deposition scheme for an atmospheric aerosol module[J]. Atmospheric Environment, 2001, 35(3): 549-560 Hirabayashi S, Kroll C N, Nowak D J. Component-based development and sensitivity analyses of an air pollutant dry deposition model[J]. Environmental Modelling & Software, 2011, 26(6): 804-816 Hsu F C, Marxmiller R L, Yang A Y S. Study of root uptake and xylem translocation of cinmethylin and related compounds in detopped soybean roots using a pressure chamber technique[J]. Plant Physiology, 1990, 93(4): 1573-1578 孟耀斌. 化学物质环境系统模拟中与植物相关的行为模拟: CN113654950B[P]. 2022-11-11 中国科学院计算机网络信息中心. 地理空间数据云[DB/OL].[2022-04-10]. http://www.gscloud.cn/ 中国科学院地理科学与资源研究所. 中国科学院资源环境科学与数据中心[DB/OL].[2022-04-10]. https://www.resdc.cn/ 中国科学院中国科学院南京土壤研究所. 土壤科学数据库[DB/OL].[2022-04-10]. http://www.issas.ac.cn/ Meng X Y, Wang H. Significance of the China meteorological Assimilation Driving Datasets for the SWAT model (CMADS) of East Asia[J]. Water, 2017, 9(10): 765-770 Meng X Y, Wang H, Chen J. Profound impacts of the China meteorological assimilation driving datasets for the SWAT model (CMADS)[J]. Water, 2019, 11(4): 832-843 中华人民共和国水利部水文局, 中华人民共和国水文年鉴[M]. 北京: 中国水利水电出版社, 2021: 160-181 Hydrological Bureau of the Ministry of Water Resources of the People's Republic of China, Hydrological Yearbook of the People's Republic of China[M]. Beijing: China Water Resources and Hydropower Press, 2021: 160 -181(in Chinese)
Environment Ministry. Reference materials for nonylphenol and nonylphenol ethoxylates[R]. Tokyo: Environment Ministry 2013: 13 Winchell M. ArcSWAT 2009用户指南[M]. 郑州: 黄河水利出版社, 2012: 36-184 尹志杰, 王容, 李磊, 等. 长江流域“2017·07”暴雨洪水分析[J]. 水文, 2019, 39(2): 86-91 Yin Z J, Wang R, Li L, et al. Analysis of storm flood occurred in the Yangtze River Basin in July, 2017[J]. Journal of China Hydrology, 2019, 39(2): 86-91(in Chinese)
周海天, 王海波. 盐城市2018年梅雨期暴雨洪水分析[J]. 水利技术监督, 2020, 28(1): 139-142 Zhou H T, Wang H B. Analysis of rainstorm and flood in 2018 Meiyu period of Yancheng City[J]. Technical Supervision in Water Resources, 2020, 28(1): 139-142(in Chinese)
陈博, 李新峰. 2018年第18号台风“温比亚”引发大范围暴雨过程的诊断分析[J]. 民航学报, 2019, 3(4): 28-34 Chen B, Li X F. The diagnostic analysis of a large-scale rainstorm process caused by typhoon Rumbia (1818)[J]. Journal of Civil Aviation, 2019, 3(4): 28-34(in Chinese)
Moriasi D N, Gitau M W, Pai N, et al. Hydrologic and water quality models: Performance measures and evaluation criteria[J]. Transactions of the Asabe, 2015, 58(6): 1763-1785 Brooke L, Thursby G B. Ambient Aquatic Life Water Quality Criteria, Nonylphenol: Final[R]. Washington DC: United States Environmental Protection Agency, Office of Water, 2005 高培. 壬基酚的水质基准探讨和生态风险评价[D]. 青岛: 中国海洋大学, 2014: 17-29 Gao P. Derivation of water quality criteria for nonylphenol and its application in ecological risk assessment[D]. Qingdao: Ocean University of China, 2014: 17 -29(in Chinese)
田军林, 郝守宁. 面源污染估算模型研究进展[J]. 中国农学通报, 2022, 38(11): 111-115 Tian J L, Hao S N. Research progress of non-point source pollution estimation model[J]. Chinese Agricultural Science Bulletin, 2022, 38(11): 111-115(in Chinese)
尹雄锐, 夏军, 张翔, 等. 水文模拟与预测中的不确定性研究现状与展望[J]. 水力发电, 2006, 32(10): 27-31 Yin X R, Xia J, Zhang X, et al. Recent progress and prospect of the study on uncertainties in hydrological modelling and forecasting[J]. Water Power, 2006, 32(10): 27-31(in Chinese)
-
点击查看大图
计量
- 文章访问数: 2587
- HTML全文浏览数: 2587
- PDF下载数: 146
- 施引文献: 0
下载:
