Making sense of ecotoxicological test results: Towards application of process-based models

被引:178
作者
Jager, Tjalling
Heugens, Evelyn H. W.
Kooijman, Sebastiaan A. L. M.
机构
[1] Vrije Univ Amsterdam, Dept Theoret Biol, FALW, NL-1081 HV Amsterdam, Netherlands
[2] Univ Amsterdam, Fac Sci, Dept Aquat Ecol & Ecotoxicol, NL-1090 GB Amsterdam, Netherlands
关键词
effects assessment; toxicity testing; dose-response modeling; DEBtox; REACH;
D O I
10.1007/s10646-006-0060-x
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The environmental risk of chemicals is routinely assessed by comparing predicted exposure levels to predicted no-effect levels for ecosystems. Although process-based models are commonly used in exposure assessment, the assessment of effects usually comprises purely descriptive models and rules-of-thumb. The problems with this approach start with the analysis of laboratory ecotoxicity tests, because only a limited amount of information is extracted. Standard summary statistics (NOEC, ECx, LC50) are of limited use in part because they change with exposure duration in a manner that varies with the tested species and the toxicant. As an alternative, process-based models are available. These models allow for toxicity measures that are independent of exposure time, make efficient use of the available data from routine toxicity tests, and are better suited for educated extrapolations (e.g., from individual to population, and from continuous to pulse exposure). These capabilities can be used to improve regulatory decisions and allow for a more efficient assessment of effects, which ultimately will reduce the need for animal testing. Process-based modeling also can help to achieve the goals laid out in REACH, the new strategy of the European Commission in dealing with chemicals. This discussion is illustrated with effects data for Daphnia magna, analyzed by the DEBtox model.
引用
收藏
页码:305 / 314
页数:10
相关论文
共 50 条
[1]   Responses to stress of Caenorhabditis elegans populations with different reproductive strategies [J].
Alvarez, OA ;
Jager, T ;
Kooijman, SALM ;
Kammenga, JE .
FUNCTIONAL ECOLOGY, 2005, 19 (04) :656-664
[2]  
[Anonymous], 2000, DYNAMIC ENERGY MASS, DOI DOI 10.1017/CBO9780511565403
[3]  
[Anonymous], 1992, ECOLOGICAL RISK ESTI
[4]   Statistical analysis of bioassays, based on hazard modelling [J].
Bedaux, J. J. M. ;
Kooijman, S. A. L. M. .
ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 1994, 1 (04) :303-314
[5]  
BRADBURY SP, 2004, ENVIRON SCI TECHNOL, V38, pA70
[6]   A critical evaluation of safety (uncertainty) factors for ecological risk assessment [J].
Chapman, PM ;
Fairbrother, A ;
Brown, D .
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 1998, 17 (01) :99-108
[7]  
CHRISTENSEN FM, 2003, GMI, V41, P5
[8]  
Crane M, 2000, ENVIRON TOXICOL CHEM, V19, P516, DOI [10.1897/1551-5028(2000)019&lt
[9]  
0516:WLOEIA&gt
[10]  
2.3.CO