Causal modelling applied to the risk assessment of a wastewater discharge
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- 作者：Warren L. Paul ; Pat A. Rokahr ; Jeff M. Webb…
- 关键词：Causal modelling ; Graph theoretical structural equation modelling ; Ecological risk assessment ; Wastewater ; Environmental impact study ; Spatiotemporal causal model
- 刊名：Environmental Monitoring and Assessment
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：188
- 期：3
- 全文大小：2,661 KB
- 参考文献：Anderson, M. J., Gorley, R. N., & Clarke, K. R. (2008). PERMANOVA+ for PRIMER: Guide to software and statistical methods. Plymouth, UK: PRIMER-E Ltd.
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Pat A. Rokahr (1)
Jeff M. Webb (2)
Gavin N. Rees (3)
Tim S. Clune (4)
1. Department of Ecology, Environment and Evolution, La Trobe University (Albury-Wodonga campus), P.O. Box 821, Wodonga, 3689, Victoria, Australia
2. Rhithron Associates, Inc., 33 Fort Missoula Road, Missoula, Mt, 59804, USA
3. Murray-Darling Freshwater Research Centre and CSIRO Land and Water Flagship, P.O. Box 991, Wodonga, Victoria, 3689, Australia
4. North East Water, PO Box 863, Wodonga, Victoria, 3689, Australia
Monitoring, Environmental Analysis and Environmental Ecotoxicology
Ecology
Atmospheric Protection, Air Quality Control and Air Pollution
Environmental Management
文摘
Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses. Keywords Causal modelling Graph theoretical structural equation modelling Ecological risk assessment Wastewater Environmental impact study Spatiotemporal causal model