| Title |
Causal modelling applied to the risk assessment of a wastewater discharge
|
|---|---|
| Published in |
Environmental Monitoring and Assessment, February 2016
|
| DOI | 10.1007/s10661-015-5074-5 |
| Pubmed ID | |
| Authors |
Warren L. Paul, Pat A. Rokahr, Jeff M. Webb, Gavin N. Rees, Tim S. Clune |
| Abstract |
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. |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 27% |
| Researcher | 9 | 20% |
| Student > Master | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Student > Doctoral Student | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 9 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 20% |
| Environmental Science | 6 | 14% |
| Computer Science | 4 | 9% |
| Nursing and Health Professions | 2 | 5% |
| Earth and Planetary Sciences | 2 | 5% |
| Other | 7 | 16% |
| Unknown | 14 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2016.
All research outputs
#21,358,731
of 23,854,458 outputs
Outputs from Environmental Monitoring and Assessment
#2,266
of 2,748 outputs
Outputs of similar age
#343,581
of 403,618 outputs
Outputs of similar age from Environmental Monitoring and Assessment
#43
of 55 outputs
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