Risk Analysis::::::::::::::::::::::::::::::::

Risk is defined in different ways in the research literature, but all are essentially similar. For example, the Australian/New Zealand (ASNZ) Risk Management Standard 4360:2004 (SA/SNZ, 2004: p 4) defines risk as ‘the chance of something happening that will have an impact on objectives’. Burgman (2005: p 1) describes risk as ‘the chance, within a time frame, of an adverse event with specific consequences’. Cohrssen and Covello (1989) define risk ‘as the possibility of suffering harm from a hazard’ (Cohrssen and Covello, 1989: p 1), where hazard is a substance or action that can cause harm.

The ASNZ Risk Management Standard 4360:2004 describes the measurement of risk in terms

‘of the magnitude of consequences of an event, should they occur and the likelihood of the event and its associated consequences... ‘ (SA/SNZ, 2004: p 17). The measurement of the level of risk is often referred to as risk analysis, which in the ASNZ Risk Management Standard, forms part of the overall risk management process and is defined as ‘ a systematic process to understand and deduce the level of risk’. However, there is much variation in terminology between different areas and between countries, particularly in the use of the terms “risk analysis”

and “risk assessment”, which are often used synonymously (Harding et al., 2009).

The focus of the present research is to measure the level of risk, and other forms of the risk management process, as defined in SA/SNZ (2004) are not considered in this study. The risk management process from the ASNZ Risk Management Standard (SA/SNZ, 2004) is shown in Figure 2.2 below. In Figure 2.2 the risk analysis phase, which is the focus of this study, has been circled in red. Earlier elements such as establishing the context and identifying risks will to some extent form a natural introduction to the risk analysis phase. However, elements after risk analysis are beyond the scope of the research.

Of the literature reviewed, many studies did not undertake risk assessment or analysis in investigating impacts of sewer overflow. However, the studies undertaken in Sydney and Brisbane, Australia (Bickford et al., 1999; Pollard et al., 2005) did undertake some form of risk assessment. Furthermore, in the USEPA’s report (USEPA, 2004b) the estimate of gastrointestinal illness from exposure to sanitary sewer overflow in recreational waters involved risk analysis methods.

Figure 2.2 Risk Management Process, AS/NZS 4360:2004 (from SA/SNZ, 2004)

The Sydney study used an ecological risk assessment (ERA) method akin to current internationally accepted ERA frameworks, including USEPA’s guidelines for ecological risk assessment (USEPA, 1998) and others (Hansen and Winton, 1995; Suter, 2007). This type of ERA is based on the principles of ecotoxicology and is commonly used in environment protection (Burgman, 2005). The USEPA Ecological Risk Assessment Guidelines (USEPA, 1998: p 2) describe the two major elements of the risk assessment process:‘... characterization of effects and characterization of exposure’. ERA is similarly defined by Parkhurst et al. (1993) as ‘.... a process that evaluates the likelihood of adverse ecological effects as a result of exposure to one or more stressors’ (Parkhurst et al., 1993: p 329). Human health risk assessment in relation to toxicants was also carried out in the Sydney study, based on methods set out in human health risk assessment guidance documents. Human health risk assessment methods are similar to the typical conceptual framework for chemical risk assessment described in the seminal report ‘Risk Assessment in the Federal Government: Managing the Process’

(NRC, 1983), which includes hazard identification, exposure assessment, dose-response assessment and risk characterisation.

Both the Brisbane study and the USEPA report to Congress in relation to estimated gastro intestinal illness in recreational users used quantitative microbial risk assessment (QMRA) methods in identifying potential public health risks. However, the Brisbane study was not able to complete the risk assessment due to lack of data on human exposure. QMRA is described in World Health Organisation Guidelines for Safe Recreational Water Environments (WHO, 2003a:

p 60) as a means whereby the risk to human health can be indirectly estimated ‘....by predicting infection or illness rates given densities of particular pathogens, assumed rates of ingestion and appropriate dose-response models for the exposed population’. QMRA is a typical risk assessment method in the field of water quality where it was developed to estimate the risk to human health from exposure to low doses of pathogens in drinking and recreational water (Haas, 1983). QMRA has since been applied in many studies investigating risks from recreational and drinking water (e.g. Ashbolt et al., 1997; Gerba et al., 1996) and has been incorporated as a risk assessment method into water quality guidelines including various wastewater recycling practices (NRMMC et al., 2006; WHO, 2006a).

ERA and QMRA are common methods used in aquatic ecological and public health risk assessment respectively. There are many other different risk analysis techniques available, both qualitative and quantitative (Burgman, 2005), and Bayesian networks (BNs) which have been chosen for this research is one such method. BNs were originally developed through research into the area of artificial intelligence concerned with reasoning under uncertainty, and were later

adapted for use in other areas such as engineering, medicine and information technology (Hart and Pollino, 2009; Jensen and Nielsen, 2007). More recently, they have been used in the environmental area including risk assessment frameworks for ecological systems (Hart and Pollino, 2009).

BNs use probability theory, an approach to reasoning under uncertainty which differs from other forms of reasoning such as logical reasoning (Jensen and Nielsen, 2007; Korb and Nicholson, 2004). Probability theory has been distinguished from other approaches to reasoning under uncertainty such as possibility theory, sometimes referred to as fuzzy logic (Jensen and Nielsen, 2007). In essence BNs are graphical models which represent causal relationships between variables in a system and allow reasoning about an uncertain domain. Nodes represent important variables within the domain or system, which are connected via a set of directed arcs, thus representing direct dependencies between variables. The strength of these casual links is represented as conditional probabilities (Korb and Nicholson, 2004). There are two types of BNs - Bayesian belief networks and Bayesian decision networks (Hart and Pollino, 2009). Bayesian decision networks differ from belief networks by the incorporation of decision and optionally utility nodes into the model. The focus of this thesis is Bayesian belief networks, which are referred to throughout the thesis by the generic term “Bayesian networks”.

A detailed review of BN applications in the ecology field was undertaken by Henderson et al.

(2008) and included areas in conservation, integrating information across disciplines and risk assessment. Many of the studies referenced in this review (Henderson et al., 2008) and elsewhere incorporated to varying degrees water quality and water pollution issues into BN models (some of these include Borsuk et al., 2004; Dorner et al., 2007; Pollino et al., 2007;

Reckhow, 1999; Shenton et al., 2010; Wooldridge and Done, 2003). However, they relate to other pollution sources or are set in a wider context with no specific focus on sewer overflows.

For example, BNs and Bayesian decision analysis have been used in the area of asset management to aid decisions in relation to upgrading CSSs to minimise CSO emissions (Korving and Clemens, 2002). To the author’s knowledge BNs have not been used to date in the investigation of ecological and health risks from wet weather SSOs and the application of BNs in this area of research is therefore novel. There are many reasons why BNs have been chosen for the analysis of ecological and health risks from sewer overflow and the main reasons are discussed below.

BNs account for uncertainty through the use of probabilities which are used to express values of variables and also relationships between variables within a system. Therefore, BNs are

particularly useful for systems where uncertainty is inherent, which is one of the reasons why they are becoming more popular in ecological risk assessment, where systems are often complex and uncertainties in the understanding of such systems may be large (Pollino et al., 2007). As discussed in the previous section, there is also much variability in the characteristics of wet weather sewer overflows and receiving waterways, thus representing a major source of uncertainty in determining impacts or risks from these events. This type of uncertainty relates to natural randomness or variability and is sometimes referred to as aleatory uncertainty (Ang and Tang, 2007). This is one of the most common sources of uncertainty that the BN is able to represent, which is often lacking in other environmental modelling techniques and in ecotoxicology (Hart and Pollino, 2009; Pollino and Hart, 2005). Other forms of uncertainty which relate to incomplete knowledge are also likely, which is commonly referred to as epistemic uncertainty. Types of epistemic uncertainty that the BN can represent, include ‘statistical variation (for example, parameter measurements) the subjectiveness of judgements through from expert elicitation of model structure to estimation of probabilities; the inherent randomness of some complex systems; and also any disagreement that may arise between multiple experts’

(Hart and Pollino, 2009: p 16).

Due to the variability inherent in the sewer overflow event, as examined in the previous section, the ability to extrapolate findings of various studies to inform management of sewer overflows is limited. BNs can be used to examine different scenarios and the resulting outcomes for a system, thus making the approach more applicable for a particular sewer overflow event or site, and enabling the supply of the necessary information for prioritisation of sites or assets for sewer overflow abatement or other management options. Scenario analysis is possible through the ability of the BN to reason about an uncertain domain, often referred to as belief updating or probabilistic inference (Korb and Nicholson, 2004). BNs can calculate the probability of events based on existing knowledge (referred to as prior probabilities), and given new observations or system changes, these probabilities can be updated in light of the changes (referred to as posterior probability).

This process not only allows different scenarios to be considered, but also allows new data or knowledge through subsequent monitoring or investigative studies to be incorporated into the BN. Therefore, whilst prior probabilities may be based on data that are initially scant, more rigorous and quantitative data from monitoring or further studies can be incorporated into the BN updating beliefs or outcomes of the system. This approach has been described as adaptive management in ecological risk assessment, and is an important step in the risk management cycle (Hart and Pollino, 2009). As shown in the previous section, to date there has been few

studies of the impacts from wet weather sewer overflows, particularly from sanitary sewers.

Since it is likely that future studies will be undertaken in this area, a risk model which allows future knowledge to be incorporated will be of benefit. Furthermore, the BN can highlight areas where data are most needed, thus further assisting the overall management of sewer overflows.

Bayesian probability theory is applied in the BN, thus allowing subjective assessments of probability such as expert opinion to be used, whilst still acknowledging the uncertainty in the data (Hart and Pollino, 2009; Korb and Nicholson, 2004). The frequentist or physical approach to probability, where probabilities are based on objective data quantifying the frequency of occurrence, can also used in the BN (Hart and Pollino, 2009; Korb and Nicholson, 2004).

Extensive quantitative data which may be required in other risk assessment methods such as QMRA and some ERA techniques used in other studies are not always available to authorities responsible for managing sewer overflows. For example, in QMRA typically pathogen rather than faecal indicator data are required which are not routinely collected in the management or monitoring of sewer overflows. The WHO Guidelines for Safe Recreational Water Environments also highlight the current lack of water quality data for many pathogens and the difficulties in applying the QMRA techniques for recreational waters (WHO, 2003a). Furthermore, quantitative data on exposure of the public to contaminated water following overflow is difficult to obtain given the many overflow sites and the sporadic nature of the events. Therefore, the fact that qualitative data can be used where data are scant to express probabilities in the BN makes it accessible for those bodies responsible for managing sewerage systems to use in their specific situations. As already mentioned, more rigorous knowledge obtained at a later time can also be incorporated into the BN, allowing the updating of risk outcomes.