Hazard Identification

multi-objective optimization framework.

To allow a flexible analysis, to address the needs of many stakeholders, and to allow for multi-objective approaches in later research, it is necessary to provide a framework that is flexible to adapt to the risk objectives demanded by different stakeholders. A similar idea has been followed by de Barros et al. (2012). They introduced environmental performance metrics, accounting for many objectives separately in one model. Here, the mass-discharge-based well vulnerability criteria provide the necessary information to derive all transport-related risk measures (see Fig. 7.2) and account for the multiple stakeholder objectives. For example, from the intermediate risk level of well down-time (W ET), the performance of risk measure”customer minutes lost“ (CML, e.g., Lindhe et al., 2009) can be derived. Can-cer risk (e.g., Rodak and Silliman, 2012), human-health-related hazard quotients (HQ, US EPA, 1989) or DALY can be derived from the cumulative contaminant load for a certain time period (Contaminant Load Exposure,CLE). Toxic units (TU, e.g., McKnight et al., 2012) can be derived from the maximum concentration load (M CR) and so forth.

As mentioned before, another advantage of using well vulnerability criteria in the STORM context is that they allow aggregation of risk sources across different contaminant types (see Section 7.5). Contrary, the information value of a chosen STORM (e.g., well exposure time, maximum concentration ratio) strongly depends on the hazard types (e.g., long-term or pulse source) that may pose a risk to water supply in the catchment. Tab. C.1 provides a brief overview on STORM and which metrics are suitable to assess the risk of certain hazard types. More details will be provided in Section 7.5.

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Figure 7.3.: Necessary hazard information to assess hazard-specific mass flux estimates at the supply well, by first retrieving hazard property information from a hazard database and secondly choosing the individual physically-based models within the logical risk assessment model.

7.2.1. Hazard Database

The hazard database contains all information collected for each risk sourceh. The hazard database stores information on all hazard locations, such as the amount of massm₀ being handled, the locationxa risk source is mapped to, the failure frequencyλfor a given time unit, and which contaminant typejis stored at the risk location. A risk source may contain several contaminantsj, which are treated independently as individual risk sourceshin the hazard database. Within a second database, the contaminant-specific transport properties such as water solubility, retardation and degradation factors of each contaminant typejare stored. Thus, both databases are linked to each other. Fig. 7.4 shows several risk sources that are categorized according to spatial (column) and temporal (row) hazard type. The additional information on spatial, temporal andcontaminant hazard type triggers the model choice within the individual modules of STORM.

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7.2.2. Spatial Hazard Types

Kourakos et al. (2012) showed that non-point sources need a different treatment than point sources in monitoring, concentration level assessment and implementation of regulatory frameworks. Indeed, transport time statistics and magnitude of impact at the well depend on the location and areal distribution of the contaminant release. Therefore, the risk ment approach has to be flexible to adapt to the spatial properties of hazards. Risk assess-ment of contaminated sites often uses point sources, neglecting the aspect of line or areal sources (e.g., Tait et al., 2004; Troldborg et al., 2008). Punctual risk sources, such as under-ground storage tanks, gas stations, sewage treatment plants, etc. are mapped in STORM to one location and to one discretization cell of numerical transport models (see Fig. 7.4). Line hazards, such as pipe networks, roads, etc., may fail at independent locations at different times, acting as individual point sources after failure. However, they may also emit contam-inant loads along an entire line segment that spans several grid cells.Areal risk sources, such

as farm fields, cemeteries, etc., release harmful mass over a larger area that often extends over many discretization cells of the numerical transport model. This inevitably leads to different arrival times and impact intensity behavior at the well for a single hazard, even without considering dispersion effects.

Hazards that follow a line source are discretized intol = 1, . . . , L single points. The initial massm_0,h,j released from a risk source at each locationx_lis assumed to be constant for line hazards. The failure rate per locationldecreases uniformly to:

λ^l_h,j = λ_h,j

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Nevertheless, it is possible to assign higher failure ratesλ^l_h,j to individual locationsland vice versa, such as representing dangerous road sections, corroded pipe material and so forth. This procedure allows failure of one single risk source at different locations at the same time. This might be true, e.g., for sewage canals that leak at more than one position at the same time.

Areal risk sources that release contaminant mass on a larger area than individual discretiza-tion cells are treated the same way as line hazards (e.g., Srinivasan et al., 2012). However, the massm_0,h,j being released due to an event is now distributed across allLlocations, leading to a lower mass release potentialm^l_0,h,j. The likelihood of failure for each location stays con-stant (λ^l_h,j =λ_h,j). After pre-processing the risk source according to the spatial hazard type (line/area), the consecutive modules1to 10of STORM can be performed as if all hazards were point hazards. For prioritization of hazards (module10), the individual point sources of a line and area hazard are convoluted to assess the total impact by the spatially hazard source on the overall risk.

7.2.3. Temporal Hazard Types

Hazards not only vary in their spatial extension, but also in their release duration (see Fig. 7.4). This is most obvious by comparing a rather short event, such as a sewage overflow due to heavy rainfall, and a more extended event, such as leachates from municipal waste landfill sites. The temporal distribution of mass release influences the level of risk and the strategy to encounter risk by available management options. Pulse events may have a higher impact at the receptor, but are limited in duration, whereas continuous releases are often less severe in their resulting concentration levels, but more severe in their duration of impact.

The most tragic combination is long-term contamination at unacceptably high concentration levels. According to Fischhoff (1990), these long term pollutions may lead to closure of the facility, depending on the risk perception of water stakeholders, the capability of post-water quality treatment and the economic feasibility to deal with this long-term contamination.

Pulse hazards, such as animal fecal releases, truck accidents, etc., are of short duration, en-abling stakeholders to manage risk on a shorter time scales, e.g., compensating well down-time by prior water storage. This is different with chlorinated solvents and other contami-nants with slow dissolution, high retardation rates and constant sources. Such contamicontami-nants can possibly act as sources for long-term contamination (e.g., Chambon et al., 2011). Thus, risk management options may unfold their effectiveness only after years.

Contaminant types of continuous hazards, such as leakage of municipal waste sites at far distance to the well, may lead at some point to long-term contamination with low concentra-tion levels. In general, these background concentraconcentra-tions are often negligible in risk assess-ment as being too small to directly cause harm. Nevertheless, it is important to notice that background concentration reduces the distance to a critical concentration levelccrit. Thus, the dilution potential of the pumped drinking water to cope with contaminants of the same type is lowered, and the risk of concentration levels induced by the hazards within the same chemical class exceeding a given threshold level increases.

7.2.4. Chemical Hazard Types

There are many different types of contaminants, ranging from microorganisms to chlori-nated solvents. Tait et al. (2004) and Verreydt et al. (2012) assume contaminants as solutes.

Troldborg et al. (2008) account for non-aqueous phase liquids (NAPL) that form either pools on top of the water table (LNAPL) or below the groundwater table (DNAPL). These pools lead to retarded and extended concentration levels at the well, as only a small amount of NAPL per time is dissolved into the by-passing groundwater flow. Therefore, transport-based risk estimates are sensitive to the chemical characteristics of hazards that dictate its miscibility with water.

The hazard database stores information on chemical properties of individual contaminant types (chemical database). Furthermore, it allows for each hazard to set the sub-model choice for dissolution into groundwater.