DOE OSTI2010
An understanding of the nature of the adverse effects that could be associated with contamination events in water distribution systems is necessary for carrying out vulnerability analyses and designing contamination warning systems. This study examines the adverse effects of contamination events using models for 12 actual water systems that serve populations ranging from about 104 to over 106 persons. The measure of adverse effects that we use is the number of people who are exposed to a contaminant above some dose level due to ingestion of contaminated tap water. For this study the number of such people defines the impact associated with an event. We consider a wide range of dose levels in order to accommodate a wide range of potential contaminants. For a particular contaminant, dose level can be related to a health effects level. For example, a dose level could correspond to the median lethal dose, i.e., the dose that would be fatal to 50% of the exposed population. Highly toxic contaminants may be associated with a particular response at a very low dose level, whereas contaminants with low toxicity may only be associated with the same response at a much higher dose level. This report focuses on the sensitivity of impacts to five factors that either define the nature of a contamination event or involve assumptions that are used in assessing exposure to the contaminant: (1) duration of contaminant injection, (2) time of contaminant injection, (3) quantity or mass of contaminant injected, (4) population distribution in the water distribution system, and (5) the ingestion pattern of the potentially exposed population. For each of these factors, the sensitivities of impacts to injection location and contaminant toxicity are also examined. For all the factors considered, sensitivity tends to increase with dose level (i.e., decreasing toxicity) of the contaminant, with considerable inter-network variability. With the exception of the population distribution (factor 4 above), sensitivity to the various factors tends to be highest at lower impact levels (e.g., impacts below the 80th percentile). Conversely, for the population distribution factor, sensitivity is lowest at the lower impact levels. For injection duration, impacts generally are higher for longer duration injections. Definite patterns are present in the sensitivity of impacts to injection time, but these vary substantially across the networks. As would be expected, impacts are larger for larger mass injections, but the sensitivity can vary dramatically depending on dose level and the network. Estimated impacts can be sensitive to assumptions about how population is distributed in a network, particularly at high impact levels and high dose levels, again with considerable variability across networks. Finally, impacts can be sensitive to assumptions about ingestion patterns in the potentially exposed population, with sensitivities varying across networks and tending to be highest for high dose levels. When considered in combination with the other factors (but not including the ingestion model used), impacts at low dose levels (levels at which the effects of highly toxic contaminants can be significant) are most sensitive to injection duration. Similarly, when considered in combination, impacts at higher dose levels (levels required for significant effects from contaminants with low toxicity) are most sensitive to injection mass. At low dose levels, for a likely range in injection masses, impacts are not particularly sensitive to injection mass. The influence of the various factors on the location of high percentile injection locations can be as important or more important than their influence on the magnitudes of impacts. In addition, the choice of contaminant has a major influence on which nodes are high impact injection locations. The sharing (overlap) of the same high-percentile injection nodes for different values of a factor can vary substantially by contaminant and impact level (percentile of impact). Overlap tends to decrease with decreasing toxicity of the contaminant and increasing impact level for all the factors considered, with considerable variability among the networks. Our results demonstrate the great variability in the impacts and the sensitivity of impacts to various factors that can occur in different water systems during a contamination event. Although definite patterns exist in the nature and magnitude of impacts and sensitivities for the diverse set of water systems examined, these results also show that substantial inter-network variability limits the ability to predict or extrapolate these results to other systems. Therefore, although water systems do exhibit some similarities in the magnitude and pattern of impacts during contamination events, each individual water system should be treated as unique.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗