Contaminant Mass Balances Obtained with 60- and 1-s Water-quality Time Steps^a.

<p>Contaminant Mass Balances Obtained with 60- and 1-s Water-quality Time Steps<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168051#t008fn001" target="_blank">^a</a>.</p

Assessing Inhalation Exposures Associated with Contamination Events in Water Distribution Systems

<div><p>When a water distribution system (WDS) is contaminated, short-term inhalation exposures to airborne contaminants could occur as the result of domestic water use. The most important domestic sources of such exposures are likely to be showering and the use of aerosol-producing humidifiers, i.e., ultrasonic and impeller (cool-mist) units. A framework is presented for assessing the potential effects of short-term, system-wide inhalation exposures that could result from such activities during a contamination event. This framework utilizes available statistical models for showering frequency and duration, available exposure models for showering and humidifier use, and experimental results on both aerosol generation and the volatilization of chemicals during showering. New models for the times when showering occurs are developed using time-use data for the United States. Given a lack of similar models for how humidifiers are used, or the information needed to develop them, an analysis of the sensitivity of results to assumptions concerning humidifier use is presented. The framework is applied using network models for three actual WDSs. Simple models are developed for estimating upper bounds on the potential effects of system-wide inhalation exposures associated with showering and humidifier use. From a system-wide, population perspective, showering could result in significant inhalation doses of volatile chemical contaminants, and humidifier use could result in significant inhalation doses of microbial contaminants during a contamination event. From a system-wide perspective, showering is unlikely to be associated with significant doses of microbial contaminants. Given the potential importance of humidifiers as a source of airborne contaminants during a contamination event, an improved understanding of the nature of humidifier use is warranted.</p></div

Estimation of Doses.

<p>Estimation of Doses.</p

Influence of the fraction of the population using ultrasonic humidifiers on 95th percentile impacts.

<p>Results are for Network E1, an injection at 0:00 with a mass equal to 10 kg, and a fill time for all humidifiers of 22:00. Humidifiers are used for eight hours each day. The different lines in the plot provide results for different fractions of the population that use humidifiers.</p

Maximum impacts from inhalation of volatile and nonvolatile contaminants during showering.

<p>Results are for Network E3 from two separate simulations, both using 10-kg injections at 0:00 hours. Nonvolatile contaminants are in aerosols in the shower air.</p

Weighted histograms of starting times of grooming events reported in ATUS from 2003 to 2012.

<p>Results are presented for cases for which one and two events were reported. Results for single events are separated into cases in which events occurred before noon and after noon (top row). For cases with two events, results are presented separately for events occurring before and after noon (middle row) and also for the first and second of the two events (bottom row).</p

Impacts associated with inhalation of a microbial contaminant due to ultrasonic humidifier use.

<p>The upper figure shows 50th to 100th percentile impacts for Network E2. The lower figure compares 95th percentile impacts for the three networks. Ten kilograms of contaminant, with 10⁸ cells/mg, were injected at 0:00 hours for all threes networks. Twenty percent of the population is assumed to use humidifiers. Humidifiers are filled at 22:00 and used for 8 hours.</p

Differences in 95th and 100th Percentile Impacts Obtained with 60- and 1-s Water-quality Time Steps.^a.

<p>Differences in 95th and 100th Percentile Impacts Obtained with 60- and 1-s Water-quality Time Steps.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168051#t009fn001" target="_blank">^a</a>.</p

Estimated weighted probability density function for starting times of single ATUS grooming events.

<p>N = 54,094.</p