Search Results (2)

The multi-pathway cancer risk (CR) assessment of trihalomethanes (THM) involves considering exposure via ingestion, dermal contact, and inhalation. Inhalation occurs during showering due to the volatilization of THMs from chlorinated water to the air. When assessing inhalation risks, exposure models commonly assume that the initial THM concentration in the shower room is zero. However, this assumption is only valid in private shower rooms where single or infrequent showering events take place. It fails to account for continuous or successive showering events in shared showering facilities. To address this issue, we incorporated the accumulation of THM in the shower room air. We studied a community (population ≈ 20,000) comprising two types of residences with the same water supply: population A with private shower rooms, and population B with communal shower stalls. The total THM concentration in the water was 30.22 ± 14.45 µg L⁻¹. For population A, the total CR was 58.5 × 10⁻⁶, including an inhalation risk of 1.11 × 10⁻⁶. However, for population B, the accumulation of THM in the shower stall air resulted in increased inhalation risk. By the tenth showering event, the inhalation risk was 2.2 × 10⁻⁶, and the equivalent total CR was 59.64 × 10⁻⁶. We found that the CR significantly increased with increasing shower duration. Nevertheless, introducing a ventilation rate of 5 L s⁻¹ in the shower stall reduced the inhalation CR from 1.2 × 10⁻⁶ to 7.9 × 10⁻⁷. Full article

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts. Full article