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Disinfection by-products (DBPs) are formed in the water in swimming pools due to reactions between disinfectants (chlorine, bromine, ozone) and the organic matter introduced by bathers and supply water. High concentrations of DBPs are also reported in the air of indoor swimming pools. Based on a robust multisampling program, the levels and variations of DBPs in the air (trichloramine [TCAM] and trihalomethanes [THMs]) and water (THM) were assessed, as well as their precursors (total organic carbon, water temperature, pH, free, and total chlorine) and proxies (CO2 and relative humidity) in four indoor chlorinated swimming pools. High-frequency sampling was conducted during one high-attendance day for each pool. This study focused on parameters that are easy to measure in order to develop models for predicting levels of THMs and TCAM in the air. The results showed that the number of bathers had an important impact on the levels of THMs and TCAM, with a two-to-three-fold increase in air chloroform (up to 110 μg/m3) and a two-to-four-fold increase in TCAM (up to 0.52 mg/m3) shortly after pools opened. The results of this study for the first time showed that CO2 and relative humidity can serve as proxies for monitoring variations in airborne THMs and TCAM. Our results highlight the good predictive capacity of the developed models and their potential for use in day-to-day monitoring. This could help optimize and control DBPs formation in the air of indoor swimming pools and reduce contaminant exposure for both pool employees and users. [ABSTRACT FROM AUTHOR]

The presence of off-flavour compounds such as geosmin, often found in raw water, significantly reduces the organoleptic quality of distributed water and diverts the consumer from its use. To adapt water treatment processes to eliminate these compounds, it is necessary to be able to identify them quickly. Routine analysis could be considered a solution, but it is expensive and delays associated with obtaining the results of analysis are often important, thereby constituting a serious disadvantage. The development of decision-making tools such as predictive models seems to be an economic and feasible solution to counterbalance the limitations of analytical methods. Among these tools, multi-linear regression and principal component regression are easy to implement. However, due to certain disadvantages inherent in these methods (multicollinearity or non-linearity of the processes), the use of emergent models involving artificial neurons networks such as multi-layer perceptron could prove to be an interesting alternative. In a previous paper (Parinet et al., Water Res 44: 5847-5856, ), the possible parameters that affect the variability of taste and odour compounds were investigated using principal component analysis. In the present study, we expand the research by comparing the performance of three tools using different modelling scenarios (multi-linear regression, principal component regression and multi-layer perceptron) to model geosmin in drinking water sources using 38 microbiological and physicochemical parameters. Three very different sources of water, in terms of quality, were selected for the study. These sources supply drinking water to the Québec City area (Canada) and its vicinity, and were monitored three times per month over a 1-year period. Seven different modelling methods were tested for predicting geosmin in these sources. The comparison of the seven different models showed that simple models based on multi-linear regression provide sufficient predictive capacity with performance levels comparable to those obtained with artificial neural networks. The multi-linear regression model ( R = 0.657, <0.001) used only four variables ( phaeophytin, sum of green algae, chlorophyll-a and potential Redox) in comparison with ten variables (potassium, heterotrophic bacteria, organic nitrogen, total nitrogen, phaeophytin, total organic carbon, sum of green algae, potential Redox, UV absorbance at 254 nm and atypical bacteria) for the best model obtained with artificial neural networks ( R = 0.843). [ABSTRACT FROM AUTHOR]

During drinking water treatment and distribution, chlorine reacts with organic matter occurring in water to form various chlorination by-products (CBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). This paper presents the occurrence of THMs and HAAs in different water distribution systems (DS) of the same region and their modelling for exposure assessment purposes. This study was conducted in eight DS supplying chlorinated water to the population of Québec City, Canada. These systems differ in type of water source (i.e. surface, ground or mixed water), in treatment applied at the plant, and in size and structure of the DS. Two spatio-temporal databases for THMs and HAAs were implemented, one for model development and the other for model validation. The analysis of the data demonstrates significant seasonal and spatial variations of these compounds. A multi-level statistical modelling approach was applied to estimate the ranges for occurrence of THMs and HAAs in the eight DS (i.e. a single model for the study region for each CBP species). The modelling approach integrates available or easily measurable parameters. For both THMs and HAAs, a two-level model considering a sampling-site random effect was selected among various models initially developed. The model capacity for estimating the presence of THMs and HAAs in drinking water and its usefulness for exposure assessment purposes in the studied region was demonstrated. [ABSTRACT FROM AUTHOR]