While standard methods for chlorine taste and odor (T&O) detection and rejection thresholds exist, little rigorous research has been conducted on T&O thresholds in humanitarian settings. To fill this gap, we estimated chlorine T&O detection and rejection thresholds using the Forced-Choice Triangle Test (FCT) and Flavor Rating Assessment (FRA) standard methods in a Ugandan refugee settlement. We conducted these tests with 410 male and female participants, aged 5–72 years, using piped and trucked surface water and bottled water. We also conducted 30 focus group discussions and 37 surveys with data collectors. Median chlorine detection thresholds were 0.56, 1.40, and 1.67 mg/L, for piped, trucked, and bottled water, respectively. Rejection was calculated using ratings (as per the method) and five different previously-used thresholds, and was 1.6, 2.0, and 1.6 mg/L (ratings) and 2.19, 1.85, and 1.67 mg/L (using the FCT threshold method with FRA data) for piped, trucked, and bottled water, respectively. Detection and rejection thresholds were significantly associated with water quality (including turbidity, pH, electrical conductivity, and temperature), participant age and education. We observed high intra- and inter-individual variability, which decreased with participant experience. We found the method used to calculate rejection thresholds influenced results, highlighting the need for a standard method to analyze FRA data. Data collectors and participants recommended shortening protocols and evaluating fewer concentrations, and highlighted difficulties in creating accurate FRC concentrations for testing. This study provides insights on using standard methods to assess T&O thresholds in untrained populations, and results are being used to develop rapid field T&O protocols for humanitarian settings.
Journal Article > ResearchFull Text
PLOS Water. 2025 February 11; Volume 4 (Issue 2); DOI:10.1371/journal.pwat.0000267
Heylen C, String G, Naliyongo D, Ali SI, Brown J, et al.
PLOS Water. 2025 February 11; Volume 4 (Issue 2); DOI:10.1371/journal.pwat.0000267
Journal Article > ResearchFull Text
PLOS Water. 2022 September 6; Volume 1 (Issue 9); e0000040.; DOI:10.1371/journal.pwat.0000040
De Santi M, Ali SI, Arnold M, Fesselet JF, Hyvärinen AMJ, et al.
PLOS Water. 2022 September 6; Volume 1 (Issue 9); e0000040.; DOI:10.1371/journal.pwat.0000040
Ensuring sufficient free residual chlorine (FRC) up to the time and place water is consumed in refugee settlements is essential for preventing the spread of waterborne illnesses. Water system operators need accurate forecasts of FRC during the household storage period. However, factors that drive FRC decay after water leaves the piped distribution system vary substantially, introducing significant uncertainty when modelling point-of-consumption FRC. Artificial neural network (ANN) ensemble forecasting systems (EFS) can account for this uncertainty by generating probabilistic forecasts of point-of-consumption FRC. ANNs are typically trained using symmetrical error metrics like mean squared error (MSE), but this leads to forecast underdispersion forecasts (the spread of the forecast is smaller than the spread of the observations). This study proposes to solve forecast underdispersion by training an ANN-EFS using cost functions that combine alternative metrics (Nash-Sutcliffe efficiency, Kling Gupta Efficiency, Index of Agreement) with cost-sensitive learning (inverse FRC weighting, class-based FRC weighting, inverse frequency weighting). The ANN-EFS trained with each cost function was evaluated using water quality data from refugee settlements in Bangladesh and Tanzania by comparing the percent capture, confidence interval reliability diagrams, rank histograms, and the continuous ranked probability. Training the ANN-EFS using the cost functions developed in this study produced up to a 70% improvement in forecast reliability and dispersion compared to the baseline cost function (MSE), with the best performance typically obtained by training the model using Kling-Gupta Efficiency and inverse frequency weighting. Our findings demonstrate that training the ANN-EFS using alternative metrics and cost-sensitive learning can improve the quality of forecasts of point-of-consumption FRC and better account for uncertainty in post-distribution chlorine decay. These techniques can enable humanitarian responders to ensure sufficient FRC more reliably at the point-of-consumption, thereby preventing the spread of waterborne illnesses.