The Safe Water Optimization Tool (SWOT) generates evidence-based point-of-distribution free residual chlorine (FRC) targets to adjust chlorine dosing by operators and ensure water quality at point-of-consumption. To investigate SWOT effectiveness in surface waters, we conducted two before-and-after mixed-method evaluations in a Uganda refugee settlement served by piped and trucked surface water systems. We surveyed 888 users on water knowledge, attitudes, and practices; collected 2768 water samples to evaluate FRC,Escherichia coli, and disinfection by-products (DBPs) concentrations; and conducted nine key-informant interviews with system operators about SWOT implementation. After baseline data collection, SWOT chlorination targets were generated, increasing point-of-distribution FRC targets from 0.2 to 0.7-0.8 mg/L and from 0.3 to 0.9 mg/L for piped and trucked systems, respectively. At endline, household point-of-consumption FRC ≥ 0.2 mg/L increased from 23 to 35% and from 8 to 42% in the two systems. With these increases, we did not observe increased chlorinated water rejection or DBPs concentrations exceeding international guidelines. Informants reported that SWOT implementation increased knowledge and capacity and improved operations. Overall, SWOT-generated chlorination targets increased chlorine dosage, which improved household water quality in surface waters although less than previously documented with groundwater sources. Additional operator support on prechlorination water treatment processes is needed to ensure maximally effective SWOT implementation for surface water sources.
Journal Article > ResearchFull Text
Water Res. 2020 November 16; Volume 189; 116642.; DOI:10.1016/j.watres.2020.116642
Ali SI, Ali SS, Fesselet JF
Water Res. 2020 November 16; Volume 189; 116642.; DOI:10.1016/j.watres.2020.116642
The current Sphere guideline for water chlorination in humanitarian emergencies fails to reliably ensure household water safety in refugee camps. We investigated post-distribution chlorine decay and household water safety in refugee camps in South Sudan, Jordan, and Rwanda between 2013-2015 with the goal of demonstrating an approach for generating site-specific and evidence-based chlorination targets that better ensure household water safety than the status quo Sphere guideline. In each of four field studies we conducted, we observed how water quality changed between distribution and point of consumption. We implemented a nonlinear optimization approach for the novel technical challenge of modelling post-distribution chlorine decay in order to generate estimates on what free residual chlorine (FRC) levels must be at water distribution points, in order to provide adequate FRC protection up to the point of consumption in households many hours later at each site. The site-specific FRC targets developed through this modelling approach improved the proportion of households having sufficient chlorine residual (i.e., ≥0.2 mg/L FRC) at the point of consumption in three out of four field studies (South Sudan 2013, Jordan 2014, and Rwanda 2015). These sites tended to be hotter (i.e., average mid-afternoon air temperatures >30°C) and/or had poorer water, sanitation, and hygiene (WASH) conditions, contributing to considerable chlorine decay between distribution and consumption. Our modelling approach did not work as well where chlorine decay was small in absolute terms (Jordan 2015). In such settings, which were cooler (20 to 30°C) and had better WASH conditions, we found that the upper range of the current Sphere chlorination guideline (i.e., 0.5 mg/L FRC) provided sufficient residual chlorine for ensuring household water safety up to 24 hours post-distribution. Site-specific and evidence-based chlorination targets generated from post-distribution chlorine decay modelling could help improve household water safety and public health outcomes in refugee camp settings where the current Sphere chlorination guideline does not provide adequate residual protection. Water quality monitoring in refugee/IDP camps should shift focus from distribution points to household points of consumption in order to monitor if the intended public health goal of safe water at the point of consumption is being achieved.
Journal Article > ResearchFull Text
Environ Health Perspect. 2024 October 9; Volume 58 (Issue 42); 18531-18540.; DOI:10.1021/acs.est.4c04240
Heylen C, String G, Naliyongo D, Ali SI, Brown J, et al.
Environ Health Perspect. 2024 October 9; Volume 58 (Issue 42); 18531-18540.; DOI:10.1021/acs.est.4c04240
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.