Present and Future Human Emissions of Rotavirus and Escherichia coli to Uganda's Surface Waters

Abstract

Rotavirus (RV) and diarrheagenic Escherichia coli are waterborne pathogens commonly causing diarrhea in children below five years old worldwide. Our study is a first step toward a loads–concentrations–risk modeling and scenario analysis framework. We analyzed current and future human RV and indicator E. coli (EC) emissions from sanitation facilities to surface waters in Uganda using two process‐based models. Emissions were estimated for the baseline year 2015 and for three scenarios in 2030 using population, excretion rates, sanitation types, and wastewater treatment. The first model is a downscaled GloWPa‐Rota H1 version, producing emissions at a 1‐km2 resolution. The second model is newly developed for Kampala and adds emissions from pit latrines and septic tanks excluded in the first model. The scenarios Business as Usual, Industrious, and Low Emissions reflect government prospects in sanitation coverage and wastewater treatment. For the first model, 6.14 × 1014 RV particles d−1 and 1.31 × 1012 EC colony‐forming units (CFU) d−1 are emitted to surface waters in 2015. The RV emissions are expected to increase in 2030 by 75% for Business as Usual and 212% for Industrious and decrease by 58% in Low Emissions. Emissions from the second model are higher for Kampala than in the first model, at 3.74 × 1014 vs. 5.95 × 1013 RV particles d−1 and 8.18 × 1011 vs. 1.75 × 1011 EC CFU d−1 in 2015, most of which come from the onsite‐not‐contained category. Simulated emissions for Kampala show the importance of including onsite sanitation in our modeling. Our study is replicable in other locations and helps identify key emission sources, their hotspots, and the importance of wastewater treatment. The scenarios can guide future sanitation safety planning. Core Ideas Rotavirus and E. coli emissions from sanitation facilities reach surface water. Using modeling and scenario analysis, we simulated these emissions for Uganda. High‐emission areas are Kampala and other urban areas. A new model including onsite sanitation shows higher emissions in Kampala. Future emissions are reducible through sanitation planning.

Mapping pathogen emissions to surface water using a global model with scenario analysis

“…However, in many parts of the world, a large part of the sludge from pits and tanks reaches the surface water after dumping on land or in the water (AECOM International Development Inc. and EAWAG/SANDEC, 2010). Therefore, in areas where large fractions of the population use pit latrines or septic tanks, these systems can lead to significant emissions (Okaali and Hofstra, 2018) that are not accounted for in this model. This emission pathway is currently being implemented in a future model version.…”

“…Whelan et al, 2014;Schijven et al, 2015). An example of an application in a data sparse area is the application to Uganda as a country (Okaali and Hofstra, 2018). Additionally, also river catchments or cities could be suitable study areas.…”

Microbial Water Quality: Monitoring and Modeling

“…Since contradictions may exist between management practices addressing different goals of restoration measures (Crohn and Bianchi, 2008), it is of paramount importance that interactions between different processes and controls of microbial water quality are reflected in the models. Quantifying such interactions should be a research priority (Oliver et al, 2016). …”

“…Low microbial water quality continues to be a daunting public health issue in developing countries . Okaali and Hofstra (2018) analyzed the effect of changes in sanitation on concentrations of Rotavirus and the indicator organism E. coli in surface water in Uganda using modeling and scenario analysis. The authors show that a plausible set of measures may substantially decrease pathogen and indicator emissions.…”

“…Monitoring and modeling are intertwined activities in microbial water quality research and applications. Substantial advances have been made in these areas (Fujioka et al, 2015; Oliver et al, 2016; Bentzon‐Tilia et al, 2016; Lothrop et al, 2018). Yet, many gaps in our knowledge remain.…”