DOI: 10.18174/426782
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Cryptosporidium in rivers of the world: the GloWPa-Crypto model

Abstract: Chapter 1 Introduction Chapter 2 Advancing waterborne pathogen modelling: lessons from global nutrient export models Chapter 3 Modelling the impact of sanitation, population growth and urbanisation on human emissions of Cryptosporidium to surface waters-a case study for Bangladesh and India Chapter 4 Impacts of population growth, urbanisation and sanitation changes on global human Cryptosporidium emissions to surface water Chapter 5 Prevalence of Cryptosporidium infection in livestock and oocyst concentrations… Show more

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Cited by 2 publications
(2 citation statements)
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“…However, when modeled emissions are integrated with hydrology, the concentrations in the surface water can be validated with measured concentrations. Cryptosporidium concentrations simulated with the GloWPa-H1 model have been compared with observational data and showed reasonable results given several assumptions made in this model to estimate livestock emissions, pathogen runoff from the land, and so on, in a case study for Bangladesh and India (Vermeulen, 2018). Simulating concentrations was beyond the scope of this study.…”
Section: Discussionmentioning
confidence: 88%
“…However, when modeled emissions are integrated with hydrology, the concentrations in the surface water can be validated with measured concentrations. Cryptosporidium concentrations simulated with the GloWPa-H1 model have been compared with observational data and showed reasonable results given several assumptions made in this model to estimate livestock emissions, pathogen runoff from the land, and so on, in a case study for Bangladesh and India (Vermeulen, 2018). Simulating concentrations was beyond the scope of this study.…”
Section: Discussionmentioning
confidence: 88%
“…Modelling water quality can take place at a range of scales from the catchment through to regions, whole continents and even more recently at the global scale [17]. For pollutants global models exist for nutrients [18], plastics [19], pathogens [20,21], pharmaceuticals [22] and in a simple way (see Section 4), ENMs [23] among others (see this issue for more details of these types of model [24,25]) . Such models are useful for scenario analysis of climate, socio-economic change, policy assessment [26], estimating concentrations where observed data are not available [27] or giving a global picture for a particular water quality indicator of concern [28].…”
Section: Boxmentioning
confidence: 99%