Exploring global Cryptosporidium emissions to surface water
Highlights
► Cryptosporidium is a major cause of diarrhoea worldwide. ► We present the first model-based inventory of global Cryptosporidium emissions. ► We estimate point and nonpoint source emissions from wastewater and agricultural lands. ► Results indicate a total global emission of 3 × 1017 oocysts per year. ► Uncertainties are large and areas for further study are identified.
Introduction
Diarrhoea is the third leading cause of death in low-income countries (WHO, 2008). In these countries 20% of all cases of childhood diarrhoea are caused by the protozoan parasite Cryptosporidium. Moreover, Cryptosporidium may cause death of immunocompromised people, for instance people suffering from AIDS (Mosier and Oberst, 2000). In developed countries outbreaks of cryptosporidiosis also occur regularly. One of the largest documented outbreaks took place in Milwaukee in 1993, due to a malfunctioning drinking water treatment facility. As a result, 403,000 people suffered from intestinal illnesses and over 100 people died (Hoxie et al., 1997, Mac Kenzie et al., 1994).
Cryptosporidium is a protozoan parasite. Cryptosporidium oocysts are highly resistant to adverse conditions and can survive for months before being ingested by new hosts (Medema et al., 2009). Environmental routes of transport of oocysts to new hosts are drinking water, recreational water and food crops that are irrigated with contaminated water (e.g., Fayer, 2004, Lake et al., 2005, Rose et al., 2001, Semenza and Menne, 2009). Only a low dose of oocysts can infect a person or animal (DuPont et al., 1995) and these hosts will spread oocysts through their faeces. Fig. 1 shows a conceptual diagram of the pathways from humans and livestock to surface water. It shows point and nonpoint sources that together form the total emissions of Cryptosporidium oocysts to the surface water. Point sources are emissions by humans via sewage systems (treated and untreated water) to surface water. Nonpoint sources are caused by spreading of excreta from livestock (manure) or grazing in an agricultural land which may enter surface water through surface and sub-surface runoff. Wildlife species, such as birds and rodents, can also be classified as nonpoint emitters.
Oocysts occur in surface water in many places worldwide (Medema et al., 2009). A reliable global inventory of Cryptosporidium concentrations is not available, as the pathogen is not regularly measured, and measurement techniques have large uncertainties due to low recovery rates (Schets et al., 2004, Schets et al., 2008). Modelling of Cryptosporidium concentrations in surface water may be useful to evaluate risk of disease caused by high Cryptosporidium concentrations. Models are used at different scales, from cow pats to water basins (Dorner et al., 2006, Ferguson et al., 2007, Haydon and Deletic, 2006, Oliver et al., 2009, Pachepsky et al., 2006, Sadeghi and Arnold, 2002, Walker and Stedinger, 1999, Wu et al., 2009) to country-scale models (Medema and Schijven, 2001), mostly focusing on industrialised countries. The reason for that is likely data availability, as these models often depend on estimates of, for instance, human excretion rates that are determined from observations at sewage treatment plants. However, problems associated with Cryptosporidium are of more concern to developing countries (Current and Garcia, 1991).
Other water pollutants, such as nitrogen, have already been studied worldwide (Seitzinger et al., 2010). So far no global inventory of Cryptosporidium occurrence in surface water has been made. A global model of Cryptosporidium may not reproduce Cryptosporidium concentrations in surface water, but it will help to identify hot-spot regions, the main drivers and to develop strategies to avoid water pollution by Cryptosporidium. An additional advantage of modelling is that it allows for analysing the impact of future global climate and land use changes on Cryptosporidium concentrations (e.g., Hofstra, 2011, Rose et al., 2001).
The aim of this paper is to develop a global Cryptosporidium model that can be used to assess worldwide emissions of Cryptosporidium to the surface water now and in the future. The results can be used to identify hot-spot regions and to obtain a general understanding of the causes of Cryptosporidium-related health problems, which will be useful to organisations such as the World Health Organization (WHO). In hot-spot regions more detailed studies can be performed to develop strategies to reduce waterborne pathogen concentrations in surface water.
Section snippets
Model construction
Fig. 1 is developed for Cryptosporidium. Although some processes differ for nutrients and microbes, such as colloid versus solute transport or die-off versus biochemical reactions, the pathways of Cryptosporidium and nitrogen to the surface water are comparable. We therefore used the global 0.5 by 0.5 degree resolution nitrogen model of Bouwman et al. (2009) together with its input data that have been collected from large databases (Table 1), as the basis for a global Cryptosporidium model.
Results and discussion
Fig. 2a shows the point source emissions of Cryptosporidium in number of oocysts per grid for the year 2000. The results differ from 107 to 1016 oocysts per year and are scattered over the globe depending on population density. Overall, the areas with the largest emissions from sewage systems are big cities in China, India and Latin America, and more generally in Europe and the US. Some lower emissions can be seen in the south-eastern part of Australia.
Most grid cells do not show any point
Concluding remarks
This paper presents the first global inventory of Cryptosporidium oocyst emissions to surface water, identifying hot-spot regions and main pollution sources. The results indicate that total global point source emissions from wastewater containing human excreta are comparable to total nonpoint source emissions from livestock production. However, the emissions from urban areas are concentrated in relatively small areas, while agricultural emissions are more diffuse. Hot-spot areas identified
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