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The protozoan parasite Cryptosporidium is a major cause of diarrhoea worldwide. This paper presents the first model-based inventory with 0.5 by 0.5 degree resolution of global Cryptosporidium emissions for the year 2000 from humans and animals to surface water. The model is based on nutrient distribution modelling, because the sources and transport of oocysts and nutrients to the surface water are comparable. Total emissions consist of point source emissions from wastewater and nonpoint source emissions by runoff of oocysts in manure from agricultural lands. Results indicate a global emission of 3 × 10(17) oocysts per year, with comparable contributions from point and nonpoint sources. Hot-spot areas for point sources are big cities in China, India and Latin America, while the area with the largest nonpoint source emissions is in China. Uncertainties in the model are large. Main areas for further study are (i) excretion rates of oocysts by humans and animals, (ii) emissions of humans not connected to sewage systems, and (iii) retention of oocysts to determine surface water pathogen concentrations rather than emissions. Our results are useful to health organisations to identify priority areas for further study and intervention., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Quantitative Microbiological Risk Assessment (QMRA) is increasingly being used to complement traditional verification of drinking water safety through the absence of indicator bacteria. However, the full benefit of QMRA is often not achieved because of a lack of appropriate data on the fate and behaviour of pathogens. In the UK, statutory monitoring for Cryptosporidium has provided a unique dataset of pathogens directly measured in large volumes of treated drinking water. Using this data a QMRA was performed to determine the benefits and limitations of such state-of-the-art monitoring for risk assessment. Estimates of the risk of infection at the 216 assessed treatment sites ranged from 10(-6.5) to 10(-2.5) person(-1) d(-1). In addition, Cryptosporidium monitoring data in source water was collected at eight treatment sites to determine how Cryptosporidium removal could be quantified for QMRA purposes. Cryptosporidium removal varied from 1.8 to 5.2 log units and appeared to be related to source water Cryptosporidium concentration. Application of general removal credits can either over- or underestimate Cryptosporidium removal by full-scale sedimentation and filtration. State-of-the-art pathogen monitoring can identify poorly performing systems, although it is ineffective to verify drinking water safety to the level of 10(-4) infections person(-1) yr(-1).

The decimal elimination capacity (DEC) of slow sand filters (SSF) for viruses, bacteria and oocysts of Cryptosporidium has been assessed from full-scale data and pilot plant and laboratory experiments. DEC for viruses calculated from experimental data with MS2-bacteriophages in the pilot plant filters was 1.5-2 log10. E. coli and thermotolerant coliforms (Coli44) were removed at full-scale and in the pilot plant with 2-3 log10. At full-scale, Campylobacter bacteria removal was 1 log10 more than removal of Coli44, which indicated that Coli44 was a conservative surrogate for these pathogenic bacteria. Laboratory experiments with sand columns showed 2-3 and >5-6 log10 removal of spiked spores of sulphite-reducing clostridia (SSRC; C. perfringens) and oocysts of Cryptosporidium respectively. Consequently, SSRC was not a good surrogate to quantify oocyst removal by SSF. Removal of indigenous SSRC by full-scale filters was less efficient than observed in the laboratory columns, probably due to continuous loading of these filter beds with spores, accumulation and retarded transport. It remains to be investigated if this also applies to oocyst removal by SSF. The results additionally showed that the schmutzdecke and accumulation of (in)organic charged compounds in the sand increased the elimination of microorganisms. Removal of the schmutzdecke reduced DEC for bacteria by +/-2 log10, but did not affect removal of phages. This clearly indicated that, besides biological activity, both straining and adsorption were important removal mechanisms in the filter bed for microorganisms larger than viruses.

Quantitative microbiological risk assessment requires quantitative data to assess consumer exposure to pathogens and the resulting health risk. The aim of this study was to evaluate data sets on the occurrence of Cryptosporidium oocysts in raw water and on the removal of model organisms (anaerobic spores, bacteriophages) to perform such a risk assessment. A tiered approach was used by first calculating approximate point estimates and when the point estimate was close to the required safety level (10(4) annual risk of infection), fitting the data to probability distributions and Monte Carlo analysis to calculate the distribution of the risk of infection. Sensitivity analysis showed that the variability in the Cryptosporidium data in raw water (largely introduced by the variability of the recovery efficiency of the detection method) determined most of the variance in the risk estimate.

Modelling the discharge of parasitic protozoa into surface water and the dispersion in rivers and streams gives insight into the contribution of the different sources of environmental contamination and in the transmission of these organisms from the point of discharge to drinking water abstraction points and bathing sites. We tested the applicability of emission (PROMISE) and dispersion (WATNAT) models developed for chemical pollutants to describe the environmental behaviour of Cryptosporidium and Giardia in the Netherlands. The annual load of Cryptosporidium and Giardia in domestic wastewater was 3.2 x 10(13) and 3.8 x 10(14) respectively. The majority (85%) of the Cryptosporidium oocysts was discharged with effluent of wastewater treatment plants. while the majority (82%) of the Giardia cysts was discharged with untreated wastewater discharges and sewer overflows. The estimated annual import through the river Rhine and Meuse was 3.2 x 1014 Cryptosporidium oocysts and 2.1 x 10(15) Giardia cysts, of which the river Rhine contributed 87 and 66%, respectively. This outweighed the total load of the discharges of treated and untreated wastewater in the Netherlands. The combination of PROMISE and WATNAT predicted concentrations of Cryptosporidium and Giardia in surface water that were in the same order of magnitude as the concentrations that were observed at 5 of the 6 sites compared. At a site with primarily agricultural contamination, the models predicted concentrations that were 1 10log-unit lower than the observed concentrations. This is a first step in the direction of a quantitative description of the transmission cycle of Cryptosporidum and Giardia through water. The use of these models combines observational occurrence data and experimental data from laboratory survival studies into a single integrated description. The description needs further improvement by incorporation of agricultural run-off and increasing the number and time frame of input monitoring data.

Oocysts of Cryptosporidium parvum can survive for several months in surface water, one of the main factors determining their success in environmental transmission and thus their health hazard via water. Several factors in the environment, e.g. temperature, presence of predators and exo-enzymes will probably influence oocyst survival. The high persistence of oocysts may also limit the value of traditional faecal indicator bacteria. The aim of this study was to determine the rate at which C parvum oocysts, E coli, faecal enterococci and C perfringens spores die in surface water and the influence of temperature and the presence of autochthonous (micro)organisms on the die-off rate. Microcosms with autoclaved river water were inoculated with the organisms. Microcosms with untreated river water were inoculated with concentrated primary effluent containing the bacteria and with C parvum oocysts. Microcosms were incubated at 5°C or 15°C at 100rpm. Viability of oocysts was monitored by in vitro excystation and dye-exclusion; viability of the bacteria was determined on appropriate selective media. When pseudo first-order die-off kinetics were assumed, the die-off rate of oocysts at 5°C was 0.010 log10/d and at 15°C, 0.006-0.024 log10/d. These rates underestimate die-off since oocyst disintegration was not accounted for. Incubation in autoclaved or untreated water did influence the die-off rate of oocysts at 15°C but not at 5°C. The die-off rate of E coli and enterococci was faster in the non-sterile river water than in autoclaved water at both temperatures. At 15°C, E coli (and possibly E faecium) even multiplied in autoclaved water. In untreated river water, the die-off of E coli and enterococci was approximately 10x faster than die-off of oocysts but die-off rates of C perfringens were lower than those of oocysts. As for oocysts, die-off of the bacteria and spores was faster at 15°C than at 5°C. Oocysts are very persistent in river water: the time required for a 10x reduction in viability being 40-160d at 15°C and 100d at 5°C. Biological/biochemical activity influenced oocyst survival at 15°C and survival of both vegetative bacteria at 5 and 15°C. The rapid die-off of E coil and enterococci makes them less suitable as indicators of oocyst presence in water. As C perfringens survived longer in untreated river water than oocysts, it may prove useful as an indicator of the presence of C parvum. [ABSTRACT FROM AUTHOR]

The design and evaluation of cost-effective treatment systems that produce safe drinking water with respect to Cryptosporidium and Giardia is based on the collection of quantitative information on: 1. theiroccurrence at abstraction points, which is used to set treatment goals or as design criterion, and 2. the elimination of surrogate parameters (spores) by treatment processes, which is used to demonstrate ifthe treatment goal is met under full-scale conditions. Both pathogenconcentrations in source water and the efficiency of a treatment process vary considerably over time. Multiple barriers in water treatment not only increased the overall treatment efficiency, but also reduced the variation in drinking water quality and thereby the probability of pathogen breakthrough. [ABSTRACT FROM AUTHOR]