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The protozoan parasite Cryptosporidium parvum is known to occur widely in both source and drinking water and has caused waterborne outbreaks of gastroenteritis. To improve monitoring, the U.S. Environmental Protection Agency developed method 1622 for isolation and detection of Cryptosporidium oocysts in water. Method 1622 is performance based and involves filtration, concentration, immunomagnetic separation, fluorescent-antibody staining and 4,6-diamidino-2-phenylindole (DAPI) counterstaining, and microscopic evaluation. The capsule filter system currently recommended for method 1622 was compared to a hollow-fiber ultrafilter system for primary concentration of C. parvum oocysts in seeded reagent water and untreated surface waters. Samples were otherwise processed according to method 1622. Rates of C. parvum oocyst recovery from seeded 10-liter volumes of reagent water in precision and recovery experiments with filter pairs were 42% (standard deviation [SD], 24%) and 46% (SD, 18%) for hollow-fiber ultrafilters and capsule filters, respectively. Mean oocyst recovery rates in experiments testing both filters on seeded surface water samples were 42% (SD, 27%) and 15% (SD, 12%) for hollow-fiber ultrafilters and capsule filters, respectively. Although C. parvum oocysts were recovered from surface waters by using the approved filter of method 1622, the recovery rates were significantly lower and more variable than those from reagent grade water. In contrast, the disposable hollow-fiber ultrafilter system was compatible with subsequent method 1622 processing steps, and it recovered C. parvum oocysts from seeded surface waters with significantly greater efficiency and reliability than the filter suggested for use in the version of method 1622 tested.Cryptosporidium parvum, a coccidian protozoan parasite, remains a risk to drinking water consumers despite extensive efforts put forth by water providers and the U.S. Environmental Protection Agency (EPA) (7,12,14,15,20,21,22). Oocysts are present in many environmental waters because Cryptosporidium is not only a human pathogen but also a zoonotic pathogen infecting livestock, as well as feral animals, in many watersheds used as sources of drinking water. Oocysts persist in the environment and are resistant to the chlorine disinfection routinely used for drinking water (2,11,13,17,23). Therefore, physical removal by chemical pretreatment and filtration is the primary means for reducing oocysts in source waters. When deficiencies in chemical pretreatment and filtration processes occur, oocysts can breach the treatment system and cause disease outbreaks of the magnitude of the 1993 Milwaukee cryptosporidiosis outbreak (14).Detection of Cryptosporidium oocysts in raw water sources is considered an important component in the management, prevention, and control of Cryptosporidium in drinking water supplies. Methods have been developed to detect C. parvum in both raw source waters and finished drinking waters. The EPA developed an Information Collection Rule requiring large munici...

cPredictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

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A total of 136 stream water and 143 groundwater samples collected in five important hydrologic systems of the United States were analyzed for microbiological indicators to test monitoring concepts in a nationally consistent program. Total coliforms were found in 99%, Escherichia coli in 97%, and Clostridium perfringens in 73% of stream water samples analyzed for each bacterium. Total coliforms were found in 20%, E. coli in less than 1%, and C. perfringens in none of the groundwater samples analyzed for each bacterium. Although coliphage analyses were performed on many of the samples, contamination in the laboratory and problems discerning discrete plaques precluded quantification. Land use was found to have the most significant effect on concentrations of bacterial indicators in stream water. Presence of septic systems on the property near the sampling site and well depth were found to be related to detection of coliforms in groundwater, although these relationships were not statistically significant. A greater diversity of sites, more detailed information about some factors, and a larger dataset may provide further insight to factors that affect microbiological indicators.