Geology topics

Michael W. Ware

Publications and source records attributed to Michael W. Ware.

4 recordsLinked to original sources

USGS2016

Microbial pathogens in source and treated waters from drinking water treatment plants in the United States and implications for human health

An occurrence survey was conducted on selected pathogens in source and treated drinking water collected from 25 drinking water treatment plants (DWTPs) in the United States. Water samples were analyzed for the protozoa Giardia and Cryptosporidium (EPA Method 1623); the fungi Aspergillus fumigatus , Aspergillus niger and Aspergillus terreus (quantitative PCR [qPCR]); and the bacteria Legionella pneumophila (qPCR), Mycobacterium avium , M. avium subspecies paratuberculosis , and Mycobacterium intracellulare (qPCR and culture). Cryptosporidium and Giardia were detected in 25% and in 46% of the source water samples, respectively (treated waters were not tested). Aspergillus fumigatus was the most commonly detected fungus in source waters (48%) but none of the three fungi were detected in treated water. Legionella pneumophila was detected in 25% of the source water samples but in only 4% of treated water samples. M. avium and M. intracellulare were both detected in 25% of source water, while all three mycobacteria were detected in 36% of treated water samples. Five species of mycobacteria, Mycobacterium mucogenicum , Mycobacterium phocaicum , Mycobacterium triplex , Mycobacterium fortuitum , and Mycobacterium lentiflavum were cultured from treated water samples. Although these DWTPs represent a fraction of those in the U.S., the results suggest that many of these pathogens are widespread in source waters but that treatment is generally effective in reducing them to below detection limits. The one exception is the mycobacteria, which were commonly detected in treated water, even when not detected in source waters.

Science of the Total Environment↗

USGS2013

Reply to “Ranking filter methods for concentrating pathogens in lake water”

Accurately comparing filtration methods is indeed difficult. Our method ( 1 ) and the method described by Borchardt et al. for determining recoveries are both acceptable approaches; however, each is designed to achieve a different research goal. Our study was designed to compare recoveries of multiple microorganisms in surface-water samples. Because, in practice, water-matrix effects come into play throughout filtration, concentration, and detection processes, we felt it important to incorporate those effects into the recovery results.

Applied and Environmental Microbiology↗

USGS2013

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

Predictive 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.

Ohio↗

USGS2013

Comparison of filters for concentrating microbial indicators and pathogens in lake-water samples

Bacterial indicators are used to indicate increased health risk from pathogens and to make beach closure and advisory decisions; however, beaches are seldom monitored for the pathogens themselves. Studies of sources and types of pathogens at beaches are needed to improve estimates of swimming-associated health risks. It would be advantageous and cost-effective, especially for studies conducted on a regional scale, to use a method that can simultaneously filter and concentrate all classes of pathogens from the large volumes of water needed to detect pathogens. In seven recovery experiments, stock cultures of viruses and protozoa were seeded into 10-liter lake water samples, and concentrations of naturally occurring bacterial indicators were used to determine recoveries. For the five filtration methods tested, the highest median recoveries were as follows: glass wool for adenovirus (4.7%); NanoCeram for enterovirus (14.5%) and MS2 coliphage (84%); continuous-flow centrifugation (CFC) plus Virocap (CFC+ViroCap) for Escherichia coli (68.3%) and Cryptosporidium (54%); automatic ultrafiltration (UF) for norovirus GII (2.4%); and dead-end UF for Enterococcus faecalis (80.5%), avian influenza virus (0.02%), and Giardia (57%). In evaluating filter performance in terms of both recovery and variability, the automatic UF resulted in the highest recovery while maintaining low variability for all nine microorganisms. The automatic UF was used to demonstrate that filtration can be scaled up to field deployment and the collection of 200-liter lake water samples.

Michigan, New York, Ohio, Wisconsin↗