Fecal indicator organisms in northern oligotrophic rivers: An explorative study on Escherichia coli prevalence in a mountain region with intense tourism and reindeer herding

Abstract

Increasing pollution levels in waters from remote mountain areas in northern Sweden have been observed. To support a sustainable water quality management, it is necessary to know which environmental and antrophogenic factors influence the water quality. The purpose of this study was to map the Escherichia coli prevalence in the catchment area of the upper part of a large northern Scandinavian river and investigate the controlling factors of microbial contamination. A total of 112 water samples were collected from various locations in the research area between July 2020 and December 2020. These samples were analyzed for microbial and chemical characteristics, and information about tourism and reindeer herding was compiled. Additionally, microbial and physicochemical water characteristics collected by Indalsälven Water Conservation Association (IWCA, 1993–2020) and Swedish Meteorological and Hydrological Institute (SMHI, 2004–2020) were analyzed. The results showed that E. coli enumerations ranged between 0 and 500 CFU/100 ml. There was generally no obvious relation between suspected point sources, e.g., sewage treatment plants at mountain stations, and E. coli levels at downstream sampling points. Principal component analysis showed that E. coli was correlated to coliforms, total heterotrophic count, river discharge, CODMn and river color. Since microbial analyses are time-consuming, expensive and difficult to perform in remote areas, it is important to find more easily extracted water parameters that can serve as a proxy for E. coli. In particular, river color and discharge are promising parameters that may serve as an early indication of bacterial outbreak and fecal contamination in mountain waters.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking

“…), however, that could also be introducing fecal contamination to the sampling region, were not represented in this analysis, and E. coli isolates derived from these host sources could comprise a considerable proportion of the environmental isolates that were left unclassified in this study. Alternatively, the presence of E. coli isolates in the study area could be due to the influence of contamination and/or runoff from the wastewater and sewage treatment infrastructure of the surrounding mountain stations ( 27 ). A significant proportion of the unclassified isolates could thus be anthropogenic (i.e., human-derived) in nature; however, these strains might have been left without a final host designation during the classification analysis due to the lack of representation of various important human E. coli populations that were not included during the model building process.…”

“…Using a whole genome-based, in silico approach, we first demonstrate the utility of logic regression for identifying highly host-specific biomarkers within E. coli strains recovered from a wide range of host and niche sources. Adapting our methodology for practical use, we then generate human-, reindeer- and beaver-specific biomarkers to classify E. coli isolates recovered via water samples taken from the Indalsälven river in Northwestern Sweden ( 27 )—thus highlighting the potential of logic regression modeling as a novel source tracking approach.…”