Patterns of Host-Associated Fecal Indicators Driven by Hydrology, Precipitation, and Land Use Attributes in Great Lakes Watersheds

Abstract

Fecal contamination from sewage and agricultural runoff is a pervasive problem in Great Lakes watersheds. Most work examining fecal pollution loads relies on discrete samples of fecal indicators and modeling land use. In this study, we made empirical measurements of human and ruminant-associated fecal indicator bacteria and combined these with hydrological measurements in eight watersheds ranging from predominantly forested to highly urbanized. Flow composited river samples were collected over low-flow ( n = 89) and rainfall or snowmelt runoff events ( n = 130). Approximately 90% of samples had evidence of human fecal pollution, with highest loads from urban watersheds. Ruminant indicators were found in ∼60-100% of runoff-event samples in agricultural watersheds, with concentrations and loads related to cattle density. Rain depth, season, agricultural tile drainage, and human or cattle density explained variability in daily flux of human or ruminant indicators. Mapping host-associated indicator loads to watershed discharge points sheds light on the type, level, and possible health risk from fecal pollution entering the Great Lakes and can inform total maximum daily load implementation and other management practices to target specific fecal pollution sources.

Spatiotemporal variability and key influencing factors of river fecal coliform within a typical complex watershed

“…It should be noted that the FC concentration increased in April and May with less precipitation, which may have been due to the advance of the rainy season and the long antecedent dry days, according to the previous studies in which the runoff associated with rainfall was a driver of degraded water quality in receiving waters (Dila et al, 2018) and antecedent dry days increased the chance of pollutant accumulation (Nabiul Afrooz and . In addition, the whole watershed was highly polluted by FC in December and January.…”

“…In addition, the whole watershed was highly polluted by FC in December and January. Studies have shown that the FC concentration was approximately 10 times higher in snowmelt runoff (Galfi et al, 2016;Dila et al, 2018), which corresponds to the snowy months of January and December in the Beiyun watershed. Consequently, the existence of FC in the dry seasons would be more complicated, indicating a detailed study should be carried out in the next step.…”

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

“…Given the number of factors (e.g., bottom substrate, upstream land use) that differ between waterways and may affect water quality, it is not surprising that microbial water quality would vary between waterways. In fact, multiple studies have found associations between microbial water quality and upstream land use (Bradshaw et al, 2016; Brendel and Soupir, 2017; Dila et al, 2018; Falardeau et al, 2017; Lyautey et al, 2007; Pandey et al, 2012; Verhougstraete et al, 2015). For instance, Pandey et al (Pandey et al, 2012) tracked water quality at 46 sites in an Iowa watershed and found that E. coli levels were positively associated with the amount of cropland in Thiessen polygons around each site.…”

“…For instance, Pandey et al (Pandey et al, 2012) tracked water quality at 46 sites in an Iowa watershed and found that E. coli levels were positively associated with the amount of cropland in Thiessen polygons around each site. Associations between microbial water quality, and proximity to upstream livestock operations (Bond and Partyka, 2004; Lyautey et al, 2010; Wilkes et al, 2011), the number of septic systems in a watershed (Verhougstraete et al, 2015), and livestock and human population density (Dila et al, 2018; Falardeau et al, 2017) have also been reported. Post-hoc identification of factors that drive spatial variation in water quality is difficult and requires data that were not collected as part of the current study (e.g., on upstream activity and land use).…”

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

“…Given the number of factors that differ between waterways and may affect water quality, this is not surprising. In fact, multiple studies have found associations between microbial water quality and upstream land use (Lyautey et al, 2007;Pandey et al, 2012;Verhougstraete et al, 2015;Bradshaw et al, 2016;Brendel and Soupir, 2017;Dila et al, 2018). For instance, Pandey et al (2012) tracked water quality at 46 sites in an Iowa watershed and found that E. coli levels were positively associated with the amount of cropland around each site.…”