Does climate influence the content of indicator bacteria in stormwater?

Published: 29 June 2020

Stormwater that originates from rain and snowmelt is an important component in the transport of pollutants to receiving waters that can be used for drinking water production and for water-based recreation. In Helen Galfi's recently published PhD thesis, potential sources and transport pathways of microbiological pollutants as well as other pollutants such as metals have been studied in Östersund and in laboratory experiments.

On May 5, 2020, Helen Galfi defended her thesis “Assessment of stormwater and snowmelt quality based on water management priorities and the consequent water quality parameters”. The dissertation took place at Luleå University of Technology with Associate Professor Jon Hathaway, The University of Tennessee, Knoxville, USA as opponent.

The overall aim of the PhD thesis was to investigate the climate as a factor of influence for the content of indicator bacteria in stormwater from both rain and snowmelt. The goals have been to quantify and assess the content of indicator bacteria in stormwater at outlets from catchments with different land uses, to investigate whether there are water quality parameters whose concentrations are related to indicator bacteria and to investigate the possibility of using such parameters as explanatory variables in statistical modeling of indicator bacteria concentrations.

The field studies carried out in this PhD project showed uncertainties in measured levels of bacteria and suspended solids. Given widely varying bacteria levels and automated sampling, the results were affected by residual water in the sampling tubes from the previous sample. The studies showed that this can be minimized by using shorter sampling tubes and avoiding bends on the tubing. Furthermore, it was shown that the amount of suspended matter in stormwater was underestimated with the conventional TSS method, which involves analysis of partial samples instead of full samples. Error sources in estimation can be reduced by means of full-sample analysis using either the existing SSC method (suspended sediment concentration) or MFP (multiple filter procedure) proposed in this study, which involves filtering the entire sample through successively finer filters with pore sizes of 25, 1.6 and 0.45 μm. The MFP generated results that were equivalent to those obtained with SSC and the original sediment content of the sample, which is confirmed by statistical methods.