title: Application of a linear regression model to assess the influence of urbanised areas and grazing pastures on the microbiological quality of rural streams.
abstract: Faecal coliform (FC) bacteria were used as a proxy of faecal indicator organisms (FIOs) to assess the microbiological pollution risk for eight mesoscale catchments with increasing lowland influence across north-east Scotland. This study sought to assess the impact of urban areas on microbial contaminant fluxes. Fluxes were lowest in upland catchments where populations are relatively low. By contrast, lowland catchments with larger settlements and a greater number of grazing populations have more elevated FC concentrations throughout the year. Peak FC counts occurred during the summer months (April-September) when biological activity is at its highest. Lowland catchments experience high FC concentrations throughout the year whereas upland catchments exhibit more seasonal variations with elevated summer conditions and reduced winter concentrations. A simple linear regression model based on catchment characteristics provided scope to predict FC fluxes. Percentage of improved grazing pasture and human population explained 90 and 62 % of the variation in mean annual FC concentrations. This approach provides scope for an initial screening tool to predict the impact of urban space and agricultural practice on FC concentrations at the catchment scale and can aid in pragmatic planning and water quality improvement decisions. However, greater understanding of the short-term dynamics is still required which would benefit from higher resolution sampling than the approach undertaken here.
authors.affiliation: Centre for Environmental Health Engineering, Department of Civil and Environmental Engineering, University of Surrey, Guildford, Surrey, GU2 7XY, UK, s.mcgrane@surrey.ac.uk.
authors.forename: Scott J
authors.initials: SJ
authors.lastname: McGrane
authors.forename: Doerthe
authors.initials: D
authors.lastname: Tetzlaff
authors.forename: Chris
authors.initials: C
authors.lastname: Soulsby
mesh_terms: D000383:Agriculture / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000379:methods*; D005243:Feces / Q000382:microbiology; D006801:Humans; D016014:Linear Models; D045483:Rivers / Q000382:microbiology*; D012621:Seasons; D014507:Urbanization / Q000639:trends; D014871:Water Microbiology; D014876:Water Pollution / Q000706:statistics & numerical data*