Animal Waste, Water Quality and Human Health

Transport of microbial pollution in catchment systems 177water compliance with Directive 160/76/EEC (Kay et al. 2007b, Dickson et al.2005). The separate effects of RBS and steading dirty water control have beenaddressed in a longitudinal study of 60 monitored catchments in Scotland byKay et al. (2005d). Here, significant improvements were recorded in FIO fluxwhen compared to “control” catchments but a relatively high intensity of“measures” was required (i.e. >30% of stream bank length protected by RBSs).The regional (i.e. multi-catchment) scale sources of agricultural diffusepollution on Ayrshire bathing waters has been assessed by Aitken et al. (2001).Following from this work, The impacts of faecal indicator fluxes from thiscatchment was examined using three modelling strategies, the first a soiltransport model, the second a regression model and the third a more distributedcatchment model (PAMIMO). The regression model gave the best prediction ofbathing water quality and the authors concluded that preventing surface runoffwould prove most protective of bathing water quality (Vinten et al. 2004b).Bacterial source tracking has been employed by Hyer and Moyer (2004) toinform TMDL studies in the USA. Pond et al. (2004), Domingo et al. (2007),Wuertz and Field (2007) and USEPA (2005) provide excellent overviews of thepotential for the source tracking methods currently available to contribute to FIOflux source apportionment. These methods use either species and or sub-speciesof organisms thought to be associated with faecal matter from humans ordefined animal groups, or chemical markers indicative of human sewage. Thereis currently no single and definitive approach with which to identify exactproportions of human and animal derived FIOs, but this area is developingrapidly and may provide operationally useful data in the medium term (Stapletonet al. 2009).A consortium commissioned by the regulators and government in Scotland toproduce a screening tool as part of the preparation for WFD implementationreviewed a series of FIO modelling approaches (Anon. 2006). The screeningtool is loosely based on research carried out at the Scottish Agricultural College(SAC) (Vinten et al. 2002, Ogden et al. 2001, McGechan & Vinten 2003,McGechan & Vinten 2003, Vinten et al. 2004a). It provides what is termed a“smart dynamic export coefficient” approach. It is driven by land use data at aone km 2 resolution and provides export estimates for each one km 2 grid based on:(i)(ii)(iii)(iv)(v)steading and other farm losses;soil applied organic wastes;livestock numbers or soil burden;channel contribution related to ditch and small stream density in each cell;a flow-dependent mobility factor incorporating sub-surface and overlandcomponents; and