The research field of catchment microbial dynamics has been rapidly expanding due to the adoption of the WFD (IGES, 2008). Such is the importance of the field to the successful implementation of the WFD, Haygarth et al. (2005) have gone so far as to describe the policy imperative to understand catchment microbial pollution concentrations and fluxes as “the new challenge of the 21st century”.
Despite the research field being of national and international importance, significant data gaps exist that hinder efforts to characterise riverine microbial pollution. These data gaps are now identified and the efforts to address them discussed.
The sources of river pollution are varied spatially. Much of our riverine microbial pollution comes from diffuse agricultural sources (Bateman et al., 2006a; Haygarth et al., 2005; Horsey, 2006) but urban point sources of pollution, such as wastewater treatment works (WwTW), account for substantial pollution discharges into rivers; particularly during periods of high rainfall when aging (and often inadequate) wastewater infrastructures overflow due to their inability to process high volumes of wastewater. It has been estimated that point source discharges from WwTWs can contribute significant proportions of the total phosphorus load in UK rivers, with significant increases in concentrations downstream from WwTWs under high-flow conditions (Young et al., 1999). The ecological and microbial aspects of water quality can be distinct and typically have unique pollution sources requiring different remediation strategies (Haygarth et al., 2005). Remediation, particularly of microbial pollution, requires the correct identification of pollution vectors (typically agricultural livestock waste or overflows from human WwTWs) to ascribe liability and enforce accountability. Remedial action is hampered by a lack of accurate FIO modelling (Stapleton et al., 2008), a shortage of empirical measurement of sewage overflows (Wither et al., 2005) and a lack of research into the effectiveness of different sewage treatment types (Kay et al., 2008a). In addition, many routine water quality monitoring programmes tend to be systematically flawed as they habitually sample during low flow (base-flow) conditions, rather than capture the full range of river discharge rates (Crowther et al., 2011). This shortage of accurate empirical data leads to flawed assessments of the magnitude of high-flow FIO
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concentrations from both diffuse and point sources (Mattikalli and Richards, 1996; Kay et al., 2005a; Kay et al., 2008a). The lack of basic data on hydrological fluxes is disturbing. The mass movement of FIOs from urban sources is typically associated with relatively short duration high rainfall events. For example, Stapleton et al. (2008) found that urban point source discharges were directly responsible for 90% of the total organism load to the Ribble estuary during high rainfall. This poses significant risks to human health as elevated microbial pollution causes unacceptably poor recreational water quality (Wither et al., 2005; Defra, 2008a). Microbiologically polluted water has been shown to have a dose- response relationship with the risk of ill-health (i.e. the rate of infection among recreational users increases steadily with increasing concentrations of harmful microorganisms and, for a constant concentration of microorganisms, the rate of infection is higher for those recreational users who have higher exposure) (WHO, 2003). The evidence used to calculate the dose-response relationship and the detrimental effect elevated microbial pollution concentrations can have on human health is discussed in Chapter 2.
Microbial pollution from diffuse sources is also elevated under high-flow conditions. Kay et al. (2008a) found that FIO concentrations and discharge volumes typically increase by an order of magnitude in rural catchments under high-flow conditions. This c.100-fold increase in export coefficients is due to a range of factors, not limited to the increased run-off of faecal material from agricultural land or the increased mobilisation and transport of FIOs due to increased turbidity within watercourses (Wilkinson et al., 2006).
The investigative monitoring of thousands of discharge sites for the presence of FIOs presents an expensive logistical challenge for the regulator as it is simply infeasible to measure pollution concentrations at every location (Environment Agency, 2008a). Because of this, there is a real and necessary requirement for cost-effective diagnostic tools capable of predicting microbial pollution sources and distributions. Although a range of statistical methods have been developed and used to model riverine pollution (Fraser et al., 1998; Tian et al., 2002; Vinten et al., 2004; Lawler et al., 2006) they are not without inaccuracies or disadvantages when applied to modelling FIOs. Watershed modelling tools such as Hydrological Simulation Program Fortran (HSPF) (Bicknell et al., 1997;
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Donigian et al. 1995), Simulated Catchments (SIMCAT) (Warn, 1987) or Soil and Water Assessment Tool (SWAT) (Gassman et al., 2007). are frequently used to assess nutrient or sediment loadings in watercourses. The use of these systems to reliably model FIOs in watercourses is restricted by the poor availability of empirical data with which to parameterise or assess the accuracy of modelled results (Crowther, 2011). The Scotland and Northern Ireland Forum for Environmental Research (SNIFFER) screening tool has been useful for identifying and characterising diffuse pollution, providing insights into the sources of FIO pollution and enabling FIO export coefficients for catchments to be determined (SNIFFER, 2006a and 2006b). However, SNIFFER does not characterise both base- and high-flow FIO concentrations and the accuracy of SNIFFER’s predicted export coefficients, in common with the previous tools, has not been evaluated against data from monitored catchments.
Another approach to catchment-scale FIO modelling has been to use linear regression techniques to model relationships between the geometric mean (GM) FIO concentrations recorded at monitored sites and the dominant land use characteristics within the catchments draining into those monitored sites; e.g. the proportions of grassland or built-up land act as proxies for the key sources of faecal pollution.
This approach allows the correlations between FIO concentrations and land use types to be examined and water quality maps to be generated. By examining the locations of anomalous standardised residuals revealed by the spatially referenced regression models it is possible to identify pollution sources in need of remediation (Crowther et al., 2001; Kay et al., 2007b).
This methodological approach to FIO modelling has proved to be a cost effective exploratory tool to predict microbial concentrations both within and at subcatchment outlets (Crowther et al., 2003; Kay et al., 2005a). Another advantage of the regression modelling approach is that the costs of obtaining empirical data are minimised. Research by Crowther et al. (2001) proved it is possible to investigate and predict FIO concentrations in coastal water by combining only secondary data sources. Similar FIO studies have obtained good results with a minimum of primary data (Wither et al., 2005). However, desktop
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studies that rely on secondary data are not without complications. These complications are now discussed.
FIO studies have shown that land use is a statistically significant determinant of microbial concentrations in rivers. Spatial variations in water quality closely reflect the distributions of developed land, meaning that urban land, with its associated sewage outflows, is one of the most critical sources of microbial pollution (Crowther et al., 2003). Unfortunately, inaccuracies in remotely sensed data can cause land-use misclassification. A comparison of the CEH LCM1990 (NERC, 2008b) with field survey data has revealed marked discrepancies in that map, particularly in the urban land use category (Kay et al., 2005a). A similar comparison of the CEH LCM1990 with OS 1:50,000 maps revealed substantial misclassification of urban and woodland areas in the Ribble catchment (Kay et al., 2005a). Misclassification also occurs within agricultural land use categories. The problem is in part caused by the light reflectance values of similar surfaces. For example, mapping software may misclassify bare rock as urban areas (CEH, 2008a). The methods used to rectify these errors are not without their own inaccuracies. During one correction exercise, mapped areas of built-up land extracted from OS 1:50,000 maps systematically underestimated urban land (Stapleton et al., 2006).
If uncorrected, misclassification can cause significant systematic errors, particularly in heavily urbanized catchments. Some misclassification can be corrected manually by reclassifying the seventeen pre-defined CEH land use classes into a reduced number of principal land use categories. The accuracy of land use classification can also be further improved by using the most current data sets, such as the LCM2000 (Kay et al., 2005a) which offers accuracy improvements over its predecessor, the Land Cover Map of Great Britain (LCMGB). For example, procedures were developed and incorporated within the LCM2000 for segmenting satellite images to produce vector outlines (Fuller et al., 2005) and the LCM2000 also incorporates upgrade improvements in structure, thematic detail and associated metadata (Smith and Fuller, 2002).
Despite the restricted availability of primary empirical data, and the difficulties associated with the use of secondary data, successive generations of desktop
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studies have tended to predict FIO concentrations with increasing accuracy (Kay et al., 2005a; Stapleton et al., 2008). Since 1995 CREH have been assembling an empirical database of enumerated FIO concentrations and accurate runoff and discharge data for catchments. This database is continuing to expand and develop. As of 2010, FIO concentration and export coefficient data have been collected from 205 sampling points across 15 process based catchment studies. One of the most recent modelling exercises undertaken by CREH combined and reanalysed those datasets within a meta-analysis to improve characterization of FIO fluxes within those catchments and assess the effectiveness of different sewage treatment types (Kay et al., 2008a).
The ChREAM project examined the agricultural costs and key non-market benefits associated with the introduction of the WFD and considered the impacts of alternative implementations of that policy in terms of its impact upon rural land use and the farming sector (Bateman et al., 2006a). These impacts will involve geographically varied changes in land use patterns and water quality. This large- scale study enabled collaboration with experts in the field of catchment microbial dynamics based at CREH and enabled limited access to CREH’s commercially sensitive FIO concentration and export coefficient database. As previously mentioned, ChREAM required a generic transferable model capable of predicting riverine FIO concentrations using standardised data surfaces, enabling integration with other aspects of ChREAM land use and hydrological modelling. The meta-analysis reported in Kay et al. (2008a) was calculated using the disparate data sources detailed in Table 6 and had not been assessed for its ability to predict FIO concentrations in other catchments. Consequently, that model could not meet ChREAM requirements. To achieve full integration with all aspects of ChREAM land use modelling it was necessary to reanalyse the CREH datasets using standardised data surfaces and standardised predictor variables. In addition to the creation of standardised data surfaces from which CREHs primary FIO concentration and export coefficient database could be remodelled, this research also aimed to extend the regression modelling approach by investigating whether improved models might be achieved by including human population and livestock density data as direct measures of the key FIO sources
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as previous CREH research has relied solely upon ordinal change within land use categories.
Other independent variables that are known to affect FIO source strength, mobilisation, transport, die-off and sedimentation within catchments (e.g., volume of runoff, soil hydrology and catchment size) have also been incorporated within the meta-analyses reported here. This has resulted in comprehensive generic and transferable models which can be used to predict FIO concentrations in UK rivers.
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