Urban runoff quality in the UK: problems, prospects and procedures

Ellis, B.S.

doi:10.1016/0143-6228(91)90029-9

Cited by 21 publications

(13 citation statements)

References 13 publications

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“…However, whilst it has long been known that diffuse urban loadings can vary by an order of magnitude or more between 'clean' and 'dirty' urban catchments (Ellis, 1991), little effort has been made to identify sites that are significant in terms of load and potential impact on receiving waters, and hence which deserve further detailed assessment and possible SuDS implementation. This omission is significant within the context of the EU Water Framework Directive (2000/60/EC), which requires that all emissions to water be identified, quantified and managed.…”

Section: Introductionmentioning

confidence: 99%

Mapping hazard from urban non-point pollution: a screening model to support sustainable urban drainage planning

Mitchell

2005

Journal of Environmental Management

109

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Article:Mitchell, G. (2005) AbstractNon-point sources of pollution are difficult to identify and control, and are one of the main reasons that urban rivers fail to reach the water quality objectives set for them.Whilst sustainable drainage systems (SuDS) are available to help combat this diffuse pollution, they are mostly installed in areas of new urban development. However, SuDS must also be installed in existing built areas if diffuse loadings are to be reduced.Advice on where best to locate SuDS within existing built areas is limited, hence a semi-distributed stochastic GIS-model was developed to map small-area basin-wide loadings of 18 key stormwater pollutants. Load maps are combined with information on surface water quality objectives to permit mapping of diffuse pollution hazard to beneficial uses of receiving waters. The model thus aids SuDS planning and strategic management of urban diffuse pollution. The identification of diffuse emission 'hotspots'within a water quality objectives framework is consistent with the 'combined' (risk assessment) approach to pollution control advocated by the EU Water FrameworkDirective.

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Section: Introductionmentioning

confidence: 99%

Mapping hazard from urban non-point pollution: a screening model to support sustainable urban drainage planning

Mitchell

2005

Journal of Environmental Management

109

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“…Fecal pollution within storm water runoff can cause significant health risks as a result of the presence of various infectious microorganisms such as Escherichia coli (Brion and Lingireddy 2003;Ellis 1991;He and He 2008;Kay et al 2005;McCorquodale et al 2004;Scholz 2006). It is well understood that dog fouling is the major source of fecal contamination in urban runoff.…”

Section: Microbial Contaminationmentioning

confidence: 99%

“…Total coliforms and Enterococcus are the most commonly used indicators (Ellis 1991;NRC 2004) due to their relative ease of application and low determination costs (Brion and Lingireddy 2003;Kay et al 2005).…”

Section: Microbial Contaminationmentioning

confidence: 99%

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Assessing Storm Water Detention Systems Treating Road Runoff with an Artificial Neural Network Predicting Fecal Indicator Organisms

Yazdi

Scholz

2009

Water Air Soil Pollut

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This paper examines whether multiple regression analysis and neural network models can be applied successfully for the indirect prediction of the runoff treatment performance with water quality indicator variables in an experimental storm water detention system rig. Five biologically mature experimental storm water detention systems with different designs treating concentrated gully pot liquor (spiked with dog droppings) were assessed. The systems were located on The King's Buildings campus at The University of Edinburgh and were monitored for a period of 18 months. Multiple regression analyses indicated a relatively successful prediction of the biochemical oxygen demand and total suspended solids for most systems, but due to a relatively weak correlation between the predictors and both microbial indicators, multiple regression analyses were not applied for the prediction of intestinal enterococci and total coliform colony-forming units. However, artificial neural network models predicted microbial counts relatively well for most detention systems.

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“…For example, the 1998 to 2000 surface water quality report published by the Environmental Protection Commission of Hillsborough County indicates increased urbanization rates resulted in heightened levels of degradation observed in many of the freshwater bodies in Florida's Tampa Bay region (Boler 2002). Urbanized areas replace natural land, which protects aquatic environments by functioning as a filter (Osborne and Wiley 1988, Ellis 1991, Lee 2002, Tong and Chen 2002, Castelletti and Soncini-Sessa 2007.…”

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confidence: 99%

Lake performance differences in response to land use and water quality: Data envelopment analysis

Alsharif

Fouad

2012

Lake and Reservoir Management

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Urban runoff quality in the UK: problems, prospects and procedures

Cited by 21 publications

References 13 publications

Mapping hazard from urban non-point pollution: a screening model to support sustainable urban drainage planning

Mapping hazard from urban non-point pollution: a screening model to support sustainable urban drainage planning

Assessing Storm Water Detention Systems Treating Road Runoff with an Artificial Neural Network Predicting Fecal Indicator Organisms

Lake performance differences in response to land use and water quality: Data envelopment analysis

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