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The use of site-specific traffic, highway and rainfall data to predict surface runoff concentrations under storm events of differing magnitudes
Mike Revitt, Lian Lundy, Bryan Ellis, Nathalie Gilbert, John Bryden

The use of site-specific traffic, highway and rainfall data to predict surface runoff concentrations under storm events of differing magnitudes

Science & Mathematics Published 2023 Verification Pending

Overview & Synopsis

The 6th EU Water Framework Directive implementation report identifies that less than 50% of Member States (MS) have met (or are on track to meet) objectives related to ‘pollution from urban areas, transport and built infrastructure’, with the remaining MS reporting their progress as none, not mapped, mixed or could not be reported. To support catchment managers address this identified pressure, vehicular emissions factors have been combined with site-specific data to predict pollutant concentrations in runoff generated from 63 sections of highways located in a North London (UK) catchment under two storm events with differing characteristics. Concentrations are predicted for total suspended solids (TSS), zinc (Zn), copper (Cu), cadmium (Cd) and benzo(a)pyrene (BaP), with modelled results falling within the range of values reported for earlier UK urban runoff field studies. The mean, median and maximum values predicted for the smaller event (15.4mm; 2.2mm/hour) exceed those associated with the larger event (33.2mm; 15.2mm/hr), with maximum total concentrations of 1516 mg/l, 5855 µg/L, 414 µg/L, 2.41 µg/L and 1.558 µg/L respectively predicted for TSS, Zn, Cu, Cd and BaP, for this smaller event. Results can be geospatially mapped to provide a visual display of their distribution and magnitude across the highway network, providing a basis for catchment managers to prioritise locations for the installation of treatment systems to minimise highway-related pollutant loads discharging to receiving waters.

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Document Information

Document Title The use of site-specific traffic, highway and rainfall data to predict surface runoff concentrations under storm events of differing magnitudes
Author / Organization Mike Revitt, Lian Lundy, Bryan Ellis, Nathalie Gilbert, John Bryden
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2023
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-244098
Original Source Link Visit Source Page on hal.science »