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Stochastic Method for Evaluating Removal, Fate and Associated Uncertainties of Micropollutants in a Stormwater Biofilter at an Annual Scale

1
LEESU, UMR MA 102, École des Ponts, AgroParisTech, UPEC, UPE, Champs-sur-Marne, 6-8 avenue Blaise Pascal, Cité Descartes, 77455 Marne-la-Vallée CEDEX 2, France
2
CEREMA, 12 Rue Léon Teisserenc de Bort, 78190 Trappes, France
3
IFSTTAR, Laboratoire Eau et Environnement- route de Bouaye CS4, 44344 Bouguenais CEDEX, France
*
Authors to whom correspondence should be addressed.
Water 2019, 11(3), 487; https://doi.org/10.3390/w11030487
Received: 18 January 2019 / Revised: 20 February 2019 / Accepted: 26 February 2019 / Published: 9 March 2019
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PDF [5292 KB, uploaded 9 March 2019]
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Abstract

A stochastic method for evaluating the in situ mass balance of micropollutants in a stormwater biofilter, accounting for inlet and outlet loads and the evolution of pollutant mass in the filter media (ΔMsoil) at an annual scale, is proposed. In the field context, this type of calculation presents a number of methodological challenges, associated with estimating water quality for unsampled rain events, reconstituting missing or invalidated flow data and accounting for significant uncertainties associated with these estimations and experimental measurements. The method is applied to a biofiltration swale treating road runoff for two trace metals, Cu and Zn and six organic micropollutants: pyrene (Pyr), phenanthrene (Phen), bisphenol-A (BPA), octylphenol (OP), nonylphenol (NP) and bis(2-ethylhexyl) phthalate (DEHP). Pollutant loads were reduced by 27–72%. While organic micropollutants are likely to be lost to degradation or volatilization processes in such systems, dissipation could not be demonstrated for any of the organic micropollutants studied due to emissions from construction materials (case of BPA, OP, NP and DEHP) or high uncertainties in ΔMsoil (case of Pyr and Phen). The necessary conditions for establishing an in situ mass balance demonstrating dissipation, which include acquisition of data associated with all terms over a period long enough that uncertainty propagation is limited and the absence of additional sources of pollutants in the field, are discussed. View Full-Text
Keywords: low impact development; swale; biofiltration; road runoff; micropollutants; treatment efficiency; monitoring; mass balance; stochastic model low impact development; swale; biofiltration; road runoff; micropollutants; treatment efficiency; monitoring; mass balance; stochastic model
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Supplementary material

  • Externally hosted supplementary file 1
    Doi: 10.17632/996zpx2ypt.1
    Link: https://data.mendeley.com/datasets/996zpx2ypt/1
    Description: The raw dataset (micropollutant concentrations in water and in soil, characteristics of sampled rain events) is available on Mendeley Data.
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Flanagan, K.; Branchu, P.; Boudahmane, L.; Caupos, E.; Demare, D.; Deshayes, S.; Dubois, P.; Kajeiou, M.; Meffray, L.; Partibane, C.; Saad, M.; Vitart de Abreu Lima, M.; Gromaire, M.-C. Stochastic Method for Evaluating Removal, Fate and Associated Uncertainties of Micropollutants in a Stormwater Biofilter at an Annual Scale. Water 2019, 11, 487.

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