Validating Nitrogen Removal Models with Field Bioretention Data
Publication: Journal of Environmental Engineering
Volume 150, Issue 8
Abstract
Bioretention is a type of stormwater best management practice that can reduce stormwater runoff volume, and with appropriate design, simultaneously reduce nitrogen loads. To meet the water quality potential of bioretention systems, improved simulation models of the transport and transformations of nitrogen are needed. In this work, two versions of a three-nitrogen-pool (3P) model (3P-1 and 3P-m) are applied to simulate observed nitrogen transport through a field bioretention system. The 3P models provide predictions of net export of dissolved organic carbon, dissolved organic nitrogen, and inorganic nitrogen species. The 3P-m model has promising results: (1) relative to the Storm Water Management Model (SWMM), it reduced the prediction error of percent removal of total dissolved nitrogen by up to 10.6% and reduces the scaled root-mean square error of total dissolved nitrogen loads by up to 53.0%; (2) it successfully captured the correlation between impactful environmental factors and event mean concentrations of total dissolved nitrogen and nitrate nitrogen in underdrain effluent; (3) its predictions on denitrification showed good alignment with observed isotope results; and (4) it demonstrated higher stability in simulated time series among calibration results than 3P-1. The 3P-m is expected to assist the design of bioretention system and valve control strategies by providing more accurate predictions on nitrogen removal under the impacts of soil moisture content and temperature. Future research and collaboration opportunities are also proposed in this article.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies. The latest code files of 3P-1 and 3P-m model are published for future research work on GitHub with the following link: https://github.com/lijiayileslie?tab=repositories. The data set used for model calibration and validation in this research is available upon request.
Acknowledgments
This research was funded as part of the National Science Foundation Grant: Award No. 1737432 (SCC-IRG Track 1: Overcoming Social and Technical Barriers for the Broad Adoption of Smart Stormwater Systems). It was also supported by the Virginia Transportation Research Council (VTRC) Project No. 413680 and the University of Virginia.
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Journal of Environmental Engineering
Volume 150 • Issue 8 • August 2024
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© 2024 American Society of Civil Engineers.
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Received: Sep 1, 2023
Accepted: Mar 7, 2024
Published online: Jun 4, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 4, 2024
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