The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach

Kefeng Zhang; Yizhou Liu; Ana Deletic; David T McCarthy; Belinda E Hatt; Emily G I Payne; Gayani Chandrasena; Yali Li; Tracey Pham; Behzad Jamali; Edoardo Daly; Tim D Fletcher; Anna Lintern

doi:10.1016/j.watres.2020.116486

The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach

Water Res. 2021 Jan 1:188:116486. doi: 10.1016/j.watres.2020.116486. Epub 2020 Oct 5.

Authors

Affiliations

¹ Water Research Centre, School of Civil and Environmental Engineering, UNSW Sydney, High St, Kensington, NSW 2052, Australia. Electronic address: kefeng.zhang@unsw.edu.au.
² Water Research Centre, School of Civil and Environmental Engineering, UNSW Sydney, High St, Kensington, NSW 2052, Australia.
³ Department of Civil Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia.
⁴ Melbourne Water Corporation, La Trobe Street, Docklands, Victoria 3008, Australia.
⁵ Water Technology, Victoria, 15 Business Park Drive, Notting Hill VIC 3168, Australia.
⁶ Centre of Smart Infrastructure and Digital Construction, Department of Civil and Construction Engineering, Swinburne University of Technology, VIC 3122, Australia.
⁷ Afflux Consulting, Emerald, VIC 3782, Australia.
⁸ School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond, VIC 3121, Australia.

PMID: 33080456
DOI: 10.1016/j.watres.2020.116486

Abstract

Biofiltration systems can help mitigate the impact of urban runoff as they can treat, retain and attenuate stormwater. It is important to select the optimal design characteristics of biofilters (e.g., vegetation, filter media depth) to ensure high treatment performance. Operational conditions (e.g., infiltration rate) can also lead to significant changes in biofilter treatment performance over time. The impact of specific operational conditions on water quality treatment performance of stormwater biofilters is still not well understood. Furthermore, despite the importance of design characteristics and operational conditions on biofilter treatment performance, there is a lack of models that can be used to determine the optimal design and operation. In this paper, we developed a series of statistical models to predict the Total Phosphorus (TP) and Total Nitrogen (TN) removal performance of stormwater biofilters using various numbers of design characteristics and operational conditions. These statistical models were tested using data collected from four extensive laboratory-scale biofilter column studies. It was found that all models performed relatively well with a Nash-Sutcliffe Efficiency (NSE) of 0.42 - 0.61 for TP and 0.37 - 0.63 for TN. The most important design characteristics were filter media type and depth for TP treatment, and vegetation type and submerged zone depth for TN treatment. In addition, infiltration rate and inflow concentrations were the operational conditions that greatly influence outflow TP and TN concentrations from stormwater biofilters. As such, these variables need to be carefully considered when designing and operating stormwater biofilters. Sensitivity analysis results indicate that the model was quite sensitive to all regression coefficients and intercepts. Additional modelling exercises show that the model could be further simplified by reducing the number of cross-correlated parameters. These models can be used by practitioners for not just optimising the design, but also operating biofilters using real-time monitoring and control to achieve optimum performance.

Keywords: Data-driven; Hierarchical model; Infiltration rate; Nutrients; Treatment.

MeSH terms

Filtration*
Models, Statistical
Nitrogen
Nutrients
Rain
Water Purification*

Substances

Nitrogen