Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands
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Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetlan...d performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.
Keywords:
cross-validation / green infrastructure / nature-based solutions / Sponge city / stormwaterSource:
Blue-Green Systems, 2021, 3, 1, 163-Publisher:
- IWA Publishing
Funding / projects:
- Australian Research Council Discovery Early Career Researcher Award – ARC DECRA (DE210101155)
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Institut za multidisciplinarna istraživanjaTY - JOUR AU - Zhang, Jiadong AU - Prodanovic, Veljko AU - Lintern, Anna AU - Zhang, Kefeng PY - 2021 UR - http://rimsi.imsi.bg.ac.rs/handle/123456789/3155 AB - Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors. PB - IWA Publishing T2 - Blue-Green Systems T1 - Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands IS - 1 SP - 163 VL - 3 DO - 10.2166/bgs.2021.024 ER -
@article{ author = "Zhang, Jiadong and Prodanovic, Veljko and Lintern, Anna and Zhang, Kefeng", year = "2021", abstract = "Constructed wetlands are a type of green infrastructure commonly used for urban stormwater treatment. Previous studies have shown that the various design characteristics have an influence on the outflow heavy metal concentrations. In this study, we develop a Bayesian linear mixed model (BLMM) and a Bayesian linear regression model (BLRM) to predict the outflow concentrations of heavy metals (Cd, Cu, Pb and Zn) using an inflow concentration (Cin) and five design variables, namely media type, constructed wetland type (CWT), hydraulic retention time, presence of a sedimentation pond (SedP) and wetland-to-catchment area ratio (Ratio). The results show that the BLMM had much better performance, with the mean Nash–Sutcliffe efficiency between 0.51 (Pb) and 0.75 (Cu) in calibration and between 0.28 (Pb) and 0.71 (Zn) in validation. The inflow concentration was found to have significant impacts on the outflow concentration of all heavy metals, while the impacts of other variables on the wetland performance varied across metals, e.g., CWT and SedP showed a positive correlation to Cd and Cu, whereas media and Ratio were negatively correlated with Pb and Zn. Results also show that the 100-fold calibration and validation was superior in identifying the key influential factors.", publisher = "IWA Publishing", journal = "Blue-Green Systems", title = "Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands", number = "1", pages = "163", volume = "3", doi = "10.2166/bgs.2021.024" }
Zhang, J., Prodanovic, V., Lintern, A.,& Zhang, K.. (2021). Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands. in Blue-Green Systems IWA Publishing., 3(1), 163. https://doi.org/10.2166/bgs.2021.024
Zhang J, Prodanovic V, Lintern A, Zhang K. Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands. in Blue-Green Systems. 2021;3(1):163. doi:10.2166/bgs.2021.024 .
Zhang, Jiadong, Prodanovic, Veljko, Lintern, Anna, Zhang, Kefeng, "Development of the data-driven models for accessing the impact of design variables on heavy metal removal in constructed wetlands" in Blue-Green Systems, 3, no. 1 (2021):163, https://doi.org/10.2166/bgs.2021.024 . .