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<p>Increasing population and climate change have transformed the urban landscape, necessitating effective stormwater management for sustainable cities. While many studies have characterized constructed wetlands’ (CWs) performance for removing conventional pollutants (TN, TP and TSS), the relationships between these parameters and metal concentrations remain unexplored—despite metals’ ecological risks and regulatory significance. Thus, this study investigates the relationships between the commonly measured pollutants (TN, TP and TSS) and metal concentrations in CWs, proposing a method to estimate metal levels using existing monitoring data. Therefore, it provides a practical, cost-effective solution to infer metal pollution without additional sampling burdens, thereby advancing urban stormwater management practices and advocating for the inclusion of heavy metals in regulatory frameworks. Monitoring from a CW in an industrialized suburb in Melbourne, Australia between 2017 and 2022 was conducted. The canonical correlation analysis confirms the significant interactions between TN, TP, TSS and metals. Multivariate modelling further highlights the potential of predicting Zn using pollutants in the current best practice management guideline and physio-chemical parameters (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10661_2025_14259_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\({R}^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> = 0.86). It is envisioned that results will offer relevant stakeholders a cost-effective approach to determine metal levels in CWs using TN, TP and TSS as proxy indicators in the face of evolving and uncertain climate conditions. Findings of this study will have implications for reducing the financial burden of ongoing monitoring and improving the design and management of future endeavours by optimizing the more influential parameters.</p>

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Statistical techniques in correlation analysis of stormwater pollutants for improved management of constructed wetlands in urban cities

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Fujia Yang,
  • Shirley Gato-Trinidad,
  • Iqbal Hossain

摘要

Increasing population and climate change have transformed the urban landscape, necessitating effective stormwater management for sustainable cities. While many studies have characterized constructed wetlands’ (CWs) performance for removing conventional pollutants (TN, TP and TSS), the relationships between these parameters and metal concentrations remain unexplored—despite metals’ ecological risks and regulatory significance. Thus, this study investigates the relationships between the commonly measured pollutants (TN, TP and TSS) and metal concentrations in CWs, proposing a method to estimate metal levels using existing monitoring data. Therefore, it provides a practical, cost-effective solution to infer metal pollution without additional sampling burdens, thereby advancing urban stormwater management practices and advocating for the inclusion of heavy metals in regulatory frameworks. Monitoring from a CW in an industrialized suburb in Melbourne, Australia between 2017 and 2022 was conducted. The canonical correlation analysis confirms the significant interactions between TN, TP, TSS and metals. Multivariate modelling further highlights the potential of predicting Zn using pollutants in the current best practice management guideline and physio-chemical parameters ( \({R}^{2}\) R 2 = 0.86). It is envisioned that results will offer relevant stakeholders a cost-effective approach to determine metal levels in CWs using TN, TP and TSS as proxy indicators in the face of evolving and uncertain climate conditions. Findings of this study will have implications for reducing the financial burden of ongoing monitoring and improving the design and management of future endeavours by optimizing the more influential parameters.