Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Civil and Environmental Engineering | en_US |
| dc.contributor.advisor | Wang, Yuhong (CEE) | en_US |
| dc.creator | Lin, Yinghong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14489 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Assessment of urban road runoff pollution and tree-based remediation methods | en_US |
| dcterms.abstract | Urban stormwater runoff is a major nonpoint source of pollution, posing significant challenges to urban water quality management due to its diffuse origins, episodic nature, and complex pollutant mixtures. Rapid urbanization has intensified these challenges, particularly in high-density cities such as Hong Kong, where extensive impervious surfaces facilitate the rapid transport and direct discharge of untreated pollutants into receiving waters. Urban road runoff is a critical contributor to this problem, carrying diverse contaminants—including nutrients, heavy metals, pathogens, organic matter, and emerging pollutants such as microplastics (MPs)—that threaten aquatic ecosystems and public health through eutrophication, toxicity, and bioaccumulation. However, the characteristics, interactions, ecological risks, and effective mitigation of multi-pollutant road runoff, especially MPs, remain insufficiently understood in compact urban environments. | en_US |
| dcterms.abstract | This study integrates large-scale field characterization with nature-based mitigation to advance the understanding and management of urban road runoff pollution in Hong Kong. First, stormwater runoff from six representative urban road sites was monitored across 11 rainfall events over 17 months (2021–2022). A total of 32 water quality parameters—including MPs, nutrients, heavy metals, conventional indicators, and microbial contaminants—were analyzed to characterize pollutant dynamics, identify dominant sources and interactions, and assess ecological risks. | en_US |
| dcterms.abstract | Results revealed that microplastic (MP) concentrations in road runoff were particularly elevated during the initial stage of rainfall events. The median MP abundance in road runoff (185 particles L⁻¹) was 4.6 times higher than that in natural rainwater (40 particles L⁻¹). Polyethylene (PE), polypropylene (PP), and polystyrene (PS) were the dominant polymers, with fragments being the most common shape, and more than 60% of MPs measuring smaller than 300 μm. Risk assessment using the Polymer Risk Index (PRI) classified most road sites as pollution classes II–III (PRI = 13.3–138.0), indicating moderate to high ecological risks. MP abundance was significantly influenced by seasonal variability, highlighting urban roads as a major source of MP pollution and underscoring the importance of controlling initial runoff. | en_US |
| dcterms.abstract | In addition, contaminant levels in road runoff were markedly higher than those in natural rainwater; notably, E. coli concentrations exceeded rainwater levels by more than four orders of magnitude. Concentrations of organic matter, suspended solids, nutrients, pathogens, and metals exceeded the Water Pollution Control Ordinance (WPCO) objectives in Hong Kong by several to dozens of times. A pronounced first-flush effect was observed for chemical oxygen demand (COD), total suspended solids (TSS), zinc (Zn), and MPs. MPs were positively correlated with pH, E. coli, phosphate (PO₄³⁻), and nitrate nitrogen (NO₃⁻–N), and negatively correlated with dissolved oxygen (DO) and iron (Fe), suggesting that MPs may act as vectors for nutrients and pathogens under oxygen-depleted, biologically enriched conditions. Pollutant concentrations varied with land use and season, with higher levels on residential roads and winter peaks for COD, chloride (Cl⁻), Zn, Fe, and TSS. Ecological risk assessment indicated very high metal-related risks, primarily driven by lead (Pb) and nickel (Ni), as well as moderate-to-high polymer risks associated with polyvinyl chloride (PVC) and polymethyl methacrylate (PMMA). | en_US |
| dcterms.abstract | Building on these findings, tree-based remediation methods were designed and evaluated to mitigate key road runoff pollutants. A total of 24 banyan tree box filters with different media compositions and six filter columns without trees were constructed and tested through 12 simulated rainfall events over 16 months (2023–2025). The tree box systems demonstrated high and consistent pollutant removal performance, achieving mean removal efficiencies exceeding 90% for NH₄⁺–N, PO₄³⁻, and Cu, and above 80% for Cr, Zn, and Fe. Compared with unvegetated filter columns, tree boxes exhibited substantially enhanced nitrate removal, underscoring the critical role of tree roots in nutrient retention and transformation. Pollutant removal was strongly influenced by pH dynamics and seasonal variability, reflecting root-mediated biogeochemical regulation. Functional media mixtures, particularly sand–granular activated carbon and sand–biochar, further enhanced nutrient and metal removal. Sustained tree growth under polluted runoff conditions confirmed the long-term viability and ecological compatibility of the system. | en_US |
| dcterms.abstract | Overall, this research demonstrates that urban road runoff represents a significant multi-pollutant and ecological risk in high-density cities, while tree-based remediation methods offer a sustainable, resilient, and multifunctional approach for urban stormwater pollution control. By integrating comprehensive pollutant characterization with field-scale remediation, this study provides new insights into multi-pollutant dynamics, first-flush behavior, and vegetation-driven treatment mechanisms, supporting the development of effective and space-efficient stormwater management strategies for compact urban environments. | en_US |
| dcterms.extent | viii, 190 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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