Zinc,Copper,and Aluminium Dynamics in Urban In-Channel Stormwater Treatment Systems Under Varying Groundwater Conditions

Abstract

The effective removal of excess heavy metals from surface stormwater is an important environmental goal due to their potential toxicity to aquatic organisms. In-channel stormwater treatment systems (ICSTS) are often used to remove pollutants from stormwater-impacted streams. However, the impact of hydraulic conditions on treatment performance is not well characterised. This research investigates the impact of varying groundwater conditions and bed media hydraulic conductivity on the dynamics of aluminium (Al), copper (Cu), and zinc (Zn) in ICSTS. Experiments conducted in a 19-m flume with gravel, bed media, and surface water under seepage, drainage, and neutral groundwater conditions revealed significant variations in heavy metal retention and release. Under groundwater seepage, there was an average decrease of dissolved Zn in the outlet and an increase in both total and dissolved Cu (5% for Zn; 16% for Cu) when using high hydraulic conductivity media (HH). Low hydraulic conductivity media (LH) under seepage led to a greater increase of dissolved and total Zn and Cu (7% for Zn; 44%–56% for Cu). Under drainage conditions, there was a decrease in dissolved Zn and Cu loads (14% for Zn; 15%–18% for Cu) with HH media and greater variation in pH and redox potential (Eh). Drainage with LH media resulted in lower Zn loads (8%) and an increase in total and dissolved Cu loads (34%–65%). Lower Zn concentrations were also observed in the groundwater (25%–46% decrease) after draining through the bed media, while Cu increased around 200% but only when using LH media. Total aluminium increased in concentration and loads while dissolved Al decreased in all scenarios. This research highlights the importance of designing ICSTS that promote drainage and high hydraulic conductivity bed sediment to enhance metal retention. It also emphasises the need for ongoing monitoring and maintenance to prevent clogging, contaminant release, and long-term performance decline.

Keywords

aluminium bed sediment copper groundwater interactions in-channel treatment systems zinc

Introduction

The pervasive presence of heavy metals in urban waterways is a pressing environmental concern due to their potential toxicity to aquatic organisms. These metals, particularly when in dissolved form, can bioaccumulate in living organisms and pass throughout the food chain, ultimately impacting humans and other top predators. While the effects on aquatic organisms are significant, the potential risks to human health should not be overlooked (Arora et al., 2008; DePinto et al., 1993; Luoma & Rainbow, 2008; Salahshoori et al., 2024; Sonone et al., 2020). On a global scale, many urban streams exhibit elevated concentrations of heavy metals in both dissolved and particulate forms (Bibby & Webster-Brown, 2006; Sekabira et al., 2010). Most heavy metals in urban stormwater are washed off from roads, carparks, and roof surfaces during stormflow events and transported in runoff directly into urban waterways. These heavy metals are associated with suspended particulate matter (SPM), contributed by diffuse sources such as motor vehicle emissions, oil drips, tyre wear, road surfaces, domestic fires, waste oil, roofing corrosion, and specific point sources such as electroplating workshops, gasworks, microelectronics, photovoltaic solar panels, electrical car batteries, and incinerators (Beck & Birch, 2012; Bibby & Webster-Brown, 2006; Brown & Peake, 2006; Charters et al., 2016; He et al., 2017; Stojić et al., 2023).

In the water column, heavy metals exist in dissolved (complexed or as ions) and particulate forms, undergoing various chemical processes like adsorption, dissociation, and degradation (Figure 1). Particulate heavy metals, owing to their specific weight, undergo sedimentation and settle on the stream bed and may undergo burial in an anoxic environment, or alternatively, can undergo degradation and dissociation into smaller components, with the metal ions re-dissolving into the water column (Luoma & Rainbow, 2008). Over time, the buried metals typically bind with sulphides, forming stable compounds that reduce their mobility. However, disturbances such as bioturbation or changes in hydrodynamic conditions can cause resuspension, bringing these metals from anoxic sediments back to the water column as freely available organic complexes or in colloidal form (Figure 1). Groundwater flow dynamics, chemical composition, and sediment depth also affect the exchange of metals between sediments and surface water. Although some studies have assessed the risks of groundwater contamination from stormwater infiltration (e.g. Pitt et al., 1999), few have experimentally explored how different groundwater flow processes (e.g. seepage vs. drainage) interact with sediment properties to affect heavy metal mobility in in-channel systems.

Figure 1.

Simplified conceptualisation of heavy metals (HM) dynamics in surface water (adapted from Burton et al., 2007; Luoma & Rainbow, 2008, and Gadd et al. (2017).

At the stream bed interface, heavy metal transformation hinges on complex interactions through chemical, physical, and biological processes with soil components, organic matter, and microorganisms within the bed sediment. The extent to which transformation occurs depends on factors such as pH, oxygen levels, and temperature, influencing the long-term fate of heavy metals in aquatic environments. Factors such as solubility, forms, mobility, and toxicity of heavy metals in water depend on their oxidation states (Snoeyink & Jenkins, 1991). Additionally, nutrient presence has been identified as a factor amplifying the bioavailability of heavy metals in aquatic environments due to bonding forming between some heavy metals and phosphorus (Miranda et al., 2021).

The primary mechanism governing heavy metal exchange at the water-soil interface is adsorption and desorption reactions with sediment that mainly happen within the porewater (the water that occupies the spaces (pores) between particles in the bed material; Violante et al., 2008; Wijesiri et al., 2019). The potential for heavy metals to adsorb to and desorb from sediment is driven by the porewater’s physico-chemical characteristics. For example, hydrogen ions (H⁺) from acidity can displace heavy metals from their binding sites. Individual heavy metal types have a pH limit at which they can be released back into water, even under stable water conditions. For instance, zinc (Zn) has a pH limit between 6.0 and 6.5, copper (Cu) 4.5, and aluminium (Al) 2.5 (Peng et al., 2009).

In terms of sediment properties, the cation ion exchange capacity and surface area are the most influential sediment factors determining the sediment’s ability to adsorb and retain heavy metals; higher cation exchange capacity allows the sediment to hold more positively charged ion, while a larger surface area provides more binding sites (Keshavarzifard et al., 2019; Miranda et al., 2021). Particle size and mineralogy also have some influence because smaller particles increase the surface area available for adsorption and mineralogy influences the types and strengths of the binding sites available for heavy metals, with different minerals having varying affinities for different metal ions (J. Huang et al., 2012; Yavar Ashayeri & Keshavarzi, 2019).

Within the sediment, each heavy metal type exhibits a distinct affinity for specific binding materials, with a preference for compounds such as iron oxide, manganese oxide, organic matter, and sulphide (Chapman et al., 1998; Eggleton & Thomas, 2004; Luoma & Rainbow, 2008). Cu preferentially binds to organic matter and SPM, while Zn tends to form complexes with iron oxide (Bibby & Webster-Brown, 2006; Zeng et al., 2020). In acidic mineral soils, Cu adsorption surpasses Zn at equivalent concentrations and the introduction of organic matter enhances Cu adsorption with minimal impact on Zn adsorption (Arias et al., 2006).

Upon bonding with other substances, the heavy metal complex adsorbs to detritus, forming a settling floc that reaches the bed sediment, trapping the heavy metal in the surficial sediment or porewater (DePinto et al., 1993). Adsorption and desorption are influenced by water properties like pH (the lower the pH, the greater the desorption), temperature, and redox conditions. Once sequestered in the sediment or porewater, these metals tend to undergo burial, descending to an anoxic region. Unlike overlying water, porewater is directly connected to sediment toxicity. In porewater, the mobility of heavy metals is relatively low, where the heavy metals concentration can be easily assessed. Assessment of pore water provides valuable insights into sediment toxicity and bioavailability of heavy metal (Ma et al., 2000; Rand, 1995).

In anoxic conditions, heavy metals in the porewater predominantly bind with sulphide, displaying a rapid and robust reaction, particularly with metals like Fe and Mn (Luoma & Rainbow, 2008). Acid-volatile Sulphide (AVS) has been shown to be a major factor controlling the bioavailability and toxicity of trace metals (including Zn and Cu) due to its availability and strong affinity to bind with heavy metals, in which exchangeable iron sulphide (FeS) and manganese sulphide (MnS) play an important role binding with heavy metals due to their strong sorption capacity (Burton et al., 2007). Mn and Fe originate from complex metals in soil, while sulphide results from bacterial processes reducing sulphate in an anoxic environment. When heavy metal concentrations exceed available sulphide, heavy metals then bind with particulate organic carbon (POC; Chapman et al., 1998). Upon depletion of the sedimentary AVS phase, Cu has the potential to displace Zn ions from their binding phase (Simpson et al., 2000).

Resuspension and transport of settled sediments in wetlands and in-channel treatment systems are infrequent during baseflow conditions (Figure 1). However, during extreme weather events, in the presence of animals (bioturbation), or turbulent streamflow conditions and high flow velocity (Reddy et al., 1999), resuspension can occur, which moves substances from anoxic into oxic conditions due to the presence of oxygen (i.e. from the bed into the water column). This alters pH and redox conditions due to changes in oxygen levels and other parameters within the hyporheic zone (Håkansson et al., 1989; Simpson et al., 1998). Resuspension studies have demonstrated an increase in Cu concentrations under resuspension, while Zn concentrations remained relatively constant (Simpson et al., 1998) yet it is unknown how groundwater conditions such as seepage and drainage would affect this.

Capturing of heavy metals by plants and microorganisms in waterways occurs through processes like absorption, adsorption, transformation, and precipitation, using their roots, cell surfaces, and metabolic activities (Raklami et al., 2022; Reddy & DeLaune, 2008). However, the subsequent decay of these organisms, such as plants, leaves, algae, and other living entities, reintroduces heavy metals back into the environment.

In-channel (stormwater) treatment systems are engineered treatment systems built inline with streams, drainage channels, and other waterways with the goal of removing contaminants through biological, chemical, and physical processes by enhancing the natural treatment capabilities of the waterway itself through interactions with soils, vegetation, and through drainage/seepage as it moves through the waterway (Moazzem et al., 2024; Walsh et al., 2016). By removing stormwater pollutants and creating a more balanced surface water flow, they can also improve habitat for aquatic organisms. Examples of these are engineered urban drainage streams (or waterways), constructed wetlands, and vegetated swales (Chen et al., 2013; Kabenge et al., 2018; Leroy et al., 2016).

In-channel treatment systems have been studied using flumes and laboratory columns because processes can be isolated and studied in detail. Using an annular flume, hydrodynamic effects were found to increase Cu and Zn concentrations in surface water due to sediment resuspension, but differences due to sediments hydraulic conductivity were not assessed (J. Huang et al., 2012). A laboratory column experiment found that temperature enhanced the suspended sediment ability to transport heavy metal transport due to the sediment’s strong adsorption, but this did not take into account drainage or groundwater seepage conditions (Bai et al., 2021). While these studies advanced the understanding of heavy metal dynamics between water and sediment, they have not considered variations in heavy metal dynamics in in-channel systems arising from factors such as variable metal concentrations and signatures, groundwater (GW) conditions, surface flow rates, and bed sediment properties.

While previous studies have examined metal transport in surface water, limited research has addressed the role of groundwater interactions in in-channel treatment systems. This study bridges this gap by assessing heavy metal retention and release under different groundwater conditions and hydraulic conductivities. A poor understanding of the influence of groundwater regimes, bed hydraulic conductivities, and metal concentrations poses a significant challenge for improving stormwater treatment and the health of surrounding water bodies.

This research thus aims to assess the dynamics of heavy metals (Zn, Al, and Cu) using a flume experiment to represent in-channel treatment systems, under different groundwater regimes (neutral, drainage, and seepage ground water conditions) and different bed media hydraulic conductivities. We hypothesise that groundwater seepage will increase heavy metal concentrations in surface water, while drainage conditions will promote retention within the bed media, with these effects varying by bed media hydraulic conductivity.

Materials and Methods

Preparation of Materials for Experimental Testing

To address the research aims, bed media was collected from an existing in-channel treatment system for column leaching tests and groundwater-controlled flume experiments. Bed media, utilised in all experiments, was obtained from the Wigram Retention Basin (WRB) in Christchurch, New Zealand, a well-established treatment pond with a three-decade history of heavy metal contamination (Black, 2018; Moores et al., 2009). WRB receives surface runoff from Haytons Stream, a groundwater-fed urban stream that receives stormwater runoff and direct discharges from a mixed industrial-residential catchment (F. C. Silveira, 2017).

The collected bed sediment was dried at 30°C and 25% air humidity for 7 days and was subsequently sieved on a stainless-steel mesh with a 32 mm aperture. To enhance hydraulic conductivity for experimentation purposes, medium to coarse silica sand supplied by Commercial Minerals Ltd, Auckland, New Zealand, with a D₆₀ value of 0.45 mm was blended with WRB’s bed sediment (Chambers 1999) (Supplemental Figure S1 and Table S1). Two mixtures of bed media were prepared: Mix 1 contained 75% sediment + 25% silica sand and Mix 2 contained 40% sediment + 60% silica sand.

Synthetic stormwater (SSW) was used to mimic urban runoff conditions (Christchurch City Council [CCC], 2022). The SSW was prepared using zinc chloride (ZnCl₂), aluminium chloride (AlCl₃), and copper chloride dihydrate (CuCl₂·2H₂O) salts dissolved in tap water to achieve target concentrations of 250 µg/L for dissolved Zn, 40 µg/L for dissolved Al, and 4 µg/L for dissolved Cu. These metals were considered priority contaminants of concern due to their high concentrations in the study site. They are also top priority for local councils in New Zealand (CCC, 2022). We screened initial results for a wide range of contaminants and these three were among the greatest concentration compared to their relevant guidelines. The composition matched groundwater quality to prevent dilution effects under seepage conditions.

Column Leaching Test

Each column had 10 cm internal diameter and 30 cm height. The column contained 5 cm of bed media mix (1:1 WRB sediment and sand), a layer of cotton cloth (1 thread per square mm), 14 cm of gravel then a layer of cotton cloth sitting on a perforated disc with 2 mm holes with 0.5 holes/cm². The cotton cloth was used between the layers to prevent the migration of particles.

Column leaching tests were performed to quantify the hydraulic conductivity of the media mixes and to determine changes in heavy metal concentrations in the water column under seepage conditions (i.e. up-flow conditions), using both deionised water (DI) and SSW to help understand the role of ions influencing heavy metals dynamics. Water was directed in from the bottom of the columns and an outlet was situated 2 cm above the top of the bed media mix.

The seepage rate was set at 6 mL/min was the equivalent seepage flow of the flume experiment. Each test was run for 90 min, preceded by a 30-min stabilisation period. Samples collected at 15-min intervals (i.e. samples taken at 30, 45, 60, 75, and 90 min after the start of flow) from the outlet. The experiments were repeated three times on different dates to check for reproducibility of results.

Flume Design and Experimental Methodology

A 19 m long flume, divided into two main sections each of 9.5 m, was made using 6 mm thick polyvinyl chloride (PVC) sheets to simulate an in-channel treatment system. The flume contained a 10 cm gravel base below a 5 cm layer of bed media. Grade 5 gravel (5–8 mm diameter) was used to represent free-draining gravel in the lower hyporheic zone. A layer of cheese cloth was placed between the gravel and bed media to prevent the migration of fine particles into the gravel. Surface water was then applied from a high-density polyethylene (HDPE) feeder tank under gravity to achieve a 2 cm depth water level over the bed media, representing an inflow of 21.8 L/min at a 0.5% slope to represent a typical hydraulic gradient in a system with minimal backflow. A flow sensor installed at the outlet of the stormwater feeder tank (Electromagnetic flow meter Krohne Optiflux 4100) was used to monitor the target flows. Seepage, drainage, and neutral groundwater conditions were simulated via an adjustable side channel (Figure 2). Drainage conditions were simulated by lowering the groundwater channel relative to the flume to drain a total of 20% of the surface water flow, resulting in a total drainage rate of 4.36 L/min. Seepage conditions were simulated by raising the groundwater channel to achieve a groundwater inflow of 20% of the surface water flow. Groundwater contributions in lowland streams typically range from 30% to 60% (Aquilina et al., 2012; Clément et al., 2003; Munro & Callander, 2015). A 20% contribution, supported by field data (Guinn Garrett et al., 2012), was adopted as a conservative estimate for this study.

Figure 2.

Experiment setup showing both schematic flume flows and set up, showing water flow between flume sections and groundwater channels, with green circle as sampling location and hydraulic regime control. Qs represent seepage inflows and Qd represent drainage outflows.

Flume Simulations

Experiments were run under three different groundwater conditions (neutral, drainage, and seepage), and with three bed media compositions: only gravel (one replication for each groundwater conditions), 40% sediment + 60% sand (high hydraulic conductivity; three runs for each groundwater condition), and 75% sediment + 25% sand (low hydraulic conductivity; five runs for each groundwater condition, with the bed media replaced after three runs, resulting in two runs with new bed media for each groundwater condition). Gravel and bed media remained saturated with SSW between runs. Before each run, fresh SSW was applied to replace the SSW from the previous run and a stabilisation period of 15 min was run before any water quality sampling started. Experiments with low bed media hydraulic conductivity were repeated twice for quality control and quality assurance (QA/QC) to verify changes in the bed media during the study. After three runs for each groundwater condition, the bed media mix was replaced. Subsequently, two additional runs were conducted with the new media mix to verify the consistency and reliability of the results. The experimental study was conducted over a 13-month period, from 13 March 2020 to 18 April 2021.

Samples were collected from the inlet, middle, and outlet positions of the flume (Figure 2) using 1-L HDPE containers. The samples were promptly stored at 4°C before preservation/analysis. Inlet samples were specifically collected at 0, 10, 20, and 30 min from the start of surface flow. Steady-state conditions were established by circulating synthetic stormwater through the flume for 15 min prior to data collection, ensuring consistent water quality in the system. Middle and outlet samples were collected after a stabilisation period of 15 min (to allow pollutants concentration to stabilise) and collected at 15, 20, 25, and 30 min (with additional samples at 35 min for the high hydraulic conductivity experiment).

Under seepage conditions, four samples were collected at 0 and 20 min from groundwater channel 1 (GW1) and at 10 and 30 min from groundwater channel 2 (GW2). Under drainage conditions, groundwater samples were collected at 0, 10, 20, and 30 min from both groundwater channels because greater changes in water quality were expected.

For dissolved heavy metals analysis, samples were filtered at 0.45 μm and preserved with nitric acid (70% analytical grade) to reach a pH below 2 (following Rice et al., 2012, Method 3030 B). Total heavy metals (per modified Rice et al., 2012, method 3030 E) were prepared via digestion of 25 mL of sample with 5 mL of nitric acid for 1 hr at 125°C. After cooling, the digested sample was filtered using a 0.45 μm membrane. Both dissolved and total metal were analysed via inductively coupled plasma mass spectrometry (ICP-MS). Blank solutions and duplicate analyses were implemented to enhance confidence in the accuracy of the results.

In addition to discrete sampling, a YSI Professional Plus multi-parameter meter was positioned at each sampling location to monitor real-time variations in pH, specific conductance, temperature, and oxidation-reduction potential (ORP; which was later calculated to Redox Potential (Eh) by adding 200 mV to the ORP values as per Environmental (2005) at 1-min intervals throughout each run. All probes were calibrated following the manufacturer’s recommendations.

Percentage changes in heavy metal concentrations for each run were calculated from the difference between middle or outlet concentrations and the average inlet concentration (inlet concentrations varied only slightly). T-tests (α = .05) were conducted to identify statistically significant differences in the percentage change under different groundwater conditions. Additional t-tests were performed to compare inlet concentrations with those at the middle and outlet of each site, as well as to assess differences related to hydraulic conductivity and seasonal variation. Analyses were conducted independently for each site to preserve data independence and avoid overgeneralisation. ANOVA was not used in order to prevent potential “p-hacking” risks associated with pooled data and confounded effects across sites.

The mass balance of heavy metals was calculated by subtracting the outflow mass from the inflow mass (i.e. the flow was multiplied by concentration, resulting in a mass flux with units of µg/min) under each groundwater conditions (neutral, drainage, and seepage, Figure 2). Under neutral groundwater conditions, only surface water was considered for outflow mass calculations (Qout and concentration at the outlet) and inflow mass calculations (Qin and concentration at the inlet). For drainage water conditions, outflow mass included water collected at the groundwater channel (Qd1 and Qd2 and concentration at both GW1 and GW2, respectively) and at the outlet (Qout and concentration at the outlet), while inflow mass considered only the inlet flow. For groundwater seepage conditions, the outflow mass was measured at the outlet, with inflow mass including both inlet and groundwater seepage contributions (Qs1 and Qs2 and concentration at both GW1 and GW2, respectively). Positive results indicated an increase in heavy metal mass in the surface water, while negative results indicated that heavy metals were removed from surface water.

Results

Bed Media Characteristics

Sediment collected from WRB had concentrations of 6,600 mg Al/kg dry weight of sediment, 24 mg Cu/kg and 770 mg Zn/kg. The Cu concentration is below the local sediment quality guideline range (ANZECC, 2000) of 65 to 270 mg Cu/kg dry weight and above the Zn guideline range of 200 to 410 mg Zn/kg dry weight. Similarly, the Cu concentration falls below the probable effect concentration (PEC; i.e. above which harmful effects are likely to be observed) of 149 mg Cu/kg dry weight and above the PEC of 459 mg Zn/kg dry weight (MacDonald et al., 2000). Additional sediment characteristics can be found in supplement information Supplemental Table S1. The residence time for bed media with high hydraulic conductivity was estimated to be 119 and 147 s low hydraulic conductivity (F. C. Silveira et al., 2025).

Column Leaching Experiment

The seepage of deionised water leached more Zn, Al, and Cu compared to the seepage of SSW using a bed media containing 50% WRB sediment and 50% sand. However, the proportion of the dissolved metal compared to the total metal remained similar in all cases: dissolved zinc (DZn) was around 95% of total zinc (TZn), dissolved copper (DCu) was around 50% of total copper (TCu) and only 5% of total aluminium (TAl) was in dissolved form (DAl; Figure 3, Supplemental Tables S2 and S3).

Figure 3.

Water collected from the column leaching test at times 15, 30, 45, and 60 min, respectively. Dissolved (light grey) and Total (dark grey), respectively, with Zn (top), Al (middle), and Cu (bottom); Dotted line represents SSW concentrations. The box shows the middle 50% of the data, the line inside marks the median, and the whiskers extend to values within 1.5 times the interquartile range from the lower and upper quartiles (n = 6).

Pollutant Dynamics Along the Flume Channel Under Varied Groundwater Conditions

TCu and DCu concentrations showed a statistically significant increase in average concentration from the inlet (2.30 and 1.74 µg/L, respectively) to the outlet (3.34 and 2.65 µg/L, respectively; refer to Figure 4 and Supplemental Table S4) under seepage conditions with low conductivity bed media.

Figure 4.

Percent change in total and dissolved Zn, Al, and Cu in surface water under neutral, drainage, and groundwater seepage conditions. The box shows the middle 50% of the data, the line inside marks the median, the whiskers extend to values within 1.5 times the interquartile range from the lower and upper quartiles, and the circles represent outliers beyond this range. HH media (n = 3); LH media (n = 5).

For both low and high hydraulic conductivity bed media, TAl increased from inlet to outlet of flume (29–40 µg/L) however, the DAl concentration exhibited a statistically significant decrease between the inlet and outlet (from around 24 to 20 µg/L, respectively; Figure 4, Supplemental Table S4).

Zn was mostly in its dissolved form, and concentrations had greater variations under groundwater seepage conditions. However, there was no statistical difference between neutral, seepage, and drainage water conditions using both high and low hydraulic conductivity media (Figure 4, Supplemental Table S4).

Pollutants Dynamics Between Bed Media and Water Column

Mass balance calculations for the water column (Table 1, Supplemental Tables S5 and S6) showed clear trends of TAl increasing in all groundwater conditions, but in greater mass under groundwater seepage conditions using high hydraulic bed media. DAl, however, showed a clear trend of decrease in all scenarios.

Table 1.

Overall Mass Balance Results for Al, Cu, and Zn in the Bed Media.

Bed media hydraulic conductivity	Groundwater condition	Compound
Bed media hydraulic conductivity	Groundwater condition	TAl	TCu	TZn	DAl	DCu	DZn
High	Neutral	−	I	I	+	I	+
	Drainage	−	I	+	+	+	+
	Seepage	−	I	I	+	I	+
Low	Neutral	−	I	I	+	I	I
	Drainage	−	I	I	+	I	+
	Seepage	−	−	I	+	−	−

Note. Positive values indicate an increase in total mass in the bed media, negative a decrease in total mass in the bed media (e.g. bed media releases) and I is inconclusive.

Cu had a decrease in mass flux (µg/min) under drainage water conditions using high hydraulic conductivity media and an increase in mass flux using low hydraulic conductivity media. Zn mass decreased under drainage water conditions using high hydraulic bed media and increased under groundwater seepage conditions with low hydraulic bed media. When analysing mass balance on each part of the flume (Supplemental Table S5 and S6) there was inconsistency on the magnitude and results for both Zn and Cu, probably due to experimental variability, low concentration of these substances, or spatial heterogeneity in the flume system.

Changes in Groundwater Quality Under Drainage Water Conditions

Groundwater collected under drainage conditions (GW1 and GW2) exhibited lower concentrations of TZn, DZn, and TAl compared to the inlet surface water concentrations (Figure 5). However, for Cu, there was a ~150% increase in both TCu and TCu concentrations with the low hydraulic conductivity bed media and a 20% decrease with the high hydraulic conductivity bed media (Figure 4, Figure 5, and Table 1).

Figure 5.

Percent change in total and dissolved Zn, Al, and Cu in groundwater under drainage water conditions. The box shows the middle 50% of the data, the line inside marks the median, the whiskers extend to values within 1.5 times the interquartile range (IQR) from the lower and upper quartiles, and the circles represent outliers beyond this range. HH media (n = 3); LH media (n = 5).

pH, Conductivity, Eh, and Temperature Changes Throughout Flume Channel Over Time

Under groundwater seepage conditions, substantial variations were noted in pH, specific conductance, and Eh values (Supplemental Table S7). pH values exhibited a decrease from pH 7.8 at the inlet to 7.2 (both high and low hydraulic conductivity bed media) at the outlet under groundwater seepage conditions, with certain values registering a substantial decrease to pH 6.6.

Specific conductance mean values ranged between 112 and 120.8 μS/cm, with the highest values (198 μS/cm) and greatest standard deviation when using high hydraulic conductivity bed media.

Similarly, in the case of high hydraulic conductivity media, Eh values decreased from around 0.55 V at the inlet to a median of 0.30 V in the middle, reaching 0.25 V at the outlet. With low hydraulic conductivity media, the median value decreased from 0.58 V at the inlet to a range of 0.32 V at the outlet (see Supplemental Table S7). There was no specific trend observed for temperature as tests happened in a room with temperature control (mean of 17.5°C; range of 16°C–19°C).

Discussion

Impact of Groundwater Conditions on Heavy Metals Dynamics in Surface Water

Under seepage conditions, Cu concentrations increased at the outlet compared to the inlet. This increase suggests Cu desorption from bed sediment due to changes in Eh and pH. The effect was stronger in low hydraulic conductivity media, where metals remained in contact with water for longer (Figure 4, Supplemental Table S4).

The increase in TAl at the outlet of the flume indicated a release of TAl from the bed media to the surface water, due to the soil’s high aluminium content, regardless of whether the system was under seepage or drainage (Figures 3 and 4, Supplemental Table S1). Conversely, there was a clear decrease in DAl at the outlet of the flume in all scenarios, most pronounced under groundwater seepage (Figure 4, Supplemental Table S1). Al is well known for its strong binding and complexing capacity in natural waters (Hawke et al., 1996). Due to its high charge and trivalent state, Al can bind and form complexes with organic and inorganic molecules, facilitating strong electrostatic attractions and the formation of stable bonds, transitioning from dissolved form to particulate (Liu et al., 2024; Vance et al., 2020), which may explain the decrease in DAl and increase in TAl. Groundwater seepage might have provided more binding opportunities for DAl in the surface column.

The slight decrease in Zn observed at the outlet in all scenarios (Figure 4, Supplemental Table S4), although not statistically significant, supports the hypothesis of rapid adsorption of Zn in the bed sediment as previously reported (Eggleton & Thomas, 2004).

Heavy Metal Dynamics Between the Water Column and Bed Media

The most important observations were that under groundwater seepage, there was an increase in TZn, DZn, TCu, and DCu in the water column while under drainage water, there was a decrease of these in the water column (Table 1). Under drainage water, the downward movement of water drains surface water through the bed media, facilitating interaction between oxygen-rich surface water and oxygen-depleted, potentially anoxic, bed media, which promotes adsorption, leading to the retention of metals within the bed media.

In contrast, under groundwater seepage conditions, the upward fluxes expose adsorbed metals within the bed media to the surface water, increasing their exposure to oxygen and higher oxidation-reduction potential in the surface water. Under groundwater seepage and low hydraulic bed media, there was a release of both DCu and DZn (Table 1).

Al, Cu, and Zn speciation in aquatic environments is governed by pH and redox potential (Eh), as illustrated by their respective Pourbaix (also known as Eh-pH) diagrams at 25 °C (H.-H. Huang, 2016). Under oxidising conditions (Eh >+0.3 V, pH <6.5), Cu²⁺ is the dominant aqueous species, while under mildly reducing and near-neutral conditions (Eh ~0 V, pH 6–8), low-solubility compounds such as cuprous oxide (Cu₂O) or cupric oxide (CuO) may form. At strongly reducing conditions (Eh <0 V), copper is stable as elemental Cu⁰ (H.-H. Huang, 2016). In contrast, Al and Zn speciation are primarily pH-dependent: Al exists predominantly as Al³⁺ under acidic conditions (pH <4), as insoluble Al₂O₃·3H₂O near neutral pH (4–9), and as soluble AlO₂^– or Al(OH)₄^– under alkaline conditions (pH >9), with minimal influence from redox potential in environmentally relevant ranges (El-Rabiei et al., 2020). Zn²⁺ prevails below pH 8.0 across a wide Eh range (–0.5 to +1.5 V). At higher pH, zinc forms soluble hydroxide complexes (e.g. Zn(OH)₂(aq), Zn(OH)₃^–) and may precipitate as Zn(OH)₂(s), while Zn⁰ is stable only under highly reducing conditions (Eh <–1.0 V; Beverskog & Puigdomenech, 1997). Although Zn and Cu tend to remain in their dissolved forms, Cu preferentially binds to organic matter and suspended particulate matter (SPM) when forming complexes, while Zn tends to form complexes with iron oxides (Bibby & Webster-Brown, 2006; Zeng et al., 2020).

Under the observed groundwater seepage conditions (pH 6.6–7.8, Eh 0.25–0.58 V), both Cu and Zn were likely present predominantly in their dissolved ionic forms (Cu²⁺ and Zn²⁺). Limited formation of Cu₂O may occur at the lower Eh range, and at pH values around 6.5 and under moderately oxidising conditions, CuO may form as a low soluble solid, representing a potential pathway for copper immobilisation and reduced mobility in the system. On the other hand, aluminium would primarily exist as the solid phase Al₂O₃·3H₂O, indicating limited solubility and low mobility under these conditions. This explains the observed decrease in DAl alongside an increase in TAl across all scenarios (Figures 3 and 4, Supplemental Table S1) and supports the observation that most Zn remained in its dissolved forms. There was a negative relationship and strong correlation between pH and both TCu and DCu concentrations at the surface water of the flume, using high hydraulic conductivity (Figure S2), weak correlation when using low hydraulic conductivity bed media. No strong trend was found between Eh and DZn, TCu, and DCu.

The lower oxidation-reduction potential values (Supplemental Table S7) under groundwater seepage conditions indicate greater electrons availability and anoxic conditions in the bed media. This change from static, low-oxygen conditions to dynamic and high-oxygen conditions favour the desorption of heavy metals to the surface water (Håkansson et al., 1989; J. Huang et al., 2012; Simpson et al., 1998). This process occur because the presence of oxygen alters the anoxic sediment matrix, facilitating the desorption of trace metal (i.e. Cu, Zn, Fe, and Mn) into the water column (Gerringa, 1990; Saulnier & Mucci, 2000). Increasing oxygen concentrations can enhance the mobility of trace metals, which may lead to a release of bed sediment-bound metals to the water column, even in undisturbed water (De Jonge et al., 2012).

Additionally, variations in sediment porewater chemistry, influenced by changes in pH and Eh (see Supplemental Table S7), may impact the availability and mobility of metals, thereby influencing adsorption and desorption mechanisms (Bibby & Webster-Brown, 2006; M. L. Silveira et al., 2006; Zeng et al., 2020). In anoxic conditions, FeS and MnS form strong bonds with heavy metals (Burton et al., 2007; Zeng et al., 2020). The highest concentrations of iron (Fe) and manganese (Mn) at the outlet of the flume were observed under groundwater seepage conditions (Supplemental Table S4). The release of both Fe and Mn under groundwater seepage conditions, along with highly positive ORP values, indicates an oxidising environment. This suggests that heavy metals bound to both FeS and MnS may be released, supporting potential release of sorbed heavy metal from Fe and Mn sulphides upon sulphide oxidation into the water column (Caetano et al., 2003).

Groundwater Concentrations Under Drainage Water Conditions

Concentrations of heavy metals in the groundwater (as measured in the flume’s groundwater channel) under drainage water conditions are explained by specific heavy metal’s dynamics. A clear decrease in Zn concentrations, using both high and low hydraulic conductivity bed media (Figure 5), relates to the strong adsorption capacity of Zn in the bed media, even though concentrations of Zn in the WRB sediment were elevated (770 mg/kg dry weight; Supplemental Table S2). Fe and Mn oxides in the sediment provide preferential adsorptive opportunities for Zn (Bibby & Webster-Brown, 2006; Eggleton & Thomas, 2004; Jain et al., 2004; Simpson et al., 1998). Large quantities of Fe and Mn were detected in the groundwater under drainage conditions, suggesting a large amount of Zn bonding sites in the bed media (Supplemental Table S1).

The increase in TAl and decrease in DAl in groundwater under drainage align with the observations of the change in Al concentrations in the surface water and column leaching experiment (Figures 2 and 4, Supplemental Table S1), where TAl leached from the bed media and DAl formed strong electrostatic attractions and the formation of stable bonds (Hawke et al., 1996; Liu et al., 2024; Vance et al., 2020).

The increase in Cu occurred mainly when using the low hydraulic conductivity bed media, likely due to the greater Cu content in this bed media mix. Cu binds strongly in bed media, preferentially to organic matter and SPM (Bibby & Webster-Brown, 2006; Zeng et al., 2020). Drainage water could transport organic matter and SPM from the bed media to the groundwater channel. It is also likely that the high-oxygen surface water within the bed media could weaken these Cu bonds (Arias et al., 2006; De Jonge et al., 2012; Simpson et al., 1998).

Effect of Bed Media Hydraulic Conductivity on Heavy Metal Dynamics

Flume experiments under groundwater seepage with low hydraulic conductivity media showed an increase of DCu and DZn in the water column, while high hydraulic conductivity bed media resulted in a decrease of these heavy metals (Figures 4 and 5, Supplemental Tables S4 and S8). High hydraulic conductivity bed media enhances pollutant removal but may reduce residence time, limiting adsorption efficiency. Regular sediment replacement or bioaugmentation could help maintain optimal sorption capacity.

High hydraulic conductivity bed media resulted in more pronounced variations in Eh and pH due to the increased permeability and water flux in the bed media. The increased permeability enhances water distribution within the bed media, extending the water flow path, enlarging the contact area, and facilitating greater porewater exchange with water column. These factors increased the likelihood that heavy metals will encounter sorption sites within the bed media. The magnitude of the increase in heavy metals concentrations under low hydraulic conductivity bed media was greater than the magnitude of the decrease in heavy metals concentrations under high hydraulic conductivity bed media, due to the higher proportion of WRB sediment in the low hydraulic conductivity bed media (low with 75% WRB; high with 40% WRB).

Urban runoff contains total suspended solids (TSS) and associated heavy metals (Charters et al., 2016; Ferreira & Stenstrom, 2013; Miranda et al., 2021; Selbig et al., 2016), which, over time, can accumulate within the bed media. This accumulation leads to an increase in heavy metal content and a reduction in hydraulic conductivity, as suspended particles tend to deposit within the bed media of in-channel treatment system. This pattern is comparable to the bed media used in the flume experiment, where low hydraulic conductivity bed media has greater heavy metals accumulation in the sediments.

Over time, as the bed media becomes saturated with pollutants from urban runoff, its hydraulic conductivity can decrease further while and heavy metals content may increase due to continued exposure to stormwater. This reduction in hydraulic conductivity decreases the likelihood of heavy metals encountering sorption sites within the bed media. Once the concentration of heavy metals in the bed media reaches a certain threshold, there is a risk of these metals being released back into the surface water. This has significant implications for the efficiency, maintenance, and lifespan of bed media in in-channel treatment systems.

Implications for Heavy Metal Dynamics in Waterways and In-Channel Treatment Systems

The design of in-channel stormwater treatment systems should promote drainage water infiltration to maximise metal retention, while minimising groundwater seepage, which could otherwise lead to the release of heavy metal into surface waters. Additionally, designing bed media with high hydraulic conductivity sediment can further enhance heavy metal removal in the system. However, a significant increase in permeability could also lead to reduced residence times, limiting adsorption efficiency.

Over time, saturation of the bed media with pollutants, suspended solids, and organic matter can reduce its hydraulic conductivity and increase heavy metal content. Subsequently, saturation of the bed media will reduce treatment performance, increase the risk of clogging, and increase the likelihood of heavy metal release back into the surface water, potentially harming aquatic organisms. Media saturation directly impact maintenance schedules, emphasising the need to remove bed media with high heavy metals concentrations and restore high hydraulic conductivity to facilitate water transport through the media.

In addition, factors such as wet versus dry seasons could affect treatment efficiency by altering groundwater levels, which in turn influence heavy metal dynamics. Furthermore, storm surges may elevate groundwater levels, reduce residence time, mobilise previously retained contaminants, and introduce additional heavy metals into the system, further compromising treatment performance. Moreover, the acidic nature of stormwater can lower surface water pH below 6.5, promoting the release of Cu from bed media.

In natural channels, it is particularly challenging, if not impossible, to control groundwater regimes—whether seepage, drainage, or neutral conditions. This lack of control, combined with the inability to modify bed media or adjust hydraulic conductivity, poses challenges to improving treatment efficiency. However, natural systems can still be optimised by identifying areas where natural drainage occurs. Prioritising restoration in these naturally draining reaches could enhance the channel’s functionality, as these locations may serve as more effective treatment zones.

In contrast, engineered systems offer the flexibility to manipulate bed materials and control groundwater conditions through drainage systems. In-channel treatment systems can thus be designed with specific hydraulic properties and incorporate measures to assess and regulate groundwater interactions, optimising treatment processes, and schedule maintenance. Annual monitoring of sediment metal concentrations, along with on-going hydraulic conductivity assessment, helps determine when replacement or regeneration is needed. This prevents clogging, reducing contaminant release risks, and supporting aquatic health. Additionally, regular monitoring helps guide maintenance intervals more accurately, making the treatment process both effective and sustainable. Monitoring metal mass balance and drainage water frequency further prevents clogging and efficiency loss. Drainage water conditions can also simplify maintenance by facilitating the removal of gross contaminants, which remain on top of the bed media and can be more readily removed.

Limitations

Further research is needed to study heavy metal dynamics under a wider range of flow conditions. In-channel treatment systems operate under a wider range of flow rates, depths, and channel widths than those considered in this study. Additionally, the use of synthetic stormwater, while necessary for controlled experiments, does not capture the diversity and complexity of contaminants typically found in urban stormwater. In typical field conditions, it is unlikely that surface water and groundwater would have the same heavy metals concentrations. Surface water could contain higher concentrations of heavy metals due to surface runoff discharges. Future studies should aim to explore these interactions, particularly regarding the mobility of heavy metals under more complex conditions.

Different media characteristics can alter the hydraulic conductivity of the system and the adsorption behaviour of heavy metals. Exploring these aspects in future research would help guide the optimisation of in-channel system design and strengthen the applicability of heavy metal dynamics in various environmental contexts. Moreover, the long-term performance of different bed media under repeated contamination events remains uncertain. Investigating media resilience and effectiveness over time is essential to understanding the durability and sustainability of these treatment systems.

Additionally, further assessment is needed on the effects of plants and microorganisms on heavy metal removal. Including these biological factors in future research could provide a more comprehensive understanding of the ecological impacts and treatment effectiveness within these systems. The role of organic matter in modulating metal mobility also warrants further study, as organic compounds may influence both adsorption and desorption processes, thereby affecting overall treatment performance.

Finally, while flume experiments offer controlled conditions to examine system behaviour, field validation is necessary to confirm the applicability of laboratory findings to real-world settings. Variability in climate, land use, and catchment characteristics can significantly influence system performance, and field studies are needed to ensure that experimental insights translate effectively to practical implementation.

Conclusions

This study highlights the critical role of groundwater interactions and bed media hydraulic conductivity in in-channel treatment systems. Groundwater seepage conditions increase Al, Cu and Zn loads, driven by upward flux that remobilised sorbed metals. In contract drainage conditions enhanced metal retention, particularly Zn.

Low hydraulic conductivity bed media induced greater changes in heavy metal concentrations, leading to an increase in concentrations in the surface water. This was due to the higher proportion of sediments, which contained elevated levels of heavy metals within the bed media. In contrast, high hydraulic conductivity media caused more pronounced variations in Ev and pH and decreased heavy metals concentration in the water column, promoting metal retention through enhanced permeability and water flux.

Designing in-channel treatment systems with drainage water conditions and high hydraulic conductivity bed media supports more effective metal removal. Regular maintenance is essential to prevent pollutant accumulation and preserve permeability, ensuring long-term system performance.

These findings underscore the importance of integrating groundwater regimes and bed media selection into system design and maintenance to optimise surface water quality outcomes.

Supplemental Material

sj-docx-1-asw-10.1177_11786221251342906 – Supplemental material for Zinc, Copper, and Aluminium Dynamics in Urban In-Channel Stormwater Treatment Systems Under Varying Groundwater Conditions

Supplemental material, sj-docx-1-asw-10.1177_11786221251342906 for Zinc, Copper, and Aluminium Dynamics in Urban In-Channel Stormwater Treatment Systems Under Varying Groundwater Conditions by Fabio C. Silveira, Thomas A. Cochrane, Ricardo Bello-Mendoza and Frances Charters in Air, Soil and Water Research

Supplemental Material

sj-docx-2-asw-10.1177_11786221251342906 – Supplemental material for Zinc, Copper, and Aluminium Dynamics in Urban In-Channel Stormwater Treatment Systems Under Varying Groundwater Conditions

Supplemental material, sj-docx-2-asw-10.1177_11786221251342906 for Zinc, Copper, and Aluminium Dynamics in Urban In-Channel Stormwater Treatment Systems Under Varying Groundwater Conditions by Fabio C. Silveira, Thomas A. Cochrane, Ricardo Bello-Mendoza and Frances Charters in Air, Soil and Water Research

Footnotes

Acknowledgements

The authors wish to thank Rob Stainthorpe for his assistance with heavy metal analyses. Gratitude is also extended to David MacPherson and Aude Thierry for their professional support and encouragement throughout the preparation of this work.

ORCID iDs

Fabio C. Silveira

Ricardo Bello-Mendoza

Frances Charters

Author Contributions

Fabio C. Silveira: methodology, formal analysis, investigation, writing—original draft, and project administration. Thomas A. Cochrane: conceptualisation, methodology, writing—review & editing, supervision, and funding acquisition. Ricardo Bello-Mendoza: methodology, writing—review & editing, and supervision. Frances Charters: methodology, writing—review & editing, and supervision.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by: Department of Civil and Natural Resources Engineering and College of Engineering scholarship, University of Canterbury; Environment Canterbury Regional Council; Waterways Centre for Freshwater Management; and Christchurch City Council. The authors would like to express their gratitude for the financial support provided by these institutions.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available within the paper and its Supplementary Information files. Additional data, if needed, are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

ANZECC. (2000). Australian and New Zealand guidelines for fresh and marine water quality (National water quality management strategy paper, 4).

Aquilina

Vergnaud-Ayraud

Labasque

Bour

Molénat

Ruiz

de Montety

De Ridder

Roques

Longuevergne

(2012). Nitrate dynamics in agricultural catchments deduced from groundwater dating and long-term nitrate monitoring in surface- and groundwaters. Science of The Total Environment, 435–436, 167–178. https://doi.org/10.1016/j.scitotenv.2012.06.028

Arias

Pérez-Novo

López

Soto

(2006). Competitive adsorption and desorption of copper and zinc in acid soils. Geoderma, 133(3), 151–159. https://doi.org/10.1016/j.geoderma.2005.07.002

Arora

Kiran

Rani

Kaur

Mittal

(2008). Heavy metal accumulation in vegetables irrigated with water from different sources. Food Chemistry, 111(4), 811–815.

Bai

Nie

Zhang

Wang

(2021). Cotransport of heavy metals and SiO2 particles at different temperatures by seepage. Journal of Hydrology, 597, Article 125771. https://doi.org/10.1016/j.jhydrol.2020.125771

Beck

H. J.

Birch

G. F.

(2012). Spatial and temporal variance of metal and suspended solids relationships in urban stormwater—Implications for monitoring. Water, Air, & Soil Pollution, 223, 1005–1015.

Beverskog

Puigdomenech

(1997). Revised pourbaix diagrams for zinc at 25–300 C. Corrosion Science, 39(1), 107–114.

Bibby

R. L.

Webster-Brown

J. G.

(2006). Trace metal adsorption onto urban stream suspended particulate matter (Auckland region, New Zealand). Applied Geochemistry, 21(7), 1135–1151. https://doi.org/10.1016/j.apgeochem.2006.03.014

Black

L. W. J.

(2018). Understanding and quantifying contaminant discharges to Haytons Stream: A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Environmental Science at the University of Canterbury [Doctoral dissertation].

10.

Brown

J. N.

Peake

B. M.

(2006). Sources of heavy metals and polycyclic aromatic hydrocarbons in urban stormwater runoff. Science of The Total Environment, 359(1), 145–155. https://doi.org/10.1016/j.scitotenv.2005.05.016

11.

Burton

G. A.

Green

Baudo

Forbes

Nguyen

L. T.

Janssen

C. R.

Kukkonen

Leppanen

Maltby

Soares

Kapo

Smith

Dunning

(2007). Characterizing sediment acid volatile sulfide concentrations in European streams. Environmental Toxicology and Chemistry, 26(1), 1–12.

12.

Caetano

Madureira

M.-J.

Vale

(2003). Metal remobilisation during resuspension of anoxic contaminated sediment: Short-term laboratory study. Water, Air, and Soil Pollution, 143, 23–40.

13.

Chambers

(1999). The seismic response of drilled shaft foundations. University of Canterbury. http://dx.doi.org/10.26021/2790.

14.

Chapman

P. M.

Wang

Janssen

Persoone

Allen

H. E.

(1998). Ecotoxicology of metals in aquatic sediments: Binding and release, bioavailability, risk assessment, and remediation. Canadian Journal of Fisheries and Aquatic Sciences, 55(10), 2221–2243. https://doi.org/10.1139/f98-145

15.

Charters

F. J.

Cochrane

T. A.

O’Sullivan

A. D.

(2016). Untreated runoff quality from roof and road surfaces in a low intensity rainfall climate. Science of The Total Environment, 550, 265–272. https://doi.org/10.1016/j.scitotenv.2016.01.093

16.

Christchurch City Council. (2022). Christchurch city surface water quality annual report 2021.

17.

Chen

Peltier

Sturm

B. S. M.

Young

C. B.

(2013). Nitrogen removal and nitrifying and denitrifying bacteria quantification in a stormwater bioretention system. Water Research, 47(4), 1691–1700.

18.

Clément

J.-C.

Holmes

R. M.

Peterson

B. J.

Pinay

(2003). Isotopic investigation of denitrification in a riparian ecosystem in western France. Journal of Applied Ecology, 40(6), 1035–1048. https://doi.org/10.1111/j.1365-2664.2003.00854.x

19.

De Jonge

Teuchies

Meire

Blust

Bervoets

. (2012). The impact of increased oxygen conditions on metal-contaminated sediments part I: Effects on redox status, sediment geochemistry and metal bioavailability. Water Research, 46(7), 2205–2214. https://doi.org/10.1016/j.watres.2012.01.052

20.

DePinto

J. V.

Schmidt

Paul

J. F.

(1993). Transport and transformation of contaminants near the sediment-water interface. CRC Press.

21.

Eggleton

Thomas

K. V.

(2004). A review of factors affecting the release and bioavailability of contaminants during sediment disturbance events. Environment International, 30(7), 973–980. https://doi.org/10.1016/j.envint.2004.03.001

22.

El-Rabiei

Abd El-Hafez

Ali

(2020). Effects of Alloying Elements (Ti and xAl) on the Electrochemical Corrosion Behaviour of Iron-Based Alloys in Corrosive Solutions of Different pH. Journal of Bio- and Tribo-Corrosion, 6, Article 7. https://doi.org/10.1007/s40735-020-0325-6

23.

Environmental

(2005). Measuring ORP on YSI 6-Series sondes: Tips, cautions and limitations.

24.

Ferreira

Stenstrom

M. K.

(2013). The importance of particle characterization in stormwater Runoff. Water Environment Research, 85(9), 833–842. https://doi.org/10.2175/106143013X13736496909103

25.

Gadd

J. M.

Juliet, M. Hickey

(2017, May). Copper and Zinc aquatic toxicity (2017129AK). National Institue of Water and Atmospheric Research Ltd.

26.

Gerringa

L. J. A.

(1990). Aerobic degradation of organic matter and the mobility of Cu, Cd, Ni, Pb, Zn, Fe and Mn in marine sediment slurries. Marine Chemistry, 29, 355–374. https://doi.org/10.1016/0304-4203(90)90023-6

27.

Guinn Garrett

Vulava

V. M.

Callahan

T. J.

Jones

M. L

. (2012). Groundwater–surface water interactions in a lowland watershed: Source contribution to stream flow. Hydrological Processes, 26(21), 3195–3206. https://doi.org/10.1002/hyp.8257

28.

Håkansson

Karlsson

Allard

(1989). Effects of pH on the accumulation and redistribution of metals in a polluted stream bed sediment. Science of The Total Environment, 87, 43–57.

29.

Hawke

Powell

Gregor

(1996). Determination of the aluminium complexing capacity of fulvic acids and natural waters, with examples from five New Zealand rivers. Marine and Freshwater Research, 47(1), 11–17. https://doi.org/10.1071/MF9960011

30.

Sun

Zeng

Cheng

(2017). Comparison of soil heavy metal pollution caused by e-waste recycling activities and traditional industrial operations. Environmental Science and Pollution Research, 24(10), 9387–9398. https://doi.org/10.1007/s11356-017-8548-x

31.

Huang

H.-H.

(2016). The Eh-pH diagram and its advances. Metals, 6(1), Article 23.

32.

Huang

Wang

(2012). Distribution of heavy metals in the water column, suspended particulate matters and the sediment under hydrodynamic conditions using an annular flume. Journal of Environmental Sciences, 24(12), 2051–2059. https://doi.org/10.1016/S1001-0742(11)61042-5

33.

Jain

C. K.

Singhal

D. C.

Sharma

M. K.

(2004). Adsorption of zinc on bed sediment of River Hindon: Adsorption models and kinetics. Journal of Hazardous Materials, 114(1), 231–239. https://doi.org/10.1016/j.jhazmat.2004.09.001

34.

Kabenge

Ouma

Aboagye

Banadda

(2018). Performance of a constructed wetland as an upstream intervention for stormwater runoff quality management. Environmental Science and Pollution Research, 25(36), 36765-36774. https://doi.org/10.1007/s11356-018-3580-z

35.

Keshavarzifard

Moore

Sharifi

(2019). The influence of physicochemical parameters on bioavailability and bioaccessibility of heavy metals in sediments of the intertidal zone of Asaluyeh region, Persian Gulf, Iran. Geochemistry, 79(1), 178–187. https://doi.org/10.1016/j.geoch.2018.12.007

36.

Leroy

M.-c.

Portet-Koltalo

Legras

Lederf

Moncond’huy

Polaert

Marcotte

(2016). Performance of vegetated swales for improving road runoff quality in a moderate traffic urban area. Science of The Total Environment, 566-567, 113–121.

37.

Liu

Graham

N. J.

(2024). Uncovering the neglected role of anions in trivalent cation-based coagulation processes. Water Research, 254, Article 121352.

38.

Luoma

S. N.

Rainbow

P. S.

(2008). Metal contamination in aquatic environments: Science and lateral management. Cambridge university Press.

39.

Dai

Huang

(2000). Distribution of tributyltin chloride in laboratory simulated estuarine microcosms. Water Research, 34(10), 2829–2841.

40.

MacDonald

D. D.

Ingersoll

C. G.

Berger

T. A.

(2000). Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Archives of Environmental Contamination and Toxicology, 39(1), 20–31. https://doi.org/10.1007/s002440010075

41.

Miranda

L. S.

Wijesiri

Ayoko

G. A.

Egodawatta

Goonetilleke

(2021). Water-sediment interactions and mobility of heavy metals in aquatic environments. Water Research, 202, Article 117386. https://doi.org/https://doi.org/10.1016/j.watres.2021.117386

42.

Moazzem

Bhuiyan

Muthukumaran

Fagan

Jegatheesan

(2024). A critical review of nature-based systems (NbS) to treat stormwater in response to climate change and urbanization. Current Pollution Reports, 10(2), 286–311. https://doi.org/10.1007/s40726-024-00297-8

43.

Moores

Gadd

Wech

Flanagan

(2009). Haytons Stream catchment water quality investigation. Niwa. http://ecan.govt.nz/publications/Reports/haytons-stream-catchment-water-quality-investigation-october-2009.pdf

44.

Munro

Callander

(2015). CCC instream spring water quality project – Waimairi and Wairarapa stream. Pattle Delamore Partners Ltd.

45.

Peng

J.-f.

Song

Y.-h.

Yuan

Cui

X.-y.

Qiu

G.-l.

(2009). The remediation of heavy metals contaminated sediment. Journal of Hazardous Materials, 161(2), 633–640. https://doi.org/10.1016/j.jhazmat.2008.04.061

46.

Pitt

Clark

Field

(1999). Groundwater contamination potential from stormwater infiltration practices. Urban Water, 1(3), 217–236. https://doi.org/10.1016/S1462-0758(99)00014-X

47.

Raklami

Meddich

Oufdou

Baslam

(2022). Plants—Microorganisms-based bioremediation for heavy metal cleanup: Recent developments, phytoremediation techniques, regulation mechanisms, and molecular responses. International Journal of Molecular Sciences, 23(9), Article 5031.

48.

Rand

G. M.

(1995). Fundamentals of aquatic toxicology: Effects, environmental fate and risk assessment. CRC Press.

49.

Reddy

K. R.

DeLaune

R. D.

(2008). Biogeochemistry of wetlands: science and applications. Taylor & Francis. https://doi.org/10.1201/9780203491454

50.

Reddy

K. R.

Kadlec

R. H.

Flaig

Gale

P. M.

(1999). Phosphorus retention in streams and Wetlands: A review. Critical Reviews in Environmental Science and Technology, 29(1), 83–146. https://doi.org/10.1080/10643389991259182

51.

Rice

E. W.

Bridgewater

, & Association, A. P. H. (2012). Standard methods for the examination of water and wastewater (Vol. 10). American Public Health Association.

52.

Salahshoori

Nobre

M. A. L.

Yazdanbakhsh

Eshaghi Malekshah

Asghari

Ali Khonakdar

Mohammadi

A. H.

(2024). Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach. Journal of Molecular Liquids, 410, Article 125592. https://doi.org/10.1016/j.molliq.2024.125592

53.

Saulnier

Mucci

(2000). Trace metal remobilization following the resuspension of estuarine sediments: Saguenay Fjord, Canada. Applied Geochemistry, 15(2), 191–210.

54.

Sekabira

Origa

H. O.

Basamba

T. A.

Mutumba

Kakudidi

(2010). Assessment of heavy metal pollution in the urban stream sediments and its tributaries. International Journal of Environmental Science & Technology, 7(3), 435–446. https://doi.org/10.1007/bf03326153

55.

Selbig

Fienen

Horwatich

Bannerman

(2016). The effect of particle size distribution on the design of urban stormwater control measures. Water, 8(1), Article 17.

56.

Silveira

F. C.

(2017). Sources and transformation of nitrogen compounds in Haytons Stream, a low lying urban drainage stream in Christchurch, New Zealand. University of Canterbury. https://go.exlibris.link/fCFyfPhT

57.

Silveira

F. C.

Cochrane

T. A.

Bello-Mendoza

Charters

(2025). The influence of surface-groundwater interactions on nutrient dynamics in urban in-channel treatment systems. Environmental Monitoring and Assessment, 197(1), 1–6.

58.

Silveira

M. L.

Alleoni

L. R. F.

O’Connor

G. A.

Chang

A. C.

(2006). Heavy metal sequential extraction methods—A modification for tropical soils. Chemosphere, 64(11), 1929–1938. https://doi.org/10.1016/j.chemosphere.2006.01.018

59.

Simpson

S. L.

Apte

S. C.

Batley

G. E.

(1998). Effect of short-term resuspension events on trace metal speciation in polluted anoxic sediments. Environmental Science & Technology, 32(5), 620–625. https://doi.org/10.1021/es970568g

60.

Simpson

S. L.

Rosner

Ellis

(2000). Competitive displacement reactions of cadmium, copper, and zinc added to a polluted, sulfidic estuarine sediment. Environmental Toxicology and Chemistry: An International Journal, 19(8), 1992–1999.

61.

Snoeyink

V. L.

Jenkins

(1991). Water chemistry. John Wiley & Sons.

62.

Sonone

S. S.

Jadhav

Sankhla

M. S.

Kumar

(2020). Water contamination by heavy metals and their toxic effect on aquaculture and human health through food Chain. Letters in Applied NanoBioScience, 10(2), 2148–2166.

63.

Stojić

Štrbac

Ćurčić

Pucarević

Prokić

Stepanov

Stojić

(2023). Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars. Transportation Research Part D: Transport and Environment, 125, Article 103966. https://doi.org/10.1016/j.trd.2023.103966

64.

Vance

G. F.

Stevenson

F. J.

Sikora

F. J.

(2020). Environmental chemistry of aluminum–organic complexes. In G. Sposito (Eds.), The environmental chemistry of aluminum (2nd ed., pp. 169–220). CRC Press.

65.

Violante

Huang

P. M.

Gadd

G. M.

(2008). Biophysico-chemical processes of heavy metals and metalloids in soil environments. Wiley Online Library.

66.

Walsh

C. J.

Booth

D. B.

Burns

M. J.

Fletcher

T. D.

Hale

R. L.

Hoang

L. N.

Livingston

Rippy

M. A.

Roy

A. H.

Scoggins

Wallace

(2016). Principles for urban stormwater management to protect stream ecosystems. Freshwater Science, 35(1), 398–411. https://doi.org/10.1086/685284

67.

Wijesiri

Liu

Yang

Zhao

Ayoko

Goonetilleke

(2019). Behaviour of metals in an urban river and the pollution of estuarine environment. Water Research, 164, Article 114911. https://doi.org/10.1016/j.watres.2019.114911

68.

Yavar Ashayeri

Keshavarzi

. (2019). Geochemical characteristics, partitioning, quantitative source apportionment, and ecological and health risk of heavy metals in sediments and water: A case study in Shadegan Wetland, Iran. Marine Pollution Bulletin, 149, Article 110495. https://doi.org/10.1016/j.marpolbul.2019.110495

69.

Zeng

Qiao

Duan

Liu

(2020). Heavy metal accumulation and release risks in sediments from groundwater–River water interaction zones in a contaminated river under restoration. ACS Earth and Space Chemistry, 4(12), 2391–2402.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.37 MB

1.00 MB