The influence of surface-groundwater interactions on nutrient dynamics in urban in-channel treatment systems

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Silveira, Thomas A. Cochrane, Ricardo Bello-Mendoza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4936228/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 7 You are reading this latest preprint version Abstract In-channel water treatment systems remove excess nutrients through biological, chemical, and physical processes associated with the hyporheic zone. However, the impact of surface and groundwater interactions on these treatment processes is poorly understood. This research aims to assess the influence of varying groundwater conditions (neutral, drainage, and seepage) and different bed sediment hydraulic conductivities on nitrogen and phosphorus dynamics in in-channel treatment systems. A flume containing bed sediment was used to study changes in surface water quality under different groundwater and bed sediment conditions. Results show that groundwater interactions influence nutrient concentrations in the surface water. An elevation in dissolved reactive phosphorus and ammoniacal nitrogen and a decrease in nitrate concentrations in the surface water under seepage groundwater conditions was evident. In addition, low hydraulic conductivity sediment led to greater changes in nutrients concentration while high hydraulic conductivity sediment led to greater variations in pH and Eh values. Water-saturated bed sediment promoted a reduction of nitrate concentrations in the surface water. The findings could assist the design and monitoring of in-channel treatment systems where groundwater and surface water interact. Nitrate Phosphate Ammonia Nutrients Seepage Water quality monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Elevated nutrients levels in rural and urban waterways lead to eutrophication and degrade the health of aquatic ecosystem. Nutrient dynamics in waterways are influenced by the input of nitrogen and phosphorus from surface runoff, and by filtration and accumulation over time in the hyporheic zone, where shallow groundwater mixes with surface water around the streambed interface (Pescimoro et al., 2019 ). Surface-groundwater interactions in the hyporheic zone can change nutrient fluxes due to the impact of physical, chemical, and microbiological factors (i.e. particle size, sediment composition, microbiological species composition, and others). Residence time in the hyporheic zone, pore clogging due to fine sediments, carbon availability, and seasonal variation of stratified groundwater contribution impact nutrients dynamics and availability and, subsequently, influence the aquatic ecology (Bohlke et al., 2007 ; Marçais et al., 2022 ; McCormack et al., 2016 ; Sunjidmaa et al., 2022 ). Oxidized nitrogen removal in waterways is primarily occurs through denitrification process at the water-sediment boundary, driven by factors such as porosity, carbon content, oxygen levels, and residence time (Hampton et al., 2020 ). Among these, residence times within the hyporheic zone has been identified to be the main driver of biochemical reactions (Zarnetske et al., 2011 ). A longer resident time increases the likelihood and extent of these reactions (Boano et al., 2014 ). In small streams, the large contact area between sediment and water flow area increases the chance of reactions to occur (Anderson et al., 2005 ). Phosphorus retention in waterways is primarily achieved through adsorption to the soil in the hyporheic zone and plant uptake (Jin et al., 2022 ; Withers & Jarvie, 2008 ). In acid soils, aluminium (Al) and iron (Fe) are the key sorbents for phosphorus (Reddy & DeLaune, 2008 ). Nevertheless, phosphorus bonded to iron oxides in the bed sediments can be mobilised when conditions became anaerobic and/or anoxic (Forsmann & Kjaergaard, 2014 ; House & Denison, 2002 ). In-channel treatment systems are commonly defined to be stormwater treatment systems engineered in-channel with streams, drainage channels and other waterways to which intend to remove an excess of contaminants (in this case nutrients) through biological, chemical, and physical processes. However, the impact of surface and groundwater interactions on these treatment processes is poorly understood, especially with ongoing changes in surface and groundwater flow regimes as impacted by spring-fed waterways and climate change (Mangangka et al., 2016 ; Webster-Brown & Barr, 2016 ). Experimental flumes can be used to isolate physical, chemical, and biological processes and to understand them in detail. Simplified flume experiments, like annular and linear flumes, have proved useful in understanding, modelling and predicting processes between bed sediment and water tables, such as advection-driven transport, production and removal of nutrients, resuspension, colloidal particles exchange, and others (Blom et al., 2003 ; Clark et al., 2019 ; Eylers et al., 1995 ; Harvey et al., 2011 ; Huang et al., 2015 ; Postma et al., 2008 ). For example, flume experiments have shown that nutrients bound to colloidal clay particles were trapped in the bed sediment at high surface water concentration, and released under low clay concentrations (Packman & Brooks, 1995 ). Furthermore, the release and entrapment of pore water due to sediment turnover were found to contribute to surface-groundwater water exchange under baseflow conditions, increasing its concentration in the surface water (Mutz & Rohde, 2003 ). Similarly, a study of phosphorus mobility triggered by sediment resuspension in a flume experiment with a recirculation system identified a strong relationship between phosphorus concentration in the surface water and sediment, greater concentrations in the bed sediment leads to great concentration in the surface water (Huang et al., 2015 ). Chemical transport has been found to be significantly influenced by surface runoff and the groundwater table, where tracers (bromate) increased in soil under drainage groundwater conditions and low groundwater table levels (Tian et al., 2009 ). Furthermore, field and model studies have shown that sediment permeability and surface water velocity through the sediment can increase nutrient supply and change the residence time of water within the streambed (Bardini et al., 2012 ). Furthermore, water flow gradients within sediments or groundwater could lead to the movement of dissolved phosphorus (Withers & Jarvie, 2008 ) and leaching of nutrients into the groundwater under drainage groundwater conditions have been reported (Scott & Hanson, 2013 ; Yoder, 2014 ). However, despite these findings of nutrients dynamics within water table, bed sediment and groundwater, significant research gaps remain. There is still a need to better understand nutrients transport and transformation under different flow conditions, bed sediment composition (e.g., gravel, sand, clay, organic matter etc.) with different hydraulic conductivity and groundwater influence (seepage, neutral, and drainage conditions). Current studies have not fully explored how these factors interact to affect nutrient dynamics in in-channel treatment systems, especially considering ongoing changes in surface and groundwater flow regimes seasonal variations and/or climate change, and modifications in waterways through geomorphic processes (between flow, sediment, and vegetation) and engineering interventions for stream restoration and improving water quality (Hatt et al., 2007 ; Hester & Doyle, 2008 ; Suddick et al., 2013 ; Vicente-Serrano et al., 2020 ; Vietz et al., 2016 ; Vietz et al., 2015 ). Moreover, quantifying nutrients transport and transformations under groundwater neutral, drainage, or seepage conditions and varying bed sediment hydraulic conductivity in in-channel treatment systems is necessary to understand the removal of nutrients via physical, chemical, and biological processes. This understanding is crucial for guiding stream management decisions, designing effective in-channel treatment systems, and ultimately improving water quality in waterways. This research thus aims to investigate nutrient dynamics (nitrogen and phosphorus) within in-channel treatment systems under different surface-water and ground-water interactions (neutral, drainage, and seepage groundwater conditions) and bed sediment with low and high hydraulic conductivity. It is hypothesized that the surface-groundwater interactions and bed sediment properties influence the levels and form of nutrients, and their in-channel mobility. A better understanding of these process could help the sustainable management of groundwater and surface water resources. Methodology Column leaching test, followed by flume experiments, were conducted to address the aims of the study. Contaminated bed sediments were sourced locally and mixed with coarse sand to enhance conductivity for flume and benchtop experiments. Synthetic stormwater was used in all experiments, with contaminant concentrations derived from observed surface-water quality from a local stream. Bed sediment The bed sediment, used in all experiments, was sourced from Wigram Retention Basin (WRB) in Christchurch, New Zealand, a 30-year-old wet pond with a history of nutrient contamination (Black, 2018 ; Moores et al., 2009 ; Silveira et al., 2022 ). The 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, including a fertilizer factory (Silveira, 2017 ). WRB sediment’s pH was 6.0, Olsen phosphorus concentration 37 mg/L (35.2 mg/kg soil based on volume weight 1.05 g/mL), organic matter 5.5%, total carbon 3.2%, and C/N ratio 13.3 (see Supplementary Information Figure A1 , Table A1 and A2 ). Collected sediment was dried in a temperature-controlled room at 30°C and 25% air humidity for 7 days, then sieved on a steel mesh with 32 mm aperture. Silica sand (medium to coarse sand with D60 = 0.45mm) was then mixed with WRB’s bed sediment to enhance hydraulic conductivity. Two mixtures of bed media were prepared consisting of 40% sediment + 60% sand and 75% sediment + 25% sand. Synthetic stormwater The synthetic stormwater (SSW) used in the experiments was prepared using potassium nitrate (KNO 3 ), ammonium chloride (NH 4 Cl), and potassium dihydrogen phosphate (KH 2 PO 4 ). It was then diluted with water to reach target concentrations of 0.4 mg/L of nitrate nitrogen (NO 3 -N), 0.2 mg/L of both ammoniacal nitrogen (NH 4 -N) and dissolved reactive phosphorus (DRP). These concentrations simulated observed in-channel water quality in Haytons Stream (CCC, 2022 ), while providing a consistent feed water quality between all experiments (see Table A3 ). Column leaching test methodology Column leaching test was initially performed to assess hydraulic conductivity of the sediment mixes with 1:1 and 1:4 WRB sediment to silica sand, and to inform the selection of the blended sediment composition for the flume experiments. Water was applied to the top of columns with a static hydraulic head of 2 cm. Each column had 14 cm of gravel at the bottom, 4 or 5 cm of sediment, and the diameter of the columns were 10 cm. The target flow rate was 6 mL/min, as this represented the equivalent scaled down flow of the subsequent flume experiment (Table A4 ; Table A5 ). Subsequent tests were conducted to determine changes in nutrients concentrations in the water column under seepage conditions using both deionized water (DI – low electrical conductivity) and synthetic stormwater (SSW, selected to have a low (DI) and – high electrical conductivity). The tests were conducted in duplicates for each condition and only NO 3 -N and DRP were measured. For this, water was supplied to the bottom of two parallel columns, with a target seepage rate of 6 mL/min achieved by creating a hydraulic head of approximately 3 cm created between a 20-L tank (with DI or SSW) and the experimental columns, with no differences between them (Fig. 1 ). Each column had 14 cm of gravel at the bottom, 5 cm of sediment-sand mix, 2 cm of standing water, and the diameter of the columns were 10 cm. Each test was run for 90 minutes, with an initial 30-minute stabilization period, after which samples were taken at 15 minutes intervals (i.e., samples taken at 30, 45, 60, 75 and 90 mins). Samples were collected from an outlet point in each water column 2 cm above the sediment. The experiments were repeated three times on different dates. Flume design and experimental methodology A 19 m long flume, divided into two main sections each of 9.5 m, was constructed using 6 mm thick polyvinyl chloride (PVC) sheets. This setup simulated an in-channel treatment system containing gravel, bed sediment, surface water, and a mechanism for managing groundwater interactions (Fig. 2). The flume’s length was determined by the maximum available laboratory space to promote enough time and area for sediment-water interactions. A 0.5% slope was set to represent a typical hydraulic gradient in a system with minimal backflow. The hydraulic regime (i.e., seepage, neutral or drainage groundwater conditions) was varied by adjusting the groundwater channel (GW1 and GW2) height relative to the flume height via a pulley system. The surface water and groundwater used in this experiment was the same synthetic stormwater (SSW) previously described. Grade 5 gravel (5–8 mm diameter) was used to represent free-draining gravel in the lower hyporheic zone, while the bed media consisted of mixtures of sand and contaminated bed sediment as previously described. A layer of cheese cloth (Cotton, 28 by 24 threads per square inch) was used between the layers to prevent migration of fine particles into the gravel. The flume was gravity fed to achieve a low water level (1 cm depth) with an inflow rate of 7.2 L/min of SSW and a high-water level (2 cm depth) with an inflow of 21.8 L/min (water velocity of 0.04 and 0.06 m/s, respectively). A flow sensor installed at the outlet of the stormwater feeder tank intermediate bulk containers (IBC) was used to monitor the target flows. No interaction between surface water and groundwater occurred under neutral groundwater conditions. To induce drainage conditions, the groundwater channel system was lowered to facilitate draining 10% of the surface water flow (1.42 L/min under 1 cm water depth and 4.36 L/min under 2 cm water depth of the incoming surface water) for each length of the flume (GW1 and GW2 as per Fig. 2). Under seepage groundwater conditions, the groundwater channel was raised to seep SSW into the flume at a total rate of 20% of the surface water flow. The composition of inflowing groundwater under seepage was the same as the surface water to avoid dilution. Flume simulations Experiments were run under three different groundwater conditions (neutral, drainage, and seepage), two different surface water depths (1 and 2 cm) and corresponding flows (7.2 L/ min and 21.8 L/ min, respectively), and with three bed sediment compositions (only gravel, 40% sediment + 60% sand – high hydraulic conductivity, and 75% sediment + 25% sand – low hydraulic conductivity; Table 1 ). The proportion of 40% sediment and 60% sand was determined based on the results of the hydraulic conductivity test (Appendix Tables A1 and A2). This composition was chosen for its ability to increase the proportion of WRB's bed sediment within the range of 1:4 to 1:1 (WRB sediment to sand), thus achieving the desired hydraulic conductivity for the flume. Additionally, this mixture incorporates a significant portion of WRB bed sediment while assuring the ability to seep/drain 20% of surface water flow. Between runs, gravel layer and bed sediment remained saturated with SSW. However, prior each run, fresh SSW was applied to replace the SSW from the previous run and the system was allowed to stabilize for 15 minutes prior to sample collection (Table 1 ). Water resident time in the sediment was approximately 13 minutes using high water depth and 38 minutes using low water depth (Table A6 ). Experiments with low hydraulic conductivity sediment and high surface water flow were repeated twice due to high variance in nutrients concentrations during the study and potential accumulation of nutrients in the bed sediment throughout the experiment. The flume’s bed sediment was removed and replaced with similar mix of silica sand and WRB bed sediment. Table 1 Groundwater condition, water depth and flow and type of bed sediment used in flume experiments. Groundwater Condition Water depth and Surface Water Flow (L/min) Bed Sediment Replications Neutral Low (1 cm and 7.2 L/min) Gravel only 1 High (2 cm and 21.8 L/min) 1 Drainage Low 1 High 1 Seepage Low 1 High 1 Neutral Low High hydraulic conductivity: 40% WRB sediment and 60% silica sand w/ gravel layer 3 High 3 Drainage Low 3 High 3 Seepage Low 3 High 3 Neutral High Low hydraulic conductivity: 75% WRB sediment and 25% silica sand w/ gravel layer 3 Drainage High 3 Seepage High 3 Neutral High 2 Drainage High 2 Seepage High 2 Total of runs performed 39 Sample collection Samples were taken from the inlet, midpoint, and outlet of the flume (Fig. 1 ) using 1L high density polyethylene (HDPE) containers and stored at 4°C before analysis. Inlet water quality was sampled at 10-minute intervals from the start of the flume trial (t = 0). Middle and outlet concentrations were found to stabilise after 15 minutes. Therefore, samples were taken from the midpoint and outlet of the flume at 10-minute intervals, starting at t = 15 mins. Groundwater samples were collected at t = 15 and 35 in the first channel and t = 25 and 45 minutes in the second channel under seepage conditions. Under drainage conditions, groundwater samples were collected at t = 15, 25, 35 and 45 minutes from both groundwater channels. More samples were taken under drainage conditions as a greater change in groundwater quality was expected than under seepage conditions, where the groundwater was expected to change from its synthetic stormwater source. Samples were filtered with a 0.45 µm filter and frozen at -18°C for storage before processing. Method of analyses NO 3 -N and DRP were then analysed with a Dionex ICS-2100 ion chromatography (IC) system using 38 mM KOH as eluent at 0.3 mL/min, with the preserved samples thawed at room temperature directly prior to IC analysis. Ammoniacal nitrogen was analysed using flow injection analysis with a modified method based on Foss A/N 5206 & 5232. The detection limit for this method was 0.10 mg/L. For the in-situ monitoring in the flume, a YSI Professional Plus multi parameter meter was positioned at each collection point to record pH, electrical conductivity, oxidation-reduction potential (ORP), and temperature data at 1-minute intervals during each run. ORP values were converted to Standard Hydrogen Electrode (SHE), displaying redox potential as Eh (V) by adding 200 mV to the ORP values (Environmental, 2005 ). Quality control and quality assurance A QA/QC plan was implemented to minimize errors in data analysis. All samples were collected, preserved, and analysed following the APHA (2007) guidelines. Duplicates were generated for ammoniacal nitrogen, oxidized nitrogen, and DRP. Sampling instruments were calibrated and maintained according to the manufacturer’s manual. Statistical analysis To account for variation in inlet concentration, samples’ nutrient concentrations taken in the midpoint and outlet of the flume were evaluated against the mean inlet levels for each run. Resulting percent changes in each contaminant concentrations were then evaluated across all runs. T-tests (with α = 0.05) were performed to assess statistically significant differences in the percentage changes of each dataset under varying groundwater conditions, aiming to determine their effect on the surface water quality. Results Change in nitrate and DRP over time under column leaching conditions Under DI water application, there was an initial high export of NO 3 -N with concentrations up to 4.4 mg/L which then decreased to less than 0.5 mg/L after 60 minutes (Figure 3, Table A7). Subsequent repetitions resulted in mean concentrations close or below the detection limit (0.045 mg/L). Under SSW application, nitrate concentrations were initially below the SSW concentration of 0.4 mg/L NO 3 -N and later increased to similar values to SSW concentrations. For DRP however, DI water application through the media showed mean DRP concentrations increasing overtime. Under SSW application, leachate concentrations were similar to the influent concentrations (Figure 3). Pollutant dynamics along a flume channel under varied groundwater conditions Results for high water level conditions in the flume showed that the GW condition influenced nutrient levels in the surface water for NO 3 -N, NH 4 -N and DRP (Table 2, Figure 4). Under seepage groundwater conditions, NO 3 -N levels reduced by 8% at the midpoint and 13 % at the outlet, whereas NH 4 -N and DRP concentrations raised by 4 and 6%, respectively, at both locations, relative to the inlet concentrations, when using high hydraulic conductivity sediment. Under neutral and drainage conditions, no significant difference in percentage change were observed (Table 2). Results of pollutant dynamics under low water levels were inconsistent and nutrient concentrations under different groundwater conditions did not exhibit any significant trend. Inconsistent results under low water levels can likely be attributed to difficulties around controlling a low groundwater flow rate of 0.72 L/min in or out the flume. The exchange of new water after the previous test may have been slower and more variable in time required under low flow conditions with less driving head, resulting in only partial exchange within the 15 minutes stabilization period. Under drainage groundwater conditions, substantial shifts were noticed in samples concentration taken from the groundwater channel, with a sharp decline in NO 3 -N levels and an increase DRP concentrations. The increase was greater using low hydraulic conductivity sediment (Figure 5, Table 2). The decrease in NO 3 -N suggests that it is retained within the bed sediment as surface water flows through it under drainage groundwater conditions, whereas DRP appears to be released or flushed out from the bed sediment. In all runs there was an export of NH 4 -N. It is important to note that the bed was water-saturated (using SSW) during all runs except during the first run, where the bed sediment/gravel was unsaturated because the flume was previously drained and not fully filled before this initial run (Figure 5). Table 2: Results of NO 3 -N, NH 4 -N and DRP concentrations in the inlet, midpoint and outlet of the flume using low and high hydraulic conductivity sediment under different groundwater conditions. Sediment Hydraulic Conductivity GW Condition Location Observations NO 3 -N Average and range (mg/L) NH 4 -N Average and range (mg/L) DRP Average and range (mg/L) High Neutral Inlet 10 0.39 (0.37-0.41) 0.19 (0.17-0.21) 0.20 (0.18-0.22) Middle 11 0.39 (0.36-0.43) 0.18 (0.17-0.20) 0.20 (0.19-0.22) Outlet 11 0.38 (0.36-0.41) 0.18 (0.15 -0.20) 0.20 (0.20-0.22) Drainage Inlet 12 0.38 (0.36-0.40) 0.16 (0.12 -0.18) 0.21 (0.20-0.22) Middle 12 0.38 (0.37-0.39) 0.16 (0.12-0.18) 0.21 (0.20-0.22) Outlet 12 0.38 (0.39-0.39) 0.17 (0.14-0.18) 0.21 (0.19-0.22) Seepage Inlet 12 0.37 (0.36-0.39) 0.17 (0.13-0.19) 0.20 (0.19-0.20) Middle 12 0.34 (0.32-0.37)* 0.18 (0.16-0.19)* 0.22 (0.20-0.29)* Outlet 12 0.33 (0.31-0.36)* 0.18 (0.17-0.19)* 0.22 (0.20-0.25)* Low 1 Neutral Inlet 20 0.39 (0.35-0.43) 0.25 (0.24-0.25) 0.20 (0.19-0.21) Middle 22 0.40 (0.36-0.44) 0.25 (0.25-0.26 0.21 (0.19-0.22) Outlet 22 0.40 (0.35-0.43) 0.25 (0.24-0.26 0.21 (0.19-0.22) Drainage Inlet 20 0.39 (0.36-0.42) 0.25 (0.24-0.26) 0.21 (0.20-0.22) Middle 25 0.38 (0.36-0.42) 0.25(0.25-0.26) 0.21 (0.20-0.23) Outlet 25 0.38 (0.36-0.42) 0.25(0.24-0.26) 0.21 (0.20-0.23) Seepage Inlet 20 0.38 (0.37-0.40) 0.25 (0.24-0.26) 0.21 (0.18-0.24) Middle 25 0.35 (0.33-0.37)* 0.31 (0.27-0.40)* 0.31 (0.20-0.45)* Outlet 25 0.34 (0.31-0.35)* 0.38 (0.29-0.56)* 0.34 (0.22-0.56)* High Drainage Groundwater 22 0.14 (0.08-0.18)* 0.18 (0.11-0.30)* 0.43 (0.04-0.68)* Seepage 12 0.36 (0.34-0.39) 0.19 (0.18-0.20) 0.17 (0.12-0.19) Low Drainage 40 2 0.58 (0-3.36)* 1.02 (0.66-1.50)* 2.28 (0.18-6.16)* Seepage 20 2 0.37 (0.36-0.40) 0.25 (0.24-0.25) 0.22 (0.18-0.28) *significant statistical difference between inlet concentrations (P-value < 0.05). 1 NH 4 -N had twelve observations at all inlet sampling and under all neutral conditions, fifteen observations at middle and outlet under drainage and seepage conditions. 2 NH 4 -N observations were 24 and 12, respectively. Comparison of hydraulic conductivity characteristics of bed sediment on nutrients dynamics Both low and high hydraulic conductivity sediments were found to have a similar type of influence on nitrogen and phosphorus., i.e., both resulted in reduction of NO 3 -N and increase of NH 4 -N and DRP concentrations in surface water under groundwater seepage conditions. However, the scale of the change and range of variation in concentrations was much larger for the low conductivity sediment (Figure 4, Table 2). In contrast, the high conductivity sediment produced greater variation in Eh, and pH under seepage groundwater conditions (Figure 6). The decrease in Eh values observed under seepage groundwater conditions indicates potential anoxic bed sediment conditions. Water collected at the groundwater channel under drainage groundwater conditions had an export of NO 3 -N on the first run; however, there was almost 100% reduction of NO 3 -N on the following runs (Figure 5). DRP concentrations, on the contrary, showed a greater export on runs 2 and 3 compared to the first run, with percentage changes going from around 100 % increase to up to 2500 % increase (Figure 5). pH, conductivity, Eh and temperature changes throughout channel over time pH, specific conductance, and Eh changed under seepage groundwater conditions. The average inlet pH values decreased from 7.8 to 7.4 at the midpoint and 7.3 at the outlet (Figure 6). Specific conductivity remained consistent, ranging between 106 to 132 μS/cm, with average values between 112 and 121 μS/cm. Under seepage conditions, the mean Eh values at the inlet, middle and outlet were around 0.6 V, 0.5 V and 0.3 V, respectively, using low and high hydraulicity conductivity sediment. The 0.3 V reduction in average Eh values under seepage groundwater condition, with just a 20% input from groundwater, suggesting anoxic conditions within the bed sediment. Temperature showed no specific pattern, with a mean of 17.5°C and a range between 16 and 19°C. Discussion Implications for nutrients dynamics in waterways and in-channel treatment systems Saturating the flume’s bed sediment with SSW likely stablished an anoxic environment, as suggested by the Eh values observed under seepage groundwater conditions. Each flume run, lasting up to 120 minutes (including preparatory steps such as replacing the water in the water-saturated bed sediment and applying a 30-minute flow through the flume), might have allowed anoxic zones to persist within the bed sediment, facilitating denitrification processes. However, given the short water resident time, denitrification alone cannot account for the observed reduction in NO 3 -N concentrations. Sandy bed sediment have non-uniform (preferential) groundwater flow, which might have prevented all water in the bed sediment to be replaced before the start of each run on the flume experiment (MahmoodPoor Dehkordy et al., 2019 ). Thus, the dilution of SSW seeping from the bed sediment within the flume could account for the reduction in NO 3 -N levels in the surface water under seepage groundwater conditions (Silveira et al., 2021 ). The ammonification processes of organic matter releases ammonia which can later be transported from the pore water in the sediment bed to the surface water. The observed release of ammonia under seepage conditions contributes to this understanding. Nitrite and nitrogen oxide is also generated within small stream and channels, but this was not measured nor quantified in this research. The release of DRP detected into groundwater under drainage groundwater conditions aligns with prior findings where phosphorus leaching under seepage groundwater conditions increased phosphorus load into the surface water (Yoder, 2014 ). Consequently, the groundwater seeping through the bed sediment facilitated the transport of phosphorus, subsequently leaching from the bed sediment into the surface water. Influence of sediment conditions on nitrogen and phosphorus transformation Changes in Eh values (from 0.6 V to around 0.25 V, Fig. 6 ) suggests that conditions go from aerobic to anoxic (or perhaps anaerobic in micro-sites) in the sediment conditions, which could have influenced microbial activity when the flume had water-saturated bed sediment. This supports denitrification processes, highlighting the importance of a saturated sediment in the nitrogen cycle. In addition, water-saturated sediment was an important parameter to reduce nitrate concentrations in the surface water (Fig. 5 ). The first run bed sediment was not saturated with SSW (unsaturated aerobic conditions), but the following runs had the bed sediments saturated, suggesting water saturation of bed sediment can change the pollutant’s dynamics. Water-saturated sediment is the main difference between dry vs wet basin, where dry basin might not promote denitrification processes as well as wet basins. The high amount of phosphorus in the bed sediment could be attributed to increased phosphorus in the surface water. Bed sediment needs to be characterised as part of any in-channel treatment development or river management planning. Impact of findings Groundwater seepage conditions contribute significantly to the dynamics of nutrients in surface water, and thus it is important to consider both natural and anthropogenic groundwater variations when assessing surface water quality. Locations where groundwater can experience reductions during extended periods without rain, seasonal droughts, excess of groundwater extraction should be identified. Conversely, prolonged periods of rain and sea-level rise can elevate groundwater levels in low-lying catchments and unconfined coastal areas, impacting in-channel treatment systems within these regions. These considerations are vital for the monitoring and design of in-channel treatment systems, recognizing the intricate interplay between groundwater table and surface water in nutrients dynamics. Additionally, the assessment of hydraulic conductivity in bed sediment is important, given the increased variability observed in nutrient dynamics and related physical parameters in surface water. Furthermore, the saturation of bed sediment in in-channel treatment systems emerges as a significant factor influencing nutrient dynamics, particularly in regions where elevated nitrate in surface-groundwater is a concern. All these factors should be taken in account in the decision-making process when designing in-channel treatment systems that involve interactions between groundwater and surface water. Limitations This laboratory-based study aimed to isolate processes occurring among surface water, groundwater, and bed sediment to evaluate the effects of various groundwater conditions (seepage, neutral, and drainage) and varying bed sediment hydraulic conductivities on nitrogen and phosphorus dynamics within in-channel treatment systems. It was assumed that the bed sediment within the flume was homogeneous and that consistent groundwater conditions prevailed throughout the experimental setup. However, the study did not account for other biotic and abiotic processes involving organisms such as fauna, flora, photosynthesis, as well as physical processes such as sunlight and rainfall. Additionally, the use of synthetic stormwater, achieved by adding nutrient salts, may not fully capture the complexity of natural stormwater, which encompasses diverse substances, temperature variations, and fluctuating flow rates. Conclusions This study elucidates the critical influence of groundwater interaction on nutrient dynamics in surface water, with direct implications for engineering interventions for the sustainable management of groundwater and surface water systems. There was an observed pattern under seepage conditions, where NO 3 -N concentrations decrease and NH 4 -N and DRP concentrations increase alongside shifts in pH and Eh. Furthermore, low conductivity sediment induces greater changes in nutrient concentration, while high conductivity sediment leads to more pronounced variations in Eh and pH. Additionally, the level of bed sediment saturation with stormwater influences nutrient dynamics and thus highlights the importance of incorporating this parameter into engineering strategies for improved nutrient prediction and removal. The lessons learned from this research can be applied to a broad range of in-channel systems where groundwater and surface water interact, and nutrients contaminants are of concern. This is particularly relevant where sea-water levels could affect groundwater levels under low-laying streams. Declarations The authors have no relevant financial or non-financial interests to disclose. 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. Funding Declaration 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. Author Contribution Fabio C. Silveira: Methodology, Formal Analysis, Investigation, Writing - Original Draft, Project administrationThomas A. Cochrane: Conceptualization, Methodology Writing - Review & Editing, Supervision, Funding acquisitionRicardo Bello-Mendoza: Methodology, Writing - Review & Editing, SupervisionFrances Charters: Methodology, Writing - Review & Editing, Supervision Acknowledgement We would like to express our gratitude for the invaluable assistance from Peter McGuigan for his role in designing and Kevin Wines’ contribution in constructing the flume, as well as to Matt Cockcroft for his efforts in conducting nutrient analysis. Data Availability 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. References Anderson, J. K., Wondzell, S. M., Gooseff, M. N., & Haggerty, R. (2005). Patterns in stream longitudinal profiles and implications for hyporheic exchange flow at the H.J. Andrews Experimental Forest, Oregon, USA. Hydrological Processes, 19 (15), 2931-2949. https://doi.org/https://doi.org/10.1002/hyp.5791 Bardini, L., Boano, F., Cardenas, M. B., Revelli, R., & Ridolfi, L. (2012, 2012/05/01/). Nutrient cycling in bedform induced hyporheic zones. Geochimica et Cosmochimica Acta, 84 , 47-61. https://doi.org/https://doi.org/10.1016/j.gca.2012.01.025 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 Blom, A., Ribberink, J. S., & de Vriend, H. J. (2003). Vertical sorting in bed forms: Flume experiments with a natural and a trimodal sediment mixture. Water Resources Research, 39 (2), n/a-n/a. https://doi.org/10.1029/2001WR001088 Boano, F., Harvey, J. W., Marion, A., Packman, A. I., Revelli, R., Ridolfi, L., & Wörman, A. (2014). Hyporheic flow and transport processes: Mechanisms, models, and biogeochemical implications. Reviews of Geophysics, 52 (4), 603-679. https://doi.org/10.1002/2012rg000417 Bohlke, J. K., O'Connell, M. E., & Prestegaard, K. L. (2007). Ground Water Stratification and Delivery of Nitrate to an Incised Stream under Varying Flow Conditions. Journal of environmental quality, 36 (3), 664-680. https://doi.org/10.2134/jeq2006.0084 CCC. (2022). Christchurch City Surface Water Quality Annual Report 2021 . Christchurch City Council Clark, J. J., Qian, Q., Voller, V. R., & Stefan, H. G. (2019, 2019/01/01/). Hyporheic exchange in a gravel bed flume with and without traveling surface waves. Advances in Water Resources, 123 , 120-133. https://doi.org/https://doi.org/10.1016/j.advwatres.2018.11.005 Environmental, Y. (2005). Measuring ORP on YSI 6-Series sondes: Tips, cautions and limitations Eylers, H., Brooks, N., & Morgan, J. (1995). Transport of adsorbing metals from stream water to a stationary sand-bed in a laboratory flume. Marine and Freshwater Research, 46 (1), 209-214. https://doi.org/https://doi.org/10.1071/MF9950209 Forsmann, D. M., & Kjaergaard, C. (2014). Phosphorus release from anaerobic peat soils during convective discharge—Effect of soil Fe: P molar ratio and preferential flow. Geoderma, 223 , 21-32. Hampton, T. B., Zarnetske, J. P., Briggs, M. A., MahmoodPoor Dehkordy, F., Singha, K., Day-Lewis, F. D., Harvey, J. W., Chowdhury, S. R., & Lane, J. W. (2020, 2020/06/01). Experimental shifts of hydrologic residence time in a sandy urban stream sediment–water interface alter nitrate removal and nitrous oxide fluxes. Biogeochemistry, 149 (2), 195-219. https://doi.org/10.1007/s10533-020-00674-7 Harvey, J. W., Noe, G. B., Larsen, L. G., Nowacki, D. J., & McPhillips, L. E. (2011, 2011/03/15/). Field flume reveals aquatic vegetation's role in sediment and particulate phosphorus transport in a shallow aquatic ecosystem. Geomorphology, 126 (3), 297-313. https://doi.org/https://doi.org/10.1016/j.geomorph.2010.03.028 Hatt, B. E., Fletcher, T. D., & Deletic, A. (2007, 2007/06/01/). Treatment performance of gravel filter media: Implications for design and application of stormwater infiltration systems. Water Research, 41 (12), 2513-2524. https://doi.org/https://doi.org/10.1016/j.watres.2007.03.014 Hester, E. T., & Doyle, M. W. (2008). In-stream geomorphic structures as drivers of hyporheic exchange. Water Resources Research, 44 (3). https://doi.org/10.1029/2006wr005810 House, W. A., & Denison, F. H. (2002, 2002/10/01). Exchange of Inorganic Phosphate between River Waters and Bed-Sediments. Environmental Science & Technology, 36 (20), 4295-4301. https://doi.org/10.1021/es020039z Huang, L., Fang, H., Fazeli, M., Chen, Y., He, G., & Chen, D. (2015). Mobility of phosphorus induced by sediment resuspension in the Three Gorges Reservoir by flume experiment. Chemosphere, 134 , 374-379. https://doi.org/https://doi.org/10.1016/j.chemosphere.2015.05.009 Jin, G., Chen, H., Zhang, Z., Jiang, Q., Liu, Z., & Tang, H. (2022). Transport of Phosphorus in the Hyporheic Zone. Water Resources Research, 58 (3), n/a-n/a. https://doi.org/10.1029/2021WR031292 MahmoodPoor Dehkordy, F., Briggs, M. A., Day-Lewis, F. D., Singha, K., Krajnovich, A., Hampton, T. B., Zarnetske, J. P., Scruggs, C., & Bagtzoglou, A. C. (2019, 2019/06/01/). Multi-scale preferential flow processes in an urban streambed under variable hydraulic conditions. Journal of Hydrology, 573 , 168-179. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.03.022 Mangangka, I. R., Liu, A., Goonetilleke, A., Egodawatta, P., & SpringerLink. (2016). Enhancing the Storm Water Treatment Performance of Constructed Wetlands and Bioretention Basins . Springer Singapore. Marçais, J., Derry, L. A., Guillaumot, L., Aquilina, L., & Dreuzy, J. R. (2022). Dynamic Contributions of Stratified Groundwater to Streams Controls Seasonal Variations of Streamwater Transit Times. Water Resources Research, 58 (3), e2021WR029659-n/a. https://doi.org/10.1029/2021WR029659 McCormack, T., Naughton, O., Johnston, P. M., & Gill, L. W. (2016, 2016 07-01). Quantifying the influence of surface water–groundwater interaction on nutrient flux in a lowland karst catchment. Hydrology and Earth System Sciences, 20 (5), 2119-2133. https://doi.org/https://doi.org/10.5194/hess-20-2119-2016 Moores, J., Gadd, J., Wech, J., & Flanagan, M. (2009). Haytons Stream catchment water quality investigation . Niwa. http://ecan.govt.nz/publications/Reports/haytons-stream-catchment-water-quality-investigation-october-2009.pdf Mutz, M., & Rohde, A. (2003). Processes of surface‐subsurface water exchange in a low energy sand‐bed stream. International Review of Hydrobiology: A Journal Covering all Aspects of Limnology and Marine Biology, 88 (3‐4), 290-303. Packman, A., & Brooks, N. (1995). Colloidal particle exchange between stream and stream bed in a laboratory flume. Marine and Freshwater Research, 46 (1), 233-236. https://doi.org/https://doi.org/10.1071/MF9950233 Pescimoro, E., Boano, F., Sawyer, A. H., & Soltanian, M. R. (2019). Modeling Influence of Sediment Heterogeneity on Nutrient Cycling in Streambeds. Water Resources Research, 55 (5), 4082-4095. https://doi.org/https://doi.org/10.1029/2018WR024221 Postma, G., Kleinhans, M. G., Meijer, P. T., & Eggenhuisen, J. T. (2008). Sediment transport in analogue flume models compared with real-world sedimentary systems: a new look at scaling evolution of sedimentary systems in a flume. Sedimentology, 55 (6), 1541-1557. https://doi.org/10.1111/j.1365-3091.2008.00956.x Reddy, K. R., & DeLaune, R. D. (2008). Biogeochemistry of wetlands: science and applications . Taylor & Francis. https://doi.org/10.1201/9780203491454 Scott, M., & Hanson, C. (2013). Risk maps of nitrate in Canterbury groundwater. Environment Canterbury, Technical report, 13 . Silveira, F., Charters, F., Bello-Mendoza, R., & Cochrane, T. (2021). The influence of groundwater conditions on nutrient dynamics in waterways Stormwater Conference, Tauranga, New Zealand. 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 Silveira, F. C., Bello-Mendoza, R., & Cochrane, T. A. (2022). Transformation of nitrogen compounds in a regenerated urban drainage stream in New Zealand. New Zealand Journal of Marine and Freshwater Research , 1-18. https://doi.org/10.1080/00288330.2022.2103158 Suddick, E., Whitney, P., Townsend, A., & Davidson, E. (2013, 2013/07/01). The role of nitrogen in climate change and the impacts of nitrogen–climate interactions in the United States: foreword to thematic issue. Biogeochemistry, 114 (1-3), 1-10. https://doi.org/10.1007/s10533-012-9795-z Sunjidmaa, N., Mendoza-Lera, C., Hille, S., Schmidt, C., Borchardt, D., & Graeber, D. (2022). Carbon limitation may override fine-sediment induced alterations of hyporheic nitrogen and phosphorus dynamics. The Science of the total environment, 837 , 155689-155689. https://doi.org/10.1016/j.scitotenv.2022.155689 Tian, K., Huang, C., Zhang, G., & Zheng, F. (2009). Chemical transport from soil into surface runoff under different ground-water tables. Journal of Northwest A & F University-Natural Science Edition, 37 (11), 193-200. Vicente-Serrano, S. M., Quiring, S. M., Peña-Gallardo, M., Yuan, S., & Domínguez-Castro, F. (2020). A review of environmental droughts: Increased risk under global warming? Earth-Science Reviews, 201 , 102953. Vietz, G. J., Rutherfurd, I. D., Fletcher, T. D., & Walsh, C. J. (2016). Thinking outside the channel: Challenges and opportunities for protection and restoration of stream morphology in urbanizing catchments. Landscape and Urban Planning, 145 , 34-44. Vietz, G. J., Walsh, C. J., & Fletcher, T. D. (2015, 2016/06/01). Urban hydrogeomorphology and the urban stream syndrome: Treating the symptoms and causes of geomorphic change. Progress in Physical Geography: Earth and Environment, 40 (3), 480-492. https://doi.org/10.1177/0309133315605048 Webster-Brown, J., & Barr, E. (2016). Changing flow regimes in the springs of Christchurch. In 37th Hydrology & Water Resources Symposium 2016: Water, Infrastructure and the Environment (pp. 588-595). Engineers Australia. Retrieved 2023/11/26, from https://search.informit.org/doi/10.3316/informit.690294130428132 Withers, P. J. A., & Jarvie, H. P. (2008, 2008/08/01/). Delivery and cycling of phosphorus in rivers: A review. Science of The Total Environment, 400 (1), 379-395. https://doi.org/https://doi.org/10.1016/j.scitotenv.2008.08.002 Yoder, C. E. (2014). Quantifying subsurface hydrology effects on chemical transport in agriculture drainage ditches using a 20 meter flume ProQuest Dissertations Publishing]. Zarnetske, J. P., Haggerty, R., Wondzell, S. M., & Baker, M. A. (2011). Dynamics of nitrate production and removal as a function of residence time in the hyporheic zone. Journal of Geophysical Research: Biogeosciences, 116 (G1). https://doi.org/https://doi.org/10.1029/2010JG001356 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 02 Oct, 2024 Reviews received at journal 02 Oct, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviewers invited by journal 25 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 19 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Cochrane","email":"","orcid":"","institution":"University of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"A.","lastName":"Cochrane","suffix":""},{"id":354498685,"identity":"34e83948-5606-4e7d-9a7a-598d564757da","order_by":2,"name":"Ricardo Bello-Mendoza","email":"","orcid":"","institution":"University of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Bello-Mendoza","suffix":""},{"id":354498686,"identity":"05ec74de-5a3a-4d8f-828f-ce357c015c06","order_by":3,"name":"Frances Charters","email":"","orcid":"","institution":"University of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Frances","middleName":"","lastName":"Charters","suffix":""}],"badges":[],"createdAt":"2024-08-19 06:15:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4936228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4936228/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13459-4","type":"published","date":"2024-12-11T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64917819,"identity":"bfcae14e-44b7-4648-97b4-be191c28a01f","added_by":"auto","created_at":"2024-09-20 11:07:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283991,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic view of the column leaching test.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/efbadccf97998aff958350a2.jpeg"},{"id":64917825,"identity":"5e189644-51b2-47b0-9647-0a470689dda3","added_by":"auto","created_at":"2024-09-20 11:07:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205845,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic layout and cross-sectional representations of the experimental setup, illustrating the controls of the groundwater hydraulic regime. The stars indicate the locations for sampling and in-situ monitoring.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/090ef89055d3e6b64800adbc.png"},{"id":64918613,"identity":"5c8f8fa7-1ca0-4dac-862f-02e45b32a470","added_by":"auto","created_at":"2024-09-20 11:15:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136834,"visible":true,"origin":"","legend":"\u003cp\u003eWater collected on the column leaching test at times 15, 30, 45 and 60 minutes, respectively. Dotted line represents SSW concentrations of 0.4 mg/L NO\u003csub\u003e3\u003c/sub\u003e-N and 0.2 mg/L DRP.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/fd2abdd4807d03074f4763cb.png"},{"id":64917823,"identity":"cf58d880-19e6-4d5b-8e7a-cc3e7a88ef61","added_by":"auto","created_at":"2024-09-20 11:07:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168699,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage changes in NO\u003csub\u003e3\u003c/sub\u003e-N, DRP and NH\u003csub\u003e4\u003c/sub\u003e-N concentrations at the midpoint and outlet of the flume under neutral, drainage, and seepage groundwater conditions; outliers are indicated by circles.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/e0ed7a70eda923e9e9eeca69.png"},{"id":64917820,"identity":"86b568ac-7eba-4081-b06e-9d385fc22195","added_by":"auto","created_at":"2024-09-20 11:07:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":213876,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in NO\u003csub\u003e3\u003c/sub\u003e-N, DRP and NH\u003csub\u003e4\u003c/sub\u003e-N concentrations in the groundwater channel under drainage condition. NH\u003csub\u003e4\u003c/sub\u003e-N concentrations were not analysed in the last two runs; outliers are indicated by circles.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/2659512198631af203d2b52c.png"},{"id":64917821,"identity":"648c5c69-8b3b-4389-a1d3-632aab5314fd","added_by":"auto","created_at":"2024-09-20 11:07:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":207766,"visible":true,"origin":"","legend":"\u003cp\u003epH, specific conductivity and Eh from the in-situ measurement after the stabilization period; outliers are indicated by circles.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/a3eb30d948cf1ce0f0ee3d49.png"},{"id":71552443,"identity":"57b7786a-3971-46da-9127-aaaf9a66991f","added_by":"auto","created_at":"2024-12-16 16:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1887831,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/3f5175b5-e9c1-427b-b6e4-86d64e079141.pdf"},{"id":64918614,"identity":"48e52f8d-d4ba-4435-b662-9118c8a70f91","added_by":"auto","created_at":"2024-09-20 11:15:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":195899,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4936228/v1/57abacfa95efccd5ed77bf9a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of surface-groundwater interactions on nutrient dynamics in urban in-channel treatment systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eElevated nutrients levels in rural and urban waterways lead to eutrophication and degrade the health of aquatic ecosystem. Nutrient dynamics in waterways are influenced by the input of nitrogen and phosphorus from surface runoff, and by filtration and accumulation over time in the hyporheic zone, where shallow groundwater mixes with surface water around the streambed interface (Pescimoro et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Surface-groundwater interactions in the hyporheic zone can change nutrient fluxes due to the impact of physical, chemical, and microbiological factors (i.e. particle size, sediment composition, microbiological species composition, and others). Residence time in the hyporheic zone, pore clogging due to fine sediments, carbon availability, and seasonal variation of stratified groundwater contribution impact nutrients dynamics and availability and, subsequently, influence the aquatic ecology (Bohlke et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mar\u0026ccedil;ais et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McCormack et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sunjidmaa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOxidized nitrogen removal in waterways is primarily occurs through denitrification process at the water-sediment boundary, driven by factors such as porosity, carbon content, oxygen levels, and residence time (Hampton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these, residence times within the hyporheic zone has been identified to be the main driver of biochemical reactions (Zarnetske et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A longer resident time increases the likelihood and extent of these reactions (Boano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In small streams, the large contact area between sediment and water flow area increases the chance of reactions to occur (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhosphorus retention in waterways is primarily achieved through adsorption to the soil in the hyporheic zone and plant uptake (Jin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Withers \u0026amp; Jarvie, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In acid soils, aluminium (Al) and iron (Fe) are the key sorbents for phosphorus (Reddy \u0026amp; DeLaune, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Nevertheless, phosphorus bonded to iron oxides in the bed sediments can be mobilised when conditions became anaerobic and/or anoxic (Forsmann \u0026amp; Kjaergaard, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; House \u0026amp; Denison, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn-channel treatment systems are commonly defined to be stormwater treatment systems engineered in-channel with streams, drainage channels and other waterways to which intend to remove an excess of contaminants (in this case nutrients) through biological, chemical, and physical processes. However, the impact of surface and groundwater interactions on these treatment processes is poorly understood, especially with ongoing changes in surface and groundwater flow regimes as impacted by spring-fed waterways and climate change (Mangangka et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Webster-Brown \u0026amp; Barr, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExperimental flumes can be used to isolate physical, chemical, and biological processes and to understand them in detail. Simplified flume experiments, like annular and linear flumes, have proved useful in understanding, modelling and predicting processes between bed sediment and water tables, such as advection-driven transport, production and removal of nutrients, resuspension, colloidal particles exchange, and others (Blom et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Eylers et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Harvey et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Postma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For example, flume experiments have shown that nutrients bound to colloidal clay particles were trapped in the bed sediment at high surface water concentration, and released under low clay concentrations (Packman \u0026amp; Brooks, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Furthermore, the release and entrapment of pore water due to sediment turnover were found to contribute to surface-groundwater water exchange under baseflow conditions, increasing its concentration in the surface water (Mutz \u0026amp; Rohde, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Similarly, a study of phosphorus mobility triggered by sediment resuspension in a flume experiment with a recirculation system identified a strong relationship between phosphorus concentration in the surface water and sediment, greater concentrations in the bed sediment leads to great concentration in the surface water (Huang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChemical transport has been found to be significantly influenced by surface runoff and the groundwater table, where tracers (bromate) increased in soil under drainage groundwater conditions and low groundwater table levels (Tian et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, field and model studies have shown that sediment permeability and surface water velocity through the sediment can increase nutrient supply and change the residence time of water within the streambed (Bardini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, water flow gradients within sediments or groundwater could lead to the movement of dissolved phosphorus (Withers \u0026amp; Jarvie, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and leaching of nutrients into the groundwater under drainage groundwater conditions have been reported (Scott \u0026amp; Hanson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yoder, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, despite these findings of nutrients dynamics within water table, bed sediment and groundwater, significant research gaps remain. There is still a need to better understand nutrients transport and transformation under different flow conditions, bed sediment composition (e.g., gravel, sand, clay, organic matter etc.) with different hydraulic conductivity and groundwater influence (seepage, neutral, and drainage conditions). Current studies have not fully explored how these factors interact to affect nutrient dynamics in in-channel treatment systems, especially considering ongoing changes in surface and groundwater flow regimes seasonal variations and/or climate change, and modifications in waterways through geomorphic processes (between flow, sediment, and vegetation) and engineering interventions for stream restoration and improving water quality (Hatt et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hester \u0026amp; Doyle, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Suddick et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vietz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vietz et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, quantifying nutrients transport and transformations under groundwater neutral, drainage, or seepage conditions and varying bed sediment hydraulic conductivity in in-channel treatment systems is necessary to understand the removal of nutrients via physical, chemical, and biological processes. This understanding is crucial for guiding stream management decisions, designing effective in-channel treatment systems, and ultimately improving water quality in waterways.\u003c/p\u003e \u003cp\u003eThis research thus aims to investigate nutrient dynamics (nitrogen and phosphorus) within in-channel treatment systems under different surface-water and ground-water interactions (neutral, drainage, and seepage groundwater conditions) and bed sediment with low and high hydraulic conductivity. It is hypothesized that the surface-groundwater interactions and bed sediment properties influence the levels and form of nutrients, and their in-channel mobility. A better understanding of these process could help the sustainable management of groundwater and surface water resources.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eColumn leaching test, followed by flume experiments, were conducted to address the aims of the study. Contaminated bed sediments were sourced locally and mixed with coarse sand to enhance conductivity for flume and benchtop experiments. Synthetic stormwater was used in all experiments, with contaminant concentrations derived from observed surface-water quality from a local stream.\u003c/p\u003e \u003cp\u003eBed sediment\u003c/p\u003e \u003cp\u003eThe bed sediment, used in all experiments, was sourced from Wigram Retention Basin (WRB) in Christchurch, New Zealand, a 30-year-old wet pond with a history of nutrient contamination (Black, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Moores et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Silveira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The 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, including a fertilizer factory (Silveira, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). WRB sediment\u0026rsquo;s pH was 6.0, Olsen phosphorus concentration 37 mg/L (35.2 mg/kg soil based on volume weight 1.05 g/mL), organic matter 5.5%, total carbon 3.2%, and C/N ratio 13.3 (see Supplementary Information Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eA1\u003c/span\u003e, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003eA1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003eA2\u003c/span\u003e). Collected sediment was dried in a temperature-controlled room at 30\u0026deg;C and 25% air humidity for 7 days, then sieved on a steel mesh with 32 mm aperture. Silica sand (medium to coarse sand with D60\u0026thinsp;=\u0026thinsp;0.45mm) was then mixed with WRB\u0026rsquo;s bed sediment to enhance hydraulic conductivity. Two mixtures of bed media were prepared consisting of 40% sediment\u0026thinsp;+\u0026thinsp;60% sand and 75% sediment\u0026thinsp;+\u0026thinsp;25% sand.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSynthetic stormwater\u003c/p\u003e \u003cp\u003eThe synthetic stormwater (SSW) used in the experiments was prepared using potassium nitrate (KNO\u003csub\u003e3\u003c/sub\u003e), ammonium chloride (NH\u003csub\u003e4\u003c/sub\u003eCl), and potassium dihydrogen phosphate (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e). It was then diluted with water to reach target concentrations of 0.4 mg/L of nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N), 0.2 mg/L of both ammoniacal nitrogen (NH\u003csub\u003e4\u003c/sub\u003e-N) and dissolved reactive phosphorus (DRP). These concentrations simulated observed in-channel water quality in Haytons Stream (CCC, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while providing a consistent feed water quality between all experiments (see Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eA3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eColumn leaching test methodology\u003c/p\u003e \u003cp\u003eColumn leaching test was initially performed to assess hydraulic conductivity of the sediment mixes with 1:1 and 1:4 WRB sediment to silica sand, and to inform the selection of the blended sediment composition for the flume experiments. Water was applied to the top of columns with a static hydraulic head of 2 cm. Each column had 14 cm of gravel at the bottom, 4 or 5 cm of sediment, and the diameter of the columns were 10 cm. The target flow rate was 6 mL/min, as this represented the equivalent scaled down flow of the subsequent flume experiment (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA4\u003c/span\u003e; Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003eA5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequent tests were conducted to determine changes in nutrients concentrations in the water column under seepage conditions using both deionized water (DI \u0026ndash; low electrical conductivity) and synthetic stormwater (SSW, selected to have a low (DI) and \u0026ndash; high electrical conductivity). The tests were conducted in duplicates for each condition and only NO\u003csub\u003e3\u003c/sub\u003e-N and DRP were measured. For this, water was supplied to the bottom of two parallel columns, with a target seepage rate of 6 mL/min achieved by creating a hydraulic head of approximately 3 cm created between a 20-L tank (with DI or SSW) and the experimental columns, with no differences between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each column had 14 cm of gravel at the bottom, 5 cm of sediment-sand mix, 2 cm of standing water, and the diameter of the columns were 10 cm. Each test was run for 90 minutes, with an initial 30-minute stabilization period, after which samples were taken at 15 minutes intervals (i.e., samples taken at 30, 45, 60, 75 and 90 mins). Samples were collected from an outlet point in each water column 2 cm above the sediment. The experiments were repeated three times on different dates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlume design and experimental methodology\u003c/p\u003e \u003cp\u003eA 19 m long flume, divided into two main sections each of 9.5 m, was constructed using 6 mm thick polyvinyl chloride (PVC) sheets. This setup simulated an in-channel treatment system containing gravel, bed sediment, surface water, and a mechanism for managing groundwater interactions (Fig.\u0026nbsp;2). The flume\u0026rsquo;s length was determined by the maximum available laboratory space to promote enough time and area for sediment-water interactions. A 0.5% slope was set to represent a typical hydraulic gradient in a system with minimal backflow. The hydraulic regime (i.e., seepage, neutral or drainage groundwater conditions) was varied by adjusting the groundwater channel (GW1 and GW2) height relative to the flume height via a pulley system. The surface water and groundwater used in this experiment was the same synthetic stormwater (SSW) previously described.\u003c/p\u003e \u003cp\u003eGrade 5 gravel (5\u0026ndash;8 mm diameter) was used to represent free-draining gravel in the lower hyporheic zone, while the bed media consisted of mixtures of sand and contaminated bed sediment as previously described. A layer of cheese cloth (Cotton, 28 by 24 threads per square inch) was used between the layers to prevent migration of fine particles into the gravel.\u003c/p\u003e \u003cp\u003eThe flume was gravity fed to achieve a low water level (1 cm depth) with an inflow rate of 7.2 L/min of SSW and a high-water level (2 cm depth) with an inflow of 21.8 L/min (water velocity of 0.04 and 0.06 m/s, respectively). A flow sensor installed at the outlet of the stormwater feeder tank intermediate bulk containers (IBC) was used to monitor the target flows.\u003c/p\u003e \u003cp\u003eNo interaction between surface water and groundwater occurred under neutral groundwater conditions. To induce drainage conditions, the groundwater channel system was lowered to facilitate draining 10% of the surface water flow (1.42 L/min under 1 cm water depth and 4.36 L/min under 2 cm water depth of the incoming surface water) for each length of the flume (GW1 and GW2 as per Fig.\u0026nbsp;2). Under seepage groundwater conditions, the groundwater channel was raised to seep SSW into the flume at a total rate of 20% of the surface water flow. The composition of inflowing groundwater under seepage was the same as the surface water to avoid dilution.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFlume simulations\u003c/h2\u003e \u003cp\u003eExperiments were run under three different groundwater conditions (neutral, drainage, and seepage), two different surface water depths (1 and 2 cm) and corresponding flows (7.2 L/ min and 21.8 L/ min, respectively), and with three bed sediment compositions (only gravel, 40% sediment\u0026thinsp;+\u0026thinsp;60% sand \u0026ndash; high hydraulic conductivity, and 75% sediment\u0026thinsp;+\u0026thinsp;25% sand \u0026ndash; low hydraulic conductivity; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The proportion of 40% sediment and 60% sand was determined based on the results of the hydraulic conductivity test (Appendix Tables A1 and A2). This composition was chosen for its ability to increase the proportion of WRB's bed sediment within the range of 1:4 to 1:1 (WRB sediment to sand), thus achieving the desired hydraulic conductivity for the flume. Additionally, this mixture incorporates a significant portion of WRB bed sediment while assuring the ability to seep/drain 20% of surface water flow.\u003c/p\u003e \u003cp\u003eBetween runs, gravel layer and bed sediment remained saturated with SSW. However, prior each run, fresh SSW was applied to replace the SSW from the previous run and the system was allowed to stabilize for 15 minutes prior to sample collection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Water resident time in the sediment was approximately 13 minutes using high water depth and 38 minutes using low water depth (Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003eA6\u003c/span\u003e). Experiments with low hydraulic conductivity sediment and high surface water flow were repeated twice due to high variance in nutrients concentrations during the study and potential accumulation of nutrients in the bed sediment throughout the experiment. The flume\u0026rsquo;s bed sediment was removed and replaced with similar mix of silica sand and WRB bed sediment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroundwater condition, water depth and flow and type of bed sediment used in flume experiments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundwater Condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater depth and Surface Water Flow (L/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBed Sediment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReplications\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (1 cm and 7.2 L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGravel only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (2 cm and 21.8 L/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeepage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHigh hydraulic conductivity:\u003c/p\u003e \u003cp\u003e40% WRB sediment and 60% silica sand w/ gravel layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeepage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLow hydraulic conductivity: 75% WRB sediment and 25% silica sand w/ gravel layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeepage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeepage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal of runs performed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eSamples were taken from the inlet, midpoint, and outlet of the flume (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using 1L high density polyethylene (HDPE) containers and stored at 4\u0026deg;C before analysis. Inlet water quality was sampled at 10-minute intervals from the start of the flume trial (t\u0026thinsp;=\u0026thinsp;0). Middle and outlet concentrations were found to stabilise after 15 minutes. Therefore, samples were taken from the midpoint and outlet of the flume at 10-minute intervals, starting at t\u0026thinsp;=\u0026thinsp;15 mins. Groundwater samples were collected at t\u0026thinsp;=\u0026thinsp;15 and 35 in the first channel and t\u0026thinsp;=\u0026thinsp;25 and 45 minutes in the second channel under seepage conditions. Under drainage conditions, groundwater samples were collected at t\u0026thinsp;=\u0026thinsp;15, 25, 35 and 45 minutes from both groundwater channels. More samples were taken under drainage conditions as a greater change in groundwater quality was expected than under seepage conditions, where the groundwater was expected to change from its synthetic stormwater source. Samples were filtered with a 0.45 \u0026micro;m filter and frozen at -18\u0026deg;C for storage before processing.\u003c/p\u003e \u003cp\u003eMethod of analyses\u003c/p\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N and DRP were then analysed with a Dionex ICS-2100 ion chromatography (IC) system using 38 mM KOH as eluent at 0.3 mL/min, with the preserved samples thawed at room temperature directly prior to IC analysis. Ammoniacal nitrogen was analysed using flow injection analysis with a modified method based on Foss A/N 5206 \u0026amp; 5232. The detection limit for this method was 0.10 mg/L.\u003c/p\u003e \u003cp\u003eFor the in-situ monitoring in the flume, a YSI Professional Plus multi parameter meter was positioned at each collection point to record pH, electrical conductivity, oxidation-reduction potential (ORP), and temperature data at 1-minute intervals during each run. ORP values were converted to Standard Hydrogen Electrode (SHE), displaying redox potential as Eh (V) by adding 200 mV to the ORP values (Environmental, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuality control and quality assurance\u003c/h2\u003e \u003cp\u003eA QA/QC plan was implemented to minimize errors in data analysis. All samples were collected, preserved, and analysed following the APHA (2007) guidelines. Duplicates were generated for ammoniacal nitrogen, oxidized nitrogen, and DRP. Sampling instruments were calibrated and maintained according to the manufacturer\u0026rsquo;s manual.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo account for variation in inlet concentration, samples\u0026rsquo; nutrient concentrations taken in the midpoint and outlet of the flume were evaluated against the mean inlet levels for each run. Resulting percent changes in each contaminant concentrations were then evaluated across all runs. T-tests (with α\u0026thinsp;=\u0026thinsp;0.05) were performed to assess statistically significant differences in the percentage changes of each dataset under varying groundwater conditions, aiming to determine their effect on the surface water quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eChange in nitrate and DRP over time under column leaching conditions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eUnder DI water application, there was an initial high export of NO\u003csub\u003e3\u003c/sub\u003e-N with concentrations up to 4.4 mg/L which then decreased to less than 0.5 mg/L after 60 minutes (Figure 3, Table A7). Subsequent repetitions resulted in mean concentrations close or below the detection limit (0.045 mg/L). Under SSW application, nitrate concentrations were initially below the SSW concentration of 0.4 mg/L NO\u003csub\u003e3\u003c/sub\u003e-N and later increased to similar values to SSW concentrations. For DRP however, DI water application through the media showed mean DRP concentrations increasing overtime. Under SSW application, leachate concentrations were similar to the influent concentrations (Figure 3).\u003c/p\u003e\n\u003ch2\u003ePollutant dynamics along a flume channel under varied groundwater conditions\u003c/h2\u003e\n\u003cp\u003eResults for high water level conditions in the flume showed that the GW condition influenced nutrient levels in the surface water for NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e-N and DRP (Table 2, Figure 4). Under seepage groundwater conditions, NO\u003csub\u003e3\u003c/sub\u003e-N levels reduced by 8% at the midpoint and 13 % at the outlet, whereas NH\u003csub\u003e4\u003c/sub\u003e-N and DRP concentrations raised by 4 and 6%, respectively, at both locations, relative to the inlet concentrations, when using high hydraulic conductivity sediment. Under neutral and drainage conditions, no significant difference in percentage change were observed (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of pollutant dynamics under low water levels were inconsistent and nutrient concentrations under different groundwater conditions did not exhibit any significant trend. Inconsistent results under low water levels can likely be attributed to difficulties around controlling a low groundwater flow rate of 0.72 L/min in or out the flume. The exchange of new water after the previous test may have been slower and more variable in time required under low flow conditions with less driving head, resulting in only partial exchange within the 15 minutes stabilization period.\u003c/p\u003e\n\u003cp\u003eUnder drainage groundwater conditions, substantial shifts were noticed in samples concentration taken from the groundwater channel, with a sharp decline in NO\u003csub\u003e3\u003c/sub\u003e-N levels and an increase DRP concentrations. The increase was greater using low hydraulic conductivity sediment (Figure 5, Table 2). The decrease in NO\u003csub\u003e3\u003c/sub\u003e-N suggests that it is retained within the bed sediment as surface water flows through it under drainage groundwater conditions, whereas DRP appears to be released or flushed out from the bed sediment. In all runs there was an export of NH\u003csub\u003e4\u003c/sub\u003e-N. It is important to note that the bed was water-saturated (using SSW) during all runs except during the first run, where the bed sediment/gravel was unsaturated because the flume was previously drained and not fully filled before this initial run (Figure 5). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Results of NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e-N and DRP concentrations in the inlet, midpoint and outlet of the flume using low and high hydraulic conductivity sediment under different groundwater conditions.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSediment Hydraulic Conductivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGW Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAverage and range (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNH\u003csub\u003e4\u003c/sub\u003e-N\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAverage and range (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAverage and range (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\" rowspan=\"9\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" rowspan=\"3\"\u003e\n \u003cp\u003eNeutral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.37-0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.19 (0.17-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.20 (0.18-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.36-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.17-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.20 (0.19-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.36-0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.15 -0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.20 (0.20-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.478260869565217%\" rowspan=\"3\"\u003e\n \u003cp\u003eDrainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.36-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.12 -0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.20-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.37-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.16 (0.12-0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.20-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.39-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.17 (0.14-0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.19-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.478260869565217%\" rowspan=\"3\"\u003e\n \u003cp\u003eSeepage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 (0.36-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.17 (0.13-0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.20 (0.19-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (0.32-0.37)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.16-0.19)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.22 (0.20-0.29)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.33 (0.31-0.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.17-0.19)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.22 (0.20-0.25)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\" rowspan=\"9\"\u003e\n \u003cp\u003eLow\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" rowspan=\"3\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.35-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.24-0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.20 (0.19-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.40 (0.36-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.25-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.19-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.40 (0.35-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.24-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.19-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.478260869565217%\" rowspan=\"3\"\u003e\n \u003cp\u003eDrainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.36-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.24-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.20-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.36-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.25(0.25-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.20-0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.36-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.25(0.24-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.20-0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.478260869565217%\" rowspan=\"3\"\u003e\n \u003cp\u003eSeepage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003eInlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.37-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.24-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.21 (0.18-0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.35 (0.33-0.37)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.31 (0.27-0.40)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.31 (0.20-0.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003eOutlet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69941060903733%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (0.31-0.35)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.29-0.56)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20039292730845%\" valign=\"top\"\u003e\n \u003cp\u003e0.34 (0.22-0.56)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.878787878787879%\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10%\" valign=\"top\"\u003e\n \u003cp\u003eDrainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\" rowspan=\"4\"\u003e\n \u003cp\u003eGroundwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.878787878787879%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.14 (0.08-0.18)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.18 (0.11-0.30)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e0.43 (0.04-0.68)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003eSeepage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.36 (0.34-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.19 (0.18-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.17 (0.12-0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.782608695652174%\" rowspan=\"2\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"top\"\u003e\n \u003cp\u003eDrainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.782608695652174%\"\u003e\n \u003cp\u003e40\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e0.58 (0-3.36)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.66-1.50)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e2.28 (0.18-6.16)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003eSeepage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e20\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 (0.36-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (0.24-0.25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.06122448979592%\" valign=\"top\"\u003e\n \u003cp\u003e0.22 (0.18-0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*significant statistical difference between inlet concentrations (P-value \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e NH\u003csub\u003e4\u003c/sub\u003e-N had twelve observations at all inlet sampling and under all neutral conditions, fifteen observations at middle and outlet under drainage and seepage conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e NH\u003csub\u003e4\u003c/sub\u003e-N observations were 24 and 12, respectively.\u003c/p\u003e\n\u003ch2\u003eComparison of hydraulic conductivity characteristics of bed sediment on nutrients dynamics\u003c/h2\u003e\n\u003cp\u003eBoth low and high hydraulic conductivity sediments were found to have a similar type of influence on nitrogen and phosphorus., i.e., both resulted in reduction of NO\u003csub\u003e3\u003c/sub\u003e-N and increase of NH\u003csub\u003e4\u003c/sub\u003e-N and DRP concentrations in surface water under groundwater seepage conditions. However, the scale of the change and range of variation in concentrations was much larger for the low conductivity sediment (Figure 4, Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the high conductivity sediment produced greater variation in Eh, and pH under seepage groundwater conditions (Figure 6). The decrease in Eh values observed under seepage groundwater conditions indicates potential anoxic bed sediment conditions.\u003c/p\u003e\n\u003cp\u003eWater collected at the groundwater channel under drainage groundwater conditions had an export of NO\u003csub\u003e3\u003c/sub\u003e-N on the first run; however, there was almost 100% reduction of NO\u003csub\u003e3\u003c/sub\u003e-N on the following runs (Figure 5). DRP concentrations, on the contrary, showed a greater export on runs 2 and 3 compared to the first run, with percentage changes going from around 100 % increase to up to 2500 % increase (Figure 5).\u003c/p\u003e\n\u003ch2\u003epH, conductivity, Eh and temperature changes throughout channel over time\u003c/h2\u003e\n\u003cp\u003epH, specific conductance, and Eh changed under seepage groundwater conditions. The average inlet pH values decreased from 7.8 to 7.4 at the midpoint and 7.3 at the outlet (Figure 6). Specific conductivity remained consistent, ranging between 106 to 132 \u0026mu;S/cm, with average values between 112 and 121 \u0026mu;S/cm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder seepage conditions, the mean Eh values at the inlet, middle and outlet were around 0.6 V, 0.5 V and 0.3 V, respectively, using low and high hydraulicity conductivity sediment. The 0.3 V reduction in average Eh values under seepage groundwater condition, with just a 20% input from groundwater, suggesting anoxic conditions within the bed sediment. Temperature showed no specific pattern, with a mean of 17.5\u0026deg;C and a range between 16 and 19\u0026deg;C.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eImplications for nutrients dynamics in waterways and in-channel treatment systems\u003c/p\u003e \u003cp\u003eSaturating the flume\u0026rsquo;s bed sediment with SSW likely stablished an anoxic environment, as suggested by the Eh values observed under seepage groundwater conditions. Each flume run, lasting up to 120 minutes (including preparatory steps such as replacing the water in the water-saturated bed sediment and applying a 30-minute flow through the flume), might have allowed anoxic zones to persist within the bed sediment, facilitating denitrification processes. However, given the short water resident time, denitrification alone cannot account for the observed reduction in NO\u003csub\u003e3\u003c/sub\u003e-N concentrations. Sandy bed sediment have non-uniform (preferential) groundwater flow, which might have prevented all water in the bed sediment to be replaced before the start of each run on the flume experiment (MahmoodPoor Dehkordy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, the dilution of SSW seeping from the bed sediment within the flume could account for the reduction in NO\u003csub\u003e3\u003c/sub\u003e-N levels in the surface water under seepage groundwater conditions (Silveira et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ammonification processes of organic matter releases ammonia which can later be transported from the pore water in the sediment bed to the surface water. The observed release of ammonia under seepage conditions contributes to this understanding. Nitrite and nitrogen oxide is also generated within small stream and channels, but this was not measured nor quantified in this research.\u003c/p\u003e \u003cp\u003eThe release of DRP detected into groundwater under drainage groundwater conditions aligns with prior findings where phosphorus leaching under seepage groundwater conditions increased phosphorus load into the surface water (Yoder, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, the groundwater seeping through the bed sediment facilitated the transport of phosphorus, subsequently leaching from the bed sediment into the surface water.\u003c/p\u003e \u003cp\u003eInfluence of sediment conditions on nitrogen and phosphorus transformation\u003c/p\u003e \u003cp\u003eChanges in Eh values (from 0.6 V to around 0.25 V, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) suggests that conditions go from aerobic to anoxic (or perhaps anaerobic in micro-sites) in the sediment conditions, which could have influenced microbial activity when the flume had water-saturated bed sediment. This supports denitrification processes, highlighting the importance of a saturated sediment in the nitrogen cycle.\u003c/p\u003e \u003cp\u003eIn addition, water-saturated sediment was an important parameter to reduce nitrate concentrations in the surface water (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The first run bed sediment was not saturated with SSW (unsaturated aerobic conditions), but the following runs had the bed sediments saturated, suggesting water saturation of bed sediment can change the pollutant\u0026rsquo;s dynamics. Water-saturated sediment is the main difference between dry vs wet basin, where dry basin might not promote denitrification processes as well as wet basins.\u003c/p\u003e \u003cp\u003eThe high amount of phosphorus in the bed sediment could be attributed to increased phosphorus in the surface water. Bed sediment needs to be characterised as part of any in-channel treatment development or river management planning.\u003c/p\u003e \u003cp\u003eImpact of findings\u003c/p\u003e \u003cp\u003eGroundwater seepage conditions contribute significantly to the dynamics of nutrients in surface water, and thus it is important to consider both natural and anthropogenic groundwater variations when assessing surface water quality. Locations where groundwater can experience reductions during extended periods without rain, seasonal droughts, excess of groundwater extraction should be identified. Conversely, prolonged periods of rain and sea-level rise can elevate groundwater levels in low-lying catchments and unconfined coastal areas, impacting in-channel treatment systems within these regions. These considerations are vital for the monitoring and design of in-channel treatment systems, recognizing the intricate interplay between groundwater table and surface water in nutrients dynamics.\u003c/p\u003e \u003cp\u003eAdditionally, the assessment of hydraulic conductivity in bed sediment is important, given the increased variability observed in nutrient dynamics and related physical parameters in surface water. Furthermore, the saturation of bed sediment in in-channel treatment systems emerges as a significant factor influencing nutrient dynamics, particularly in regions where elevated nitrate in surface-groundwater is a concern. All these factors should be taken in account in the decision-making process when designing in-channel treatment systems that involve interactions between groundwater and surface water.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis laboratory-based study aimed to isolate processes occurring among surface water, groundwater, and bed sediment to evaluate the effects of various groundwater conditions (seepage, neutral, and drainage) and varying bed sediment hydraulic conductivities on nitrogen and phosphorus dynamics within in-channel treatment systems. It was assumed that the bed sediment within the flume was homogeneous and that consistent groundwater conditions prevailed throughout the experimental setup. However, the study did not account for other biotic and abiotic processes involving organisms such as fauna, flora, photosynthesis, as well as physical processes such as sunlight and rainfall. Additionally, the use of synthetic stormwater, achieved by adding nutrient salts, may not fully capture the complexity of natural stormwater, which encompasses diverse substances, temperature variations, and fluctuating flow rates.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study elucidates the critical influence of groundwater interaction on nutrient dynamics in surface water, with direct implications for engineering interventions for the sustainable management of groundwater and surface water systems. There was an observed pattern under seepage conditions, where NO\u003csub\u003e3\u003c/sub\u003e-N concentrations decrease and NH\u003csub\u003e4\u003c/sub\u003e-N and DRP concentrations increase alongside shifts in pH and Eh.\u003c/p\u003e \u003cp\u003eFurthermore, low conductivity sediment induces greater changes in nutrient concentration, while high conductivity sediment leads to more pronounced variations in Eh and pH. Additionally, the level of bed sediment saturation with stormwater influences nutrient dynamics and thus highlights the importance of incorporating this parameter into engineering strategies for improved nutrient prediction and removal.\u003c/p\u003e \u003cp\u003eThe lessons learned from this research can be applied to a broad range of in-channel systems where groundwater and surface water interact, and nutrients contaminants are of concern. This is particularly relevant where sea-water levels could affect groundwater levels under low-laying streams.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. 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.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFabio C. Silveira: Methodology, Formal Analysis, Investigation, Writing - Original Draft, Project administrationThomas A. Cochrane: Conceptualization, Methodology Writing - Review \u0026amp; Editing, Supervision, Funding acquisitionRicardo Bello-Mendoza: Methodology, Writing - Review \u0026amp; Editing, SupervisionFrances Charters: Methodology, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our gratitude for the invaluable assistance from Peter McGuigan for his role in designing and Kevin Wines\u0026rsquo; contribution in constructing the flume, as well as to Matt Cockcroft for his efforts in conducting nutrient analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, J. K., Wondzell, S. M., Gooseff, M. N., \u0026amp; Haggerty, R. (2005). Patterns in stream longitudinal profiles and implications for hyporheic exchange flow at the H.J. Andrews Experimental Forest, Oregon, USA. \u003cem\u003eHydrological Processes, 19\u003c/em\u003e(15), 2931-2949. https://doi.org/https://doi.org/10.1002/hyp.5791\u003c/li\u003e\n\u003cli\u003eBardini, L., Boano, F., Cardenas, M. B., Revelli, R., \u0026amp; Ridolfi, L. (2012, 2012/05/01/). Nutrient cycling in bedform induced hyporheic zones. \u003cem\u003eGeochimica et Cosmochimica Acta, 84\u003c/em\u003e, 47-61. https://doi.org/https://doi.org/10.1016/j.gca.2012.01.025\u003c/li\u003e\n\u003cli\u003eBlack, L. W. J. 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Dynamics of nitrate production and removal as a function of residence time in the hyporheic zone. \u003cem\u003eJournal of Geophysical Research: Biogeosciences, 116\u003c/em\u003e(G1). https://doi.org/https://doi.org/10.1029/2010JG001356\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nitrate; Phosphate, Ammonia, Nutrients, Seepage, Water quality monitoring","lastPublishedDoi":"10.21203/rs.3.rs-4936228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4936228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn-channel water treatment systems remove excess nutrients through biological, chemical, and physical processes associated with the hyporheic zone. However, the impact of surface and groundwater interactions on these treatment processes is poorly understood. This research aims to assess the influence of varying groundwater conditions (neutral, drainage, and seepage) and different bed sediment hydraulic conductivities on nitrogen and phosphorus dynamics in in-channel treatment systems. A flume containing bed sediment was used to study changes in surface water quality under different groundwater and bed sediment conditions. Results show that groundwater interactions influence nutrient concentrations in the surface water. An elevation in dissolved reactive phosphorus and ammoniacal nitrogen and a decrease in nitrate concentrations in the surface water under seepage groundwater conditions was evident. In addition, low hydraulic conductivity sediment led to greater changes in nutrients concentration while high hydraulic conductivity sediment led to greater variations in pH and Eh values. Water-saturated bed sediment promoted a reduction of nitrate concentrations in the surface water. The findings could assist the design and monitoring of in-channel treatment systems where groundwater and surface water interact.\u003c/p\u003e","manuscriptTitle":"The influence of surface-groundwater interactions on nutrient dynamics in urban in-channel treatment systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 11:07:49","doi":"10.21203/rs.3.rs-4936228/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-03T02:11:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-02T12:18:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263622510987735766895421915963981422953","date":"2024-08-28T07:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-26T03:48:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T06:24:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T06:23:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-08-19T06:13:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cbdfc234-b877-478f-94bb-9b200f68e476","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T16:00:20+00:00","versionOfRecord":{"articleIdentity":"rs-4936228","link":"https://doi.org/10.1007/s10661-024-13459-4","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-12-11 15:57:21","publishedOnDateReadable":"December 11th, 2024"},"versionCreatedAt":"2024-09-20 11:07:49","video":"","vorDoi":"10.1007/s10661-024-13459-4","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13459-4","workflowStages":[]},"version":"v1","identity":"rs-4936228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4936228","identity":"rs-4936228","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}