Fate and Behaviour of Pollutants in a Vegetated Pond System for Road Runoff.

Georgios Roinasa, Alexandros Tsavdarisa, John B Williamsb, Catherine Mantc

aPhD Student, bPrincipal Lecturer, cResearch Fellow, School of Civil Engineering and Surveying, University

of Portsmouth, Portland Building, Portsmouth, PO1 3AH, UK

Corresponding Author:

John Williams

School of Civil Engineering and Surveying

University of Portsmouth

Portland Building

Portsmouth

PO1 3AH

Email: john.williams@port.ac.uk

Tel: 00 44 23 92 842404

Fax: 00 22 23 92 842521

Notation

ADP – Antecedent Dry Period

B - Basin

BOD – Biochemical Oxygen Demand

COD – Chemical Oxygen Demand

DO – Dissolved Oxygen

DP – Daily Precipitation

DRO – Diesel Range Organics

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EC – Electric Conductivity

ERO – Engine Oil Range Organics

GC-MS – Gas Chromatograph-Mass Spectrometer

HEM – Hexane Extractable Material

ICP-MS – Inductively Coupled Plasma - Mass Spectrometer

PAHs – Polycyclic Aromatic Hydrocarbons

PGM – Platinum Group Metals

Q – Discharge

ST – Sediment Trap

TSS – Total Suspended Solids

VSS – Volatile Suspended Solids

SuDS – Sustainable Drainage Systems

WR = River Wallington

Keywords

SuDs, Road Runoff, First-flush, PAHs, Metals

Abstract

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1 Introduction

There is increasing concern about the impact that runoff from developed areas, especially from roads, can

have on aquatic ecosystems [1]. The variety of pollutants (including silt, organic matter, heavy metals and a

range of hydrocarbons and polycyclic aromatic hydrocarbons (PAHs) [4]) and intermittent loadings mean

that predicting the impact is very complex. Most contaminants are attached to particles in the µm range and

are attributable to automobile activity [6]. The pollution risk to receiving waters has led to the development

of a more sustainable approach to the management of urban runoff; the Sustainable Drainage System concept

(SuDS). One of the common SuDS features for road runoff is a wet pond; these have various layouts,

vegetation cover and inflow/outflow control devices [7]. Although hydrological attenuation is relatively easy

to address in design, pollutant removal is more difficult and treatment can be highly dependent on site

specific parameters

Wet balancing ponds are one of the most efficient systems for treating highway runoff [8], as the complex

ecology exposes pollutants to a range of treatment mechanisms. This includes adsorption, volatilization,

photolysis, biodegradation and sedimentation . Many studies have reported large reductions in organic loads

in ponds, often in excess of 90% [9,10]. However removal of suspended solids has often been less, with

typical reductions of 60-65%, possibly due to biogenic debris from plants [8, 10].

Studies on heavy metals removal have been more varied, with reported removal rates between 0-84% [8, 9,

11]. This may be due to the variety of chemical properties of heavy metals affecting their behavior in SuDS.

For many metals sorption and subsequent sedimentation are the dominant removal mechanism, most metals

are associated with 0.45-75 μm particles [6]. Particles greater than 125 µm are readily trapped by vegetated

systems, where in 6-32µm range particles are often difficult to remove [12]. The varying behavior of these

particles, and in-situ sorption/desorption processes, have given rise to varied patterns of metal deposition.

Some studies have found that sediments located at the inlet of a pond have the highest metal concentrations

while others have shown found the opposite [13]. Longer term studies have generally found an increase in

concentrations associated with sediment accumulations over time [13, 14].

Hydrocarbons are also of concern in road runoff, with wear of road surfaces, tyres and brake pads combined

with combustion by-products and “drip loss” [15]. Loadings vary with road use and vehicle behaviour. These

emissions can be classified in a variety of ways by either carbon number (e.g. d iesel range organics (DRO)

C10-22 and engine oil range organics (ERO) C22-C40) or for pollutants of most concern by class e.g. PAHs.

These organics have an even wider range of behaviour than metals, with adsorption characteristics,

solubility, volatility and biodegradation characteristics giving a variety of possible fates. PAHs are of

particular concern due to their toxicity [16]. A range PAHs are found in urban runoff with higher

concentrations usually found the particulate phase [18]. However, due to analytical costs, organic pollutants

are often not included in monitoring of urban runoff and further investigation has been identified as a

research priority [19, 20, 21].

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Vegetated detention ponds for road runoff are highly dynamic systems, with varying flow, pollutant loads,

plant growth and other seasonal factors, such as road salting and temperature. Despite numerous studies [11,

22, 23] there are no established design criteria for optimising treatment processes. The toxicity and transport

of metals and hydrocarbons depends upon their bioavailability which is influenced by variations in

speciation, pH, redox potential, particle size distribution, organic matter and temperature [24]. Therefore an

ongoing concern is that under unfavourable conditions accumulated pollutants could be released giving

shock loads to receiving waters or exposing organisms such as invertebrates, fish, birds, amphibians and

mammals attracted by the habitat potential of the ponds [22].

This paper aims to investigate the fate of pollutants in a vegetated detention pond and contribute to further

understanding of the treatment mechanisms which will inform design and operational codes.

2 Materials and Methods

2.1 Study Site

The study site at Waterlooville, Hampshire, UK (Latitude=50.881315, Longitude= -1.037575) is a greenfield

Major Development Area (MDA) for 2,500 new homes. The impermeable clay soil means the site will be

served by storage SUDs. This study considers a detention pond which was constructed to receive runoff

from the access roads prior to house construction. The pond receives runoff from an urban commuter road

(B2150) and roundabout with peak hour flows of approximately 3,100 cars and 100 lorries, which equates to

a daily traffic flow of 40,000 (unpublished Traffic Survey 2009, Mayer Brown Ltd.). While mainly free

flowing, peak time traffic is characterised by stop start congestion associated with nearby traffic lights.

The vegetated pond system receives runoff after a swale which receives piped inflow, as well as direct

precipitation along its length. Figure 1 shows a schematic plan of the system with sampling points labelled

by letters. The plan area is 51x26 m², the two flow-balancing basins are connected by a berm with an invert

level of 1.1 m relative to the pond bed. The berm is designed to reduce short circuiting and increase the

overall retention times. The basins have fixed sediment traps (ST) to collect settling solids. Basin 1` (B1)

has 2 sediment traps (D and E) and Basin 2 (B2) has 1 trap (F). The storage capacity is 304 m ³, the

permanent water level is 1 m rising to 1.6 m at the overflow. A “hydro-brake” regulates the outflow to the

River Wallington (WR). The design inflow for the 1:30 and 1:100 year events were 70 l/s and 100 l/s

respectively. The system was planted with Phragmites australis and Typha latifolia in Spring 2009. By 2010

all the pond area was dominated by vegetation, differing in density with respect to depth of flow. Figure 2 is

a photograph from the pond inlet taken in June 2009, approximately 3 months after planting.

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Figure 1: Schematic Plan of the Vegetated Pond System. Letters Indicate Sampling Points: A = Swale Inlet;

B = Swale Mid-Point; C = Pond Inlet; D, E, F = Sediment Traps; G = Pond Outlet.

The site was equipped with a rain gauge and flumes/stage loggers on the inlet and outlet of the ponds.

Unfortunately, this equipment was not operational during the monitoring so storms were characterised by

total daily precipitation (DP) and antenacent dry period (ADP) since the last DP greater than 2.5 mm/d.

These were obtained from closest rain gauge to the site (a private gauge approximately 1.5 km away: Station

IHAMPSHI9 - www.wunderground.com).

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Figure 2: Photograph of the System in June 2009 looking from the Pond Inlet. The Sampling Points are Labelled as per Figure 1.

2.2 Experimental Methods

Studies into the fate of metal and organic pollutants have been the focus of two separate, but linked, studies,

so have slightly different sampling strategies and sampling occasions.

2.2.1 Monitoring Strategy

Monitoring was undertaken of conditions in the pond system (monthly) and of individual storm events and

for 2 years (03/2011-03/2013). The monthly monitoring aimed to assess the baseline water quality in the

pond and characteristics of bed and settling sediments. The storm event monitoring aimed to characterise the

water quality of runoff entering the pond and the transport of pollutants.

Monthly Monitoring: There were two sampling strategies in the monthly monitoring. The metal study has

focussed on sedimentation in the pond and quality of deposited sediment at the inlet/outlet bank (C, G) ,

while the organic study focussed on soil in the swale (A, B, C). Grab samples of water were collected from

the system and river (WR) via a hand pump to avoid aeration. Material accumulated in the sediment traps

was removed. Soil cores and sediments were also collected from the swale and pond bed.

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Storm Events: Potential storm events were identified from weather forecasts. The criteria for a storm event

were (i) daily precipitation (DP) had to exceed 2.5 mm (ii) storm duration had to be greater than 3 hours and

(iii) there had to be inflow to the pond for more than 3 hours. As the storm monitoring involved intensive

multivariate sampling and testing, there were also logistical constraints on which storms could be monitored.

Table 1 shows the characteristics of the 10 storms monitored: inflow was measured directly at the inlet flume

(C) by the velocity area method using Valeport 801 electromagnetic flow meter. However only 4 of these

events (3, 5. 6. 8) generated outflow due to storage deficit in the Pond (low rainfall in 2011).

Table 1: Characteristics of the monitored storm events

Storm Event

Date Antenacent Dry Period, ADP), d Daily Precipitation (DP), mm

Qmax, m³/s1 31/3/11 0 10.4 mv2 01/12/11 0 7.1 0.0083 12/12/11 0 14.5 0.0474 24/1/12 20 7.1 0.0075 04/03/12 14 12.4 0.0516 23/04/12 0 16.3 0.0647 25/4/12 0 7.1 mv8 8/06/12 8 16.8 0.0349 14/12/12 8 12.7 mv10 12/1/13 3 18.5 mv

mv= missing value

Sampling logistics meant that a sub-set of storms were monitored for general water quality and metals (7

events - 1, 2, 3, 4, 5, 6, 8), water samples were taken from the inlet (C) and outlet of the ponds (G) at specific

time intervals. The study of hydrocarbons examined another sub-set (5 events - 1, 4, 7, 9, 10). Water

samples for hydrocarbon extraction were taken directly from the swale inlet pipe (A).

All sample types were stored in a cool box (4 OC) and analysed or pre-treated within 24 h.

2.2.2 Water Quality

Biochemical oxygen demand (BOD) and total suspended solids (TSS) were measured using standard

methods [26] and chemical oxygen demand (COD) by the Hach™ micro kit. VSS was measured via the loss

on ignition method [26]. Ammoniacal nitrogen (AmmN) was measured via the Palintest™ kit. Other

variables were measured in-situ with probes, e.g. EC (Palintest Micrcomputer 900), pH (Hanna HI1925) and

DO (YSI 50B).

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2.2.3 Metals in Water and Sediments

Water: During on-site sampling 50 ml aliquots were filtered through 0.45 μm Whatman cellulose nitrate

filters using a hand pump to separate particulate matter and dissolved fractions for metal analysis, these

fractions were preserved with HNO3 [13, 6]. Metal content was analysed using an Agilent 7500ce

Inductively Coupled Plasma - Mass Spectrometer (ICP-MS) with octopole reaction cell using the semi-

quantitative method in He mode. Samples were introduced using an integrated auto-sampler and calibration

was by a tuning solution of 10 ppb of 6 elements across the mass range.

Sediments: The settling solids and bed sediments were wet sieved in-situ using pond water to two size

fractions; namely the 2 mm to >63 μm and <63 μm fractions [12, 13]. These were termed coarse grains and

fines respectively. The fractions were then dried in the dark at 80°C and digested for metal analysis with

HNO3 [13] and analysed using ICP-MS.

2.2.4 DRO, ERO and PAH

Water samples were taken in amber bottles, 50 or 100 ml was used for hexane extraction using Solid Phase

Extraction Empore C18 Discs (3M) as per EPA method 1664 revision A. The extract was passed through a 1

g anhydrous Na2SO4 cartridge (Bond Elut) to remove residual water. 50 µl of nonane was added and samples

concentrated down to 1 ml at 40oC in a stream of N2 prior injection on the Gas Chromatograph-Mass

Spectrometer (GC-MS).

The extraction of the PAHs used the same procedure using dichloromethane as the solvent, based on EPA

Method 550.1[27] using application note 54 from SUPERLCO (Sigma Aldrich) for C18 discs [28].

Soils: mild steel tubes 60 mm long and 50.8 mm diameter were hammered into the ground, cores were

extracted and transported to the laboratory covered in foil inside sealed bags. Accelerated solvent extraction

(ASE 200 Dionex) was used to extract the hydrocarbons in soils following manufacturer’s application Note

324 [29] for DRO and ERO and Note 313 for PAHs, both these methods are based on EPA method 3545. In

the extraction of DROs and EROs a weighed sample of was mixed with equal parts of drying agent

HYDROMATRIX and packed between washed sand and cellulose filters in the metal cells and placed in the

ASE, a 50:50 solvent mixture of hexane:acetone at a pressure of 1500 psi was applied using N 2 gas at an

oven a temperature of 200oC. The solvent extract was passed through a Bond Elut anhydrous Na2SO4 1GM

cartridges to remove any remaining water. The extract was filtered (0.45 µm Chromacol), 50 µl of nonane

was added. Heat and N2 were used to blow down the samples to 1 ml prior to injection in to the GC-MS.

PAHs extraction was similar but a 50:50 mixture of acetone:dichloromethane was used [30] at an oven

temperature of 100oC. The extract was dried using anhydrous Na2SO4 Bond Elut tubes. After concentration

down to 1 ml there was a further clean up stage using Bond Elut silica gel cartridges 500 MG to extract the

PAHs from the solvent (EPA Method 3630C) [31], this involved conditioning the cartridge with hexane then

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adding the sample and then 5 ml of hexane:dichloromethane (60:40) to elute the PAH’s, 50 µl of nonane was

added and samples blown down to 1 ml again before GCMS analysis.

2.2.5 GC-MS

A Varian 430GC with a VF-5 Column and a Varian 210- MS detector were used. The operational

specifications of the GC-MS were:

DRO/ERO: Initial temperature 50˚C for 1.5 min. Increase at rate of 15˚C/min to 300˚C in splitless mode.

Trap: 220˚C. Manifold: 80˚C. Transfer-line: 300˚C. Injection volume of sample: 1 µl. Injector temperature:

280˚C.

PAH’s: Injector temperature 250oC. Temperature ramp: initial temperature of 60˚C hold for 1 minute; then

increase to 150oC at a rate of 30oC/min; and then increase to 186 at a rate of 6oC/min; and finally increase to

280 at a rate of 4oC/min and hold for 20 minutes.

The DRO and ERO concentrations were calculated by baseline to baseline integration over the carbon ranges

and individual PAHs peaks were integrated and calibrated against standards.

3 Results and Discussion

Statistical analysis and graphical presentation of results was performed using Minitab 16. The variables

were tested for normality and where appropriate, if it showed a better approximation to normality, log

transformed data was used for statistical analysis.

3.1 General Water Quality

3.1.2 Water Quality in the Pond

Table 1 shows the median, minimum and maximum values of the water quality descriptors in the basins (B1.

B2) and river (WR) measured during monthly monitoring and provides a baseline of conditions in the

system. Upstream, the River Wallington passes through a built-up area and so receives other sources of

urban runoff, it is therefore not pristine with a BOD of up to 20 mg/l. AmmN concentrations in the pond

basins are lower than the river; while BOD and EC are of approximately similar values. This suggests that

the ponds will not have a significant impact on the oxygen balance of the receiving water. However the

ponds do have higher COD and turbidity than the river. There is a notable increase in COD between B1 and

B2 (154 mg/l compared to about 118 mg/l) and also smaller increases in TSS and BOD. The majority of

solids suspended in the water column were composed of volatile matter (B1 53% and B2 61%). The overall

COD:BOD ratio increases from about 9:1 in B1 to 19:1 in B2, suggesting that much of the accumulated

organic material not very biodegradable. COD:BOD ratios of 30:1 have been reported in other road runoff

studies, so this is not unusual [13]. This transformation between basins suggests that the nature of solids

changes within the system, which could be due to preferential transport or accumulations of plant derived

debris.

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Table 2: Water quality in the pond basins (B1, B2) and River (WR) (n=17)

Variable Statistics LocationMedian Min Max

BOD (mg/l) 11.6 2.2 17.9 B113.5 1.7 28.0 B28.1 1.1 20.8 WR

SBOD (mg/l) 8.2 0.8 13.1 B18.2 1.3 12.8 B24.3 0.2 12.7 WR

COD (mg/l) 118 30 541 B1154 17 832 B253 2 641 WR

SCOD (mg/l) 67 17 279 B175 7 148 B232 0 128 WR

TSS (mg/l) 28.8 6.4 74.0 B133.2 10.4 88.7 B217.6 2.4 55.3 WR

VSS (mg/l) 15.3 5.0 35.1 B120.1 6.0 78.0 B2

8 1.2 49.3 WRTurbidity

(NTU)14.5 2.0 60.6 B110.0 2.5 68.0 B24.5 2.0 61.0 WR

EC (µS/cm) 725 355 1853 B1594 337 1168 B2756 225 1054 WR

Amm-N (mg/l)

0.17 0.01 1.00 B10.11 0.00 0.47 B20.29 0.07 0.96 WR

pH 6.93 6.54 7.63 B16.88 6.61 7.25 B27.25 6.8 7.65 WR

3.1.1. General Water Quality Storm Events

The storm monitoring covered a range of events, but the lack of automatic monitoring data meant that rather

coarse descriptions have been used to characterise them (Table 1). Figure 3 shows the plots of COD, SS and

flow rate at the inlet to the pond (C) over the first 3 hours of the 7 storm events studied for general water

quality, There is a clear “first flush” phenomena as inlet water quality progressively improves in terms of

COD and TSS over storm events (Fig 3i and ii), this was also seen for BOD, VSS and turbidity (data not

shown). The initial rate of decay of TSS and COD over the first 15 mins was faster than over the later stages

and did not fit well to an exponential decay model, perhaps indicating that several process were interacting.

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There were significant different pollutant loadings between the storm events (LogBOD, LogCOD, LogTSS,

LogVSS, turbidity, AmmN: all ANOVA p>0.00). There was general trend for pollutant loads to increase

over the study period (Fig 3i and ii), but there is also a trend for increasing flow rates and velocities of the

storm runoff over time (Fig 3iii) which makes interpretation difficult. Increased influent pollutant loads have

been seen in other systems as they become established [9], but this system had been operating for 2 years

prior to the start of this study so this may not be the case. There was significant association between TSS at

the start of the storms and initial flow velocity which may suggest that increased pollutant transport at high

flows was an important factor (Log TSS = 16.6 Q(m3/s) + 2.17; n=6; r=0.88; p=0.000) as soluble pollutants

such as EC, did not have a significant association with flow rate. Although there were several significant

differences between pollutant loadings and the ADP and DP, none of these showed a clear trend, perhaps due

to the relatively small number of storm data points.

Figure 3: Plot of (i) TSS, (ii) COD and (iii) Flow Rate at the Pond Inlet (C) during 3 h after start of flow

during 7 Storm Events.

Figure 4 shows the inlet (C) concentrations of BOD, EC, TSS and COD plotted against effluent (G)

concentrations for the 4 storm events that generated outflow from the pond. Inlet BOD was often over 30

mg/l; this is high compared to other studies [8, 13]. TSS was also relatively high [32] with an inlet median

of 76 mg/l, but with a peak of over 1000 mg/l. The Pond provided effective reductions of BOD, TSS, COD

11

and EC (with median reductions of 57, 70, 62 and 40% respectively), with even higher reductions were seen

for other parameters (e.g. turbidity 77% - data not shown). Compared to other studies these represent good

treatment efficiencies for TSS and turbidity and moderate treatment efficiency for BOD and VSS [10].

Figure 4. Scatterplots of (i) BOD, (ii) EC, (iii) TSS and (iv) COD of the Pond Inflow (C) against Pond

Outflow (G) for the 4 Storm Events that generated outflow.

The quality of inflow appears to have a direct influence on the quality of outflow as all the variables in

Figure 4 have significant linear associations between the inflow and outflow. The regression equations, r

and p for these fits are shown in Table 2. The best fit (r=0.89) is for EC which may reflect the conservative

nature of many ions, which could be buffered by dilution or concentrated through processes such as

evapotranspiration. The lowest fit of these 4 variables is for TSS (r=0.40). TSS is composed of a range of

materials that may have different transport/sedimentation characteristics in the Pond. There was also an

increase in the proportion of TSS composed of VSS between the influent and effluent, rising from a median

of 42% (IQR=31-56) to 86% (IQR=71-90). This suggests that there is either preferential transport of lighter

organic solids or (more likely) that the effluent solids are composed of a high proportion of biogenic material

generated in the pond possibly being resuspended in storm flows. It was sometimes observed that outlet

VSS was higher than inflow, which may support this explanation.

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Table 2. Linear regression models fitted to the relationship between water quality of inflow and outflow to

the Pond.

Outlet (y) Inlet (x) Gradient (m) Intercept (c) r pECout ECin 0.765 - 41.9 0.89 <0.000Log BODout Log BODin - 0.531 1.18 0.71 <0.000,Log TSSout Log TSSin 0.294 0.844 0.40 0.022Log CODout Log CODin 0.590 0.446 0.61 <0.000

Pond processes and treatment mechanisms will affect different pollutants in different ways. There is

ongoing work to develop a Computational Fluid Dynamics model of the Pond to investigate the fate of

different pollutants to better understand the treatment processes.

3.2. Metal Concentrations

Concentrations of a range of metals were assessed in the water (dissolved and particulate) during storm

events and in two size fractions in sediments and settling solids.

3.2.1 Metals in Water: Storm Events

Table 3 shows the median concentrations of metals in the influent (C) and effluent of the ponds (G) during 7

Storm Events (No. 1, 2, 3, 4, 5, 6, 8) along with the range and % in the soluble form (<0.45 μm). Most metal

concentrations (Cu Cr, Pd, Rh and especially Ni) were lower than most other reported values for road runoff;

Zn concentration was similar to other studies and the high Ca concentration reflects the chalk-derived soil

present at the area [8]. The pond showed a similar wide range of metal removal percentages to other studies

[8, 32] However the large removals of Cu and Zn (>60%) indicate a significant overall reduction in toxicity

[9].

Table 3: Metal Concentrations in Water at the Pond Inlet (C) and Outlet (G)

Metal Median Range % Soluble Median Range % SolubleInfluent (n=7) Effluent (n=4)

Ca (mg/l) 41 6.6-149 92 33.5 19-41 93Ni (µg/l) 2.5 0.5-11.8 49 1.5 1.5-9.1 59Cu (µg/l) 72.5 12-194 59 23.5 9.2-66 65Zn (µg/l) 115 36-378 57 37 17-81 59Cr (µg/l) 1.15 0.32-4.9 61 0.875 0.44-1.22 81Pd (ng/l) 31 16-210 77 18 9.3-39 67Rh (ng/l) 2.9 1.6-17 54 1.6 1.1-2.9 89

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Most metals entering the system were associated with particulates, and at the inlet the partitioning between

solid and dissolved phases was similar to other studies [4, 8]. However there is an increase in the soluble

fraction of Ni, Cr, and Rh across the system. The soluble fraction of Ni and Cr at the outlet were

consequently much higher than other reported values (<50%) [11, 33]. As pH changed little, this may be

more to do with sorption characteristics than changes in solubility. This may relate to changes in particulate

composition as the sorption coefficient, Kd, is highly dependent on the type of solids [33, 34]. Despite the

metal concentrations being relatively low, increased solubility might be of concern as solubility is an

indicator of bioavailability [34]. If conditions in the ponds changed dramatically (e.g. pH or conductivity

shift, or if anoxic conditions developed), the metals could be desorbed or change their speciation in the

water, which could result in higher toxicity as the metals become more bioavailable.

Several metals had shared variation and had significant correlations. Zn behaved in a similar manner to Ni at

the inlet, in both the total and dissolved fractions (r=0.680, p=0.007; r=0.702, p=0.005). Cr was also

associated with Zn in both the total and dissolved fractions (r=0.890, p=0.000; r=0.608, p=0.021

respectively) while Ni and Pd only in the total fraction (r=0.632, p =0.015; r=0.612, p=0.02). Furthermore,

Rh was correlated with Ni (r=0.629, p=0.016), but only in the dissolved fraction indicating changes in

partitioning [24]. These correlations possibly indicate a similar source, probably vehicular activity [2, 3, 26].

3.2.2 Metals in Sediments

Figure 4 shows concentrations of Ni, Zn, Cr, and Cu in bed sediments (close to C and G) and in settling

solids from the sediment traps (B1=D+E combined and B2=F), all have been split between coarse (>63 µm)

and fine (<63µm) fractions. The settling solids represent the material settling through the water column,

while the bed sediments will include the pond soil as well as settled material. The settling solids in the traps

therefore had a much higher median volatile matter content (B1=25% and B2 30%) compared to the bed

sediments (In =12% and Out=15%), indicating a higher organic content.

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Figure 5: Median concentrations of Ni, Cu, Zn, and Cr in settling solids (B1 = D+E combined and B2 = F)

and bed sediment (close to points C and G) in the > and < 63 µm Size Fractions (n=17).

The metals in Figure 4 demonstrate different behaviour patterns in the pond systems. The settling solids had

lower values of Ni and Cr than the bed sediments, while Cu was higher in the settling solids than sediments.

Cu and Zn had much lower concentrations in sediments at the outlet of the pond compared to the inlet, while

Ni and Cr showed the opposite pattern. All metals have significant association with each other, the strongest

being Zn with Cu (r=0.923; p=0.000) in settling material. Zn and Cu also had the highest mass removal from

the water phase (Table 3) and highest concentrations on settling solids suggesting that that sedimentation was

the main removal mechanism. Other studies have reported the existence of correlations between metal

concentrations in sediments and organic or clay fractions [3]. This study has revealed a complex picture: Cu

in settling solids was associated with volatile matter (r=0.288, p=0.001), while Pd and Rh had a negative

association (r=-0.253; p=0.005 and r=-0.260 p=0.004 respectively). In addition, Cr and Ni were strongly

associated both in sediments (In – r=0.919, p=0.000; Out – r=0.860, p=0.001) and settling solids (r=0.882,

p=0.000). These different patterns and behaviours of metals in sediment and settling material suggest that

their transport, sorption and deposition are driven by different processes depending on their physico-

chemical properties. The sediment processes include ion exchange reactions or sorption and

diffusion/advection of solutes through the pore water [25]. Moreover, the elevated Ni and Cr concentrations

at the deposited sediment found at the outlet (Figure 4) suggest that, Ni and Cr bearing particles are

conveyed either from the inlet deposits by re-suspension and preferential transport (under specific flow

conditions) or the conditions in the pond cause enrichment of particles at the outlet [9]. Other studies have

15

also found elevated metal concentrations at the outlet (bed sediment) of different pond systems for Ni and Cr

[27, 28]. Generally, the metal concentrations (in both settling solids and bed sediment) were lower than

other studies [9, 12, 15] and the concentrations of Ni at the outlet are still negligible compared to the Soil

Guideline Value issued by the national Environment Agency of 130 mg/kg for the most sensitive residential

use. However metal concentrations have been shown to progressively increase over time in road runoff

detention ponds [13] so this may reflect the relatively early stages of the pond development.

3.3 Hydrocarbons

DRO, ERO and PAHs were examined in storm inflows, water passing through in the system and in soils and

sediments. DRO and ERO encompass a wide range of hydrocarbons with varying properties (e.g. solubility,

partitioning coefficients), the hexane extraction method also means they encompass naturally ocuring

compounds (hexane extractable material (HEM)). PAHs arise during the combustion of fossil fuels and

vehicle emissions [38]. Naphthalene (C10H8) and pyrene (C16H10) were selected as examples of the 18 PAHs

monitored as they were frequently measured at relatively high concentrations and had distinctly different

physico-chemical properties (e.g. napthelene - mw =128.2 g/mol; logKd = 3.1; water solubility = 31700 ug/l:

pyrene - mw = 202.2 g/mol; logKd = 4.8; water solubility = 135 ug/l [38].

3.3.1 Hydrocarbons in Water

Hydrocarbon concentrations in storm runoff at the swale inlet (A) are presented as line plots in Figure 5 as

concentrations over time after start of flow for 5 storm events. There are differences in “first flush” patterns

compared to the general water quality variables in Figure 3, but as these represent a different sub-set of storm

events direct comparison is not possible. Sampling logistics also meant that inflow was not measured for all

of these events so a comparison in a similar manner to Figure 2 is not possible.

Both DRO and ERO (Fig 5i and ii) had lower concentrations after 3 h. but overall the median ERO and DRO

concentrations did not vary greatly significantly with time. The variability of ERO tends to decrease over

time, perhaps as the higher C-number compounds in ERO are more likely to be associated with solids. The

potential for natural compounds to contribute to these fractions means that patterns may not be due to

pollution from road runoff.

16

Figure 6. Line plots of Hydrocarbon Concentrations at the Swale Inlet (A) against time after flow began for 5

Storm Events.

The PAH concentrations and ranges exhibit greater variation throughout the storm events (Fig 6iii and iv).

The peak concentrations and ranges are a similar to those reported in other road runoff studies, e.g.

naphthalene 0.073-4.79 µg/l [39] and pyrene 0.36–48 µg/l and 11-191 µg/l [39, 40]. Pyrene tends to have

highest values between 15 and 45 min after the start of storms, whereas naphthalene presents a more

irregular pattern, with lower overall concentrations and peaks at 45 and 90 min. Pyrene has a higher Kd, so

may possibly become attached to smaller organic particles in runoff following the initial peak flow.

There is a significant increase in the DRO and ERO of the

Figure 6: Note: an outlier of 26,232 µg/l DRO (i) has been omitted from for clarity from event 10.

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Figure 7 shows the hydrocarbons in the water passing through the system (n=22 A, n= 11 B and C, n=4 G).

The DRO and ERO tend to decrease down the swale, but increase significantly in the pond. This may again

be due to HEM from naturally occurring organic material. At the swale inlet there are median values of >2

µg/l naphthalene and >1 µ/l pyrene. Pyrene, like the other heavier PAHs, showed significant reductions

along the swale, this is likely due to filtration of particles. Low concentrations of pyrene were therefore seen

in the pond and none detected in the outflow. A slight reduction of naphthalene was observed along the

swale, due to higher solubility it appears to be transferred from the swale into the pond. Nevertheless, there

are lower concentrations of naphthalene at the outlet of the pond (<0.05 µg/l) compared to the inlet, possibly

caused by dilution, photolysis, volatilization or biodegradation during retention. A biodegradation half life

of 7 d has been estimated naphthalene in polluted aquatic systems [41] so significant removals could occur

between storm loadings.

Fig 7

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3.3.2 Hydrocarbons in soils and sediment

Fig 8

The concentrations of hydrocarbons in soil and sediments are shown in Figure 8 from >20 sampling

occasions. All the concentrations decrease along the swale-pond system. Concentrations of ERO fell from a

median of 18.9 to 0 µg/g and DRO from 34.9 to 10.9 µg/g between point A to G. Despite having lower

soluble concentrations, the higher sorption affinity of pyrene over naphthalene was shown by the high pyrene

concentrations in soils and sediments, especially in the early parts of the system. Median pyrene

concentrations were 850 µg/g in the soil at the swale inlet (A) falling to 5.7 µg/g in sediments at the end of

the pond (G), naphalene concentrations were 82.6 (A) and 30.2 µg/g (G) at these points. Similar

concentrations of 23-130 µg/g pyrene in sediment have been seen sediments affected by road runoff [40].

The process of sorption and sediment accumulation therefore appears to be the predominant removal

mechanism for hydrophobic PAHs.

4 Concluding Remarks

The swale and pond system provided effective treatment of the highly variable inflow of pollutants from

storm events but generally pollutant loads were low to average compared to other studies. The small size of

the system allowed for intensive monitoring of a range of pollutants and system components. The

multivariate monitoring strategy allowed for treatment processes and associations to be investigated. This

19

approach is now being extended to more intensely trafficked parts of the trunk road network to assess the

impact and fate of higher pollutant loads.

The flows patterns at Waterlooville are also being modelled using Computer Fluid Dynamics to increase the

understanding of the hydraulics and sedimentation patterns in vegetated systems.

The hexane extraction of DRO and ERO recovered naturally occuring componds which made interpretation

of hydrocarbon fate difficult. This means that these broad range organic groupings are not suitable for

assessing pollutant removal, especially in vegetated systems. PAHs are therefore much more appropriate as

tracers of pollution in such systems as they give a better understanding of the fate of automobile emissions.

PAHs behaved in a relatively straightforward way in this small linear system; more complex fates and

possible release from sediments have been reported in larger systems [38]. In a similar way selecting metals

specifically associated with automobile emissions may be more apropriate than considering a broad range of

metals which occur in local soils and sediments. However high accumulations of heavier PAHs may also be

of more concern than heavy metals when considering the toxicity and disposal of sediments from ponds.

The differing patterns and behaviours of different pollutants highlights the difficulties in designing vegetated

ponds for pollutant removal, as different mechanisms remove different pollutants. There may even be

contradictory design demands in promoting different processes and some pollutants may need prioritization.

When combined with site specific factors, such as pond shape, geology, hydrology and road drainage design,

this means there is need for further study to refine the design codes for pollutant removal in vegetated SuDS

for road runoff control.

Acknowledgements

The Authors would like to thank Mayer Brown Ltd (P. Stewart and K. Chaney) for facilitating access to the

site and supplying traffic data. This study follows on from a KTP study of the site funded by Mayer Brown

Ltd and the Technology Strategy Board.

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Figure Titles

Figure 7: Box Plot of Hydrocarbon Concentrations in Water Passing Through the Pond Systems. The Box Plots Show the Inter-Quartile Range with the Median shown as the Horizontal Line Across the Boxes. Outliers are shown by Stars and Whiskers show the Upper (Q1 1.5 [Q3 Q1]) and Lower Limits (Q1 1.5 [Q3 Q1]). Note: outliers of 6438 µg/l DRO and 48 µg/l Pyrene have been omitted at the inlet for clarity.

Figure 8: Box Plot of Hydrocarbon Concentrations in Soils and Sediments. The Box Plots Show the Inter-

Quartile Range with the Median shown as the Horizontal Line Across the Boxes. Outliers are shown by Stars

and Whiskers show the Upper (Q1 1.5 [Q3 Q1]) and Lower Limits (Q1 1.5 [Q3 Q1]). Note: outliers of

361 µg/g DRO (A), 67,798 µg/g Pyrene (C) plus 1468 µg/g and 2328 µg/g (A and C) Napthalene have been

omitted for clarity.

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