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Environmental Pollution 158 (2010) 2541e2545

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Environmental Pollution

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Comparison of heavy metal loads in stormwater runoff from major and minorurban roads using pollutant yield rating curves

Brett Davis*, Gavin BirchSchool of Geosciences, University of Sydney, New South Wales 2006, Australia

A simple method for representing data onroad runoff pollution allows cdatabase.

omparisons among dissimilar sites and could form the basis for a pollution

a r t i c l e i n f o

Article history:Received 19 August 2009Received in revised form28 March 2010Accepted 26 May 2010

Keywords:RoadsRunoffStormwaterPollutionFirst flushHeavy metalsPollutant loads

* Corresponding author.E-mail address: b.davis@usyd.edu.au (B. Davis).

0269-7491/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.envpol.2010.05.021

a b s t r a c t

Trace metal export by stormwater runoff from a major road and local street in urban Sydney, Australia, iscompared using pollutant yield rating curves derived from intensive sampling data. The event loads ofcopper, lead and zinc are well approximated by logarithmic relationships with respect to total eventdischarge owing to the reliable appearance of a first flush in pollutant mass loading from urban roads.Comparisons of the yield rating curves for these three metals show that copper and zinc export ratesfrom the local street are comparable with that of the major road, while lead export from the local street ismuch higher, despite a 45-fold difference in traffic volume. The yield rating curve approach allowsproblematic environmental data to be presented in a simple yet meaningful manner with less infor-mation loss.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Stormwater runoff from urban catchments is widely recognisedas a major source of environmental contaminants (e.g. Birch andTaylor, 2002; US EPA, 1983), and the accumulation of stormwater-related pollutants in receiving waterways has been shown innumerous studies to have a severe detrimental impact on theecosystem health of affected aquatic environments (Brown andPeake, 2006; McCready et al., 2004; Pitt et al., 1995). In highlyurbanised catchments, road surfaces can typically constitute up to22% of total catchment area, and contribute up to 26% of total runoffvolumes with commensurate contributions to total ‘heavy’ metal(e.g. Cu, Pb, Zn) loads of 19e40% (Davis and Birch, 2009). Roadrunoff is second only to residential runoff in terms of runoffgeneration and pollutant export (Davis and Birch, 2009), andtherefore requires priority consideration in the development of anyintegrated stormwater management plan as a non-point source ofurban pollution. However, complicating the issue of catchment-wide remediation and responsibility for road-derived pollution isthe fact that roads within a given area often fall under multiple

All rights reserved.

jurisdictions, such as state and local governments. It is thereforeimportant to establish the relative contribution of roads of differenttypes and ownership.

In Sydney, Australia, state roads support the highest trafficvolumes but generally comprise a small fraction of the total roadlength in a catchment. In contrast, roads under local governmentownership comprise the vast majority of road length, but supportmuch lower traffic volumes. Information on the relative pollutantcontributions of major state roads and minor local roads would bevaluable for guiding pollution mitigation efforts and for developingcost-effective stormwater management strategies.

To obtain such data, it appears relatively straightforward toconduct a field investigation of relative stormwater pollutant loadsfrom different roads to construct a model of road-derived pollutantexport. However, it is in practice difficult to compare pollutantloadings from different stretches of road directly due to the stronginfluence of physical and structural factors such as the breakingregime, road grade, proximity to intersections, traffic lights, bends,and side entries and exits, without any apparent systematic rela-tionship among these parameters (Brezonik and Stadelmann,2002). The spatial and temporal variability of meteorologicalconditions, such as wind intensity and direction, and rainfallintensity and duration, can also affect the accumulation of partic-ulate pollutants and the subsequent mobilisation of accumulated

B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e25452542

material, processes that interact with the orientation and aspect ofthe stretch of road under consideration. Due to the large numberand high complexity of factors affecting the spatial and temporalvariation in road runoff composition and volume, in addition to theknown difficulties in obtaining representative time-series andsampling data for stormwater in general (Deletic and Maksimovic,1998), direct comparisons among different road sites are highlyunreliable, and it is not expected that meaningful results could beobtained by such an approach. Therefore, to extract useful infor-mation from a comparative road runoff study, an alternativeapproach that obviates many of the above confounding factorsshould be developed. Such an alternative methodology is proposedin the present study, focussing on suspended solids and the heavymetals copper, lead and zinc as representative examples ofpollutants of concern in urban waterways (Birch and Taylor, 2002;Jartun et al., 2008). It is shown that a ‘pollutant yield rating curve’can be constructed for a given site based on accumulated data ofpollutant loading per event discharge, and that these ratings curvescan be compared among different sites to provide a clear andunderstandable picture of pollutant export from roadways ofdifferent types and situations.

Heavy metals in road runoff have been studied extensively, butthe results of such studies have generally been reported in terms ofa single parameter, the event mean concentration (EMC), broadlydefined as the total pollutant load divided by the total discharge fora runoff event. The reported EMCs fall within a well-establishedorder-of-magnitude range (Fletcher et al., 2004; Kayhanian et al.,2003; Ma et al., 2009), and are characterised by pronounced vari-ability and lack of systematic relationships with environmentalparameters such as traffic volume (Brezonik and Stadelmann, 2002;Li and Barrett, 2008; Waara and Farm, 2008). The use of a singlevalue to describe the complex relationship between instantaneousflow and pollutant concentration and the variation in thesebehaviours with the size of the runoff event therefore means thatthere is little value to comparing EMC data from different sites forthe purpose of determining comparative loading rates.

Early studies byBourcier andHindin (1979), Harrison andWilson(1985) and Hewitt and Rashed (1990), and more recent studies byWu et al. (1998) and Barbosa and Hvitved-Jacobsen (1999), whileconducted in the era of leaded fuel, explored variousmethodologiesfor sampling road runoff and calculating pollutant loads. However,these studies are also based on the premise of multiplying totaldischarge by a single concentration parameter (i.e. EMC), ignoringthe complexity of the floweload relationship contained in the fielddata. This remains true for recent studies attempting more sophis-ticated sampling regimes and statistical analyses (Furami et al.,2002; Kayhanian et al., 2003), where no systematic relationshipscould be found and the calculated EMCs fall within the establishedliterature ranges. Kim et al. (2005a,b) proposed a predictive modelfor calculating EMCs at different stages of storm events based onextensive sampling data. The extreme variability in EMCs amongstorm events and in pollutant concentration over the course of anevent are clearly demonstrated in that study, and the inadequacy ofexisting approaches to calculating EMCs and characterisingpollutant loads were discussed. The solution of Kim et al. however,requires the calibration of four variables against sampling data, oneof which is an initial condition related to antecedent dry period andthe surface accumulation of pollutants, which is a parameter thatwill differ for each event.

The studies of Kim et al. (2005a,b) and others (Lee et al., 2005;Mangani et al., 2005) have provided extensive evidence of a first-flush characteristic in road runoff. The first flush, the period ofelevated pollutant concentration at the start of an event resulting inthe transport of more pollutant mass in the first half of an eventthan in the second half, is an important feature of road runoff that

renders the use of EMCs unreliable as a means of calculatingpollutant loads for all magnitudes of total discharge. Progressingfrom this establishment of the first-flush phenomenon for roads,the present study, based on sampling data obtained at two urbanroad sites over a period of eight months, presents a more usefulmodel for calculation of annualised pollutant loads and for inter-comparison of road catchments.

2. Methods

2.1. Road sites

Road runoff was sampled between August 2007 and April 2008 in roadside drainpits on a major high-volume road (Parramatta Road) with average annual dailytraffic (AADT) of 84 500 veh/day (NSW RTA, 2002), and on a typical local street(Queen Street) with AADT of 2000 veh/day (measured data). The two stretches ofroad were bounded by concrete curbs, had similar slopes and proximity to roadbend, had comparable drainage areas (Queen St, ca. 860 m2; Parramatta Rd, ca.1095 m2), and were separated by a distance of 700 m. Both stretches of road werefree of extraneous (i.e. residential) drainage inputs into the road gutter, and the drainpits in which sampling was performed were free-flowing with a single large-diameter outlet pipe and no other inputs. Situational similarity of the stretches ofroad is not necessary for application of the present model, but eliminating as manyconfounding parameters as possible allows for a more illustrative demonstration ofthe descriptive power of the pollutant yield rating curve approach. Of moreimportance is the need to sample only runoff from the road surface without inter-ference that could modulate the first-flush character of runoff discharge, such asinput from non-road surfaces or loss of runoff to pervious boundaries.

2.2. Sampling

An autosampler (Sigma 900 MAX, Hach, USA) was installed at each site, andrunoff was sampled in the concrete outlet pipe of the adjacent drain pit. The waterlevel in the outlet pipe was monitored using a pressure transducer, and instanta-neous flowwas calculated using aManning equationwith appropriate parameters ofslope (4% in both cases based on-road grade), roughness (n ¼ 0.015, float-finishconcrete channel), and geometry (0.4 m-diameter pipe), with intermittent fieldverification using the Doppler velocity sensor integrated into the depth transducer.The velocity measurements were intermittent due to the susceptibility of the probeto fouling upon accumulation of sediment or litter. Samples were taken at pre-defined time intervals upon the detection of flow, starting with an interval of 6 minand doubling every four intervals.

2.3. Sample analysis

Samples were collected in 1 L polyethylene bottles cleaned in advance bysoaking overnight in 10% HNO3, rinsing twice with ultrapure water, drying tocomplete dryness in a clean oven at 60 �C, and capping in the laboratory. In the field,bottles were loaded into the autosampler (24 bottles) and uncapped prior to sealingthe distributor of the apparatus. Samples were recovered as soon as possiblefollowing runoff events (within 24e48 h). Upon returning to the laboratory, sampleswere agitated and a homogenised fraction extracted for other analyses. Theremaining sample in the collection vessel was then acidified with 0.5% HNO3 andstored at room temperature for at least one week. Duplicate 10 mL dissolved-phasesubsamples (filtered at 0.45 mm) were then taken directly from the bottle aftervigorous shaking. The remaining sample was pre-filtered through a 250 mm nylonmesh (for normalisation with respect to maximum particle size) and then througha pre-weighed and -dried 0.45 mm membrane filter (ungridded cellulose nitrate)using a vacuum filtration manifold. The filter papers bearing the filtrand were driedin an oven at 60 �C and re-weighed, then digested in batch fashion using aqua regia(1:1 HCl:HNO3) at 120 �C. The digested sample was made up to 15mLwith ultrapurewater, from which a 10 mL 0.45 mm-filtered subsample was taken for analysis. Allsubsamples were refrigerated until analysis.

Analyses for a range of metals were performed by inductively coupled plasmaatomic emission spectroscopy (ICP-AES) using a Varian Axial ICP-AES instrument. Ofthe trace metals of possible interest, only copper, lead and zinc were reliablydetected in most samples. Total concentrations were calculated as the sum of thedissolved- and particulate-phase (post-digestion) concentrations. The precision ofICP-AES analyses was evaluated by tabulating the relative standard deviations(RSDs) of determined concentrations for each sample (3 replicates). Replicateanalyses with RSD values of greater than 20% were excluded from further consid-eration as being significantly beyond the reliable detection limit of the instrument,and analyses with RSD values in the range 5e20% were treated with additional carein subsequent calculations (i.e. not included in calculations of statistics but displayedon graphs). All copper and zinc analyses and 67% of lead analyses for digestedparticulate samples had RSDs of less than 5% (i.e. in the reliable analytical range forthe instrument). For the digested dissolved-phase samples, 93% of copper, 97% of

B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545 2543

zinc and 21% of lead analyses had analytical RSDs of less than 5%. The lowestconcentrations detected at an RSD of 5% or better were 3.16, 22.43 and 1.21 mg/Lcopper, lead and zinc, respectively.

Field and laboratory blanks (ultrapure water) were maintained throughout thesampling, preparation and analytical steps. No issues with contamination weredetected in any of the 39 laboratory and field blanks, with only three field blanksexhibiting measurable levels of themetals of interest (three cases of Zn equivalent toless than 1% of the lowest-concentration contemporaneous sample). Filtration anddigestion blanks were free of lead, and copper and zinc in these blank samples, ifdetected, were present at less than 1.2% (Cu) and 3.8% (Zn) of that for the lowest-concentration contemporaneous sample.

The NIST International Reference Material SRM-1648a (Urban ParticulateMatter) was employed as a procedural and analytical reference material for roadrunoff samples. Overall recovery from this highly contaminated sample by heat-assisted aqua regia digestion was 89% for copper, 97% for lead and 89% for zinc, withRSDs of 4.5%, 3.6% and 2.6%, respectively, over 13 reference digestions.

2.4. Data processing

Of the numerous runoff events sampled at each site over the eight months ofsampler deployment, only eight (Queen St) and nine (Parramatta Rd) events wereadequately sampled to allow load calculations to be attempted, and only four (QueenSt) and six (Parramatta Rd) events are considered to have been sufficiently wellsampled with respect to the flow record to afford reliable loading estimates. Thetime series of flow and pollutant concentration were upsampled to 1 min intervalsfrom variable-interval data and sampling times, and total loads per event werecalculated by linear interpolation of the determined instantaneous concentrations atthis 1 min resolution.

3. Results and discussion

3.1. Runoff events and pollution loads

Graphs showing the variation in water depth and pollutantconcentration over the course of the runoff events at each site areshown in the supplementary data (Figs S1 and S2), and the totalpollutant loading calculations for the same events are listed inTable 1. Event mean concentrations calculated by dividing thelinearly interpolated load by the total discharge are also reported inTable 1 for comparison with published data. In all but one of thesampling runs triggered at the onset of a rainfall event, a distinctfirst flush in metal export is apparent (see Figs. 2, S1 and S2).

3.2. Derivation of pollutant yield rating curves

The events for which samples were taken simultaneously atboth of the present road sites could not be correlated directly in

Table 1Calculated total event loads and event mean concentrations.

Event Total flow (L) Total load (mg) EMC (mg/L)

Cu Pb Zn Cu Pb Zn

Queen St20070819a 7944 425 523 1071 0.0535 0.0659 0.134820070820 3560 28 e 130 0.0077 e 0.036420070920a 12304 697 1987 3630 0.0567 0.1615 0.295020071126 16227 219 e 737 0.0135 e 0.045420071206a 45370 1791 8974 10086 0.0395 0.1978 0.222320080131a 11050 566 2001 3144 0.0512 0.1811 0.284620080206 17260 63 e 234 0.0036 e 0.013520080406 8128 1150 2489 5642 0.1415 0.3062 0.6941

Parramatta Rd20070819a 2331 155 76 540 0.0666 0.0325 0.231820070920a 2707 430 181 1350 0.1590 0.0667 0.498820071103a 106690 6132 980 17630 0.0575 0.0092 0.165220071206a 21466 1971 1316 6830 0.0918 0.0613 0.318220080118 4541 198 81 711 0.0436 0.0179 0.156520080202 38420 8194 3382 20789 0.2133 0.0880 0.541120080206a 2495 170 67 583 0.0683 0.0270 0.233520080406a 7059 992 495 4481 0.1405 0.0702 0.6348

a Reliable estimate.

a meaningful way due to dissimilarities in runoff depth andhydrograph dynamics related to differences in meteorologicalconditions, even over the relatively short distance separating thetwo sites, demonstrating the difficulty in acquiring directlycomparable data at multiple sites. However, the loadings calculatedfor the events considered to be sufficiently well sampled atappropriate times on the hydrograph to afford reliable loadingestimates are well explained by a logarithmic relationship withrespect to total discharge (Fig. 1). The explanatory power andsignificance of these relationships are very high for both road sites(r2 > 0.91, p < 0.01), even with relatively few data pairs. The fit toa logarithmic relationship is consistent with the regular appearanceof a first flush in the mass/discharge relationship (Fig. 2).

The pollutant yield rating curves given by these logarithmicrelationships for the two roads, normalised with respect to rainfalldepth and road area, are compared in Fig. 3. These curves allow thepollutant accumulation/export behaviour of the two stretches ofroad to be compared over a range of discharge volumes withoutrequiring the sampling to be performed simultaneously.

It can be observed from this comparison that although therelative pollutant yields of copper and zinc are lower for the localstreet than for the major road, the difference is quite small giventhe 45-fold difference in traffic volume. The difference is also muchsmaller than the ratio of atmospheric deposition at 1 m from theroadside determined for the same two sites in a companionatmospheric deposition study (Davis and Birch, in press). The leadyield on the local street also far exceeds that for the major road. Thepollutant load available for runoff from the major road thereforeappears to be substantially reduced from what might have other-wise been expected based on traffic volumes and atmosphericdeposition to the roadside. As traffic on the major road passes veryclose to the curb, whereas the curb on the local street is set 1e2 mback from the path of traffic, the present observations may suggest

Fig. 1. Cumulative mass/discharge curves for the sampled events on (a) Queen St and(b) Parramatta Rd. Traces plotting above the 1:1 line (in the white region) indicate theexistence of a first flush.

Fig. 2. Relationship between total event loads of copper, lead, and zinc, and total eventdischarge for sampled events on (a) Queen St and (b) Parramatta Rd. Open symbolsdenote data for unreliable events excluded from statistics. Lines plotting above theshaded area indicate a first flush in pollutant export.

Fig. 4. Heavy metal vs. total suspended solids (TSS) concentrations for road runofffrom (a) Queen St and (b) Parramatta Rd. Open symbols denote outlier data excludedfrom statistics, solid line denotes the geometric mean of the lognormal distribution ofdata, and dashed lines denote the first standard-deviation limits in log-transformedunits.

B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e25452544

that particulates on the major road are remobilized right out to theedge of the gutter and subsequently dispersed beyond the curb.

The lack of a consistent relationship between traffic volume andrunoff-entrained heavy metal export has been observed in manyprevious studies (Li and Barrett, 2008; Waara and Farm, 2008) andhas generally been explained as due to differences in drivingregimes (Hamilton et al., 1984; Li and Barrett, 2008). The results ofthe present study suggest that proximity of passing traffic to thecurb may be a major factor determining the mass of pollutant pervehicle that remains on the road to be entrained in runoff duringrainfall. The high yield rate for lead on the local street, on the otherhand, is suggestive of a substantial input that is not present for themajor road. Relatively high rates of atmospheric lead depositionwere identified in the residential area in the vicinity of Queen St aspart of a companion sampling survey (Davis and Birch, in press), buton-road lead sources such as dislodged wheel weights, whichwould be quickly ejected from the major road by vehicle passage, islikely to contribute to the high loads of lead observed for runofffrom this local street (Murakami et al., 2007; Root, 2000). Furtherinvestigation of the source(s) of on-road lead is therefore necessary.The present observations nevertheless indicate that local factors

Fig. 3. Comparison of heavy metal loading rates (in yield per unit road area) withrespect to rainfall depth between Queen St (dashed line) and Parramatta Rd (solid line).

are of considerable importance in the development of appropriateremedial activities for any particular stretch of road.

The comparative method employed in this analysis lends itselfto application for any combination of appropriate sites (i.e. drainingcurb-bounded, impervious catchments), as representative dataappear to accord well with a logarithmic relationship to totaldischarge. This approach does not require that the data be obtainedsimultaneously at multiple sites, only that the analysis shouldinclude only qualified data that is confirmed to be representative ofthe target event.

3.3. Solid-phase concentrations

The relationship between heavy metal concentration and totalsuspended solids concentration (i.e. the solid-phase metalconcentration) is shown in Fig. 4. Lognormal distributions werefitted to these data using a maximum likelihood algorithm, and thegeometric mean and upper/lower one-standard-deviation limits(�s) are indicated on the graphs and listed in Table 2.

For all of the present metals (Cu, Pb, Zn), the relationship withtotal suspended solids is very well defined, with r2 values for thelinear regression exceeding 0.70 (p< 0.01, n¼ 55e60) for the majorroad and 0.86 (p < 0.01, n ¼ 38e45) for the local street. This could

Table 2Metal concentrations in road-derived suspended sediment.

Concentration (mg/kg)

Cu Pb Zn

Queen St�1s 288 511 1285m 474 870 2008þ1s 781 1482 3136

Atmospheric depositiona 492 781 3900

Parramatta Rd�1s 715 347 2574m 1013 560 3522þ1s 1437 901 4821

Atmospheric depositiona 862 364 4617

a Companion study (Davis and Birch, in press).

B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545 2545

allow runoff samples to be analysed using suspended solids asa proxy for these metals with a considerable reduction in analyticalcost.

The summarised results of an atmospheric deposition studyconducted at the same sites over the same period as the runoffsampling study (Davis and Birch, in press) are also shown forcomparison in Table 2. Atmospherically deposited particulateswere collected at the same site as runoff sampling, 1 m from theroadside. Except for zinc on the local street, the solid-phaseconcentrations correspond very closely to those found for atmo-spherically deposited particulates. A similarly strong relationshipbetween suspended solids and these metals has been noted atother road sites (Desta et al., 2007). These results confirm that theparticulatematter deposited on roads and dispersed to the roadsidehas a consistent chemical composition with respect to copper, leadand zinc.

4. Conclusions

The pollutant yield rating curve approach provides a usefulmeans of presenting problematic environmental data in a simpleyet meaningful manner with less information loss than the routineuse of EMCs for site characterisation. This method was demon-strated in the present study to be of considerable utility for thecomparison of heavy metal loadings in runoff from two roads withmarkedly different traffic volumes, allowing intercomparison ofroad runoff characteristics without requiring simultaneouslyacquired data. Using these pollutant yield rating curves, it wasshown that runoff from the major road is not as loaded withpollutants as expected, attributable to cleaning of the road surfaceby the passage of traffic out to the very edge of the roadway. Thepollutant loads exported from the local street are only marginallylower for copper and zinc, but many times higher for lead. Althoughthese dissimilar conditions of surface accumulation and dispersalpreclude meaningful extrapolation of the present results to thewider catchment, the observation of such effects is important withrespect to the design of remediation strategies for reducingpollutant loads from roads. For example, drainage from the presentstretch of major road has comparable remediation priority to thatfrom the local street, despite the 45-fold difference in trafficvolume, and in terms of lead, the local road appears to be ofconsiderably greater concern.

The present study also demonstrated the well-constrainedtrace metal composition of particulate material entrained in runoffand deposited at the roadside. This character could allowcomparative pollutant export from major and minor roads to beconducted based on measurements of total suspended solidsrather than analyses of heavy metals at trace concentrations. Thetotal solid-phase concentrations of copper, lead and zinc are alsolargely similar to those of atmospherically deposited particulatesobtained at the same sites. A sampling campaign combining sus-pended solids sampling or monitoring, periodic atmosphericsampling for determination of nominal metal concentrations, andthe present pollutant yield rating curve analysis could be aninexpensive, simple and reliable approach for constructing a well-populated catchment-wide database of trace metal loadings byroad type or point of runoff discharge for the purposes of reme-diation planning.

Acknowledgements

This study was supported by an Australian Postgraduate Award(Industry) from the Australian Research Council (No. LP0455486).

Appendix. Supplementary material

Supplementary data associated with this article can be found inthe online version at doi:10.1016/j.envpol.2010.05.021.

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