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Comparison of heavy metal loads in stormwater runoff from major and minor urban roads using pollutant yield rating curves
Environmental Pollution 158 (2010) 2541e2545
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Comparison of heavy metal loads in stormwater runoff from major and minor urban roads using pollutant yield rating curves Brett Davis*, Gavin Birch School of Geosciences, University of Sydney, New South Wales 2006, Australia
A simple method for representing data onroad runoff pollution allows comparisons among dissimilar sites and could form the basis for a pollution database.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 August 2009 Received in revised form 28 March 2010 Accepted 26 May 2010
Trace metal export by stormwater runoff from a major road and local street in urban Sydney, Australia, is compared using pollutant yield rating curves derived from intensive sampling data. The event loads of copper, lead and zinc are well approximated by logarithmic relationships with respect to total event discharge 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 rates from the local street are comparable with that of the major road, while lead export from the local street is much higher, despite a 45-fold difference in traffic volume. The yield rating curve approach allows problematic environmental data to be presented in a simple yet meaningful manner with less information loss. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Roads Runoff Stormwater Pollution First flush Heavy metals Pollutant loads
1. Introduction Stormwater runoff from urban catchments is widely recognised as a major source of environmental contaminants (e.g. Birch and Taylor, 2002; US EPA, 1983), and the accumulation of stormwaterrelated pollutants in receiving waterways has been shown in numerous studies to have a severe detrimental impact on the ecosystem health of affected aquatic environments (Brown and Peake, 2006; McCready et al., 2004; Pitt et al., 1995). In highly urbanised catchments, road surfaces can typically constitute up to 22% of total catchment area, and contribute up to 26% of total runoff volumes with commensurate contributions to total ‘heavy’ metal (e.g. Cu, Pb, Zn) loads of 19e40% (Davis and Birch, 2009). Road runoff is second only to residential runoff in terms of runoff generation and pollutant export (Davis and Birch, 2009), and therefore requires priority consideration in the development of any integrated stormwater management plan as a non-point source of urban pollution. However, complicating the issue of catchmentwide remediation and responsibility for road-derived pollution is the fact that roads within a given area often fall under multiple
* Corresponding author. E-mail address: [email protected] (B. Davis). 0269-7491/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2010.05.021
jurisdictions, such as state and local governments. It is therefore important to establish the relative contribution of roads of different types and ownership. In Sydney, Australia, state roads support the highest traffic volumes but generally comprise a small fraction of the total road length in a catchment. In contrast, roads under local government ownership comprise the vast majority of road length, but support much lower traffic volumes. Information on the relative pollutant contributions of major state roads and minor local roads would be valuable for guiding pollution mitigation efforts and for developing cost-effective stormwater management strategies. To obtain such data, it appears relatively straightforward to conduct a field investigation of relative stormwater pollutant loads from different roads to construct a model of road-derived pollutant export. However, it is in practice difficult to compare pollutant loadings from different stretches of road directly due to the strong influence of physical and structural factors such as the breaking regime, road grade, proximity to intersections, traffic lights, bends, and side entries and exits, without any apparent systematic relationship among these parameters (Brezonik and Stadelmann, 2002). The spatial and temporal variability of meteorological conditions, such as wind intensity and direction, and rainfall intensity and duration, can also affect the accumulation of particulate pollutants and the subsequent mobilisation of accumulated
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material, processes that interact with the orientation and aspect of the stretch of road under consideration. Due to the large number and high complexity of factors affecting the spatial and temporal variation in road runoff composition and volume, in addition to the known difficulties in obtaining representative time-series and sampling data for stormwater in general (Deletic and Maksimovic, 1998), direct comparisons among different road sites are highly unreliable, and it is not expected that meaningful results could be obtained by such an approach. Therefore, to extract useful information from a comparative road runoff study, an alternative approach that obviates many of the above confounding factors should be developed. Such an alternative methodology is proposed in the present study, focussing on suspended solids and the heavy metals copper, lead and zinc as representative examples of pollutants 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 of pollutant loading per event discharge, and that these ratings curves can be compared among different sites to provide a clear and understandable picture of pollutant export from roadways of different types and situations. Heavy metals in road runoff have been studied extensively, but the results of such studies have generally been reported in terms of a single parameter, the event mean concentration (EMC), broadly defined as the total pollutant load divided by the total discharge for a runoff event. The reported EMCs fall within a well-established order-of-magnitude range (Fletcher et al., 2004; Kayhanian et al., 2003; Ma et al., 2009), and are characterised by pronounced variability and lack of systematic relationships with environmental parameters such as traffic volume (Brezonik and Stadelmann, 2002; Li and Barrett, 2008; Waara and Farm, 2008). The use of a single value to describe the complex relationship between instantaneous flow and pollutant concentration and the variation in these behaviours with the size of the runoff event therefore means that there is little value to comparing EMC data from different sites for the purpose of determining comparative loading rates. Early studies by Bourcier and Hindin (1979), Harrison and Wilson (1985) and Hewitt and Rashed (1990), and more recent studies by Wu et al. (1998) and Barbosa and Hvitved-Jacobsen (1999), while conducted in the era of leaded fuel, explored various methodologies for sampling road runoff and calculating pollutant loads. However, these studies are also based on the premise of multiplying total discharge by a single concentration parameter (i.e. EMC), ignoring the complexity of the floweload relationship contained in the field data. This remains true for recent studies attempting more sophisticated sampling regimes and statistical analyses (Furami et al., 2002; Kayhanian et al., 2003), where no systematic relationships could be found and the calculated EMCs fall within the established literature ranges. Kim et al. (2005a,b) proposed a predictive model for calculating EMCs at different stages of storm events based on extensive sampling data. The extreme variability in EMCs among storm events and in pollutant concentration over the course of an event are clearly demonstrated in that study, and the inadequacy of existing approaches to calculating EMCs and characterising pollutant loads were discussed. The solution of Kim et al. however, requires the calibration of four variables against sampling data, one of which is an initial condition related to antecedent dry period and the surface accumulation of pollutants, which is a parameter that will 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 firstflush characteristic in road runoff. The first flush, the period of elevated pollutant concentration at the start of an event resulting in the transport of more pollutant mass in the first half of an event than in the second half, is an important feature of road runoff that
renders the use of EMCs unreliable as a means of calculating pollutant loads for all magnitudes of total discharge. Progressing from this establishment of the first-flush phenomenon for roads, the present study, based on sampling data obtained at two urban road sites over a period of eight months, presents a more useful model for calculation of annualised pollutant loads and for intercomparison of road catchments. 2. Methods 2.1. Road sites Road runoff was sampled between August 2007 and April 2008 in roadside drain pits on a major high-volume road (Parramatta Road) with average annual daily traffic (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 of road were bounded by concrete curbs, had similar slopes and proximity to road bend, 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 were free of extraneous (i.e. residential) drainage inputs into the road gutter, and the drain pits in which sampling was performed were free-flowing with a single largediameter outlet pipe and no other inputs. Situational similarity of the stretches of road is not necessary for application of the present model, but eliminating as many confounding parameters as possible allows for a more illustrative demonstration of the descriptive power of the pollutant yield rating curve approach. Of more importance is the need to sample only runoff from the road surface without interference that could modulate the first-flush character of runoff discharge, such as input 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, and runoff was sampled in the concrete outlet pipe of the adjacent drain pit. The water level in the outlet pipe was monitored using a pressure transducer, and instantaneous flow was calculated using a Manning equation with appropriate parameters of slope (4% in both cases based on-road grade), roughness (n ¼ 0.015, float-finish concrete channel), and geometry (0.4 m-diameter pipe), with intermittent field verification using the Doppler velocity sensor integrated into the depth transducer. The velocity measurements were intermittent due to the susceptibility of the probe to fouling upon accumulation of sediment or litter. Samples were taken at predefined time intervals upon the detection of flow, starting with an interval of 6 min and doubling every four intervals. 2.3. Sample analysis Samples were collected in 1 L polyethylene bottles cleaned in advance by soaking overnight in 10% HNO3, rinsing twice with ultrapure water, drying to complete 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 sealing the distributor of the apparatus. Samples were recovered as soon as possible following runoff events (within 24e48 h). Upon returning to the laboratory, samples were agitated and a homogenised fraction extracted for other analyses. The remaining sample in the collection vessel was then acidified with 0.5% HNO3 and stored at room temperature for at least one week. Duplicate 10 mL dissolved-phase subsamples (filtered at 0.45 mm) were then taken directly from the bottle after vigorous shaking. The remaining sample was pre-filtered through a 250 mm nylon mesh (for normalisation with respect to maximum particle size) and then through a pre-weighed and -dried 0.45 mm membrane filter (ungridded cellulose nitrate) using a vacuum filtration manifold. The filter papers bearing the filtrand were dried in 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 15 mL with ultrapure water, from which a 10 mL 0.45 mm-filtered subsample was taken for analysis. All subsamples were refrigerated until analysis. Analyses for a range of metals were performed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) using a Varian Axial ICP-AES instrument. Of the trace metals of possible interest, only copper, lead and zinc were reliably detected in most samples. Total concentrations were calculated as the sum of the dissolved- and particulate-phase (post-digestion) concentrations. The precision of ICP-AES analyses was evaluated by tabulating the relative standard deviations (RSDs) of determined concentrations for each sample (3 replicates). Replicate analyses with RSD values of greater than 20% were excluded from further consideration as being significantly beyond the reliable detection limit of the instrument, and analyses with RSD values in the range 5e20% were treated with additional care in subsequent calculations (i.e. not included in calculations of statistics but displayed on graphs). All copper and zinc analyses and 67% of lead analyses for digested particulate samples had RSDs of less than 5% (i.e. in the reliable analytical range for the instrument). For the digested dissolved-phase samples, 93% of copper, 97% of
B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545 zinc and 21% of lead analyses had analytical RSDs of less than 5%. The lowest concentrations detected at an RSD of 5% or better were 3.16, 22.43 and 1.21 mg/L copper, lead and zinc, respectively. Field and laboratory blanks (ultrapure water) were maintained throughout the sampling, preparation and analytical steps. No issues with contamination were detected in any of the 39 laboratory and field blanks, with only three field blanks exhibiting measurable levels of the metals of interest (three cases of Zn equivalent to less than 1% of the lowest-concentration contemporaneous sample). Filtration and digestion blanks were free of lead, and copper and zinc in these blank samples, if detected, were present at less than 1.2% (Cu) and 3.8% (Zn) of that for the lowestconcentration contemporaneous sample. The NIST International Reference Material SRM-1648a (Urban Particulate Matter) was employed as a procedural and analytical reference material for road runoff samples. Overall recovery from this highly contaminated sample by heatassisted aqua regia digestion was 89% for copper, 97% for lead and 89% for zinc, with RSDs 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 of sampler deployment, only eight (Queen St) and nine (Parramatta Rd) events were adequately sampled to allow load calculations to be attempted, and only four (Queen St) and six (Parramatta Rd) events are considered to have been sufficiently well sampled with respect to the flow record to afford reliable loading estimates. The time series of flow and pollutant concentration were upsampled to 1 min intervals from variable-interval data and sampling times, and total loads per event were calculated by linear interpolation of the determined instantaneous concentrations at this 1 min resolution.
3. Results and discussion 3.1. Runoff events and pollution loads Graphs showing the variation in water depth and pollutant concentration over the course of the runoff events at each site are shown in the supplementary data (Figs S1 and S2), and the total pollutant loading calculations for the same events are listed in Table 1. Event mean concentrations calculated by dividing the linearly interpolated load by the total discharge are also reported in Table 1 for comparison with published data. In all but one of the sampling runs triggered at the onset of a rainfall event, a distinct first flush in metal export is apparent (see Figs. 2, S1 and S2).
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a meaningful way due to dissimilarities in runoff depth and hydrograph dynamics related to differences in meteorological conditions, even over the relatively short distance separating the two sites, demonstrating the difficulty in acquiring directly comparable data at multiple sites. However, the loadings calculated for the events considered to be sufficiently well sampled at appropriate times on the hydrograph to afford reliable loading estimates are well explained by a logarithmic relationship with respect to total discharge (Fig. 1). The explanatory power and significance of these relationships are very high for both road sites (r2 > 0.91, p < 0.01), even with relatively few data pairs. The fit to a logarithmic relationship is consistent with the regular appearance of a first flush in the mass/discharge relationship (Fig. 2). The pollutant yield rating curves given by these logarithmic relationships for the two roads, normalised with respect to rainfall depth and road area, are compared in Fig. 3. These curves allow the pollutant accumulation/export behaviour of the two stretches of road to be compared over a range of discharge volumes without requiring the sampling to be performed simultaneously. It can be observed from this comparison that although the relative pollutant yields of copper and zinc are lower for the local street than for the major road, the difference is quite small given the 45-fold difference in traffic volume. The difference is also much smaller than the ratio of atmospheric deposition at 1 m from the roadside determined for the same two sites in a companion atmospheric deposition study (Davis and Birch, in press). The lead yield on the local street also far exceeds that for the major road. The pollutant load available for runoff from the major road therefore appears to be substantially reduced from what might have otherwise been expected based on traffic volumes and atmospheric deposition to the roadside. As traffic on the major road passes very close to the curb, whereas the curb on the local street is set 1e2 m back from the path of traffic, the present observations may suggest
3.2. Derivation of pollutant yield rating curves The events for which samples were taken simultaneously at both of the present road sites could not be correlated directly in
Table 1 Calculated total event loads and event mean concentrations. Event
Total flow (L)
Total load (mg)
EMC (mg/L)
Cu
Pb
Zn
Cu
Pb
Zn
7944 3560 12304 16227 45370 11050 17260 8128
425 28 697 219 1791 566 63 1150
523 e 1987 e 8974 2001 e 2489
1071 130 3630 737 10086 3144 234 5642
0.0535 0.0077 0.0567 0.0135 0.0395 0.0512 0.0036 0.1415
0.0659 e 0.1615 e 0.1978 0.1811 e 0.3062
0.1348 0.0364 0.2950 0.0454 0.2223 0.2846 0.0135 0.6941
Parramatta Rd 2331 20070819a 2707 20070920a a 20071103 106690 a 21466 20071206 20080118 4541 20080202 38420 2495 20080206a 7059 20080406a
155 430 6132 1971 198 8194 170 992
76 181 980 1316 81 3382 67 495
540 1350 17630 6830 711 20789 583 4481
0.0666 0.1590 0.0575 0.0918 0.0436 0.2133 0.0683 0.1405
0.0325 0.0667 0.0092 0.0613 0.0179 0.0880 0.0270 0.0702
0.2318 0.4988 0.1652 0.3182 0.1565 0.5411 0.2335 0.6348
Queen St 20070819a 20070820 20070920a 20071126 20071206a 20080131a 20080206 20080406
a
Reliable estimate.
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 the existence of a first flush.
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B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545
Fig. 2. Relationship between total event loads of copper, lead, and zinc, and total event discharge for sampled events on (a) Queen St and (b) Parramatta Rd. Open symbols denote data for unreliable events excluded from statistics. Lines plotting above the shaded area indicate a first flush in pollutant export.
that particulates on the major road are remobilized right out to the edge of the gutter and subsequently dispersed beyond the curb. The lack of a consistent relationship between traffic volume and runoff-entrained heavy metal export has been observed in many previous studies (Li and Barrett, 2008; Waara and Farm, 2008) and has generally been explained as due to differences in driving regimes (Hamilton et al., 1984; Li and Barrett, 2008). The results of the present study suggest that proximity of passing traffic to the curb may be a major factor determining the mass of pollutant per vehicle that remains on the road to be entrained in runoff during rainfall. The high yield rate for lead on the local street, on the other hand, is suggestive of a substantial input that is not present for the major road. Relatively high rates of atmospheric lead deposition were identified in the residential area in the vicinity of Queen St as part of a companion sampling survey (Davis and Birch, in press), but on-road lead sources such as dislodged wheel weights, which would be quickly ejected from the major road by vehicle passage, is likely to contribute to the high loads of lead observed for runoff from this local street (Murakami et al., 2007; Root, 2000). Further investigation of the source(s) of on-road lead is therefore necessary. The present observations nevertheless indicate that local factors
Fig. 4. Heavy metal vs. total suspended solids (TSS) concentrations for road runoff from (a) Queen St and (b) Parramatta Rd. Open symbols denote outlier data excluded from statistics, solid line denotes the geometric mean of the lognormal distribution of data, and dashed lines denote the first standard-deviation limits in log-transformed units.
are of considerable importance in the development of appropriate remedial activities for any particular stretch of road. The comparative method employed in this analysis lends itself to application for any combination of appropriate sites (i.e. draining curb-bounded, impervious catchments), as representative data appear to accord well with a logarithmic relationship to total discharge. This approach does not require that the data be obtained simultaneously at multiple sites, only that the analysis should include only qualified data that is confirmed to be representative of the target event. 3.3. Solid-phase concentrations The relationship between heavy metal concentration and total suspended solids concentration (i.e. the solid-phase metal concentration) is shown in Fig. 4. Lognormal distributions were fitted to these data using a maximum likelihood algorithm, and the geometric 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 with total suspended solids is very well defined, with r2 values for the linear regression exceeding 0.70 (p < 0.01, n ¼ 55e60) for the major road and 0.86 (p < 0.01, n ¼ 38e45) for the local street. This could Table 2 Metal concentrations in road-derived suspended sediment. Concentration (mg/kg) Cu Queen St 1s
m
þ1s Atmospheric depositiona Parramatta Rd 1s
m
þ1s Atmospheric depositiona Fig. 3. Comparison of heavy metal loading rates (in yield per unit road area) with respect to rainfall depth between Queen St (dashed line) and Parramatta Rd (solid line).
a
Pb
Zn
288 474 781 492
511 870 1482 781
1285 2008 3136 3900
715 1013 1437 862
347 560 901 364
2574 3522 4821 4617
Companion study (Davis and Birch, in press).
B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545
allow runoff samples to be analysed using suspended solids as a proxy for these metals with a considerable reduction in analytical cost. The summarised results of an atmospheric deposition study conducted at the same sites over the same period as the runoff sampling study (Davis and Birch, in press) are also shown for comparison in Table 2. Atmospherically deposited particulates were collected at the same site as runoff sampling, 1 m from the roadside. Except for zinc on the local street, the solid-phase concentrations correspond very closely to those found for atmospherically deposited particulates. A similarly strong relationship between suspended solids and these metals has been noted at other road sites (Desta et al., 2007). These results confirm that the particulate matter deposited on roads and dispersed to the roadside has a consistent chemical composition with respect to copper, lead and zinc.
4. Conclusions The pollutant yield rating curve approach provides a useful means of presenting problematic environmental data in a simple yet meaningful manner with less information loss than the routine use of EMCs for site characterisation. This method was demonstrated in the present study to be of considerable utility for the comparison of heavy metal loadings in runoff from two roads with markedly different traffic volumes, allowing intercomparison of road runoff characteristics without requiring simultaneously acquired data. Using these pollutant yield rating curves, it was shown that runoff from the major road is not as loaded with pollutants as expected, attributable to cleaning of the road surface by the passage of traffic out to the very edge of the roadway. The pollutant loads exported from the local street are only marginally lower for copper and zinc, but many times higher for lead. Although these dissimilar conditions of surface accumulation and dispersal preclude meaningful extrapolation of the present results to the wider catchment, the observation of such effects is important with respect to the design of remediation strategies for reducing pollutant loads from roads. For example, drainage from the present stretch of major road has comparable remediation priority to that from the local street, despite the 45-fold difference in traffic volume, and in terms of lead, the local road appears to be of considerably greater concern. The present study also demonstrated the well-constrained trace metal composition of particulate material entrained in runoff and deposited at the roadside. This character could allow comparative pollutant export from major and minor roads to be conducted based on measurements of total suspended solids rather than analyses of heavy metals at trace concentrations. The total solid-phase concentrations of copper, lead and zinc are also largely similar to those of atmospherically deposited particulates obtained at the same sites. A sampling campaign combining suspended solids sampling or monitoring, periodic atmospheric sampling for determination of nominal metal concentrations, and the present pollutant yield rating curve analysis could be an inexpensive, simple and reliable approach for constructing a wellpopulated catchment-wide database of trace metal loadings by road type or point of runoff discharge for the purposes of remediation planning.
Acknowledgements This study was supported by an Australian Postgraduate Award (Industry) from the Australian Research Council (No. LP0455486).
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Total Environ. 359, 145e155. Davis, B.S., Birch, G.F., 2009. Catchment-wide assessment of the cost-effectiveness of stormwater remediation measures in urban areas. Environ. Sci. Policy 12, 84e91. Davis, B.S., Birch, G.F., Spatial distribution of bulk atmospheric deposition of heavy metals in metropolitan Sydney, Australia. Water Air Soil Poll., in press, doi:10. 1007/s11270-010-0411-3. Deletic, A., Maksimovic, C., 1998. Evaluation of water quality factors in storm runoff from paved areas. J. Environ. Eng. ASCE 124, 869e879. Desta, M.B., Bruen, M., Higgins, N., Johnston, P., 2007. Highway runoff quality in Ireland. J. Environ. Monit. 9, 366e371. Fletcher, T., Duncan, H., Poelsma, P., Lloyd, S., 2004. Stormwater Flow and Quality and the Effectiveness of Non-proprietary Stormwater Treatment Measures e A Review and Gap Analysis. Technical Report 04/8. Cooperative Research Centre for Catchment Hydrology, Australia. Furumai, H., Balmer, H., Boller, M., 2002. Dynamic behavior of suspended pollutants and particle size distribution in highway runoff, Water Sci. Technol 46, 413e418. Hamilton, R., Revitt, D., Warren, R., 1984. Levels and physico-chemical associations of Cd, Cu, Pb and Zn in road sediments. Sci. Total Environ. 33, 59e74. Harrison, R., Wilson, S., 1985. The chemical composition of highway drainage waters I. Major ions and selected trace metals. Sci. Total Environ. 43, 63e77. Hewitt, C., Rashed, M., 1990. An integrated budget for selected pollutants for a major rural highway. Sci. Total Environ. 93, 375e384. Jartun, M., Ottesen, R.T., Steinnes, E., Volden, T., 2008. Runoff of particle bound pollutants from urban impervious surfaces studied by analysis of sediments from stormwater traps. Sci. Total Environ. 396, 147e163. Kayhanian, M., Singh, A., Suverkropp, C., Borroum, S., 2003. Impact of annual average daily traffic on highway runoff pollutant concentrations. J. Environ. Eng. 129, 975e990. Kim, L.-H., Kayhanian, M., Lau, S., Stenstrom, M., 2005a. A new modeling approach for estimating first flush metal mass loading. Water Sci. Technol. 51, 159e167. Kim, L.-H., Kayhanian, M., Zoh, K.-D., Stenstrom, M., 2005b. Modeling of highway stormwater runoff. Sci. Total Environ. 348, 1e18. Lee, B., Matsui, S., Shimizu, Y., Matsuda, T., 2005. Characterizations of the first flush in storm water runoff from an urban roadway. Environ. Technol. 26, 773e782. Li, M.H., Barrett, M.E., 2008. Relationship between antecedent dry period and highway pollutant: conceptual models of buildup and removal processes. Water Environ. Res. 80, 740e747. Ma, J.S., Kang, J.H., Kayhanian, M., Stenstrom, M.K., 2009. Sampling issues in urban runoff monitoring programs: composite versus grab. J. Enviro. Eng. ASCE 135, 118e127. Mangani, G., Berloni, A., Bellucci, F., Tatano, F., Maione, M., 2005. Evaluation of the pollutant content in road runoff first flush waters. Water Air Soil Poll. 160, 213e228. 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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Comparison of heavy metal loads in stormwater runoff from major and minor urban roads using pollutant yield rating curves Brett Davis*, Gavin Birch School of Geosciences, University of Sydney, New South Wales 2006, Australia
A simple method for representing data onroad runoff pollution allows comparisons among dissimilar sites and could form the basis for a pollution database.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 August 2009 Received in revised form 28 March 2010 Accepted 26 May 2010
Trace metal export by stormwater runoff from a major road and local street in urban Sydney, Australia, is compared using pollutant yield rating curves derived from intensive sampling data. The event loads of copper, lead and zinc are well approximated by logarithmic relationships with respect to total event discharge 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 rates from the local street are comparable with that of the major road, while lead export from the local street is much higher, despite a 45-fold difference in traffic volume. The yield rating curve approach allows problematic environmental data to be presented in a simple yet meaningful manner with less information loss. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Roads Runoff Stormwater Pollution First flush Heavy metals Pollutant loads
1. Introduction Stormwater runoff from urban catchments is widely recognised as a major source of environmental contaminants (e.g. Birch and Taylor, 2002; US EPA, 1983), and the accumulation of stormwaterrelated pollutants in receiving waterways has been shown in numerous studies to have a severe detrimental impact on the ecosystem health of affected aquatic environments (Brown and Peake, 2006; McCready et al., 2004; Pitt et al., 1995). In highly urbanised catchments, road surfaces can typically constitute up to 22% of total catchment area, and contribute up to 26% of total runoff volumes with commensurate contributions to total ‘heavy’ metal (e.g. Cu, Pb, Zn) loads of 19e40% (Davis and Birch, 2009). Road runoff is second only to residential runoff in terms of runoff generation and pollutant export (Davis and Birch, 2009), and therefore requires priority consideration in the development of any integrated stormwater management plan as a non-point source of urban pollution. However, complicating the issue of catchmentwide remediation and responsibility for road-derived pollution is the fact that roads within a given area often fall under multiple
* Corresponding author. E-mail address: [email protected] (B. Davis). 0269-7491/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2010.05.021
jurisdictions, such as state and local governments. It is therefore important to establish the relative contribution of roads of different types and ownership. In Sydney, Australia, state roads support the highest traffic volumes but generally comprise a small fraction of the total road length in a catchment. In contrast, roads under local government ownership comprise the vast majority of road length, but support much lower traffic volumes. Information on the relative pollutant contributions of major state roads and minor local roads would be valuable for guiding pollution mitigation efforts and for developing cost-effective stormwater management strategies. To obtain such data, it appears relatively straightforward to conduct a field investigation of relative stormwater pollutant loads from different roads to construct a model of road-derived pollutant export. However, it is in practice difficult to compare pollutant loadings from different stretches of road directly due to the strong influence of physical and structural factors such as the breaking regime, road grade, proximity to intersections, traffic lights, bends, and side entries and exits, without any apparent systematic relationship among these parameters (Brezonik and Stadelmann, 2002). The spatial and temporal variability of meteorological conditions, such as wind intensity and direction, and rainfall intensity and duration, can also affect the accumulation of particulate pollutants and the subsequent mobilisation of accumulated
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material, processes that interact with the orientation and aspect of the stretch of road under consideration. Due to the large number and high complexity of factors affecting the spatial and temporal variation in road runoff composition and volume, in addition to the known difficulties in obtaining representative time-series and sampling data for stormwater in general (Deletic and Maksimovic, 1998), direct comparisons among different road sites are highly unreliable, and it is not expected that meaningful results could be obtained by such an approach. Therefore, to extract useful information from a comparative road runoff study, an alternative approach that obviates many of the above confounding factors should be developed. Such an alternative methodology is proposed in the present study, focussing on suspended solids and the heavy metals copper, lead and zinc as representative examples of pollutants 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 of pollutant loading per event discharge, and that these ratings curves can be compared among different sites to provide a clear and understandable picture of pollutant export from roadways of different types and situations. Heavy metals in road runoff have been studied extensively, but the results of such studies have generally been reported in terms of a single parameter, the event mean concentration (EMC), broadly defined as the total pollutant load divided by the total discharge for a runoff event. The reported EMCs fall within a well-established order-of-magnitude range (Fletcher et al., 2004; Kayhanian et al., 2003; Ma et al., 2009), and are characterised by pronounced variability and lack of systematic relationships with environmental parameters such as traffic volume (Brezonik and Stadelmann, 2002; Li and Barrett, 2008; Waara and Farm, 2008). The use of a single value to describe the complex relationship between instantaneous flow and pollutant concentration and the variation in these behaviours with the size of the runoff event therefore means that there is little value to comparing EMC data from different sites for the purpose of determining comparative loading rates. Early studies by Bourcier and Hindin (1979), Harrison and Wilson (1985) and Hewitt and Rashed (1990), and more recent studies by Wu et al. (1998) and Barbosa and Hvitved-Jacobsen (1999), while conducted in the era of leaded fuel, explored various methodologies for sampling road runoff and calculating pollutant loads. However, these studies are also based on the premise of multiplying total discharge by a single concentration parameter (i.e. EMC), ignoring the complexity of the floweload relationship contained in the field data. This remains true for recent studies attempting more sophisticated sampling regimes and statistical analyses (Furami et al., 2002; Kayhanian et al., 2003), where no systematic relationships could be found and the calculated EMCs fall within the established literature ranges. Kim et al. (2005a,b) proposed a predictive model for calculating EMCs at different stages of storm events based on extensive sampling data. The extreme variability in EMCs among storm events and in pollutant concentration over the course of an event are clearly demonstrated in that study, and the inadequacy of existing approaches to calculating EMCs and characterising pollutant loads were discussed. The solution of Kim et al. however, requires the calibration of four variables against sampling data, one of which is an initial condition related to antecedent dry period and the surface accumulation of pollutants, which is a parameter that will 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 firstflush characteristic in road runoff. The first flush, the period of elevated pollutant concentration at the start of an event resulting in the transport of more pollutant mass in the first half of an event than in the second half, is an important feature of road runoff that
renders the use of EMCs unreliable as a means of calculating pollutant loads for all magnitudes of total discharge. Progressing from this establishment of the first-flush phenomenon for roads, the present study, based on sampling data obtained at two urban road sites over a period of eight months, presents a more useful model for calculation of annualised pollutant loads and for intercomparison of road catchments. 2. Methods 2.1. Road sites Road runoff was sampled between August 2007 and April 2008 in roadside drain pits on a major high-volume road (Parramatta Road) with average annual daily traffic (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 of road were bounded by concrete curbs, had similar slopes and proximity to road bend, 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 were free of extraneous (i.e. residential) drainage inputs into the road gutter, and the drain pits in which sampling was performed were free-flowing with a single largediameter outlet pipe and no other inputs. Situational similarity of the stretches of road is not necessary for application of the present model, but eliminating as many confounding parameters as possible allows for a more illustrative demonstration of the descriptive power of the pollutant yield rating curve approach. Of more importance is the need to sample only runoff from the road surface without interference that could modulate the first-flush character of runoff discharge, such as input 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, and runoff was sampled in the concrete outlet pipe of the adjacent drain pit. The water level in the outlet pipe was monitored using a pressure transducer, and instantaneous flow was calculated using a Manning equation with appropriate parameters of slope (4% in both cases based on-road grade), roughness (n ¼ 0.015, float-finish concrete channel), and geometry (0.4 m-diameter pipe), with intermittent field verification using the Doppler velocity sensor integrated into the depth transducer. The velocity measurements were intermittent due to the susceptibility of the probe to fouling upon accumulation of sediment or litter. Samples were taken at predefined time intervals upon the detection of flow, starting with an interval of 6 min and doubling every four intervals. 2.3. Sample analysis Samples were collected in 1 L polyethylene bottles cleaned in advance by soaking overnight in 10% HNO3, rinsing twice with ultrapure water, drying to complete 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 sealing the distributor of the apparatus. Samples were recovered as soon as possible following runoff events (within 24e48 h). Upon returning to the laboratory, samples were agitated and a homogenised fraction extracted for other analyses. The remaining sample in the collection vessel was then acidified with 0.5% HNO3 and stored at room temperature for at least one week. Duplicate 10 mL dissolved-phase subsamples (filtered at 0.45 mm) were then taken directly from the bottle after vigorous shaking. The remaining sample was pre-filtered through a 250 mm nylon mesh (for normalisation with respect to maximum particle size) and then through a pre-weighed and -dried 0.45 mm membrane filter (ungridded cellulose nitrate) using a vacuum filtration manifold. The filter papers bearing the filtrand were dried in 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 15 mL with ultrapure water, from which a 10 mL 0.45 mm-filtered subsample was taken for analysis. All subsamples were refrigerated until analysis. Analyses for a range of metals were performed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) using a Varian Axial ICP-AES instrument. Of the trace metals of possible interest, only copper, lead and zinc were reliably detected in most samples. Total concentrations were calculated as the sum of the dissolved- and particulate-phase (post-digestion) concentrations. The precision of ICP-AES analyses was evaluated by tabulating the relative standard deviations (RSDs) of determined concentrations for each sample (3 replicates). Replicate analyses with RSD values of greater than 20% were excluded from further consideration as being significantly beyond the reliable detection limit of the instrument, and analyses with RSD values in the range 5e20% were treated with additional care in subsequent calculations (i.e. not included in calculations of statistics but displayed on graphs). All copper and zinc analyses and 67% of lead analyses for digested particulate samples had RSDs of less than 5% (i.e. in the reliable analytical range for the instrument). For the digested dissolved-phase samples, 93% of copper, 97% of
B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545 zinc and 21% of lead analyses had analytical RSDs of less than 5%. The lowest concentrations detected at an RSD of 5% or better were 3.16, 22.43 and 1.21 mg/L copper, lead and zinc, respectively. Field and laboratory blanks (ultrapure water) were maintained throughout the sampling, preparation and analytical steps. No issues with contamination were detected in any of the 39 laboratory and field blanks, with only three field blanks exhibiting measurable levels of the metals of interest (three cases of Zn equivalent to less than 1% of the lowest-concentration contemporaneous sample). Filtration and digestion blanks were free of lead, and copper and zinc in these blank samples, if detected, were present at less than 1.2% (Cu) and 3.8% (Zn) of that for the lowestconcentration contemporaneous sample. The NIST International Reference Material SRM-1648a (Urban Particulate Matter) was employed as a procedural and analytical reference material for road runoff samples. Overall recovery from this highly contaminated sample by heatassisted aqua regia digestion was 89% for copper, 97% for lead and 89% for zinc, with RSDs 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 of sampler deployment, only eight (Queen St) and nine (Parramatta Rd) events were adequately sampled to allow load calculations to be attempted, and only four (Queen St) and six (Parramatta Rd) events are considered to have been sufficiently well sampled with respect to the flow record to afford reliable loading estimates. The time series of flow and pollutant concentration were upsampled to 1 min intervals from variable-interval data and sampling times, and total loads per event were calculated by linear interpolation of the determined instantaneous concentrations at this 1 min resolution.
3. Results and discussion 3.1. Runoff events and pollution loads Graphs showing the variation in water depth and pollutant concentration over the course of the runoff events at each site are shown in the supplementary data (Figs S1 and S2), and the total pollutant loading calculations for the same events are listed in Table 1. Event mean concentrations calculated by dividing the linearly interpolated load by the total discharge are also reported in Table 1 for comparison with published data. In all but one of the sampling runs triggered at the onset of a rainfall event, a distinct first flush in metal export is apparent (see Figs. 2, S1 and S2).
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a meaningful way due to dissimilarities in runoff depth and hydrograph dynamics related to differences in meteorological conditions, even over the relatively short distance separating the two sites, demonstrating the difficulty in acquiring directly comparable data at multiple sites. However, the loadings calculated for the events considered to be sufficiently well sampled at appropriate times on the hydrograph to afford reliable loading estimates are well explained by a logarithmic relationship with respect to total discharge (Fig. 1). The explanatory power and significance of these relationships are very high for both road sites (r2 > 0.91, p < 0.01), even with relatively few data pairs. The fit to a logarithmic relationship is consistent with the regular appearance of a first flush in the mass/discharge relationship (Fig. 2). The pollutant yield rating curves given by these logarithmic relationships for the two roads, normalised with respect to rainfall depth and road area, are compared in Fig. 3. These curves allow the pollutant accumulation/export behaviour of the two stretches of road to be compared over a range of discharge volumes without requiring the sampling to be performed simultaneously. It can be observed from this comparison that although the relative pollutant yields of copper and zinc are lower for the local street than for the major road, the difference is quite small given the 45-fold difference in traffic volume. The difference is also much smaller than the ratio of atmospheric deposition at 1 m from the roadside determined for the same two sites in a companion atmospheric deposition study (Davis and Birch, in press). The lead yield on the local street also far exceeds that for the major road. The pollutant load available for runoff from the major road therefore appears to be substantially reduced from what might have otherwise been expected based on traffic volumes and atmospheric deposition to the roadside. As traffic on the major road passes very close to the curb, whereas the curb on the local street is set 1e2 m back from the path of traffic, the present observations may suggest
3.2. Derivation of pollutant yield rating curves The events for which samples were taken simultaneously at both of the present road sites could not be correlated directly in
Table 1 Calculated total event loads and event mean concentrations. Event
Total flow (L)
Total load (mg)
EMC (mg/L)
Cu
Pb
Zn
Cu
Pb
Zn
7944 3560 12304 16227 45370 11050 17260 8128
425 28 697 219 1791 566 63 1150
523 e 1987 e 8974 2001 e 2489
1071 130 3630 737 10086 3144 234 5642
0.0535 0.0077 0.0567 0.0135 0.0395 0.0512 0.0036 0.1415
0.0659 e 0.1615 e 0.1978 0.1811 e 0.3062
0.1348 0.0364 0.2950 0.0454 0.2223 0.2846 0.0135 0.6941
Parramatta Rd 2331 20070819a 2707 20070920a a 20071103 106690 a 21466 20071206 20080118 4541 20080202 38420 2495 20080206a 7059 20080406a
155 430 6132 1971 198 8194 170 992
76 181 980 1316 81 3382 67 495
540 1350 17630 6830 711 20789 583 4481
0.0666 0.1590 0.0575 0.0918 0.0436 0.2133 0.0683 0.1405
0.0325 0.0667 0.0092 0.0613 0.0179 0.0880 0.0270 0.0702
0.2318 0.4988 0.1652 0.3182 0.1565 0.5411 0.2335 0.6348
Queen St 20070819a 20070820 20070920a 20071126 20071206a 20080131a 20080206 20080406
a
Reliable estimate.
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 the existence of a first flush.
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Fig. 2. Relationship between total event loads of copper, lead, and zinc, and total event discharge for sampled events on (a) Queen St and (b) Parramatta Rd. Open symbols denote data for unreliable events excluded from statistics. Lines plotting above the shaded area indicate a first flush in pollutant export.
that particulates on the major road are remobilized right out to the edge of the gutter and subsequently dispersed beyond the curb. The lack of a consistent relationship between traffic volume and runoff-entrained heavy metal export has been observed in many previous studies (Li and Barrett, 2008; Waara and Farm, 2008) and has generally been explained as due to differences in driving regimes (Hamilton et al., 1984; Li and Barrett, 2008). The results of the present study suggest that proximity of passing traffic to the curb may be a major factor determining the mass of pollutant per vehicle that remains on the road to be entrained in runoff during rainfall. The high yield rate for lead on the local street, on the other hand, is suggestive of a substantial input that is not present for the major road. Relatively high rates of atmospheric lead deposition were identified in the residential area in the vicinity of Queen St as part of a companion sampling survey (Davis and Birch, in press), but on-road lead sources such as dislodged wheel weights, which would be quickly ejected from the major road by vehicle passage, is likely to contribute to the high loads of lead observed for runoff from this local street (Murakami et al., 2007; Root, 2000). Further investigation of the source(s) of on-road lead is therefore necessary. The present observations nevertheless indicate that local factors
Fig. 4. Heavy metal vs. total suspended solids (TSS) concentrations for road runoff from (a) Queen St and (b) Parramatta Rd. Open symbols denote outlier data excluded from statistics, solid line denotes the geometric mean of the lognormal distribution of data, and dashed lines denote the first standard-deviation limits in log-transformed units.
are of considerable importance in the development of appropriate remedial activities for any particular stretch of road. The comparative method employed in this analysis lends itself to application for any combination of appropriate sites (i.e. draining curb-bounded, impervious catchments), as representative data appear to accord well with a logarithmic relationship to total discharge. This approach does not require that the data be obtained simultaneously at multiple sites, only that the analysis should include only qualified data that is confirmed to be representative of the target event. 3.3. Solid-phase concentrations The relationship between heavy metal concentration and total suspended solids concentration (i.e. the solid-phase metal concentration) is shown in Fig. 4. Lognormal distributions were fitted to these data using a maximum likelihood algorithm, and the geometric 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 with total suspended solids is very well defined, with r2 values for the linear regression exceeding 0.70 (p < 0.01, n ¼ 55e60) for the major road and 0.86 (p < 0.01, n ¼ 38e45) for the local street. This could Table 2 Metal concentrations in road-derived suspended sediment. Concentration (mg/kg) Cu Queen St 1s
m
þ1s Atmospheric depositiona Parramatta Rd 1s
m
þ1s Atmospheric depositiona Fig. 3. Comparison of heavy metal loading rates (in yield per unit road area) with respect to rainfall depth between Queen St (dashed line) and Parramatta Rd (solid line).
a
Pb
Zn
288 474 781 492
511 870 1482 781
1285 2008 3136 3900
715 1013 1437 862
347 560 901 364
2574 3522 4821 4617
Companion study (Davis and Birch, in press).
B. Davis, G. Birch / Environmental Pollution 158 (2010) 2541e2545
allow runoff samples to be analysed using suspended solids as a proxy for these metals with a considerable reduction in analytical cost. The summarised results of an atmospheric deposition study conducted at the same sites over the same period as the runoff sampling study (Davis and Birch, in press) are also shown for comparison in Table 2. Atmospherically deposited particulates were collected at the same site as runoff sampling, 1 m from the roadside. Except for zinc on the local street, the solid-phase concentrations correspond very closely to those found for atmospherically deposited particulates. A similarly strong relationship between suspended solids and these metals has been noted at other road sites (Desta et al., 2007). These results confirm that the particulate matter deposited on roads and dispersed to the roadside has a consistent chemical composition with respect to copper, lead and zinc.
4. Conclusions The pollutant yield rating curve approach provides a useful means of presenting problematic environmental data in a simple yet meaningful manner with less information loss than the routine use of EMCs for site characterisation. This method was demonstrated in the present study to be of considerable utility for the comparison of heavy metal loadings in runoff from two roads with markedly different traffic volumes, allowing intercomparison of road runoff characteristics without requiring simultaneously acquired data. Using these pollutant yield rating curves, it was shown that runoff from the major road is not as loaded with pollutants as expected, attributable to cleaning of the road surface by the passage of traffic out to the very edge of the roadway. The pollutant loads exported from the local street are only marginally lower for copper and zinc, but many times higher for lead. Although these dissimilar conditions of surface accumulation and dispersal preclude meaningful extrapolation of the present results to the wider catchment, the observation of such effects is important with respect to the design of remediation strategies for reducing pollutant loads from roads. For example, drainage from the present stretch of major road has comparable remediation priority to that from the local street, despite the 45-fold difference in traffic volume, and in terms of lead, the local road appears to be of considerably greater concern. The present study also demonstrated the well-constrained trace metal composition of particulate material entrained in runoff and deposited at the roadside. This character could allow comparative pollutant export from major and minor roads to be conducted based on measurements of total suspended solids rather than analyses of heavy metals at trace concentrations. The total solid-phase concentrations of copper, lead and zinc are also largely similar to those of atmospherically deposited particulates obtained at the same sites. A sampling campaign combining suspended solids sampling or monitoring, periodic atmospheric sampling for determination of nominal metal concentrations, and the present pollutant yield rating curve analysis could be an inexpensive, simple and reliable approach for constructing a wellpopulated catchment-wide database of trace metal loadings by road type or point of runoff discharge for the purposes of remediation planning.
Acknowledgements This study was supported by an Australian Postgraduate Award (Industry) from the Australian Research Council (No. LP0455486).
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Total Environ. 359, 145e155. Davis, B.S., Birch, G.F., 2009. Catchment-wide assessment of the cost-effectiveness of stormwater remediation measures in urban areas. Environ. Sci. Policy 12, 84e91. Davis, B.S., Birch, G.F., Spatial distribution of bulk atmospheric deposition of heavy metals in metropolitan Sydney, Australia. Water Air Soil Poll., in press, doi:10. 1007/s11270-010-0411-3. Deletic, A., Maksimovic, C., 1998. Evaluation of water quality factors in storm runoff from paved areas. J. Environ. Eng. ASCE 124, 869e879. Desta, M.B., Bruen, M., Higgins, N., Johnston, P., 2007. Highway runoff quality in Ireland. J. Environ. Monit. 9, 366e371. Fletcher, T., Duncan, H., Poelsma, P., Lloyd, S., 2004. Stormwater Flow and Quality and the Effectiveness of Non-proprietary Stormwater Treatment Measures e A Review and Gap Analysis. Technical Report 04/8. Cooperative Research Centre for Catchment Hydrology, Australia. Furumai, H., Balmer, H., Boller, M., 2002. 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