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Build-up dynamics of heavy metals deposited on impermeable urban surfaces
Journal of Environmental Management 113 (2012) 347e354
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Build-up dynamics of heavy metals deposited on impermeable urban surfaces D. Wicke*, T.A. Cochrane, A. O’Sullivan University of Canterbury, Department of Civil and Natural Resources Engineering, Private Bag 4800, Christchurch 8140, New Zealand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2012 Received in revised form 29 August 2012 Accepted 10 September 2012 Available online 6 October 2012
A method using thin boards (3 cm thick, 0.56 m2) comprising different paving materials typically used in urban environments (2 asphalt types and concrete) was employed to specifically investigate air-borne deposition dynamics of TSS, zinc, copper and lead. Boards were exposed at an urban car park near vehicular traffic to determine the rate of contaminant build-up over a 13-day dry period. Concentration profiles from simulated rainfall wash-off were used to determine contaminant yields at different antecedent dry days. Maximum contaminant yields after 13 days of exposure were 2.7 kg ha1 for TSS, 35 g ha1 zinc, 2.3 g ha1 copper and 0.4 g ha1 lead. Accumulation of all contaminants increased over the first week and levelled off thereafter, supporting theoretical assumptions that contaminant accumulation on impervious surfaces asymptotically approaches a maximum. Comparison of different surface types showed approximately four times higher zinc concentrations in runoff from asphalt surfaces and two times higher TSS concentrations in runoff from concrete, which is attributed to different physical and chemical compositions of the pavement types. Contaminant build-up and wash-off behaviours were modelled using exponential and saturation functions commonly applied in the US EPA’s Stormwater Management Model (SWMM) showing good correlation between measured and modelled concentrations. Maximum build-up, half-saturation time, build-up rate constants and wash-off coefficients, necessary for stormwater contaminant modelling, were determined for the four contaminants studied. These parameters are required to model contaminant concentrations in urban runoff assisting in stormwater management decisions. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Air-borne deposition Pavement Heavy metals TSS Stormwater runoff
1. Introduction Stormwater runoff from urban areas is recognized as a major source of water pollution as contaminants deposited on impervious surfaces are washed off during rain events. Key inorganic stormwater contaminants include total suspended solids (TSS) and heavy metals, particularly zinc, copper and lead (Gobel et al., 2007; Brown and Peake, 2006). Various studies have identified the traffic sector as a main source of these metals, which originate mainly from tyre wear and brake lining abrasion (Davis et al., 2001; Adachi and Tainosho, 2004; Zanders, 2005). However, only a small portion of the pollutants emitted from traffic becomes entrapped in storm water runoff originating directly from the road; the remaining part drifts to the immediate vicinity by wind during dry days and spray action during precipitation (Gobel et al., 2007; BUWAL, 1996). Furthermore, some airborne heavy metals can travel far and will contribute to contaminant build-up on urban surfaces through both dry and wet deposition within a large area (Davis and Birch, 2011).
* Corresponding author. Tel.: þ64 3 364 2378. E-mail address: [email protected] (D. Wicke). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.09.005
Copper (Cu) and zinc (Zn) in stormwater can also originate from roofing materials and guttering (Karlen et al., 2001, 2002; Pennington and Webster-Brown, 2008). Due to its toxicity effects, untreated storm runoff negatively impacts aquatic ecosystems in receiving waterways that typically serve as stormwater drainage systems (Beasley and Kneale, 2002; Kayhanian et al., 2008). A range of studies quantifying contaminant build-up on roads have been conducted over the past several decades (Gobel et al., 2007). Methods used to quantify road contaminant build-up and wash-off include using vacuum cleaners to capture dry and wet contaminant deposition (Bris et al., 1999), rainfall simulators to wash-off portions of roads (Herngren et al., 2005a), and sampling under natural rainfall events with automated samplers (Gnecco et al., 2005). Average daily traffic, volume over capacity ratio, and surface texture depth have been identified as having strong correlations with the build-up of heavy metals in roads (Mahbub et al., 2011; Deletic and Orr, 2005). Other studies have suggested that background atmospheric deposition, distance from traffic, and meteorological effects are also significant factors in the build-up process (Mahbub et al., 2010; Deletic and Orr, 2005). Antecedent conditions are of particular importance as build-up over dry days occurs relatively quickly after a rainfall event, but
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D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
slows down after several days due to redistribution induced by wind (Vaze, and Chiew, 2002). This is consistent with early reports investigating contaminant accumulation on road surfaces, which found that particulate loadings begin to level off after 3e4 days as portions of the material may be picked up by passing traffic (Shaheen, 1975). A similar pattern of particulate accumulation on residential roads approaching a maximum level after about 7 days has been observed by Egodawatta and Goonetilleke (2006) and earlier by Sartor and Boyd (1974). However, it remains unclear if contaminants deposited on surfaces not directly in contact with traffic (i.e. airborne contaminants) show similar accumulation behaviour. In a recent related study (Wicke et al., 2012), we found that heavy metals accumulate at locations >50 m from car traffic (in a carpark) and >150 m from the nearest road. The resulting contaminant yields were comparable to atmospheric deposition at background locations and low traffic volume roads (2000 vehicles/ day) determined for a residential area in Sydney (Davis and Birch, 2011). Much of the current work on quantifying heavy metal build-up is used in numerical models aiming to predict stormwater contaminant discharge from larger urban areas (May and Sivakumar, 2009). A wide range of stormwater runoff and contamination simulation models for urban catchments were reviewed by Zoppou (2001). Contaminant behaviour in most models is simulated using mathematical relationships, usually incorporating contaminant accumulation over time (build-up function) and subsequent dislodgement during a storm event (wash-off function). One of the most well-known stormwater models is the US Environmental Protection Agency’s stormwater management model (SWMM), which allows users to select from a range of available functions to mathematically describe contaminant build-up and wash-off (Huber and Dickinson, 1988). However, in order to accurately model different contaminant behaviour on different pavement surfaces, specific coefficients must be provided, which need to be derived from experimental data. The objective of this study was thus to quantify the build-up dynamics of heavy metals and TSS specifically from atmospheric deposition over time on different impervious urban surfaces. These surfaces were excluded from vehicle traffic. Differences in contaminant build-up behaviour on asphalt and concrete surfaces were specifically distinguished. A method that uses thin boards of these pavement types (previously detailed by Wicke et al., 2012) was employed for this purpose. Contaminant concentrations from the experiments were then used to derive build-up and wash-off functions that mathematically describe contaminant behaviour for asphalt and concrete surfaces, essential parameter inputs for stormwater contaminant modelling. 2. Materials and methods Fifteen thin asphalt and concrete boards (75 cm L 75 cm W 3 cm height) were used to capture air-borne contaminants depositing in an urban catchment. Each board was filled with one of three impervious materials (five boards of each material, poured in 2009) to replicate different surface roughness and composition in common urban pavements: concrete, ‘smooth’ asphalt of 3 mm max. aggregate size, and ‘coarse’ asphalt of 14 mm max. aggregate size. Detailed descriptions of the board materials (including concrete composition), the method to assess differences in surface roughness, and further details on the methodology can be found in Wicke et al. (2012). Standard deviations in surface elevation used to quantify the surface roughness were 0.13 mm for concrete, 0.51 mm for smooth asphalt and 0.76 mm for coarse asphalt (Wicke et al., 2012).
2.1. Exposure of boards and rainfall simulation To investigate the build-up of particulates (total suspended solids, TSS) and three heavy metals (zinc, copper, lead), boards were exposed over a 13-day dry period on the largest carpark of the University of Canterbury (1.6 ha, 650 lots) in Christchurch, New Zealand. The campus is surrounded primarily by residential area and main roads are a distance of 150e600 m away (traffic volumes: 13,000e15,000 vehicles/d at distances of 150e300 m, and 32,000 vehicles/d at distances of 600 m away; RAMM, 2011). All 15 boards were placed on a blocked-off (unused) parking area preventing cars from parking or driving over the experimental boards. A wireless camera (DCS-920, D-Link Corporation) overlooking the investigated carpark area was installed and programmed to take a photograph every 5 min verifying that no traffic breached the experimental zone area or that no tampering occurred with the experimental set-up. To determine contaminant build-up over time, one pair of boards (coarse asphalt and concrete) were collected after 2, 4, 6, 9, and 13 dry days. The five smooth asphalt boards (replicates) were collected after 13 dry days to assess variability between replicates. Boards were then carefully covered with cardboard sheets to prevent cross contamination, transported to the laboratory, and placed under a rainfall simulator at an inclination of 4 degrees to generate surface runoff. A two-nozzle (Veerjet 80100) Norton type rainfall simulator (Herngren et al., 2005b) was used to simulate rainfall for contaminant wash-off. A rainfall intensity of 20 mm h1 was applied for 60 min for each experimental run, resulting in a rainfall depth that is comparable to a 2 yr return period for a 2 h rain event in Christchurch. This represents a high local rainfall intensity (99% of Christchurch rainfall is <20 mm h1, measured every minute for the past 3 years with a PARSIVEL laser disdrometer, Ott, Germany). This relatively high rainfall intensity was chosen to ensure full contaminant dislodgement from the pavement surfaces so that total contaminant deposition could be quantified. Droplet size distributions generated by the rainfall simulator and those from a local natural rain event (45 min duration and peak intensity of 20 mm h1) were very similar (Wicke et al., 2012). Wind speed and direction were measured at a nearby weather station (Campbell Scientific) located 300 m from the experimental site (Table 1). Tap water used for the simulated rainfall was pre-filtered through a 10 mm cartridge filter and adjusted to a pH of 6.0 using concentrated nitric acid in order to simulate the pH of local rainwater in Christchurch. During pH-adjustment, the pH-buffering capacity of the tap water became depleted, thus making the feed water susceptible to pH changes as occurs with natural rain. Specific conductance of the water after pH-adjustment was 180 mS cm1. Local rainfall pH measured on-campus during the experimental period was comparable to reported rainfall pH in Auckland, New Zealand ranging from 5.8 to 6.4 (Pennington and Webster-Brown, 2008). Results were adjusted to account for background feed water metal concentrations (measured before and Table 1 Wind speeds during time of contaminant accumulation. Days
0e2 2e4 4e6 6e9 9e13
Daily average wind speeds
Daily max wind speeds
[m/s]
Beaufort #a
[m/s]
Beaufort #a
4.1e4.7 2.4e6.3 2.6e4.4 2.4e4.0 2.7e4.7
3 2e4 2e3 2e3 2e3
7.5e7.9 6.0e9.5 6.7e11.2 6.0e8.6 5.7e9.0
4 4e5 4e5 4e5 4e5
a Beaufort scale: 2-light breeze, 3-gentle breeze, 4-moderate breeze, 5-fresh breeze.
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
after each experiment; <0.5 mg Pb L1, <10 mg Cu L1, <40 mg Zn L1). Prior to their deployment, all boards underwent an initial ‘flushing’ with simulated rainfall by applying the highest possible rainfall intensity of 120 mm h1 for 15 min. Control samples taken at the end of the washing step showed low concentrations of zinc (15.6 mg Zn L1 from concrete, 9.4 mg Zn L1 from smooth asphalt and 37.4 mg Zn L1 from coarse asphalt), whereas copper, lead and TSS concentrations were below detection limits. 2.2. Chemical analysis Samples were collected from runoff in new polypropylene containers at 0 (first runoff), 5, 10, 20, 40 and 60 min after applying the simulated rainfall. The first two samples had a volume of 300 mL; 700 mL volumes were collected thereafter. Samples were instantaneously measured for pH (YSI Model 60 pH field meter) and turbidity (Hach Model 2100P portable turbidimeter) with instruments pre-calibrated on the day of the experimental runs. Heavy metal concentrations (Zn, Cu and Pb) were determined by ICP-MS (Agilent) following APHA Method 3125B after HNO3 digestion (APHA, 2005). Total metal digestions were prepared by thoroughly mixing samples on a magnetic stir plate and transferring 25 mL to a 50 mL polypropylene centrifuge tube. After the addition of 5 mL concentrated (69%) nitric acid (Fisher, trace analysis grade), tubes were placed in a heating block and samples were boiled for 1 h. Cooled samples were then filtered through an encapsulated 0.45 mm PVDF filter (47 mm, Labserv) and analysed via ICP-MS. Total suspended solid (TSS) concentrations were measured within 24 h following APHA Method 2540D (APHA, 2005). Dissolved organic carbon (DOC) concentrations were analysed using an Apollo 9000 TOC Analyzer (Teledyne Tekmar, US) after 0.45 mm filtration. Quality assurance protocols including blanks, duplicates (10% of samples), spiked samples (5% of samples) and instrument calibration were conducted throughout each batch analysis and adhered to the QA/QC procedures detailed in sampling and analysis (e.g. APHA) methods. 3. Results and discussion 3.1. Runoff concentrations Concentrations for total heavy metals (Zn, Cu and Pb) and total suspended solids (TSS) determined in runoff from concrete and coarse asphalt boards exposed for 2, 4, 6, 9, and 13 dry days are presented in Fig. 1. Results for the five smooth asphalt boards exposed for 13 dry days are shown in Fig. 2a. Maximum total heavy metal concentrations from all surface types in initial runoff were 1121 mg Zn L1 and 85.3 mg Cu L1 from the coarse asphalt and 15.4 mg Pb L1 from concrete (Fig. 1). These first flush runoff concentrations exceeded relevant toxicity-based guideline values for fresh and marine water quality (ANZECC, 2000) several-fold (e.g. the 90% trigger value for zinc of 15 mg L1 was exceeded 75fold and the 90% trigger value for copper of 1.8 mg L1 was exceeded 47-fold) which would negatively affect local waterways receiving untreated runoff (O’Halloran and Harding, 2008; Beasley and Kneale, 2002). Concentrations for all contaminants showed exponential decline for all three impervious surface types, concurring with a previous related study (Wicke et al., 2012). Furthermore, concentration profiles for all contaminants increased with increasing exposure time, typically up to 6 days with not much increase thereafter (Fig. 1). This indicates that contaminants accumulate up to a maximum amount in a relatively short period of time.
349
Maximum TSS concentrations after 13 dry days of exposure were higher in runoff from concrete (64 mg L1) than from coarse asphalt (37 mg L1, Fig. 2b). Particles deposited on the smooth concrete surfaces were more easily dislodged during wash-off compared to the rougher asphalt surface, whose cavities and possibly greater surface adhesion properties impeded particulate wash-off (Wicke et al., 2012). Zinc and copper concentrations were higher in runoff from asphalt, while lead (being mainly particlebound in stormwater runoff, e.g. Sansalone and Buchberger, 1997) showed higher concentrations in runoff from concrete boards (Fig. 1 and Wicke et al., 2012). This was attributed to distinct differences in runoff pH between the two pavement types. The average runoff pH from asphalt was 5.3 vs. 8.9 from concrete (Fig. 2c). The increased runoff pH from concrete boards was attributed to the dissolution of calcium hydrates at the concrete surface (Davies et al., 2010), whereas the lower asphalt pH during the initial 20 min of runoff (pH: 4.3e5.5 compared to the feed water pH of 6) may have resulted from localized metal hydrolyzation during initial surface wash-off (Stumm and Morgan, 1996). Particulate metals detained in the cavities of the asphalt most likely became dissolved at a lower pH resulting in higher metal concentrations in the wash-off. The pH can greatly influence metal speciation because an increased pH can lead to complexation and hence reduction of the bioavailable metal fraction (Bahar et al., 2008), whereas a lower pH of 6.5 can enhance metal dissolution (Campbell and Tessier, 1991; Pitt, 1995). Dissolved organic carbon (DOC) concentrations in runoff after 13 dry days of exposure are shown in Fig. 2d. High initial DOC concentrations in runoff from both asphalt types (136e151 mg L1) compared to concrete runoff (16e24 mg L1) were attributed to visible vegetative material (e.g. tree seeds, pollen) detained in the asphalt cavities, which resulted in a yellow runoff colour (up to 500 Pt/Co). The sharp decline in DOC concentrations during the first 10 min of runoff indicates that organic compounds did not leach from the asphalt material (as leaching would occur over the whole time of wash-off). 3.2. Contaminant yields Contaminant deposition yields (mg m2) and rates (mg m2 d1) were calculated from runoff concentration profiles and volumes for all three surface types (Table 2). Variability in contaminant yields (determined from the smooth asphalt boards, n ¼ 5 for 13 dry days) showed standard deviations between 24% and 34% for all 4 contaminants (Table 2), concurring with the variability reported in other experimental studies on contaminant accumulation (Davis and Birch, 2011). Since all boards were exposed under the same conditions at the same location, we expect similar variability for the other two surface types. After 13 antecedent dry days, 123e272 mg m2 TSS, 0.8e 3.5 mg m2 total Zn, 0.15e0.23 mg m2 total Cu and 0.028e 0.038 mg m2 total Pb accumulated on the different surfaces through atmospheric deposition alone (Table 2). Differences of TSS and zinc yields between asphalt and concrete (e.g. higher zinc yields on asphalt, higher TSS yields on concrete) were statistically significant at 95% confidence as demonstrated by a two-tailed t-test (p-values <0.02). Daily contaminant yields (mg m2 d1, Table 2) for concrete and asphalt surfaces after 6 dry exposure days are consistent with results from a previous related study investigating contaminant build-up for 7 days at the same location (Wicke et al., 2012). Furthermore, the range of daily contaminant yields (Table 2) were similar to average pollutant fluxes of urban background determined in a study on atmospheric deposition of heavy metals in the Sydney metropolitan area (in mg/m2 day1: TSS 19-30, zinc 0.118-0.13, copper 0.011-0.02, lead 0.012-0.029), but lower than
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D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
a
b
c
d
e
f
g
h
Fig. 1. Zn, Cu, Pb and TSS concentrations in runoff from coarse asphalt (14 mm aggregate) and concrete boards after exposure at a car park for 2e13 days.
pollutant fluxes measured directly beside major roads (Davis and Birch, 2011). These data highlight the significance of air-borne contaminant deposition in urban catchments. Contaminant accumulation occurs only for a limited period of time and asymptotically reaches a saturation maximum (Fig. 3aed) as previously described by Shaheen (1975) for particulates. Contaminant build-up amounts level off after at least 6 days of atmospheric exposure. Wind data recorded near the experimental site (Table 1) show similar wind conditions throughout the experiment reducing the likelihood that wind patterns affected contaminant accumulation after 6 days. Instead, it is believed that
an equilibrium is established between particles deposited and blown away from the surface, influenced somewhat by pavement physical characteristics. For TSS, similar characteristics of build-up were shown by Egodawatta and Goonetilleke (2006), Hossain et al. (2010) and Sartor et al. (1974), who report a maximum contaminant build-up after about 7 days. This study shows that depositions of zinc, copper and lead behave similarly. As the number of antecedent dry days increases, a decline in contaminant accumulation rate is observed. For instance, Zn build-up rates on asphalt for 2, 6 and 13 day periods were 0.83, 0.41 and 0.27 mg m2 d1, respectively (Table 2).
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
a
b
c
d
351
Fig. 2. (a) Zn, Cu and Pb concentrations in runoff from smooth (3 mm) asphalt boards exposed for 13 days on a car park (average, n ¼ 5, inset shows results for copper and lead at different scale), (bed) TSS, pH and DOC concentrations in runoff from smooth (3 mm) asphalt (average, n ¼ 5) compared to concrete board runoff (n ¼ 1) after being exposed for 13 days at a car park.
3.3. Modelling contaminant build-up and wash-off
B ¼
The SWMM model was chosen for this study’s application because it is a widely used hydrological and water quality simulation programme developed primarily for urban areas (Temprano et al., 2006; Tsihrintzis and Hamid, 1998). Furthermore, functions provided in SWMM have been successfully applied in other studies to model contaminant build-up and wash-off from road and roof surfaces (Hossain et al., 2010). Providing that these functions adequately describe the experimental data, resulting parameters are applicable for stormwater modelling under similar conditions.
Bmax $t Aþt
(1)
where B is the contaminant build-up [mg m2], Bmax is the maximum build-up [mg m2], A is the half saturation time [d], and t is the number of antecedent dry days. The half saturation time A represents the number of days it takes to reach half of the maximum build-up Bmax. For the exponential function (Equation (2)), proposed by Shaheen (1975), build-up follows an exponential curve that also approaches a maximum limit asymptotically:
B ¼ Bmax 1 eC1 t
3.3.1. Contaminant build-up The concept of contaminant build-up describes the contaminant accumulation process during dry weather periods. Since our experimental results indicate an asymptotic approximation to a maximum value (Fig. 3aed), the saturation (SAT, also referred to as Michaelis-Menton) and the exponential (EXP) functions were used that are based on those principles (Huber and Dickinson, 1988). The saturation function (Equation (1)) mathematically describes contaminant build-up that begins at a linear rate which continuously declines until a saturation value is reached:
(2)
with C1 being the build-up rate constant [d1]. The parameters Bmax, A and C1 were determined by minimizing the sum of the squared differences between modelled and experimental results using EXCEL solver (Microsoft EXCEL, 2007). Modelled results for both the saturation (SAT) and exponential (EXP) functions were plotted with measured contaminant build-up yields (from Table 2) in Fig. 3aed. Both modelled functions resulted in close representations of the actual experimental results (see R2-
Table 2 Contaminant yields (bold: amounts in mg m2, below: yields per day in mg m2 d1). n ¼ 5 for smooth asphalt (3 mm). Concrete Days TSS Total zinc Total copper Total lead
Asphalt (coarse)
Asphalt (smooth)
2
4
6
9
13
2
4
6
9
13
13
S(x)
85 43 0.29 0.15 0.12 0.058 0.018 0.0091
138 34 0.39 0.10 0.12 0.030 0.024 0.0061
234 39 0.65 0.11 0.19 0.032 0.037 0.0062
188 21 0.47 0.05 0.16 0.018 0.033 0.0037
272 21 0.77 0.06 0.21 0.016 0.038 0.0029
54 27 1.7 0.83 0.12 0.061 0.011 0.0054
78 19 2.3 0.56 0.16 0.040 0.020 0.0050
113 19 2.5 0.41 0.23 0.039 0.025 0.0042
102 11 3.6 0.40 0.20 0.022 0.029 0.0032
135 10 3.5 0.27 0.23 0.018 0.028 0.0021
123 9 2.9 0.23 0.15 0.011 0.035 0.0027
33.5 0.70 0.05 0.012
352
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
a
e
b
f
c
g
d
h
Fig. 3. Experimental and modelling results of build-up (aed) and wash-off (eeh) relationships for concrete and asphalt during 13 days of exposure.
values in Table 3), demonstrating that both functions are suitable for modelling urban contaminant build-up. Coefficients for half saturation time A, maximum build-up Bmax and the build-up rate constant C1 are summarized in Table 3. Values for A range from 2.4 to 6.4 days (average of 4 days) demonstrating contaminant loads accumulate over relatively short periods of time. Values for C1 were between 0.2 and 0.36 with little difference between pavement types (slightly lower for concrete, Table 3). These build-up constants are similar to a value of 0.3 determined by Temprano et al. (2006) for dust accumulation used in an SWMM model to predict suspended solids concentration in runoff from a residential area. They also aligned to values between 0.22 and 0.38 determined
in TSS build-up on residential roads (Hossain et al., 2010). Maximum build-up (Bmax) was up to 47 g ha1 zinc and 2.7 g ha1 copper on asphalt and 3.9 kg ha1 TSS and 0.5 g ha1 lead on concrete for an antecedent dry period of 13-days (Table 3). The exponential function resulted in 15e30% lower maximum build-up values for all contaminants (Table 3). Contaminant build-up is likely to be influenced by other factors beside number of antecedent dry days and pavement type. These could include distance from sources, traffic density, high wind speeds and wind direction. A change in any of these parameters could likely result in different contaminant deposition behaviour. Maximum build-up values in this study were determined in a low
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
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Table 3 Coefficients for build-up (SAT e saturation function, EXP e exponential function) and wash-off functions for concrete and asphalt surfaces. Concrete
Asphalt
Build-up
Wash-off
SAT
TSS Total zinc Total copper Total lead
EXP
Build-up
Wash-off
SAT
EXP
Bmax mg/m2
A [d]
R2
Bmax mg/m2
C1 [d1]
R2
C2
R2
Bmax mg/m2
A [d]
R2
Bmax mg/m2
C1 [d1]
R2
C2
R2
388 1.0 0.26 0.048
6.4 4.9 3.5 3.2
0.82 0.70 0.64 0.86
276 0.73 0.2 0.04
0.2 0.22 0.28 0.3
0.82 0.68 0.60 0.87
0.24 0.32 0.20 0.29
0.90 0.92 0.77 0.86
173 4.7 0.27 0.039
4.4 4.2 2.4 3.9
0.89 0.90 0.80 0.92
134 3.7 0.23 0.03
0.23 0.24 0.36 0.31
0.88 0.89 0.81 0.94
0.27 0.32 0.34 0.33
0.95 0.75 0.90 0.85
traffic density (15,000 vehicles/d) catchment at a distance >150 m away from the nearest road. Sansalone and Buchberger (1997) found that traffic density did influence air-borne contaminant deposition. Other studies have found an exponential decline in contaminant amounts with increasing distance from the roadway edge with a maximum reach of 20e50 m (Zafra et al., 2011) but usually investigated contaminant deposition along major roads (e.g. Davis and Birch, 2011). 3.3.2. Contaminant wash-off SWMM’s exponential wash-off equation describes wash-off as being dependent on the remaining amount of contaminants on the surface during each modelling time-step (Equation (3)):
W ¼ C2 $qC3 $B
(3)
where W is the wash-off rate [mg h1], q is the runoff rate (i.e. rainfall intensity) [mm h1], B is the remaining contaminant amount [mg], C2 is the wash-off coefficient and C3 is the wash-off exponent. Since rainfall intensity was kept constant, the wash-off exponent could not be independently determined and thus a value of C3 ¼ 1 was used. The value of 1.0 was adopted from a study by Temprano et al. (2006) to predict pollution in a residential urban area. They determined the wash-off exponent for this equation in SWMM to be 1.0, each for suspended solids, chemical oxygen demand and total nitrogen. Similarly, Hossain et al. (2010) determined values for C3 for wash-off of TSS from residential roads also to be around 1 (0.75e1.27). The wash-off load W was modelled with a time step of 1 min with C2 values determined for each contaminant and surface type using EXCEL solver (Microsoft EXCEL, 2007). Modelled curves reproduced experimental values well (Fig. 3eeh); resulting coefficients and respective R2 are listed in Table 3. Wash-off concentrations exhibited exponential decline behaviour in both the measured and modelled results (Fig. 1, 3eeh). Values for the wash-off coefficient (C2) range from 0.2 to 0.34 with slightly lower values for concrete indicating that contaminants are washed off a little faster from the smoother concrete (Table 3). A benefit in using exponential wash-off relationships in comparison to often applied event mean concentrations (EMC) is that these better represent actual contaminant behaviour as evidenced by the experimental data. 4. Conclusions This study applied the method of using portable boards made of different impervious pavement materials previously described by Wicke et al. (2012) to quantify air-borne contaminant deposition in a low traffic location >150 m from the nearest road. Accumulation of TSS and metal contaminants increased over the first week and levelled off thereafter, supporting theories that contaminant accumulation on impervious surfaces asymptotically approaches
a maximum. Comparison of different surface types showed approximately four times higher zinc concentrations in runoff from asphalt surfaces and two times higher TSS concentrations in runoff from concrete, which can likely be attributed to different surfaces roughness and compositions of the pavement types. Contaminant build-up and wash-off behaviour was then modelled to determine build-up and wash-off parameters (applicable for urban background locations) necessary to better predict contaminant behaviour in urban stormwater runoff when using modelling programs such as SWMM. Both exponential and saturation functions for contaminant build-up as well as the wash-off function used in this study were applicable to mathematically describe the build-up and wash-off process for key urban contaminants of interest in New Zealand; TSS, zinc, copper and lead. Further studies are necessary to investigate how wind behaviour, distance from contaminant sources, and other variables impact contaminant deposition on impermeable pavements as a means to help elucidate the complexities of urban contaminant deposition. Acknowledgements The authors wish to thank the following people, who significantly contributed to the outcome of this study: Ingrid Cooper and William Jacobson for sampling and analysis, Joseph Good for digestion of samples as preparation for total metal analysis, Christchurch City Council for funding a student, Fulton Hogan for supplying asphalt, and Tim Perigo for his help regarding construction of concrete boards. References Adachi, K., Tainosho, Y., 2004. Characterization of heavy metal particles embedded in tire dust. Environ. Int. 30, 1009e1017. ANZECC, 2000. Australian and New Zealand Guidelines for Fresh and Marine Water Quality. Australian and New Zealand Environment and Conservation Council. APHA, 2005. Standards Methods for the Examination of Water and Wastewater, first ed. American Public Health Association, Washington DC. Bahar, B., Herting, G., Wallinder, I.O., Hakkila, K., Leygraf, C., Virta, M., 2008. The interaction between concrete pavement and corrosion-induced copper runoff from buildings. Environ. Monit. Assess. 140 (1e3), 175e189. Beasley, G., Kneale, P., 2002. Reviewing the impact of metals and PAHs on macroinvertebrates in urban watercourses. Prog. Phys. Geog. 26 (2), 236e270. Bris, F.J., Garnaud, S., Apperry, N., Gonzalez, A., Mouchel, J.M., Chebbo, G., Thevenot, D.R., 1999. A street deposit sampling method for metal and hydrocarbon contamination assessment. Sci. Total Environ. 235, 211e220. Brown, J.N., Peake, B.M., 2006. Sources of heavy metals and polycyclic aromatic hydrocarbons in urban stormwater runoff. Sci. Total Environ. 359 (1e3), 145e155. BUWAL, 1996. Gewässerschutzmaßnahmen beim Straßenbau. Bundesamt für Umwelt, Wald und Landschaft, Bern, Schweiz. Schriftenreihe Umwelt Nr. 263. Campbell, P., Tessier, A., 1991. Biological availability of metals in sediments: analytical approaches. In: Vernet, J.P. (Ed.), Heavy Metals in the Environment. Elsevier, Amsterdam, pp. 161e174. Davies, P.J., Wright, I.A., Jonasson, O.J., Findlay, S.J., 2010. Impact of concrete and PVC pipes on urban water chemistry. Urban Water J. 7 (4), 233e241. Davis, B.S., Birch, G.F., 2011. Spatial distribution of bulk atmospheric deposition of heavy metals in metropolitan Sydney, Australia. Water Air Soil Pollut. 214, 147e162.
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Davis, A.P., Shokouhian, M., Ni, S.B., 2001. Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources. Chemosphere 44 (5), 997e1009. Deletic, A., Orr, D., 2005. Pollution Buildup on road surfaces. J. Environ. Eng. 131 (1), 49e59. Egodawatta, P., Goonetilleke, A., 2006. Characteristics of pollution build-up on residential road surfaces. In: Proceedings of the Seventh International Conference on Hydroscience and Engineering (ICHE 2006), Philadelphia, USA. Gobel, P., Dierkes, C., Coldewey, W.C., 2007. Storm water runoff concentration matrix for urban areas. J. Contam. Hydrol. 91 (1e2), 26e42. Gnecco, I., Berretta, C., Lanza, L.G., La Barbera, P., 2005. Storm water pollution in the urban environment of Genoa, Italy. Atmos. Res. 77, 60e73. Herngren, L., Goonetilleke, A., Ayoko, G.A., 2005a. Understanding heavy metal and suspended solids relationships in urban stormwater using simulated rainfall. J. Environ. Manage. 76 (2), 149e158. Herngren, L., Goonetilleke, A., Sukpum, R., De Silva, D.Y., 2005b. Rainfall simulation as a tool for urban water quality research. Environ. Eng. Sci. 22, 378e383. Hossain, I., Imteaz, M., Gato-Trinidad, S., Shanableh, A., 2010. Development of a catchment water quality model for continuous simulations of pollutants build-up and wash-off. Int. J. Civil Environ. Eng. 2, 210e217. Huber, W., Dickinson, R., 1988. Stormwater Management Model. Version 4, Part A. User’s Manual. U.S. Environmental Research Agency. Office of Research and Development. EPA/600/3e88/001a. Karlen, C., Wallinder, I.O., Heijerick, D., Leygraf, C., Janssen, C.R., 2001. Runoff rates and ecotoxicity of zinc induced by atmospheric corrosion. Sci. Total Environ. 277 (1e3), 169e180. Karlen, C., Wallinder, I.O., Heijerick, D., Leygraf, C., 2002. Runoff rates, chemical speciation and bioavailability of copper released from naturally patinated copper. Environ. Pollut. 120 (3), 691e700. Kayhanian, M., Stransky, C., Bay, S., Lau, S.L., Stenstrom, M.K., 2008. Toxicity of urban highway runoff with respect to storm duration. Sci. Total Environ. 389 (2e3), 386e406. Mahbub, P., Goonetilleke, A., Ayoko, G.A., Egodawatta, P., Yigitcanlar, T., 2011. Analysis of build-up of heavy metals and volatile organics on urban roads in gold coast, Australia. Water Sci. Technol. 63, 2077e2085. Mahbub, P., Ayoko, G.A., Goonetilleke, A., Egodawatta, P., Kokot, S., 2010. Impacts of traffic and rainfall characteristics on heavy metals build-up and wash-off from urban roads. Environ. Sci. Technol. 44, 8904e8910.
May, D.B., Sivakumar, M., 2009. Prediction of heavy metal concentrations in urban stormwater. Water Environ. J. 23, 247e254. O’Halloran, K., Cavagannah, J.H., Harding, J., 2008. Response of a New Zealand mayfly to toxicity from acid mine drainage. Environ. Toxicol. Chem. 27 (5), 1135e1140. Pennington, S.L., Webster-Brown, J.G., 2008. Stormwater runoff quality from copper roofing, Auckland, New Zealand. New Zeal. J. Mar. Fresh 42 (1), 99e108. Pitt, R.E., 1995. Biological effects of urban runoff discharges. In: Herricks, E.E. (Ed.), Stormwater Runoff and Receiving Systems. Impact, Monitoring, and Assessment. Lewis Publishers, Boca Raton, pp. 127e162. RAMM (Road Assessment, Management & Maintenance), Data from the Crash Analysis System (CAS) Accessed in July 2011, NZ Transport Agency, Wellington, New Zealand. Sansalone, J.J., Buchberger, S.G., 1997. Partitioning and first flush of metals in urban roadway storm water. J. Environ. Eng.-Asce 123 (2), 134e143. Sartor, J.D., Boyd, G.B., 1974. Water pollution aspects of street surface contaminants. J. WPCF (Water Pollut. Control Fed.) 46 (3), 458e467. Shaheen, D.G., 1975. Contributions of Urban Roadway Usage to Water Pollution. U.S. Environmental Protection Agency, Washington, D.C., USA. Report No. EPA-600/’275-004. Stumm, W., Morgan, J.J., 1996. Aquatic Chemistry, Chemical Equilibria and Rates in Natural Waters, third ed. John Wiley and Sons Inc., New York. Temprano, J., Arango, O., Cagiao, J., Suarez, J., Tejero, I., 2006. Stormwater quality calibration by SWMM: a case study in northern Spain. Water Sa 32 (1), 55e63. Tsihrintzis, V.A., Hamid, R., 1998. Runoff quality prediction from small urban catchments using SWMM. Hydrol. Process. 12 (2), 311e329. Vaze, J., Chiew, F.H.S., 2002. Experimental study of pollutant accumulation on an urban road surface. Urban Water 4, 379e389. Wicke, D., Cochrane, T.A., O’Sullivan, A.D., 2012. Atmospheric deposition and storm induced runoff of heavy metals from different impermeable urban surfaces. J. Environ. Monitor 14 (1), 209e216. Zafra, C.A., Temprano, J., Tejero, I., 2011. Distribution of the concentration of heavy metals associated with the sediment particles accumulated on road surfaces. Environ. Technol. 32 (9), 997e1008. Zanders, J.M., 2005. Road sediment: characterisation and implications for the performance of vegetated strips treating road runoff. Sci. Total Environ. 339 (1e 3), 41e47. Zoppou, C., 2001. Review of urban storm water models. Environ. Modell. Softw. 16 (3), 195e231.
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Build-up dynamics of heavy metals deposited on impermeable urban surfaces D. Wicke*, T.A. Cochrane, A. O’Sullivan University of Canterbury, Department of Civil and Natural Resources Engineering, Private Bag 4800, Christchurch 8140, New Zealand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 January 2012 Received in revised form 29 August 2012 Accepted 10 September 2012 Available online 6 October 2012
A method using thin boards (3 cm thick, 0.56 m2) comprising different paving materials typically used in urban environments (2 asphalt types and concrete) was employed to specifically investigate air-borne deposition dynamics of TSS, zinc, copper and lead. Boards were exposed at an urban car park near vehicular traffic to determine the rate of contaminant build-up over a 13-day dry period. Concentration profiles from simulated rainfall wash-off were used to determine contaminant yields at different antecedent dry days. Maximum contaminant yields after 13 days of exposure were 2.7 kg ha1 for TSS, 35 g ha1 zinc, 2.3 g ha1 copper and 0.4 g ha1 lead. Accumulation of all contaminants increased over the first week and levelled off thereafter, supporting theoretical assumptions that contaminant accumulation on impervious surfaces asymptotically approaches a maximum. Comparison of different surface types showed approximately four times higher zinc concentrations in runoff from asphalt surfaces and two times higher TSS concentrations in runoff from concrete, which is attributed to different physical and chemical compositions of the pavement types. Contaminant build-up and wash-off behaviours were modelled using exponential and saturation functions commonly applied in the US EPA’s Stormwater Management Model (SWMM) showing good correlation between measured and modelled concentrations. Maximum build-up, half-saturation time, build-up rate constants and wash-off coefficients, necessary for stormwater contaminant modelling, were determined for the four contaminants studied. These parameters are required to model contaminant concentrations in urban runoff assisting in stormwater management decisions. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Air-borne deposition Pavement Heavy metals TSS Stormwater runoff
1. Introduction Stormwater runoff from urban areas is recognized as a major source of water pollution as contaminants deposited on impervious surfaces are washed off during rain events. Key inorganic stormwater contaminants include total suspended solids (TSS) and heavy metals, particularly zinc, copper and lead (Gobel et al., 2007; Brown and Peake, 2006). Various studies have identified the traffic sector as a main source of these metals, which originate mainly from tyre wear and brake lining abrasion (Davis et al., 2001; Adachi and Tainosho, 2004; Zanders, 2005). However, only a small portion of the pollutants emitted from traffic becomes entrapped in storm water runoff originating directly from the road; the remaining part drifts to the immediate vicinity by wind during dry days and spray action during precipitation (Gobel et al., 2007; BUWAL, 1996). Furthermore, some airborne heavy metals can travel far and will contribute to contaminant build-up on urban surfaces through both dry and wet deposition within a large area (Davis and Birch, 2011).
* Corresponding author. Tel.: þ64 3 364 2378. E-mail address: [email protected] (D. Wicke). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2012.09.005
Copper (Cu) and zinc (Zn) in stormwater can also originate from roofing materials and guttering (Karlen et al., 2001, 2002; Pennington and Webster-Brown, 2008). Due to its toxicity effects, untreated storm runoff negatively impacts aquatic ecosystems in receiving waterways that typically serve as stormwater drainage systems (Beasley and Kneale, 2002; Kayhanian et al., 2008). A range of studies quantifying contaminant build-up on roads have been conducted over the past several decades (Gobel et al., 2007). Methods used to quantify road contaminant build-up and wash-off include using vacuum cleaners to capture dry and wet contaminant deposition (Bris et al., 1999), rainfall simulators to wash-off portions of roads (Herngren et al., 2005a), and sampling under natural rainfall events with automated samplers (Gnecco et al., 2005). Average daily traffic, volume over capacity ratio, and surface texture depth have been identified as having strong correlations with the build-up of heavy metals in roads (Mahbub et al., 2011; Deletic and Orr, 2005). Other studies have suggested that background atmospheric deposition, distance from traffic, and meteorological effects are also significant factors in the build-up process (Mahbub et al., 2010; Deletic and Orr, 2005). Antecedent conditions are of particular importance as build-up over dry days occurs relatively quickly after a rainfall event, but
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slows down after several days due to redistribution induced by wind (Vaze, and Chiew, 2002). This is consistent with early reports investigating contaminant accumulation on road surfaces, which found that particulate loadings begin to level off after 3e4 days as portions of the material may be picked up by passing traffic (Shaheen, 1975). A similar pattern of particulate accumulation on residential roads approaching a maximum level after about 7 days has been observed by Egodawatta and Goonetilleke (2006) and earlier by Sartor and Boyd (1974). However, it remains unclear if contaminants deposited on surfaces not directly in contact with traffic (i.e. airborne contaminants) show similar accumulation behaviour. In a recent related study (Wicke et al., 2012), we found that heavy metals accumulate at locations >50 m from car traffic (in a carpark) and >150 m from the nearest road. The resulting contaminant yields were comparable to atmospheric deposition at background locations and low traffic volume roads (2000 vehicles/ day) determined for a residential area in Sydney (Davis and Birch, 2011). Much of the current work on quantifying heavy metal build-up is used in numerical models aiming to predict stormwater contaminant discharge from larger urban areas (May and Sivakumar, 2009). A wide range of stormwater runoff and contamination simulation models for urban catchments were reviewed by Zoppou (2001). Contaminant behaviour in most models is simulated using mathematical relationships, usually incorporating contaminant accumulation over time (build-up function) and subsequent dislodgement during a storm event (wash-off function). One of the most well-known stormwater models is the US Environmental Protection Agency’s stormwater management model (SWMM), which allows users to select from a range of available functions to mathematically describe contaminant build-up and wash-off (Huber and Dickinson, 1988). However, in order to accurately model different contaminant behaviour on different pavement surfaces, specific coefficients must be provided, which need to be derived from experimental data. The objective of this study was thus to quantify the build-up dynamics of heavy metals and TSS specifically from atmospheric deposition over time on different impervious urban surfaces. These surfaces were excluded from vehicle traffic. Differences in contaminant build-up behaviour on asphalt and concrete surfaces were specifically distinguished. A method that uses thin boards of these pavement types (previously detailed by Wicke et al., 2012) was employed for this purpose. Contaminant concentrations from the experiments were then used to derive build-up and wash-off functions that mathematically describe contaminant behaviour for asphalt and concrete surfaces, essential parameter inputs for stormwater contaminant modelling. 2. Materials and methods Fifteen thin asphalt and concrete boards (75 cm L 75 cm W 3 cm height) were used to capture air-borne contaminants depositing in an urban catchment. Each board was filled with one of three impervious materials (five boards of each material, poured in 2009) to replicate different surface roughness and composition in common urban pavements: concrete, ‘smooth’ asphalt of 3 mm max. aggregate size, and ‘coarse’ asphalt of 14 mm max. aggregate size. Detailed descriptions of the board materials (including concrete composition), the method to assess differences in surface roughness, and further details on the methodology can be found in Wicke et al. (2012). Standard deviations in surface elevation used to quantify the surface roughness were 0.13 mm for concrete, 0.51 mm for smooth asphalt and 0.76 mm for coarse asphalt (Wicke et al., 2012).
2.1. Exposure of boards and rainfall simulation To investigate the build-up of particulates (total suspended solids, TSS) and three heavy metals (zinc, copper, lead), boards were exposed over a 13-day dry period on the largest carpark of the University of Canterbury (1.6 ha, 650 lots) in Christchurch, New Zealand. The campus is surrounded primarily by residential area and main roads are a distance of 150e600 m away (traffic volumes: 13,000e15,000 vehicles/d at distances of 150e300 m, and 32,000 vehicles/d at distances of 600 m away; RAMM, 2011). All 15 boards were placed on a blocked-off (unused) parking area preventing cars from parking or driving over the experimental boards. A wireless camera (DCS-920, D-Link Corporation) overlooking the investigated carpark area was installed and programmed to take a photograph every 5 min verifying that no traffic breached the experimental zone area or that no tampering occurred with the experimental set-up. To determine contaminant build-up over time, one pair of boards (coarse asphalt and concrete) were collected after 2, 4, 6, 9, and 13 dry days. The five smooth asphalt boards (replicates) were collected after 13 dry days to assess variability between replicates. Boards were then carefully covered with cardboard sheets to prevent cross contamination, transported to the laboratory, and placed under a rainfall simulator at an inclination of 4 degrees to generate surface runoff. A two-nozzle (Veerjet 80100) Norton type rainfall simulator (Herngren et al., 2005b) was used to simulate rainfall for contaminant wash-off. A rainfall intensity of 20 mm h1 was applied for 60 min for each experimental run, resulting in a rainfall depth that is comparable to a 2 yr return period for a 2 h rain event in Christchurch. This represents a high local rainfall intensity (99% of Christchurch rainfall is <20 mm h1, measured every minute for the past 3 years with a PARSIVEL laser disdrometer, Ott, Germany). This relatively high rainfall intensity was chosen to ensure full contaminant dislodgement from the pavement surfaces so that total contaminant deposition could be quantified. Droplet size distributions generated by the rainfall simulator and those from a local natural rain event (45 min duration and peak intensity of 20 mm h1) were very similar (Wicke et al., 2012). Wind speed and direction were measured at a nearby weather station (Campbell Scientific) located 300 m from the experimental site (Table 1). Tap water used for the simulated rainfall was pre-filtered through a 10 mm cartridge filter and adjusted to a pH of 6.0 using concentrated nitric acid in order to simulate the pH of local rainwater in Christchurch. During pH-adjustment, the pH-buffering capacity of the tap water became depleted, thus making the feed water susceptible to pH changes as occurs with natural rain. Specific conductance of the water after pH-adjustment was 180 mS cm1. Local rainfall pH measured on-campus during the experimental period was comparable to reported rainfall pH in Auckland, New Zealand ranging from 5.8 to 6.4 (Pennington and Webster-Brown, 2008). Results were adjusted to account for background feed water metal concentrations (measured before and Table 1 Wind speeds during time of contaminant accumulation. Days
0e2 2e4 4e6 6e9 9e13
Daily average wind speeds
Daily max wind speeds
[m/s]
Beaufort #a
[m/s]
Beaufort #a
4.1e4.7 2.4e6.3 2.6e4.4 2.4e4.0 2.7e4.7
3 2e4 2e3 2e3 2e3
7.5e7.9 6.0e9.5 6.7e11.2 6.0e8.6 5.7e9.0
4 4e5 4e5 4e5 4e5
a Beaufort scale: 2-light breeze, 3-gentle breeze, 4-moderate breeze, 5-fresh breeze.
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after each experiment; <0.5 mg Pb L1, <10 mg Cu L1, <40 mg Zn L1). Prior to their deployment, all boards underwent an initial ‘flushing’ with simulated rainfall by applying the highest possible rainfall intensity of 120 mm h1 for 15 min. Control samples taken at the end of the washing step showed low concentrations of zinc (15.6 mg Zn L1 from concrete, 9.4 mg Zn L1 from smooth asphalt and 37.4 mg Zn L1 from coarse asphalt), whereas copper, lead and TSS concentrations were below detection limits. 2.2. Chemical analysis Samples were collected from runoff in new polypropylene containers at 0 (first runoff), 5, 10, 20, 40 and 60 min after applying the simulated rainfall. The first two samples had a volume of 300 mL; 700 mL volumes were collected thereafter. Samples were instantaneously measured for pH (YSI Model 60 pH field meter) and turbidity (Hach Model 2100P portable turbidimeter) with instruments pre-calibrated on the day of the experimental runs. Heavy metal concentrations (Zn, Cu and Pb) were determined by ICP-MS (Agilent) following APHA Method 3125B after HNO3 digestion (APHA, 2005). Total metal digestions were prepared by thoroughly mixing samples on a magnetic stir plate and transferring 25 mL to a 50 mL polypropylene centrifuge tube. After the addition of 5 mL concentrated (69%) nitric acid (Fisher, trace analysis grade), tubes were placed in a heating block and samples were boiled for 1 h. Cooled samples were then filtered through an encapsulated 0.45 mm PVDF filter (47 mm, Labserv) and analysed via ICP-MS. Total suspended solid (TSS) concentrations were measured within 24 h following APHA Method 2540D (APHA, 2005). Dissolved organic carbon (DOC) concentrations were analysed using an Apollo 9000 TOC Analyzer (Teledyne Tekmar, US) after 0.45 mm filtration. Quality assurance protocols including blanks, duplicates (10% of samples), spiked samples (5% of samples) and instrument calibration were conducted throughout each batch analysis and adhered to the QA/QC procedures detailed in sampling and analysis (e.g. APHA) methods. 3. Results and discussion 3.1. Runoff concentrations Concentrations for total heavy metals (Zn, Cu and Pb) and total suspended solids (TSS) determined in runoff from concrete and coarse asphalt boards exposed for 2, 4, 6, 9, and 13 dry days are presented in Fig. 1. Results for the five smooth asphalt boards exposed for 13 dry days are shown in Fig. 2a. Maximum total heavy metal concentrations from all surface types in initial runoff were 1121 mg Zn L1 and 85.3 mg Cu L1 from the coarse asphalt and 15.4 mg Pb L1 from concrete (Fig. 1). These first flush runoff concentrations exceeded relevant toxicity-based guideline values for fresh and marine water quality (ANZECC, 2000) several-fold (e.g. the 90% trigger value for zinc of 15 mg L1 was exceeded 75fold and the 90% trigger value for copper of 1.8 mg L1 was exceeded 47-fold) which would negatively affect local waterways receiving untreated runoff (O’Halloran and Harding, 2008; Beasley and Kneale, 2002). Concentrations for all contaminants showed exponential decline for all three impervious surface types, concurring with a previous related study (Wicke et al., 2012). Furthermore, concentration profiles for all contaminants increased with increasing exposure time, typically up to 6 days with not much increase thereafter (Fig. 1). This indicates that contaminants accumulate up to a maximum amount in a relatively short period of time.
349
Maximum TSS concentrations after 13 dry days of exposure were higher in runoff from concrete (64 mg L1) than from coarse asphalt (37 mg L1, Fig. 2b). Particles deposited on the smooth concrete surfaces were more easily dislodged during wash-off compared to the rougher asphalt surface, whose cavities and possibly greater surface adhesion properties impeded particulate wash-off (Wicke et al., 2012). Zinc and copper concentrations were higher in runoff from asphalt, while lead (being mainly particlebound in stormwater runoff, e.g. Sansalone and Buchberger, 1997) showed higher concentrations in runoff from concrete boards (Fig. 1 and Wicke et al., 2012). This was attributed to distinct differences in runoff pH between the two pavement types. The average runoff pH from asphalt was 5.3 vs. 8.9 from concrete (Fig. 2c). The increased runoff pH from concrete boards was attributed to the dissolution of calcium hydrates at the concrete surface (Davies et al., 2010), whereas the lower asphalt pH during the initial 20 min of runoff (pH: 4.3e5.5 compared to the feed water pH of 6) may have resulted from localized metal hydrolyzation during initial surface wash-off (Stumm and Morgan, 1996). Particulate metals detained in the cavities of the asphalt most likely became dissolved at a lower pH resulting in higher metal concentrations in the wash-off. The pH can greatly influence metal speciation because an increased pH can lead to complexation and hence reduction of the bioavailable metal fraction (Bahar et al., 2008), whereas a lower pH of 6.5 can enhance metal dissolution (Campbell and Tessier, 1991; Pitt, 1995). Dissolved organic carbon (DOC) concentrations in runoff after 13 dry days of exposure are shown in Fig. 2d. High initial DOC concentrations in runoff from both asphalt types (136e151 mg L1) compared to concrete runoff (16e24 mg L1) were attributed to visible vegetative material (e.g. tree seeds, pollen) detained in the asphalt cavities, which resulted in a yellow runoff colour (up to 500 Pt/Co). The sharp decline in DOC concentrations during the first 10 min of runoff indicates that organic compounds did not leach from the asphalt material (as leaching would occur over the whole time of wash-off). 3.2. Contaminant yields Contaminant deposition yields (mg m2) and rates (mg m2 d1) were calculated from runoff concentration profiles and volumes for all three surface types (Table 2). Variability in contaminant yields (determined from the smooth asphalt boards, n ¼ 5 for 13 dry days) showed standard deviations between 24% and 34% for all 4 contaminants (Table 2), concurring with the variability reported in other experimental studies on contaminant accumulation (Davis and Birch, 2011). Since all boards were exposed under the same conditions at the same location, we expect similar variability for the other two surface types. After 13 antecedent dry days, 123e272 mg m2 TSS, 0.8e 3.5 mg m2 total Zn, 0.15e0.23 mg m2 total Cu and 0.028e 0.038 mg m2 total Pb accumulated on the different surfaces through atmospheric deposition alone (Table 2). Differences of TSS and zinc yields between asphalt and concrete (e.g. higher zinc yields on asphalt, higher TSS yields on concrete) were statistically significant at 95% confidence as demonstrated by a two-tailed t-test (p-values <0.02). Daily contaminant yields (mg m2 d1, Table 2) for concrete and asphalt surfaces after 6 dry exposure days are consistent with results from a previous related study investigating contaminant build-up for 7 days at the same location (Wicke et al., 2012). Furthermore, the range of daily contaminant yields (Table 2) were similar to average pollutant fluxes of urban background determined in a study on atmospheric deposition of heavy metals in the Sydney metropolitan area (in mg/m2 day1: TSS 19-30, zinc 0.118-0.13, copper 0.011-0.02, lead 0.012-0.029), but lower than
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a
b
c
d
e
f
g
h
Fig. 1. Zn, Cu, Pb and TSS concentrations in runoff from coarse asphalt (14 mm aggregate) and concrete boards after exposure at a car park for 2e13 days.
pollutant fluxes measured directly beside major roads (Davis and Birch, 2011). These data highlight the significance of air-borne contaminant deposition in urban catchments. Contaminant accumulation occurs only for a limited period of time and asymptotically reaches a saturation maximum (Fig. 3aed) as previously described by Shaheen (1975) for particulates. Contaminant build-up amounts level off after at least 6 days of atmospheric exposure. Wind data recorded near the experimental site (Table 1) show similar wind conditions throughout the experiment reducing the likelihood that wind patterns affected contaminant accumulation after 6 days. Instead, it is believed that
an equilibrium is established between particles deposited and blown away from the surface, influenced somewhat by pavement physical characteristics. For TSS, similar characteristics of build-up were shown by Egodawatta and Goonetilleke (2006), Hossain et al. (2010) and Sartor et al. (1974), who report a maximum contaminant build-up after about 7 days. This study shows that depositions of zinc, copper and lead behave similarly. As the number of antecedent dry days increases, a decline in contaminant accumulation rate is observed. For instance, Zn build-up rates on asphalt for 2, 6 and 13 day periods were 0.83, 0.41 and 0.27 mg m2 d1, respectively (Table 2).
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
a
b
c
d
351
Fig. 2. (a) Zn, Cu and Pb concentrations in runoff from smooth (3 mm) asphalt boards exposed for 13 days on a car park (average, n ¼ 5, inset shows results for copper and lead at different scale), (bed) TSS, pH and DOC concentrations in runoff from smooth (3 mm) asphalt (average, n ¼ 5) compared to concrete board runoff (n ¼ 1) after being exposed for 13 days at a car park.
3.3. Modelling contaminant build-up and wash-off
B ¼
The SWMM model was chosen for this study’s application because it is a widely used hydrological and water quality simulation programme developed primarily for urban areas (Temprano et al., 2006; Tsihrintzis and Hamid, 1998). Furthermore, functions provided in SWMM have been successfully applied in other studies to model contaminant build-up and wash-off from road and roof surfaces (Hossain et al., 2010). Providing that these functions adequately describe the experimental data, resulting parameters are applicable for stormwater modelling under similar conditions.
Bmax $t Aþt
(1)
where B is the contaminant build-up [mg m2], Bmax is the maximum build-up [mg m2], A is the half saturation time [d], and t is the number of antecedent dry days. The half saturation time A represents the number of days it takes to reach half of the maximum build-up Bmax. For the exponential function (Equation (2)), proposed by Shaheen (1975), build-up follows an exponential curve that also approaches a maximum limit asymptotically:
B ¼ Bmax 1 eC1 t
3.3.1. Contaminant build-up The concept of contaminant build-up describes the contaminant accumulation process during dry weather periods. Since our experimental results indicate an asymptotic approximation to a maximum value (Fig. 3aed), the saturation (SAT, also referred to as Michaelis-Menton) and the exponential (EXP) functions were used that are based on those principles (Huber and Dickinson, 1988). The saturation function (Equation (1)) mathematically describes contaminant build-up that begins at a linear rate which continuously declines until a saturation value is reached:
(2)
with C1 being the build-up rate constant [d1]. The parameters Bmax, A and C1 were determined by minimizing the sum of the squared differences between modelled and experimental results using EXCEL solver (Microsoft EXCEL, 2007). Modelled results for both the saturation (SAT) and exponential (EXP) functions were plotted with measured contaminant build-up yields (from Table 2) in Fig. 3aed. Both modelled functions resulted in close representations of the actual experimental results (see R2-
Table 2 Contaminant yields (bold: amounts in mg m2, below: yields per day in mg m2 d1). n ¼ 5 for smooth asphalt (3 mm). Concrete Days TSS Total zinc Total copper Total lead
Asphalt (coarse)
Asphalt (smooth)
2
4
6
9
13
2
4
6
9
13
13
S(x)
85 43 0.29 0.15 0.12 0.058 0.018 0.0091
138 34 0.39 0.10 0.12 0.030 0.024 0.0061
234 39 0.65 0.11 0.19 0.032 0.037 0.0062
188 21 0.47 0.05 0.16 0.018 0.033 0.0037
272 21 0.77 0.06 0.21 0.016 0.038 0.0029
54 27 1.7 0.83 0.12 0.061 0.011 0.0054
78 19 2.3 0.56 0.16 0.040 0.020 0.0050
113 19 2.5 0.41 0.23 0.039 0.025 0.0042
102 11 3.6 0.40 0.20 0.022 0.029 0.0032
135 10 3.5 0.27 0.23 0.018 0.028 0.0021
123 9 2.9 0.23 0.15 0.011 0.035 0.0027
33.5 0.70 0.05 0.012
352
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
a
e
b
f
c
g
d
h
Fig. 3. Experimental and modelling results of build-up (aed) and wash-off (eeh) relationships for concrete and asphalt during 13 days of exposure.
values in Table 3), demonstrating that both functions are suitable for modelling urban contaminant build-up. Coefficients for half saturation time A, maximum build-up Bmax and the build-up rate constant C1 are summarized in Table 3. Values for A range from 2.4 to 6.4 days (average of 4 days) demonstrating contaminant loads accumulate over relatively short periods of time. Values for C1 were between 0.2 and 0.36 with little difference between pavement types (slightly lower for concrete, Table 3). These build-up constants are similar to a value of 0.3 determined by Temprano et al. (2006) for dust accumulation used in an SWMM model to predict suspended solids concentration in runoff from a residential area. They also aligned to values between 0.22 and 0.38 determined
in TSS build-up on residential roads (Hossain et al., 2010). Maximum build-up (Bmax) was up to 47 g ha1 zinc and 2.7 g ha1 copper on asphalt and 3.9 kg ha1 TSS and 0.5 g ha1 lead on concrete for an antecedent dry period of 13-days (Table 3). The exponential function resulted in 15e30% lower maximum build-up values for all contaminants (Table 3). Contaminant build-up is likely to be influenced by other factors beside number of antecedent dry days and pavement type. These could include distance from sources, traffic density, high wind speeds and wind direction. A change in any of these parameters could likely result in different contaminant deposition behaviour. Maximum build-up values in this study were determined in a low
D. Wicke et al. / Journal of Environmental Management 113 (2012) 347e354
353
Table 3 Coefficients for build-up (SAT e saturation function, EXP e exponential function) and wash-off functions for concrete and asphalt surfaces. Concrete
Asphalt
Build-up
Wash-off
SAT
TSS Total zinc Total copper Total lead
EXP
Build-up
Wash-off
SAT
EXP
Bmax mg/m2
A [d]
R2
Bmax mg/m2
C1 [d1]
R2
C2
R2
Bmax mg/m2
A [d]
R2
Bmax mg/m2
C1 [d1]
R2
C2
R2
388 1.0 0.26 0.048
6.4 4.9 3.5 3.2
0.82 0.70 0.64 0.86
276 0.73 0.2 0.04
0.2 0.22 0.28 0.3
0.82 0.68 0.60 0.87
0.24 0.32 0.20 0.29
0.90 0.92 0.77 0.86
173 4.7 0.27 0.039
4.4 4.2 2.4 3.9
0.89 0.90 0.80 0.92
134 3.7 0.23 0.03
0.23 0.24 0.36 0.31
0.88 0.89 0.81 0.94
0.27 0.32 0.34 0.33
0.95 0.75 0.90 0.85
traffic density (15,000 vehicles/d) catchment at a distance >150 m away from the nearest road. Sansalone and Buchberger (1997) found that traffic density did influence air-borne contaminant deposition. Other studies have found an exponential decline in contaminant amounts with increasing distance from the roadway edge with a maximum reach of 20e50 m (Zafra et al., 2011) but usually investigated contaminant deposition along major roads (e.g. Davis and Birch, 2011). 3.3.2. Contaminant wash-off SWMM’s exponential wash-off equation describes wash-off as being dependent on the remaining amount of contaminants on the surface during each modelling time-step (Equation (3)):
W ¼ C2 $qC3 $B
(3)
where W is the wash-off rate [mg h1], q is the runoff rate (i.e. rainfall intensity) [mm h1], B is the remaining contaminant amount [mg], C2 is the wash-off coefficient and C3 is the wash-off exponent. Since rainfall intensity was kept constant, the wash-off exponent could not be independently determined and thus a value of C3 ¼ 1 was used. The value of 1.0 was adopted from a study by Temprano et al. (2006) to predict pollution in a residential urban area. They determined the wash-off exponent for this equation in SWMM to be 1.0, each for suspended solids, chemical oxygen demand and total nitrogen. Similarly, Hossain et al. (2010) determined values for C3 for wash-off of TSS from residential roads also to be around 1 (0.75e1.27). The wash-off load W was modelled with a time step of 1 min with C2 values determined for each contaminant and surface type using EXCEL solver (Microsoft EXCEL, 2007). Modelled curves reproduced experimental values well (Fig. 3eeh); resulting coefficients and respective R2 are listed in Table 3. Wash-off concentrations exhibited exponential decline behaviour in both the measured and modelled results (Fig. 1, 3eeh). Values for the wash-off coefficient (C2) range from 0.2 to 0.34 with slightly lower values for concrete indicating that contaminants are washed off a little faster from the smoother concrete (Table 3). A benefit in using exponential wash-off relationships in comparison to often applied event mean concentrations (EMC) is that these better represent actual contaminant behaviour as evidenced by the experimental data. 4. Conclusions This study applied the method of using portable boards made of different impervious pavement materials previously described by Wicke et al. (2012) to quantify air-borne contaminant deposition in a low traffic location >150 m from the nearest road. Accumulation of TSS and metal contaminants increased over the first week and levelled off thereafter, supporting theories that contaminant accumulation on impervious surfaces asymptotically approaches
a maximum. Comparison of different surface types showed approximately four times higher zinc concentrations in runoff from asphalt surfaces and two times higher TSS concentrations in runoff from concrete, which can likely be attributed to different surfaces roughness and compositions of the pavement types. Contaminant build-up and wash-off behaviour was then modelled to determine build-up and wash-off parameters (applicable for urban background locations) necessary to better predict contaminant behaviour in urban stormwater runoff when using modelling programs such as SWMM. Both exponential and saturation functions for contaminant build-up as well as the wash-off function used in this study were applicable to mathematically describe the build-up and wash-off process for key urban contaminants of interest in New Zealand; TSS, zinc, copper and lead. Further studies are necessary to investigate how wind behaviour, distance from contaminant sources, and other variables impact contaminant deposition on impermeable pavements as a means to help elucidate the complexities of urban contaminant deposition. Acknowledgements The authors wish to thank the following people, who significantly contributed to the outcome of this study: Ingrid Cooper and William Jacobson for sampling and analysis, Joseph Good for digestion of samples as preparation for total metal analysis, Christchurch City Council for funding a student, Fulton Hogan for supplying asphalt, and Tim Perigo for his help regarding construction of concrete boards. References Adachi, K., Tainosho, Y., 2004. Characterization of heavy metal particles embedded in tire dust. Environ. Int. 30, 1009e1017. ANZECC, 2000. Australian and New Zealand Guidelines for Fresh and Marine Water Quality. Australian and New Zealand Environment and Conservation Council. APHA, 2005. Standards Methods for the Examination of Water and Wastewater, first ed. American Public Health Association, Washington DC. Bahar, B., Herting, G., Wallinder, I.O., Hakkila, K., Leygraf, C., Virta, M., 2008. The interaction between concrete pavement and corrosion-induced copper runoff from buildings. Environ. Monit. Assess. 140 (1e3), 175e189. Beasley, G., Kneale, P., 2002. Reviewing the impact of metals and PAHs on macroinvertebrates in urban watercourses. Prog. Phys. Geog. 26 (2), 236e270. Bris, F.J., Garnaud, S., Apperry, N., Gonzalez, A., Mouchel, J.M., Chebbo, G., Thevenot, D.R., 1999. A street deposit sampling method for metal and hydrocarbon contamination assessment. Sci. Total Environ. 235, 211e220. Brown, J.N., Peake, B.M., 2006. Sources of heavy metals and polycyclic aromatic hydrocarbons in urban stormwater runoff. Sci. Total Environ. 359 (1e3), 145e155. BUWAL, 1996. Gewässerschutzmaßnahmen beim Straßenbau. Bundesamt für Umwelt, Wald und Landschaft, Bern, Schweiz. Schriftenreihe Umwelt Nr. 263. Campbell, P., Tessier, A., 1991. Biological availability of metals in sediments: analytical approaches. In: Vernet, J.P. (Ed.), Heavy Metals in the Environment. Elsevier, Amsterdam, pp. 161e174. Davies, P.J., Wright, I.A., Jonasson, O.J., Findlay, S.J., 2010. Impact of concrete and PVC pipes on urban water chemistry. Urban Water J. 7 (4), 233e241. Davis, B.S., Birch, G.F., 2011. Spatial distribution of bulk atmospheric deposition of heavy metals in metropolitan Sydney, Australia. Water Air Soil Pollut. 214, 147e162.
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