Multi-isotope signatures (Cu, Zn, Pb) of different particle sizes in road-deposited sediments: a case study from industrial area

Abstract

Road-deposited sediments (RDS) are major sources of heavy metal contamination in urban areas and adversely affect surrounding environments and human health. Multi-isotope combinations (Cu, Zn, and Pb), which serve as environmental tracers, enable the identification and management of metal contaminants in RDS. Here, we present Cu, Zn, and Pb isotopic data for the first time in size-fractionated RDS samples collected from industrial areas to describe the relationship between the RDS and total suspended solids (TSS) in runoff, and to explore the feasibility of using multi-isotopes to identify sources of metal contamination. RDS in the industrial study areas had high concentrations of Cu, Zn, and Pb, and their δ⁶⁵Cu_AE647, δ⁶⁶Zn_IRMM3702, and ²⁰⁶Pb/²⁰⁷Pb values ranged from − 0.33 to + 0.73‰, − 0.36 to + 0.01‰, and 1.1418 to 1.1616, respectively. The variation in δ⁶⁵Cu_AE647 (δ⁶⁵Cu_max-min) was larger than that of δ⁶⁶Zn_IRMM3702 (i.e., δ⁶⁶Zn_max-min), and the isotope values of Zn and Pb (²⁰⁶Pb/²⁰⁷Pb) tended to increase with the concentrations of these elements. Meanwhile, the fine RDS particles (< 63 µm) had similar Cu, Zn, and Pb isotopic compositions to those of TSS. Hierarchical cluster analyses revealed that the < 63 µm RDS fractions were associated with the TSS. Our results also showed that a combination of Pb and either Cu or Zn could be used to distinguish between RDS and non-exhaust emissions (e.g., brake pads, tires, etc.). Multi-isotope approaches utilizing Cu, Zn, and Pb and more robust isotopic data on individual sources of metal contamination could be useful for identifying pollution sources and understanding their environmental impacts.

Introduction

Road surfaces are hot spots for heavy metal pollution because of the accumulation of road-deposited sediments (RDS) via human activities. Road-deposited sediments in urban environments are highly polluted with Cu, Zn, and Pb due to traffic-related activities, such as tire, and brake pad wear and engine emissions (Adamiec et al. 2016; Amato et al. 2011; Iijima et al. 2007; Jeong et al. 2020a; Lanzerstorfer 2020). They are a major source of metal pollution in surrounding atmospheric particles, roadside soils, and stream sediments, as they can be readily transported into aquatic and atmospheric environments by stormwater runoff and resuspension by the wind (Bretón et al. 2019; Jeong et al. 2020b; Khalid et al. 2018; Nawrot et al. 2020; Popoola et al. 2017; Wang and Zhang 2018). Heavy metal concentrations such as Cu, Zn, and Pb in RDS have been used as tracers to identify metal pollution associated with traffic activities, but they are of limited use in discerning the exact source of pollution for each metal (Amato et al. 2009, 2011, 2020c; Bourotte et al. 2019; Hwang et al. 2016; Jeong et al. 2020a).

Many researchers have determined the isotopic compositions of single elements, such as Cu (Little et al. 2017; Song et al. 2016; Wang et al. 2020), Zn (Desaulty et al. 2020; Spinks and Uvarova 2019; Tu et al. 2020), and Pb (Gmochowska et al. 2019; Graney et al. 2019; Mil-Homens et al. 2017) during their environmental pollution studies. In a study area with a simple pollution source, it is possible to classify the pollutant source through a single isotope study. Heavy metals in the environment are derived from complex mixtures of natural and anthropogenic sources introduced through various pollution pathways and most environments are contaminated with many metals. In order to overcome the limitations of single elements and to distinguish multiple metal contamination sources, isotopic studies are now being conducted using combinations of two elements, namely Cu and Pb (Jeong et al. 2020b; Mihaljevič et al. 2019), Cu and Zn (Araújo et al. 2019a; Novak et al. 2016), and Zn and Pb (Rosca et al. 2019). Determining multi-elements isotopic compositions of Cu, Zn, and Pb can be a powerful tool for more accurate identification of various sources of pollution. For example, multi-isotope approaches have been applied to identify and discriminate among individual metal sources in urban aerosols (Souto-Oliveira et al. 2018, 2019; Schleicher et al. 2020) and coastal sediments (Araújo et al. 2019b).

Heavy metals in RDS occur at higher concentrations in finer particles, and these size fractions account for a higher proportion of all RDS (Jeong et al. 2020d; Zhao et al. 2010). Because the particle size distribution of RDS is closely related to particle mobility (Amato et al. 2012; Li et al. 2016), a consideration of particle size is needed to trace metal pollutants more clearly and to understand their associated pollution pathways. In this study, we aim to understand the variations in the Cu, Zn, and Pb isotopic compositions of different RDS particle sizes, as well as the relationship between RDS and total suspended solids (TSS) in runoff. We also consider whether multi-isotope studies wherein Cu, Zn, and Pb are combined can provide meaningful insights into the sources of metal pollution in the RDS of industrial regions.

Material and methods

Study area and sample collection

The Shihwa National Industrial Complex in Ansan, South Korea, has 18,612 small-scale facilities in operation. According to our previous study, Siheung Stream showed the highest pollution levels of Cu (10,977 mg/kg), Zn (3875 mg/kg), and Pb (2669 mg/kg) among the five streams flowing through this industrial complex (Jeong et al. 2019). These metals caused by industrial activities transport to stream and coastal environments via atmospheric deposition or rainfall runoff. In the study area, there is a significant level of metal contamination nearby the industrial area. This suggests that the influence of rainfall runoff is greater than atmospheric deposition. Therefore, we selected a small upstream catchment area of Siheung Stream as the study area. A total of 37 industrial facilities operates over a drainage area of 0.166 km² (Fig. 1). As copper smelting and refining facilities are present here, cable manufacturing and nonferrous and primary metal manufacturing industries where Cu is used are also active and account for 70% of all industries in the study area.

Fig. 1

Study area and sampling sites of RDS and TSS in rainfall runoff at industrial complex of Korea (The purple area represents the drainage area for runoff, base map from Google Earth)

Sampling the RDS and TSS in runoff was conducted in May 2018. Road-deposited sediments were collected from 11 sites at equal intervals using a Dyson vacuum cleaner (DC-35, Dyson Ltd., UK). Four subsamples were taken per site to ensure that sampling was representative. Before RDS sampling, the vacuum cleaner was disassembled and cleaned to prevent cross-contamination. After removing the coarse RDS particles (> 2 mm), the RDS samples were divided into six different size fractions (> 1000, 500–1000, 250–500, 125–250, 63–125, and < 63 µm) using automatic sieves (Analysette 3, Fritsch Co., Germany) in accordance with Wentworth (1992). Total suspended solid sampling was also conducted for a rainfall event that had a 5-day antecedent dry period (total rainfall = 10.9 mm). Twelve of the TSS samples were collected during a 17-h period and were separated using an acid-cleaned, 0.4 µm polycarbonate filter (47 mm diameter, Whatman, MilliporeSigma, USA) in a Class 1000 clean room.

Heavy metal analysis

The size-fractionated RDS samples were weighed after oven drying at 60 °C and pulverized using an automatic grinding machine (Pulverisette 6, Fritsch Co., Germany). Homogenized RDS and TSS samples were digested with high-purity mixed acids (fluoric, hydrochloric, and perchloric) on a hot plate. Seven metals (Cr, Ni, Cu, Zn, As, Cd, and Pb) were then analyzed via inductively coupled plasma mass spectrometry (ICP-MS; iCAP Q, Thermo Fisher Scientific, Germany) at the Korea Institute of Ocean Science and Technology (KIOST). Certified reference materials were also analyzed to ensure the quality of our metal analyses. The recoveries of two types of certified reference materials (MESS-4 and PACS-3 from the National Research Council Canada) ranged from 92.0% (Ni) to 99.7% (Pb) for MESS-4 and 90.9% (Pb) to 103.7% (Cr) for PACS-3.

Cu, Zn, and Pb isotopes analysis

For Cu and Zn isotopic analyses, these elements were separated from matrix elements, such as Na, Mg, Ca, Ti, Cr, Fe, and Ba, using an anion exchange resin (AG-MP1, Bio-Rad Laboratories, Inc., USA) (Jeong et al. 2021). The Pb in RDS and TSS samples was separated from interference elements using a Pb-specific resin (Eichrom Technologies, Inc., USA). The detailed chemical procedures and isotopic analyses were reported in our previous study (Jeong et al. 2021).

Isotopic measurements were performed using a Neptune Plus multicollector-ICP-MS (Thermo Fisher Scientific, Germany) at the KIOST. A mass-bias correction was performed via the standard sample bracketing (SSB) method, using the measurements of ERM-AE647 for Cu, IRMM-3702 for Zn, and NIST SRM 981 for Pb. Purified Cu, Zn, and Pb concentrations were matched to less than ± 5% to yield precise isotopic measurements. The Cu and Zn isotopic data are expressed in delta (δ) notation as permil (‰) deviations from the isotopic standards (ERM-AE647 and IRMM-3702, respectively) as follows:

$$\updelta ^{65} {\text{Cu}} \left( \permil \right) = \left( {\frac{{\left( {{}_{{}}^{65} {\text{Cu}}/{}_{{}}^{63} {\text{Cu}}} \right)_{{{\text{sample}}}} }}{{\left( {{}_{{}}^{65} {\text{Cu}}/{}_{{}}^{63} {\text{Cu}}} \right)_{{\text{ERM - AE647}}} }} - 1} \right) \times 1000$$

(1)

$$\updelta ^{66} {\text{Zn}} \left( \permil \right) = \left( {\frac{{\left( {{}_{{}}^{66} {\text{Zn}}/{}_{{}}^{64} {\text{Zn}}} \right)_{{{\text{sample}}}} }}{{\left( {{}_{{}}^{66} {\text{Zn}}/{}_{{}}^{64} {\text{Zn}}} \right)_{{\text{IRMM - 3702}}} }} - 1} \right) \times 1000$$

(2)

For comparison with our results, previously reported Cu and Zn isotopic data were converted from SRM 976 to ERM-AE647 and JMC-Lyon to IRMM-3702 (Moeller et al. 2012; Araújo et al. 2017), respectively (Eqs. 3 and 4).

$$\updelta ^{65} {\text{Cu}}_{{\text{ERM - AE647}}} = \updelta ^{65} {\text{Cu}}_{{\text{NIST 976}}} - 0.21\permil$$

(3)

$$\updelta ^{66} {\text{Zn}}_{{\text{IRMM - 3702}}} = \updelta ^{66} {\text{Zn}}_{{{\text{JMC}}}} - 0.27\permil$$

(4)

The repeatability and bias for Cu and Zn were obtained by measuring ERM-AE647 (0.00 ± 0.02‰, 2 s, N = 8) and IRMM-3702 (0.00 ± 0.01‰, 2 s, N = 7). The average analytical uncertainty (2 s) of sample measurements using SSB method yielded between ± 0.01‰ and ± 0.04‰ for δ⁶⁵Cu_AE647 and δ⁶⁶Zn_IRMM3702.

Heavy metal loadings on a grain size fraction

Grain size fraction loadings (GSF_loading) were determined by calculating the metal loading ratios of the six grain size fractions of RDS, and GSF_loading was expressed as follows:

$${\text{GSF}}_{{{\text{loading}}}} = 100 \times \left[ {\frac{{X_{i} \times {\text{GS}}_{i} }}{{\mathop \sum \nolimits_{i = 1}^{6} X_{i} \times {\text{GS}}_{i} }}} \right].$$

(5)

where X_i is the metal concentration in each particle size fraction and GS_i is the percentage of the total mass of the sample accounted for by each size fraction. The total GSF_loading is 100% (Sutherland 2003).

Results and discussion

Heavy metal concentrations in different RDS size fractions and TSS

Mean metal concentrations of RDS are shown in Table 1. The Cu concentration was significantly higher than those of the other metals owing to the copper smelting facilities concentrated in the area. We found that the mean concentrations of Cu in RDS were in the following order by particle size: below 63 µm (20,089 mg/kg) > 63–125 µm (8245 mg/kg) > 125–250 µm (4056 mg/kg) > 250–500 µm (2340 mg/kg) > 500–1000 µm (1273 mg/kg) > above 1000 µm (353 mg/kg). Zn concentrations of 250–500 µm, 125–250 µm, and 63–125 µm fractions were higher than other urban RDS (Jeong et al. 2020e; Zafra et al. 2011). Additionally, Zn concentration of < 63 µm fraction was similar to another industrial RDS in Korea (Jeong et al. 2020f) and Zn concentration of TSS in this study was 3 to 16 times higher than other urban and industrial roads (Table 1). Mean Pb concentration of < 63 µm fraction (5797 mg/kg) was 2 times higher than TSS in this study and was 16 to 28 times higher than urban/industrial regions (Table 1). Other metal concentrations (Cr, Ni, Zn, As, Cd, and Pb) also tended to increase in smaller particle sizes. However, Li showed no difference in concentrations according to the RDS sizes, indicating that the metal concentrations in the RDS were significantly affected by anthropogenic sources. In the fine RDS (< 63 µm), the mean metal concentrations in descending order were: Cu (20,089 mg/kg) > Pb (5797 mg/kg) > Zn (2541 mg/kg) > Cr (1488 mg/kg) > Ni (638 mg/kg) > As (28.5 mg/kg) > Cd (10.2 mg/kg) (Fig. 2).

Table 1 Means and standard deviations (1 s) of heavy metal concentrations in six different particle sizes of RDS and TSS in runoff in this study

Fig. 2

Comparison of heavy metal concentrations in different sizes of RDS and TSS in this study

The mean concentrations of heavy metals in TSS samples are as follows: Cu (16,294 mg/kg), Zn (9921 mg/kg), Pb (2391 mg/kg), Cr (737 mg/kg), Ni (436 mg/kg), Cd (50.2 mg/kg), and As (26.1 mg/kg) (Table 2). Zn and Cd in TSS samples showed higher concentrations than the finest particles (< 63 µm) in RDS. The mean concentrations of other metals showed values similar to < 125 µm, but showed higher value than that of particles over 125 µm. After the beginning of precipitation, the concentration of TSS increased by 13.6 times for Li, 10.1 times for Cu, 17.3 times for Pb, 3.0 times for Zn, 12.1 times for Cr, 4.7 times for As, and 3.2 times for Cd. These results suggest that the fine particles accumulated on the road surface were discharged by rainfall runoff. There are copper smelter and refinery in this study area, and copper alloy manufacturers (generator, copper wire, and cable manufacturer) are concentrated in the vicinity. The Cu concentrations in this industrial region were more than 100 times higher than urban/industrial RDS elsewhere in South Korea (Jeong et al. 2020e, f) and Spain (Zafra et al. 2011) (Table 1), and the mean concentration of Cu was higher than the maximum (8980 mg/kg) in highly contaminated soils near the Mufulira copper smelter in Zambia (Konečný et al. 2014). Due to the influence of Cu smelting facilities and Cu alloy processing industries, the Cu concentration was extremely high compared to urban RDS (mean of 370 mg/kg, Jeong et al. 2020a), but Cr, Ni, Cd, and Pb were also higher than urban RDS. Cr and Ni showed the highest concentrations of RDS of most particle sizes at sampling site 3, where copper smelting and refinery are concentrated. Our previous study reported that RDS around the paper and cardboard manufacturing industry were highly contaminated with Cr (2565 mg/kg), Pb (2263 mg/kg), and Cd (20.9 mg/kg) (Jeong et al. 2020d). There are also manufacturers of corrugated cardboard and paper in this study area and various industrial facilities exist. Therefore, the metal contamination of the RDS in this study area may be affected not only by copper smelting and processing activities, but also by various industrial and traffic activities.

Table 2 Comparison of mean, minimum, and maximum values for Cu, Zn, and Pb isotopic composition of this study and other reported literature data

Cu isotopic compositions of different RDS size fractions

The δ⁶⁵Cu_AE647 values of the different RDS size fractions are shown in Table 2. The Cu isotopic composition ranged from − 0.33 to + 0.73‰, and the mean δ⁶⁵Cu_AE647 values were − 0.01‰ (> 1000 µm), + 0.08‰ (500–1000 µm), − 0.08‰ (250–500 µm), + 0.10‰ (125–250 µm), + 0.03‰ (63–125 µm), and + 0.03‰ (< 63 µm) (Table 2). Figure 3a illustrates the relationship between δ⁶⁵Cu_AE647 and its concentration in the different RDS size fractions. No correlation was found between δ⁶⁵Cu_AE647 and its concentration.

Fig. 3

Comparison of isotopic composition and concentration (mg/kg) for Cu (a), Zn (b), and Pb (c) in the different particle sizes of RDS samples of this study

Variation of Cu isotopic composition (~ 1.05‰) was greater than Zn isotopic composition (~ 0.37‰) in this study. In case of Cu isotopes, there were large variations in low temperature environments (~ 5.86‰) and various natural materials (~ 36.53‰) (Wang et al. 2017). Redox reaction, adsorption, complexation with organic matters/minerals, and biological process can affect the Cu isotopic composition, and heavy Cu isotope was readily oxidized and preferentially released into solution (Rodríguez et al. 2015; Lv et al. 2016; Wang et al. 2017). The difference in the Cu isotopic composition (δ⁶⁵Cu_max- δ⁶⁵Cu_min) was ~ 0.80‰ for 125–250 µm and ~ 0.16‰ for 63–125 µm particles, with the fine particles having a narrow range of isotopic variation among the sampling sites. Because fine particles are more easily dispersed by wind and vehicle movement than coarse particles and these concentrations are very high, the isotopic variations are likely to be small. The Cu concentrations of < 250 µm particles were high at sites 10 and 11, where small-scale, nonferrous metal processing, polishing, and manufacturing facilities are concentrated. The mean δ⁶⁵Cu_AE647 of < 250 µm particles was − 0.01‰, and it ranged from − 0.07‰ to 0.11‰ at sites 10 and 11. Copper product (e.g., rod, billet, slab, etc.) manufacturers are located around site 4. Particles larger than 500 µm had a relatively low Cu concentration, but the Cu isotopic values were relatively high, with a mean of + 0.30‰ (125–250 µm: Cu 1270 mg/kg, δ⁶⁵Cu_AE647 + 0.73‰; 500–1000 µm: Cu 161 mg/kg, δ⁶⁵Cu_AE647 + 0.51‰) (Fig. 3a).

Figure 4 presents a comparison of the Cu isotopic compositions in this study and those from the literature related to road dust and non-exhaust emissions (e.g., road paint, brake pads, and tires). The Cu isotopic signatures of RDS in this study had similar values to the road dust from São Paulo, Brazil (Souto-Oliveira et al. 2018). Mean Cu isotopic ratios in this study (δ⁶⁵Cu_AE647: + 0.03‰) differed only slightly from those of road paint (δ⁶⁵Cu_AE647: + 0.03‰) and tires (δ⁶⁵Cu_AE647: + 0.05‰) (Fig. 4). Meanwhile, the mean δ⁶⁵Cu_AE647 of brake pads was 0.24‰ (Dong et al. 2017), which was significantly different from the isotopic ratios found in this study. The δ⁶⁵Cu_AE647 values of road paint and brake pads display large variations, ranging from − 0.39 to + 0.35‰ (N = 3) and + 0.07 to + 0.42‰ (N = 3), respectively (Dong et al. 2017). The δ⁶⁵Cu_AE647 of brake-derived particles was − 0.03 ± 0.14‰ (N = 19, 2 s, Gonzalez et al. 2016). A previous study reported δ⁶⁵Cu_AE647 values of sludge (+ 0.60‰), solid waste (− 0.05‰), and fly ash (− 0.10‰) (Bigalke et al. 2010). Cu isotopes of RDS in this study showed a wide range and there was no significant difference from that of brake-derived particles, solid waste, and fly ash (Gonzalez et al. 2016; Bigalke et al. 2010). Additionally, the Cu concentrations in this study were extremely high compared to RDS in urban areas (151 mg/kg), and those of tires (37 mg/kg) and brake pads (12 mg/kg) (Dong et al. 2017; Souto-Oliveira et al. 2018). Our results indicate that the Cu contamination of RDS seems to be more affected by Cu-related industries than by traffic activities.

Fig. 4

Comparison of Cu, Zn, and Pb isotopic composition in this study and other literature data (^aSouto-Oliveira et al. 2018; ^bDong et al. 2017)

Zn isotopic compositions of different RDS size fractions

The overall variation of δ⁶⁶Zn_IRMM3702 in this study was ~ 0.37‰, and ranged from − 0.36‰ to + 0.01‰ (Table 2). The variation in δ⁶⁶Zn_IRMM3702 was less than that of δ⁶⁵Cu_NIST976 (~ 1.05‰), and the mean δ⁶⁶Zn_IRMM3702 values of RDS were − 0.21‰ (> 1000 µm), − 0.19‰ (500–1000 µm), − 0.17‰ (250–500 µm), − 0.18‰ (125–250 µm), − 0.20‰ (63–125 µm), and − 0.13‰ (< 63 µm) (Table 2). Figure 3b shows a comparison between the δ⁶⁶Zn_IRMM3702 and Zn concentration in the different RDS size fractions. The Zn isotopic values (δ⁶⁶Zn_IRMM3702) tended to increase in smaller particle sizes. The differences of the Zn isotopic compositions (δ⁶⁶Zn_max- δ⁶⁶Zn_min) in the 63–125 µm fraction displayed a narrow range (~ 0.14‰), while the < 63 µm fraction showed a wide range (~ 0.29‰) compared to other particle sizes.

The mean Zn concentration in this study was slightly higher than that found in the RDS of urban areas (337 mg/kg) and brake pads (28 mg/kg), but lower than that of tires (9500 mg/kg) (Dong et al. 2017; Souto-Oliveira et al. 2018). Tires contain a high Zn concentration of 8700 mg/kg (Klöckner et al. 2019). Therefore, Zn contamination of RDS caused by tire wear is widely regarded as a major source of Zn pollution in urban areas. Baensch-Baltruschat et al. (2020) reported that the tire and road wear particles generated on roads during driving of vehicles can contribute to airborne non-exhaust emissions and the average global value of tire wear emissions is about 0.8 kg/capita ranging from 0.2 to 5.5 kg. The abrasion processes increase the density of tire and road wear particles on the road surface from 1.5 to 2.2 g/cm3 (Kayhanian et al. 2012; Klöckner et al. 2019). In industrial areas, heavy-duty vehicles are used to transport raw materials and final products, so tire wear will be more severe than in urban areas. Therefore, Zn contamination in RDS derived from tire wear in industrial areas is higher than in urban areas.

Additionally, galvanized steel sheets have excellent corrosion resistance and are widely used in the automobile, steel, and metal industries, as well as for the exterior panels of facilities across South Korea. Zinc contamination has also been reported in the RDS of industrial areas in the country (Jeong et al. 2020f, g).

The mean δ⁶⁶Zn_IRMM3702 of RDS from São Paulo was − 0.15‰ (Souto-Oliveira et al. 2018), which was similar to the range of − 0.13‰ (< 63 µm) to − 0.21‰ (> 1000 µm) in this study (Fig. 4). Zinc isotopic compositions in road paints ranged from − 0.20 to + 0.31‰, with a mean of + 0.10‰ (Dong et al. 2017). Even considering the large variability of Zn isotopic compositions, the δ⁶⁶Zn_IRMM3702 in road paints was heavier than the mean RDS in urban (Souto-Oliveira et al. 2018) and industrial areas (this study). The Zn isotopic compositions in brake pads (− 0.10 ‰) and tires (− 0.07 ‰) were found to be within the range of RDS in urban and industrial areas (Table 2). Light Zn isotope was associated with high-temperature industrial activity, and negative Zn isotope values have been reported in the vicinity of the metallurgical industry in Barcelona (Gonzalez et al. 2016). A previous study has reported δ⁶⁶Zn_IRMM3702 values of sludge (− 0.71‰), solid waste (+ 0.30‰), and fly ash (− 0.36‰) (Bigalke et al. 2010). Zn isotopic composition of RDS in this study was similar to PM₁₀ in Barcelona, however, different from sludge, solid, and fly ash (Gonzalez et al. 2016; Bigalke et al. 2010).

In this study, Zn contamination of RDS was not significant when compared to Cu, and there were small Zn isotopic variations among the sampling sites and different particle sizes. Therefore, our results indicate that Zn contamination may be influenced by the traffic activities of large vehicles related to the transportation of raw materials and products, as well as to industrial activities, such as the use of galvanized steel and Cu-alloy brass containing Zn in the metal manufacturing and processing facilities at the study sites.

Pb isotopic compositions of different RDS size fractions

The ²⁰⁶Pb/²⁰⁷Pb ratio in all RDS samples ranged from 1.1418 to 1.1616, with the mean of 1.1538 (Table 2). A wide variation (0.0143) in the Pb isotopic composition (²⁰⁶Pb/²⁰⁷Pb) was observed in the > 1000 µm RDS fraction, and a narrow variation (0.0057) was found in the < 63 µm fraction. The Pb isotopic composition (²⁰⁶Pb/²⁰⁷Pb) tended to increase as the RDS particle size decreased.

Lead has been widely used in various industries, in addition to battery plates, paint additives, cable covers, and anti-corrosion materials (van der Kuijp et al. 2013; Yang et al. 2020). It is generally contained in paint additives as a stabilizer to increase their durability and moisture resistance (Apanpa-Qasim et al. 2016; Hall and Tinklenberg 2003). Megertu and Bayissa (2020) reported that extremely high Pb concentrations were found in paint samples ranging from 41 to 51,204 mg/kg. Another application for the use of Pb is the cable industry, wherein polyvinyl chloride-coated wires and cables for electrical use are stabilized with Pb. Electrical cables have been reported to contain 9518 mg/kg of Pb (Balaram et al. 2018). Copper alloys, such as brass and bronze, contain several mass percentages of Pb and are widely used in pipes due to their excellent corrosion resistance and machinability (Nakano et al. 2005). Pb concentrations showed the highest values at sampling site 4 (< 125 µm), site 6 (> 1000 µm and 500–1000 µm) site 7 (125–250 µm), and site 8 (250–500 µm), where many printing-related industries exist. Especially, for both particle sizes of < 63 µm (11,169 mg/kg) and 63–125 µm (4409 mg/kg), the highest Pb concentrations were observed at site 4 and the ²⁰⁶Pb/²⁰⁷Pb ratio was also higher than other sampling sites.

As shown in Table 2 and Fig. 4, the mean ²⁰⁶Pb/²⁰⁷Pb ratio in this study was slightly lower than that of the RDS in urban São Paulo (Souto-Oliveira et al. 2018). Differences were also observed in the mean Pb isotopic values (²⁰⁶Pb/²⁰⁷Pb) relative to road paints, brake pads, and tires, which are affected by traffic activities (Dong et al. 2017). The mean Pb concentration in the industrial area was extremely high when compared to RDS in urban areas (37 mg/kg), tires (11 mg/kg), and brake pads (16 mg/kg; Dong et al. 2017, Souto-Oliveira et al. 2018). Although there are no Pb isotope data expected as a potential of Pb contamination source in this study, our results showed that Pb contamination in RDS was found to be mainly affected by industrial activities (e.g., many printings, wire manufacturing, and Cu-related manufacturing and processing) rather than traffic activity. To more clearly identify potential sources of RDS contamination in industrial areas, it is necessary to establish isotopic data for raw materials and final products of the study area.

Relationship between different RDS size fractions and TSS in runoff

Generally, RDS on the road surface exists as a mixture of various particle sizes. Sutherland (2003) reported that the most important index of RDS pollution is the mass loading of heavy metals in each grain size fraction. Grain size mass partitioning revealed that the 250–500-µm fraction was dominant, and accounted for 22.4% of the RDS samples (Fig. 5). Although the finest fraction (< 63 µm) accounted for 17.1% of total RDS as a mass fraction, the amounts of metal accumulated in the fines fraction (< 63 µm) of RDS were higher than other RDS sizes. The mean of metal loading of this fraction accounted for 40.0% of Cr, 45.4% of Ni, 56.3% of Cu, 33.9% of Zn, 32.4% of As, 50.2% of Cd, and 54.3% of Pb in the total RDS.

Fig. 5

Heavy metal loading percentage (%) of six different particle sizes in RDS

The mobility of RDS on the road surface during rainfall events strongly depends on particle size. Fine particle fractions are easily transported by rainfall, which is important because the concentrations and GSF_loading of metals are higher in smaller particles in urban and industrial regions (Lanzerstorfer 2020; Sutherland 2003; Jeong et al. 2020d; Zhao et al. 2010). Indeed, the fine particles (< 63 µm) had higher Cu, Zn, and Pb concentrations and isotopic values than the coarser fractions (Fig. 3).

The mean concentration of Cu in TSS was 16,294 mg/kg, which was similar to that observed in the < 63 µm RDS (20,089 mg/kg) (Table 1). Moreover, the Cu isotopic composition of TSS varied between − 0.11 and + 0.15 ‰ (mean δ⁶⁵Cu_AE647: + 0.01‰), which was within the range of RDS (Table 2, Fig. 4). A higher concentration of Cu was observed in the < 63 µm fraction in RDS than in the TSS, but a 3.9 times higher concentration of Zn was found in the TSS (9921 mg/kg) than in the < 63 µm fraction in RDS (2541 mg/kg). The mean Zn isotopic value (δ⁶⁶Zn_IRMM3702) was − 0.11‰ and ranged from − 0.21 to − 0.02‰; similar to the Cu isotopes, the Zn isotopic ratio of the TSS was also the most similar value to the < 63 µm RDS fraction (mean δ⁶⁶Zn_IRMM3702: − 0.13‰) (Table 2). The mean Pb concentration in the < 63 µm fraction of RDS (5797 mg/kg) was higher than that of the TSS (2391 mg/kg), in the latter of which the mean ²⁰⁶Pb/²⁰⁷Pb value was 1.1534. The Pb isotopic compositions showed less variability in the TSS than in all RDS size fractions (Fig. 4).

A hierarchical cluster analysis was conducted to examine the relationships between different sizes of RDS and TSS (Fig. 6). The clustering procedure generated two groups—Group 1, which included the 125–250, 250–500, 500–1000, and > 1000 µm RDS, and Group 2, which included the < 63 µm in RDS and TSS. These results indicate that the finest RDS and TSS were correlated. Brodie and Dunn (2009) investigated stormwater monitoring with different rainfall events and reported that the runoff particle size distribution was dominated by particles < 63 µm. Our results show that the Cu, Zn, and Pb isotopic values of the TSS were closely related to the < 63 µm RDS fraction.

Fig. 6

Dendrogram showing clustering of the TSS in runoff and six different particle sizes of RDS in this study

Research on rainfall runoff has been conducted focusing on fine particle size fractions. However, different size fractions, such as < 38 µm (Kayhanian et al. 2012), < 44 µm (Zhao et al. 2010), < 63 µm (Camponelli et al. 2010), < 75 µm (Li and Zuo 2013), < 125 µm (Jeong et al. 2020d), and < 250 µm (Aryal et al. 2017), have been proposed for different regions and by different researchers. In our previous study, we reported that when the total rainfall was 74 mm and the mean rainfall intensity was 7 mm/h, the < 125 µm fraction accounted for 53.9–98.7% (Jeong et al. 2020d). This difference may have been affected by various environmental conditions, such as rainfall intensity, antecedent dry period, and land use type. In this study, TSS data were collected when precipitation was low; hence, fine particles were preferentially discharged, and the TSS showed much higher metal concentrations than the < 63 µm RDS. The total amount of rainfall in the study area in 2018 was 1135 mm, and there were 95 annual precipitation days. The number of days with a daily rainfall of < 10 mm was 67 days, accounting for 70% of all annual precipitation days. Recently, Lanzerstorfer (2020) suggested that 63 µm would be the upper size limit for runoff studies. Our results also indicate that small particles (< 63 µm) are more suitable for tracing pollution sources than larger ones in runoff studies using a Cu, Zn, and Pb multi-isotope approach.

Application of multi-isotopes as a useful tool for source identification in RDS studies

Studies on the concentrations and elemental ratios of heavy metals in urban RDS are still used to assess metal pollution levels and their adverse effects on surrounding environments and human health (e.g., Bian et al. 2015; Hwang et al. 2016; Roy et al. 2019). However, this approach is limited in its utility for determining the specific sources of contamination and discriminating among them. Figure 7 shows the bivariate, multi-isotope plots produced in this study and from existing data in the literature, including Cu, Zn, and Pb isotopic compositions. Because we divided the RDS samples into six different size fractions, the variations in the Cu and Zn isotopic compositions were large. The combination of Cu and Zn isotopes could not be used to distinguish between urban road dust (Souto-Oliveira et al. 2018) and industrial RDS (this study) (Fig. 7a). Road dust in São Paulo seems to be distinct from brake pad and tire wear (Dong et al. 2017), yet the RDS data in this study could not be differentiated due to differences in the isotopic values by land use type and country (Fig. 7a). This may be due to the lack of data and differences between countries and/or manufacturers.

Fig. 7

Multi-isotope diagrams of this study and other literature data using δ⁶⁵Cu_AE647 vs δ⁶⁶Zn_IRMM3702 (a), δ⁶⁵Cu_AE647 vs ²⁰⁶Pb/²⁰⁷Pb (b) and δ⁶⁶Zn_IRMM3702 vs ²⁰⁶Pb/²⁰⁷Pb (c)

Figure 7b shows the relationship between the Cu and Pb isotopic compositions in this study and previously reported values. Our results suggest that the combination of Pb and Cu isotopes provides better information than that of Cu and Zn isotopes for the purpose of differentiating among pollution sources and urban and industrial RDS. No difference was observed between the urban road dust and industrial RDS in this study using Zn isotopes alone, but the combination of Pb and Zn isotopes yielded clearly differentiated isotopic values (Fig. 7c). Meanwhile, the Pb and Zn isotopes in brake pads and tires, which are major causes of metal contamination from traffic activities, displayed different characteristics from industrial RDS and urban road dust (Dong et al. 2017; Souto-Oliveira et al. 2018).

Isotopic data for road dust, road paint, brake pads, and tires related to traffic activities have been limited so far, and Cu, Zn, and Pb isotopic values may be influenced by natural and anthropogenic factors, depending upon the region. The application of a multi-isotope approach using Cu, Zn, and Pb as environmental tracers may be key to identifying potential sources of metal pollution, as well as for understanding the environmental pollution and contribution of RDS in various environments, especially urban and industrial areas. However, the Cu, Zn, and Pb isotopic compositions in various environments are still poorly reported. In this study, the combination of a Pb and either Cu or Zn isotopic signature revealed the differences among road paints, brake pads, and tires. Multi-isotope (Cu, Zn, and Pb) data on a variety of anthropogenic pollutants from different countries and manufacturers are necessary, and the establishment of a database on multi-isotope values would greatly contribute to the identification and management of individual sources of metal pollution.

Conclusions

We evaluated a multi-isotope (Cu, Zn, and Pb) signature for the purpose of characterizing the differences in the isotopic values of different RDS sizes fractions and to understand the relationship between RDS and TSS in runoff from industrial areas in Korea. The Cu concentration in RDS was highest when compared to other metal elements due to the influence of industrial copper smelting/refining facilities and adjacent Cu-related industries. There was a wide variation in δ⁶⁵Cu_AE647 of ~ 1.05‰ and no correlation was observed between Cu isotope and concentration values. The Cu isotopes of RDS around a copper smelting facility were different from those of Cu-related metal processing facilities. Meanwhile, Zn isotopic compositions showed less variation (~ 0.37‰) than Cu, and the δ⁶⁶Zn_IRMM3702 and ²⁰⁶Pb/²⁰⁷Pb ratios tended to increase in the smaller RDS particle sizes. The combination of Pb and either Cu or Zn isotopic signatures revealed the differences in road paint, brake pad, and tire from traffic activities. The Cu, Zn, and Pb isotopic compositions of TSS in runoff were closely related to the fine RDS particle sizes (< 63 µm) in this study, suggesting that small particles are more suitable for tracing pollution sources than large particles in runoff studies using a multi-isotope (Cu, Zn, and Pb) approach. We also proposed for the first time that multi-isotopes (Cu, Zn, and Pb) in the RDS studies of industrial areas could serve as valuable tracers for the identification and discrimination of sources of metal pollution. To gain a more accurate understanding and improve the source identification of metal pollution processes, further investigation and more isotopic information from a variety of potential sources by natural, industrial, and traffic activities are needed. The application of multi-isotope (Cu, Zn, and Pb) approaches will contribute to the identification and management of individual sources of metal pollution in urban environments.