This study examined the effects of land-use changes on heavy metal pollution in runoff in a catchment of Tehran, Iran. Urban runoff samples were collected from six stations, including five various land uses and mixed land uses. The event mean concentration (EMC) was applied to determine heavy metals, including mercury (Hg), arsenic (As), cadmium (Cd), zinc (Zn), lead (Pb), and copper (Cu), in five land uses. Sampling was done during six events with different antecedent dry days (ADDs) during 2019–2020. The result revealed higher heavy metal concentrations in runoff in the industrial land use compared to other land-use types in the catchment. The calculated EMC rates were as follows: EMC Zn > EMC Pb > EMC Cu > EM As > EMC Hg > EMC Cd. This study also found that the maximum and minimum EMCs of heavy metals were associated with rainfall events with 115 and 1 dry days, respectively. In comparison to other heavy metals, mercury and arsenic were at a higher level in runoff as determined by EMC data analysis. In order to minimize the risk of heavy metal contamination of runoff, the relocation of industrial land uses from urban environments to non-urban areas is recommended.

  • Event mean concentrations (EMCs) of heavy metals for typical land uses in a high-density urban area were assessed.

  • Heavy metal concentrations are higher in runoff in the industrial land use compared to other land-use types.

  • The calculation of the EMC rate showed that, in general, EMC Zn > EMC Pb > EMC Cu > EM As > EMC Hg > EMC Cd.

  • The heavy metal pollution load meaningfully increased in the urban runoff in dry seasons.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The quality of urban runoff is negatively affected by trends in urban development and land-use changes (Ying & Sansalone 2010; Yazdi et al. 2021). The type of human activities in different urban land uses is one of the reasons for the decrease in the quality of urban runoff (Müller et al. 2020; Yang et al. 2020). As a result of urbanization, pollutant loads are increased at least one time higher than they were in the natural conditions of the watershed (Ahiablame et al. 2012). In addition, land-use characteristics and specifications of a catchment, such as the intensity and duration of rainfall (Nazahiyah et al. 2007; Liu et al. 2013), soil type, catchment slope, vegetation cover, and antecedent dry days (ADDs), could affect the urban runoff pollution (Yazdi et al. 2019). Furthermore, the consequences of climate change and global warming could lead to long dry periods (Müller et al. 2020), and ADDs complicate the analysis of urban runoff quality (Kim et al. 2018). Heavy metals are one of the most important pollutants in urban runoff (Reddy et al. 2021). Trace elements, such as copper (Cu), zinc (Zn), and lead (Pb), are common compounds in runoff (Zhao et al. 2019). The extensive distribution of heavy metals regarding their interaction in different parts of the biosphere, including the atmosphere, hydrosphere, and lithosphere (Golomb et al. 1997; Cabon 1999; Morselli et al. 2003), and its toxic effects on the environment are the most important issues (Domingo 1994; Christensen 1995; Passarii et al. 2001). Moreover, high concentrations of heavy metals in the environment are difficult and can easily accumulate in the human body and plants after entering the water bodies of the downstream catchment through the food chain and other ways, and impose high pressure on the biogeochemical cycle (Kelly et al. 1996; Cheng et al. 2018; Ukah et al. 2019).

Furthermore, heavy metals have bio-accumulative, persistent, and poorly biodegradable properties as well as carcinogenic, systematic teratogenic, and mutagenic effects. Heavy metals accumulated on different land uses during runoff could be washed off and transferred to water sources, thereby threatening human health (Liu et al. 2015; Aendo et al. 2018). Humans and animals, such as fish, could take heavy metals through contaminated water. Arsenic (As) in water could cause skin diseases and cancer; Pb and Zn would trigger neurotoxicity and risk of cardiovascular diseases; mercury (Hg) and Cu intake creates harmful effects such as brain damage. Cadmium (Cd) is reported as one of the carcinogens (Yadav et al. 2017; Mishra et al. 2019). Increasing the toxicity of heavy metals in the ecosystem also triggers significant changes in the composition of the plant community and heavy metal soil pollution (Fernando & Lynch 2015; Kong et al. 2018). As a result, the accumulation of heavy metals in ecosystems leads to a slow process in the ecological restoration of barren lands (Dou et al. 2009; Zhu et al. 2018).

In addition, stormwater runoff could affect the physicochemical properties of heavy metals in the riverbed. It would even lead to the release of heavy metals into water sources and cause significant environmental damage (Villanueva et al. 2016). Due to a relatively higher fraction of fine particles, wet sediments could contribute more to heavy metal runoff than the dry sediment (Gunawardena et al. 2013) and could regenerate heavy metals in sediments of water bodies (Sun et al. 2015; Botwe et al. 2017). Generally, understanding the relationship between heavy metals in water bodies and rainfall properties is essential both to divide the main sources and to control pollution effectively (Laurenson et al. 2013).

Heavy metals in urban runoff are triggered mainly by vehicle exhaust, industrial dust, fossil fuel burning, dust storm sand, and corrosion of various metal facilities (Nilsson et al. 2007; Gillis et al. 2022). There are several functional zones in urban lands, including business, residential, and industrial areas (IAs) (Chen et al. 2022; Li et al. 2015). Land-use changes may directly or indirectly affect the distribution of heavy metals in the environment among different anthropogenic stressors (Joimel et al. 2016; Zhang et al. 2017; Arfaeinia et al. 2019; Wang et al. 2020). Furthermore, urban land use heavily impacts water quality through the discharge of industrial, commercial, and residential wastes, which may contain heavy metals (Ren et al. 2003). Runoff from roads with a higher density is associated with a higher concentration of heavy metals (Brown & Peake 2006). In urban catchments, the total road surface accounts for about 10–15% of the total area (Bannerman et al. 1993; Ball 2002). Commercial areas (CAs) and IAs and parking lots could account for up to 46% of the total area (Bannerman et al. 1993). Different urban land uses have different shares in the production of metal pollutants in urban runoff. For example, in most cases, highway runoff contains higher amounts of heavy metals than other runoff in drainage systems, such as conventional roof runoff (Schueler 2000; Ball 2002). Up to 80% of the total mass flow in combined sewer systems is comprised of metals from the roof and road runoff (Ellis et al. 1987; Boller 1997; Huber et al. 2016). The level of runoff toxicity in traffic areas has been investigated by various researchers (Gjessing et al. 1984; Pitt et al. 1995; McQueen et al. 2010; Lerat-Hardy et al. 2021). Kayhanian et al. (2008) identified Cu and Zn solutions as the main reasons for toxicity in the highway runoff. Brown & Peake (2006) and Tiefenthaler et al. (2008) found high levels of Pb and Cu in industrial applications.

The most commonly used methods for estimating urban runoff pollution loads are the event mean concentration (EMC) and the exponential build-up wash-off methods (Yazdi et al. 2019; Behrouz et al. 2022). Although it is costly to collect samples from land uses in order to calculate EMCs (Coville et al. 2018), researchers, due to greater precision (Charbeneau & Barrett 1998) and simpler calibration than other models (Gaume et al. 1998; Niazi et al. 2017), the EMC model is preferred (Rossman & Huber 2016).

Spatio-temporal variations in heavy metals in runoff have been considered in previous studies (Helios Rybicka et al. 2005; Li & Zhang 2010; Su et al. 2013; Banerjee et al. 2016; Ciazela & Siepak 2016). Nevertheless, few studies have scrutinized the characteristics of heavy metal changes in runoff in different land-use types simultaneously (Zhu et al. 2021). Likewise, the amount of metals, such as Hg and As, in the use of various urban runoff lands that are essential to reduce the risk of heavy metals on human health and ecosystem safety (Githaiga et al. 2021) has not been considered and addressed in previous studies. In addition, the role of long dry days with a duration of 90 days and more in the EMC of heavy metal estimation in semi-arid regions has been ignored in the literature. The objectives of this study were to (1) investigate the amount of heavy metal pollutants, such as EMCs of Zn, Pb, Cu, As, Hg, and Cd, in different land-use types; (2) fill an existing information gap on how rainfall and land-use types interact and affect such loading with different ADDs in a densely populated urban area in Tehran city with a semi-arid climate. Hence, the current study investigated six rainfall events in six stations accessing runoff with different land-use types.

Study area

District 17 of Tehran municipality, which is located at a longitude of 51°21′10″ and latitude of 35°38′11″, covers an area of 744.17 ha (Figure 1). Residential areas (RAs), with a great number of buildings and houses, which constitute the largest share of the district, account for 38.5% of the total land (305.7 ha). The land allocated to the traffic road area (TA) accounts for 28.3% of the total district area (224.7 ha). The IA and CA, which are predominantly occupied by car accessories manufacturing workshops, furniture making, and carton making, cover approximately 10.6% (84.4 ha) and 15.3% (121.8 ha) of the total area of the district. Also, of the total land area, only 7.3% (7.57 ha) has been dedicated to open space areas (OAs), including wellness facilities, green spaces, and municipal and infrastructural amenities of the area. All parking lots and roads have been paved and are 100% waterproof. The mixed land use has an approximate population of 350,000 people, and additionally, the district is situated in a highly congested traffic area of the city. The area also lacks urban runoff treatment systems.
Figure 1

District 17 of Tehran municipality.

Figure 1

District 17 of Tehran municipality.

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Figure 2

Stations for the sampling of urban runoff in District 17 according to land use (Google Earth 2019).

Figure 2

Stations for the sampling of urban runoff in District 17 according to land use (Google Earth 2019).

Close modal

Region climate

Given its semi-arid climate, the area features relatively long periods of drought, from dry summer days until rainy days of autumn, as well as very different levels of precipitation. The nearest weather station to the area, i.e., Mehrabad International Station, indicates the average precipitation of 232.8 mm. The evaluated runoff coefficient of this district is about 37%.

Monitoring methods

The EMC is a widely applied method for calculating the concentrations of heavy metal pollutants in different land uses and stormwater collection systems (Lindfors et al. 2020; Du et al. 2021). The concentrations of pollutants in urban runoff are assumed to be constant in the EMC method during an event (Charbeneau & Barrett 1998). In order to evaluate the pollutant load emanating from land use, the EMC method has been employed. EMC is a simple, convenient, and quick technique used to estimate the contamination of nonpoint source pollution in small rural catchments (Shen et al. 2012). In addition, EMC is employed as a unique indicator used for determining and expressing concentration in milligrams per litre (mg/L) (Sansalone & Buchberger 1997). Environmental mean concentration is also considered a gravimetric flow technique of the average concentration for stream pollutants. This technique demands some data pertaining to flow rate, time, and pollutant concentration (Wu et al. 1998; Li 2003). Environmental mean concentration can be estimated as follows by dividing the total mass of the pollutant (M) discharged in the course of an incident by the total volume (V) of runoff water (Ma et al. 2009):
formula
(1)
where Ci (mg/L) is the concentration of stormwater runoff pollutant during the time interval Δt, Qi (L/min) is the discharge during the time interval Δt, and (min) is the length of time in the ith interval (min).

For three reasons, the EMC was introduced to assess heavy metal pollution in urban runoff: first, the monitored rainfall area is not representative of the entire region's rainfall situation. Second, the concentration of constituents can vary by several orders of magnitude during a runoff event (Huber 1993). Third, the rate of change in pollutant concentration runoff is much lower than that in receiving waters in drainage systems (Huber 1993; Xue et al. 2020).

In accordance with different land uses, including TA, RA, IA, OA, CA, and a high development area (HDA) featuring mixed land uses, which allow the runoff of the whole above-mentioned uses, six stations of flow and water quality control were chosen in order to prepare samples from the pollution of municipal runoff in sub-catchments. Figure 2 shows stations for the sampling of urban runoff in District 17 according to land use. In HDA stations, the land-use percentages are commercial (18%), open space (4%), transportation (26%), residential (41%), and industrial (11%). Each station was set up in the vicinity of the discharge channel entrance of the urban drainage network, and their lands were covered with asphalt and concrete. Two stations featured WS-9004-IT rain gauges. An acoustic Doppler velocimeter installed at the catchment area outlet estimated the flow. In order to analyse and find the effect of ADDs on heavy metal runoff pollution, six events were selected in the rainy months from April 2019 to February 2020 with different ADDs (Table 1), and the rainfall events with the same ADDs were not considered.

Table 1

Rainfall details for the six events observed in the catchment

DateADDsRainfall intensity (mm/h)
12 April 2019 0.97 
22 October 2019 115 0.77 
16 November 2019 16 1.02 
3 December 2019 1.709 
5 January 2020 0.89 
23 February 2020 10 0.93 
DateADDsRainfall intensity (mm/h)
12 April 2019 0.97 
22 October 2019 115 0.77 
16 November 2019 16 1.02 
3 December 2019 1.709 
5 January 2020 0.89 
23 February 2020 10 0.93 

Sampling began as soon as the flow at the discharge points was monitored. In general, for the primary 1-h precipitation runoff period, the sampling is conducted in 15–20 min intervals, followed by 1-h intervals for the runoff period. In the course of each event, the characteristics of the precipitation, including ADDs and the duration and intensity of precipitation, were recorded. ADDs range between 1 and 115 days. The precipitation depth ranges between 3 and 11.1 mm, and the range of average precipitation intensity is between 0.77 and 1.709 mm/h. The collection of samples was carried out manually. Based on the duration of continued rainfall events, the number of samples collected varied (in events 1, 3, 4, and 6, there were six samples collected, and in events 3 and 5, there were five samples collected). The major specifications of the six storms recorded for the catchment are shown in Table 1.

Acid digestion of raw runoff samples was carried out using aqua regia before determining the contents of heavy metals. In order to determine the concentration of heavy metals in various fractions, dissolved samples, and total concentration in the runoff, ultra-pure water was used to cool and dilute the solutions to 100 mL following acid digestion.

The U.S. environmental protection agency (EPA) technique was employed for the analysis and sampling in this investigation. Using 5% HNO3, the sample containers were cleansed and rinsed with ultra-pure deionized water carefully in advance of application. The runoff samples were gathered from six stations in equal volumes. Each subsample had an approximate volume of 0.3 L. Within 1 h after the end of the sampling time in the stations, all samples were sent to the laboratory. They were kept at 4 °C until the tests were carried out (U.S. EPA 1992). Then, using three replications for each sample, the six indicators such as Hg, As, Cd, Zn, Pb, and Cu were estimated in the laboratory. In order to estimate the concentrations of the studied metals, an inductively coupled plasma mass spectrometry (ICP-MS) model (HP-4500 USA) with an ASX-520 autosampler was utilized for water runoff samples. By determining the calibration curve, the test accuracy was approved since r2 = 0.9971–0.9996 was regarded as a highly accurate range. The evaluated values of the limit of detection (LOD) for Zn and Pb concentrations were 0.1, and for Cu, Cd, As, and Hg concentrations were μg/L. In addition, the obtained relative standard deviation (RSD) % was lower than 5% for all investigated elements. The mean concentrations of events were estimated as the concentration of a pollutant in stormwater by employing the stormwater monitoring data obtained for a watershed in a land-use.

Heavy metal concentrations varied between the sampling intervals in all runoff events. Furthermore, the EMCs of heavy metal were higher in the early phase of runoff events than those in the later phases. The reason that heavy metal concentrations are high at the beginning of runoff events is that most of the pollutants are washed away by the first part of the runoff event (Costa et al. 2021). Generally, urban stormwater runoff demonstrates a ‘first flush’ (FF) effect, where the discharge during the initial phase contains more pollutants than that during the later phase (Maniquiz-Redillas et al. 2022). The subsequent samples of all events after the FF showed low pollutant concentrations. In addition, urban runoff after the dry season had disproportionately higher mass discharges based on the type of land use. For the estimation of the average concentration of the samples for all heavy metal indicators, Equation (1) was employed. The results showed that in all rainfall events, EMC Zn > EMC Pb > EMC Cu > EM As > EMC Hg > EMC Cd. Figure 3 illustrates heavy metal concentrations in different land-use types during six rainfall events using the EMC method. The error bars in Figure 3 represent the standard deviation from the mean of heavy metal concentrations based on the EMC method.
Figure 3

Heavy metal concentrations based on the EMC method in different land uses in six rainfall events.

Figure 3

Heavy metal concentrations based on the EMC method in different land uses in six rainfall events.

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The stormwater quality data recorded in the catchment varied by the event specifications and ADDs from one storm event to the other. The present study found that high concentrations of heavy metals were associated with a greater number of ADDs. Figure 4 illustrates the relationship between ADDs and EMCs of heavy metals across all land-use stations. In all land uses except OA, all heavy metal pollutants increased in outflow concentrations relative to the EMC as ADDs increased. However, an increasing trend in As, Pb, and Cu in the OA was almost tangible as ADDs increase, whereas other runoff pollutants did not exhibit significant outflow changes concerning the EMC.
Figure 4

The relationship between ADDs and the average EMCs of heavy metals across all land-use stations.

Figure 4

The relationship between ADDs and the average EMCs of heavy metals across all land-use stations.

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Due to an unequal number of samples across different events with different time intervals, the pooled EMC data were not normally distributed. To examine whether EMC data for each of the ADDs had similar distributions, IBM®'s SPSS® Statistics (Version 22) was used. Kolmogorov–Smirnov and Shapiro-Wilk tests were conducted to confirm the non-normality of EMC data. As a result, the Kruskal–Wallis test was applied as a non-parametric technique. Based on pairwise comparisons using the Kruskal–Wallis method, it was determined that Hg and Cd were not significantly different with increasing amounts of dry days without rainfall. It was also found that Zn, Cu, and As were significantly different in event 2 with 1 ADD compared to event 4 with 115 ADDs (p < 0.05). In addition, significant differences were found between event 3 with 16 ADDs and events 2 and 4 for Pb EMC.

In addition, the minimum EMC (EMCmin) and the maximum (EMCmax) were recorded in the fourth event on 3 December 2019 and the second event on 22 October 2019, lasting for 1 and 115 dry days, respectively. Figure 5 illustrates the maximum and minimum FF and EMCs of heavy metals.
Figure 5

Maximum and minimum FF and heavy metal concentrations based on the EMC method.

Figure 5

Maximum and minimum FF and heavy metal concentrations based on the EMC method.

Close modal

In this study, it is perceived that a greater number of ADDs led to a high concentration of heavy metals. Furthermore, the results demonstrated that road-deposited sediments had higher levels of Zn and Cu pollution with longer dry days. Zhang et al. (2017) demonstrated that ADDs have evident effects on the concentration of metals in the solid phase and the concentration of metals on the surface in areas subjected to traffic. Likewise, Ladislas et al. (2012) observed high pollution loads in road runoff after long periods of dry weather. Moreover, in dry weather, metals and other toxic substances accumulate on urban surfaces like roads, roofs, and parking lots (Shajib et al. 2019). By increasing ADDs, the difference in the level of pollutants in mixed land uses compared to single land use increases significantly. The difference in the amount of EMCs can have reasons, such as uncertainties related to build-up pollutants in the dry season and the differences in human activities (Soltaninia et al. 2022).

Furthermore, uncertainty in ADDs could constitute a major factor in the inappropriate simulation of EMCs. Uncertainty is associated with the predictive hydrologic models (Zhao et al. 2013) and across longer periods (Jung et al. 2011). Hence, considering long dry days in semi-arid areas is crucial for urban runoff pollution modelling. ADDs as a parameter of climatic change affected heavy metal runoff pollution in Tehran, which is located in a semi-arid area. Chaudhary et al. (2022) also found that catchments in the tropics exhibit stronger FF than temperate zone catchments.

Figure 6 illustrates the relationship between rainfall intensity, ADDs, and the average of EMCs across all land-use stations. It can be observed that rainfall intensity does not correlate well with EMCs all the time, as evidenced by a large number of studies, often with mixed results. Furthermore, several studies reported the same observation (Bertrand-Krajewski et al. 1998; Taebi & Droste 2004). Chaudhary et al. (2022) found no relationship between FF and runoff characteristics such as rainfall intensity. However, Kim (2002) studied runoff from highways and found that rainfall intensity is negatively correlated with the EMCs of various parameters. Maniquiz et al. (2010) also revealed that EMC values are influenced by rainfall intensity since large rainfalls with longer durations and high intensities result in greater dilution.
Figure 6

The relationship between rainfall intensity, ADDs, and the average of EMCs across all land-use stations.

Figure 6

The relationship between rainfall intensity, ADDs, and the average of EMCs across all land-use stations.

Close modal
Figure 7 illustrates the average EMCs of heavy metals during six rainfall events across all land uses within the area. The error bars in Figure 7 show the standard deviations from the mean of heavy metal concentrations. The number of heavy metals, such as Zn, Pb, Cu, Cd, and Hg, for industrial land use and transportation was higher than that for other land uses. Next, commercial and residential land use and outdoor space have the largest share in the amount of these heavy metals.
Figure 7

Average EMCs of heavy metals during six rainfall events across all land uses within the area.

Figure 7

Average EMCs of heavy metals during six rainfall events across all land uses within the area.

Close modal

The results show that heavy metal pollutants in mixed land use are significantly higher than those in each land use separately (Figure 7). High concentrations of heavy metals in the urban runoff for the station with the HDA could be caused by several uncertainties, such as the presence of other Nonpoint Source (NPS) pollution and the diversity of industrial activities in the area. In addition, high traffic volumes and high population density in HDA intensify the pollution accumulated on land surfaces (Huber et al. 2016). Hence, other influencing factors and NPS-induced pollution should be considered for the mixed land-use areas. Furthermore, human activities are a major contributor to heavy metal pollution (Shao et al. 2014; Zhang et al. 2020). The mixed land-use environment is subject to a variety of human activities that increase the EMC of heavy metals. Due to the wide variety of industrial activities conducted in mixed land uses, heavy metal EMCs are higher than for each land-use type. Moreover, there are other industrial activities in the station with mixed land use, which were not assessed in the station with industrial lands. Therefore, the pollution produced by each industrial activity should be considered separately to assess runoff pollution.

The higher levels of heavy metal pollution in industrial and traffic land uses in the urban area were consistent with the findings of Tiefenthaler et al. (2008) and Liu et al. (2018), who concluded that industrial and traffic areas produced higher metal concentrations in stormwater runoff when compared to other land uses, such as RAs, especially concerning Zn, Pb, and Cu. Moreover, Khademi et al. (2019) discovered that heavy dust from workshops and factories pollutes the industrial area as part of economic growth. As a result of the presence of heavy duty vehicles in IAs, which represents one of the most significant sources of metal generation, heavy metal build-up loads were found to be higher in IAs than in other land-use type areas (Liu et al. 2012). In traffic sites, there is a wide range of vehicles from passenger cars to public transportation and other equipment, and these activities result in heavy metal pollutant emissions (Müller et al. 2020). The dominant heavy metals of Zn, Pb, and Cu in the road runoff could be caused by tire wear of vehicles for Zn (De Silva et al. 2016), petrol for Pb (Todd et al. 2010), and brake systems for Cu (Yu et al. 2016). Cd may come from motor oil and vehicle breaks, building siding and roofs, and automobile tire shaving in vehicles (Davis et al. 2001). Vehicle washing releases various chemicals and materials attached to the vehicles or contained in car-care products (Sörme & Lagerkvist 2002). Rain falling on stationary or moving cars could potentially have similar washing effects. Local fossil fuel combustion, metal manufacturing, cement production, medical and industrial waste discharges, and cremations are sources of Hg (Selin 2009; Pirrone et al. 2010). In CAs, population density increases metal bioavailability (Miranda et al. 2022), which can be transferred into the urban runoff. RAs are typically less congested than IAs and CAs. As a result, a small amount of heavy metals accumulates in RAs compared to industrial and commercial lands prior to rain events. The highest arsenic EMC was found in open space land uses. Al Masum et al. (2022) found that green areas contained the lowest concentrations of pollutants in runoff compared with other urban lands. Likewise, in this study, heavy metal EMCs were significantly lower in open spaces and green areas as compared to other areas. Fertilizers and pesticides used in green spaces may contribute to high arsenic levels (Cai et al. 2015). Measurements of regional background heavy metal EMCs are unavailable. Hence, the results were compared with different standards to determine the effect of land-use types and ADDs on the risk level of heavy metal pollutants. Currently, no standard has been established for the quality of runoff in Tehran. The comparison of the results of heavy metals in the urban runoff with the content of different class standards of Surface Water Environmental Quality Standard (GB 3838-2002) in China (Standardization Administration of China, 2013) and the World Health Organization (World Health Organization 2011) standard (Table 2) showed that in the case of the maximum heavy metals EMC with prolonged ADDs, all metals in all land uses except the OA are at the high-risk level.

Table 2

Standards of surface water quality in China (GB 3838-2002) and drinking water guidelines of the WHO (mg/L)

Trace elementIIIIIIIVVWHO (upper limit range)
As ≤ 0.05 0.05 0.05 0.1 0.1 0.001 
Hg ≤ 0.00005 0.00005 0.0001 0.001 0.001 0.006 
Zn ≤ 0.05 1.0 1.0 2.0 2.0 – 
Pb ≤ 0.01 0.01 0.05 0.05 0.1 0.01 
Cu ≤ 0.01 1.0 1.0 1.0 1.0 2.0 
Cd ≤ 0.001 0.005 0.005 0.005 0.001 0.003 
Trace elementIIIIIIIVVWHO (upper limit range)
As ≤ 0.05 0.05 0.05 0.1 0.1 0.001 
Hg ≤ 0.00005 0.00005 0.0001 0.001 0.001 0.006 
Zn ≤ 0.05 1.0 1.0 2.0 2.0 – 
Pb ≤ 0.01 0.01 0.05 0.05 0.1 0.01 
Cu ≤ 0.01 1.0 1.0 1.0 1.0 2.0 
Cd ≤ 0.001 0.005 0.005 0.005 0.001 0.003 

I: Applicable to national nature reserves and water sources.

II: Applicable to the source of centralized drinking surface water – primary conservation area.

III: Applicable to source of centralized drinking surface water – second conservation area.

IV: Applicable to water spaces for general industry and entertainment of indirect physical contact.

V: Applicable to water spaces for agriculture and general landscape.

WHO: The symbol ‘–’ indicates the absence of national standards due to a lack of health concern sounding levels found in drinking water.

In the case of a minimum EMC with short ADDs, Hg metal in mixed and industrial land uses is still at a high-risk level. After that, the heavy metals such as As, Zn, Pb, and Cd are at moderate risk, respectively. Cu metal has a lower risk than other metals.

Likewise, for the average EMCs (Table 3), although the concentrations decreased significantly, Cu heavy metals were at the level of high risk in mixed and industrial land uses. In some classes, the average cadmium EMC is also at high-level risk.

Table 3

Average heavy metal EMCs measured in five events

Land useHgAsCdZnPbCu
Residential 0.00566 0.02876 0.00484 0.6762 0.2758 0.13268 
Commercial 0.009026 0.05054 0.00662 0.8694 0.3992 0.16236 
Industrial 0.01332 0.07326 0.01146 1.3596 0.6616 0.29202 
Traffic 0.0095 0.0571 0.00972 1.1664 0.5704 0.22662 
Open space 0.00062 0.11104 0.00088 0.2766 0.333 0.0462 
Mixed 0.02004 0.15758 0.01684 2.4794 0.8084 0.409 
Land useHgAsCdZnPbCu
Residential 0.00566 0.02876 0.00484 0.6762 0.2758 0.13268 
Commercial 0.009026 0.05054 0.00662 0.8694 0.3992 0.16236 
Industrial 0.01332 0.07326 0.01146 1.3596 0.6616 0.29202 
Traffic 0.0095 0.0571 0.00972 1.1664 0.5704 0.22662 
Open space 0.00062 0.11104 0.00088 0.2766 0.333 0.0462 
Mixed 0.02004 0.15758 0.01684 2.4794 0.8084 0.409 

The main focus of this study is to investigate the effects of land-use types and climate conditions, such as ADDs and rainfall intensity, on heavy metal pollutants in urban runoff in Tehran, which is characterized by a semi-arid climate. The study also examined the effects of land use on urban runoff quality using the EMC, and the method suggests that industrial lands and ADDs have further influence on runoff quality. Furthermore, sampling and analysis to determine the runoff heavy metal pollution in an urban catchment with different land uses in a semi-rigid area located in Tehran, Iran, yielded the following results:

  • 1.

    Regarding the data collected using the EMC method, the heavy metal pollution load meaningfully increased in the urban runoff in dry seasons, indicating the accumulation of pollutants in long dry periods.

  • 2.

    The EMCs calculated in the study district suggested the high levels of heavy metal pollution in the land uses compared to the figures reported in other districts. The difference could be explained by differences in human activities and behaviour between the districts.

  • 3.

    The EMC is significantly affected by land use and catchment size, such as the study catchment.

  • 4.

    The number of heavy metals except As was highest in industrial land uses. The share of industrial land use in the urban runoff pollution was higher than other land uses.

  • 5.

    The highest and lowest variations for both events, EMCmax and EMCmin, were reported for Zn and Cd, respectively. In other words, the mean Zn concentration acts as an indicator of the greater variability of EMC events.

Rainfall-runoff events can contribute significantly to the transfer of heavy metal pollution loads to surface water bodies (Borthakur et al. 2021). Furthermore, heavy metal in runoff could lead to soil and groundwater contamination (El Khalil et al. 2008). Heavy metal bioaccumulation in biota is vital from an environmental, ecological, and human health perspective and has significant implications for wildlife and human health (Ali et al. 2019). Consequently, it is crucial to understand how much heavy metal pollution is present in the runoff. In the down catchment of Tehran, there are a number of agricultural zones and runoff pollutants that could cause ecological and health hazards.

As land use affects urban runoff adversely, it is recommended that land-use management strategies address semi-arid areas in the regulation of land-use spatial configurations and that land-use types should be planned from a catchment perspective. Furthermore, any harmful human activities in arid and semi-arid areas, such as industrialization, must be strictly prohibited. Relocating industrial land uses from urban environments to non-urban areas is also recommended.

Likewise, urban runoff with high concentrations of heavy metals suggests that the NPS pollution control system is inefficient. In order to address runoff heavy metal pollution, which is a major environmental problem in the study area, low-impact developments (LIDs) are essential. Furthermore, the potential impact on human and animal health due to the contamination of heavy metals in the catchment is a great concern. Using LIDs, including permeable pavement in urban environments, such as catchments, can effectively eliminate heavy metals (Selbig et al. 2019).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Aendo
P.
,
Netvichian
R.
,
Viriyarampa
S.
,
Songserm
T.
&
Tulayakul
P.
2018
Comparison of zinc, lead, cadmium, cobalt, manganese, iron, chromium and copper in duck eggs from three duck farm systems in Central and Western, Thailand
.
Ecotoxicology and Environmental Safety
16
(
1
),
691
698
.
https://doi.org/10.1016/j.ecoenv.2018.06.052
.
Ahiablame
L.
,
Engel
B. A.
&
Chaubey
I.
2012
Representation and evaluation of low impact development practices with L-THIA-LID: an example for site planning
.
Environmental Pollution Journal
2
(
1
).
http://dx.doi.org/ep.v1n2p1
.
Ali
H.
,
Khan
E.
&
Ilahi
I.
2019
Environmental chemistry and ecotoxicology of hazardous heavy metals: environmental persistence, toxicity, and bioaccumulation
.
Journal of Chemistry
2019
.
https://doi.org/10.1155/2019/6730305
.
Al Masum
A.
,
Bettman
N.
,
Read
S.
,
Hecker
M.
,
Brinkmann
M.
&
McPhedran
K.
2022
Urban stormwater runoff pollutant loadings: GIS land use classification vs. sample-based predictions
.
Environmental Science and Pollution Research
29
,
45349
45363
.
https://doi.org/10.1007/s11356-022-18876-x
.
Arfaeinia
H.
,
Dobaradaran
S.
,
Moradi
M.
,
Pasalari
H.
,
Mehrizi
E. A.
,
Taghizadeh
F.
,
Esmaili
A.
&
Ansarizadeh
M.
2019
The effect of land use configurations on concentration, spatial distribution, and ecological risk of heavy metals in coastal sediments of northern part along the Persian Gulf
.
Science of the Total Environment
653
,
783
791
.
https://doi.org/10.1016/j.scitotenv.2018.11.009
.
Ball
J. E.
2002
Stormwater Quality at Centennial Park. University of New South Wales, School of Civil and Environmental Engineering
.
Water Research Laboratory
,
Sydney
,
Australia
.
Banerjee
S.
,
Kumar
A.
,
Maiti
S. K.
&
Chowdhury
A.
2016
Seasonal variation in heavy metal contaminations in water and sediments of Jamshedpur stretch of Subarnarekha River, India
.
Environmental Earth Sciences
75
(
265
).
https://doi.org/10.1007/s12665-015-4990-6
.
Bannerman
R. T.
,
Owens
D. W.
,
Dodds
R. B.
&
Hornewer
N. J.
1993
Sources of pollutants in Wisconsin stormwater
.
Water Science and Technology
28
(
1
),
241
259
.
https://doi.org/10.2166/wst.1993.0426
.
Behrouz
M. S.
,
Yazdi
M. N.
&
Sample
D. J.
2022
Using random forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff
.
Journal of Environmental Management
317
,
115412
.
https://doi.org/10.1016/j.jenvman.2022.115412
.
Bertrand-Krajewski
J.-L.
,
Chebbo
G.
&
Saget
A.
1998
Distribution of pollutant mass vs volume in stormwater discharges and the first flush phenomenon
.
Water Research
32
(
8
),
2341
2356
.
https://doi.org/10.1016/S0043-1354(97)00420-X
.
Boller
M.
1997
Tracking heavy metals reveals sustainability deficits of urban drainage systems
.
Water Science and Technology
35
(
1
),
77
87
.
https://doi.org/10.1016/S0273-1223(97)00186-8
.
Borthakur
A.
,
Wang
M.
,
He
M.
,
Ascencio
K.
,
Blotevogel
J.
,
Adamson
D. T.
,
Mahendra
S.
&
Mohanty
S. K.
2021
Perfluoroalkyl acids on suspended particles: significant transport pathways in surface runoff, surface waters, and subsurface soils
.
Journal of Hazardous Materials
417
,
126159
.
https://doi.org/10.1016/j.jhazmat.2021.126159
.
Botwe
B. O.
,
Abril
J. M.
,
Schirone
A.
,
Barsanti
M.
,
Delbono
I.
,
Delfanti
R.
,
Nyarko
E.
&
Lens
P. N. L.
2017
Settling fluxes and sediment accumulation rates by the combined use of sediment traps and sediment cores in Tema Harbour (Ghana)
.
Science of the Total Environment
609
(
1
),
1114
1125
.
https://doi.org/10.1016/j.scitotenv.2017.07.139
.
Brown
J. N.
&
Peake
B. M.
2006
Sources of heavy metals and polycyclic aromatic hydrocarbons in urban stormwater runoff
.
Science of the Total Environment
359
(
1–3
),
145
155
.
https://doi.org/10.1016/j.scitotenv.2005.05.016
.
Cabon
J. Y.
1999
Chemical characteristics of precipitation at an Atlantic station
.
Water, Air, and Soil Pollution
111
(
1
),
399
416
.
https://doi.org/10.1023/A:1005000532449
.
Cai
L.
,
Xu
Z.
,
Bao
P.
,
He
M.
,
Dou
L.
&
Chen
L.
2015
Multivariate and geostatistical analyses of the spatial distribution and source of arsenic and heavy metals in the agricultural soils in Shunde, Southeast China
.
Journal of Geochemical Exploration
148
,
189
195
.
https://doi.org/10.1016/j.gexplo.2014.09.010
.
Charbeneau
R. J.
&
Barrett
M. E.
1998
Evaluation of methods for estimating stormwater pollutant loads
.
Water Environment Research
70
(
7
),
1295
1302
.
https://doi.org/10.2175/106143098X123679
.
Chaudhary
S.
,
Chua
L. H.
&
Kansal
A.
2022
Event mean concentration and first flush from residential catchments in different climate zones
.
Water Research
118594
.
https://doi.org/10.1016/j.watres.2022.118594
.
Chen
Y.
,
Yang
J.
,
Yang
R.
,
Xiao
X.
&
Xia
J. C.
2022
Contribution of urban functional zones to the spatial distribution of urban thermal environment
.
Building and Environment
216
,
109000
.
https://doi.org/10.1016/j.buildenv.2022.109000
.
Cheng
Z.
,
Chen
L.-J.
,
Li
H.-H.
,
Lin
J.-Q.
,
Yang
Z.-B.
,
Yang
Y.-X.
,
Xu
X.-X.
,
Xian
J.-R.
,
Shao
J.-R.
&
Zhu
X.-M.
2018
Characteristics and health risk assessment of heavy metals exposure via household dust from urban area in Chengdu, China
.
The Science of the Total Environment
619–620
,
621
629
.
https://doi.org/10.1016/j.scitotenv.2017.11.144
.
Christensen
J. M.
1995
Human exposure to toxic metals: factors influencing interpretation of biomonitoring results
.
The Science of the Total Environment
166
(
1
),
89
135
.
https://doi.org/10.1016/0048-9697(95)04478-J
.
Ciazela
J.
&
Siepak
M.
2016
Environmental factors affecting soil metals near outlet roads in Poznan, Poland: impact of grain size, soil depth, and wind dispersal
.
Environmental Monitoring and Assessment
188
(
323
).
https://doi.org/10.1007/s10661-016-5284-5
.
Coville
R.
,
Nowak
D.
,
Atchison
R.
,
Stephan
E.
,
Taggart
T.
&
Endreny
T.
2018
Modeling Tree Cover Effects in Eight Hydrological Units of Northeast Kansas
.
Kansas State University Agricultural Experiment Station and Cooperative Extension Service
,
Manhattan, KS
, p.
58
.
Davis
A. P.
,
Shokouhian
M.
&
Ni
S.
2001
Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources
.
Chemosphere
2001
(
44
),
997
1009
.
https://doi.org/10.1016/S0045-6535(00)00561-0
.
De Silva
S.
,
Ball
A. S.
,
Huynh
T.
&
Reichman
S. M.
2016
Metal accumulation in roadside soil in Melbourne, Australia: effect of road age, traffic density and vehicular speed
.
Environmental Pollution
208
,
102
109
.
https:// doi.org/10.1016/j.envpol.2015.09.032
.
Domingo
J. L.
1994
Metal-induced developmental toxicity in mammals
.
Journal of Toxicology and Environmental Health
42
(
1
),
123
141
.
https://doi.org/10.1080/15287399409531868
.
Dou
C. M.
,
Fu
X. P.
,
Chen
X. C.
,
Shi
J. Y.
&
Chen
Y. X.
2009
Accumulation and detoxification of manganese in hyperaccumulator Phytolacca Americana
.
Plant Biology
11
(
5
),
664
670
.
https://doi.org/10.1111/j.1438- 8677.2008.00163.x
.
Du
X.
,
Liang
H.
,
Fang
X.
,
Cui
S.
&
Junqi
L.
2021
Characteristics of colloids and their affinity for heavy metals in road runoff with different traffic in Beijing, China
.
Environmental Science and Pollution Research
28
,
20082
20092
.
https://doi.org/10.1007/s11356-020-12020-3
.
El Khalil
H.
,
El Hamiani
O.
,
Bitton
G.
,
Ouazzani
N.
&
Boularbah
A.
2008
Heavy metal contamination from mining sites in South Morocco: monitoring metal content and toxicity of soil runoff and groundwater
.
Environmental Monitoring and Assessment
136
(
1
),
147
160
.
https://doi.org/10.1007/s10661-007-9671-9
.
Ellis
J. B.
,
Revitt
D. J.
,
Harrop
D. O.
&
Beckwith
P. R.
1987
The contribution of high-way surfaces to urban stormwater sediments and metal loadings
.
Science of the Total Environment
59
(
1
),
339
349
.
https://doi.org/10.1016/0048-9697(87)90457-8
.
Fernando
D. R.
&
Lynch
J. P.
2015
Manganese phytotoxicity: new light on an old problem
.
Annals of Botany
116
(
3
),
313
319
.
https://doi.org/10.1093/aob/mcv111
.
Gaume
E.
,
Villeneuve
J.
&
Desbordes
M.
1998
Uncertainty assessment and analysis of the calibrated parameter values of an urban stormwater quality model
.
Journal of Hydrology
210
(
1
),
38
50
.
https://doi.org/10.1016/S0022-1694(98)00171
.
Gillis
P. L.
,
Parrott
J. L.
&
Helm
P.
2022
Environmental fate and effects of road run-off
.
Archives of Environment Contamination and Toxicology
38
(
1
).
https://doi.org/10.1007/s00244-021-00906-3
.
Githaiga
K. B.
,
Njuguna
S. M.
,
Gituru
R. W.
&
Yan
X.
2021
Water quality assessment, multivariate analysis and human health risks of heavy metals in eight major lakes in Kenya
.
Journal of Environmental Management
297
,
113410
.
https://doi.org/10.1016/j.jenvman.2021.113410
.
Gjessing
E.
,
Lygren
E.
,
Andersen
S.
,
Berglind
L.
,
Carlberg
G.
,
Efraimsen
H.
,
Källqvist
T.
&
Martinsen
K.
1984
Acute toxicity and chemical characteristics of moderately polluted runoff from high-ways
.
Science of the Total Environment
33
,
225
232
.
https://doi.org/10.1016/0048-9697(84)90396-6
.
Golomb
D.
,
Ryan
D.
,
Eby
N.
,
Underhill
J.
&
Zemba
S.
1997
Atmospheric deposition of toxics onto Massachusetts Bay – I. Metals
.
Atmospheric Environment
31
(
1
),
1349
1359
.
https://doi.org/10.1016/S1352-2310(96)00276-2
.
Google Earth Web 2019 District 17 Tehran. 35 39′. 30 “N, 51 21′.31”. https://earth.google.com/web. Accessed 15th January 2019
.
Gunawardena
J.
,
Egodawatta
P.
,
Ayoko
G. A.
&
Goonetilleke
A.
2013
Atmospheric deposition as a source of heavy metals in urban stormwater
.
Atmospheric Environment
68
(
1
),
235
242
.
https://doi.org/10.1016/j.atmosenv.2012.11.062
.
Helios Rybicka
E.
,
Adamiec
E.
&
Aleksander-Kwaterczak
U.
2005
Distribution of trace metals in the Odra River system: water-suspended matter–sediments
.
Limnologica
35
(
1
),
185
198
.
https://doi.org/10.1016/j.limno.2005.04.002
.
Huber
W. C.
,
1993
Contaminant transport in surface water
. In:
Handbook of Hydrology
(
Maidment
D. R.
ed.).
McGraw-Hill Inc.
,
New York
.
Chapter 14
.
Huber
M.
,
Welker
A.
&
Helmreich
B.
2016
Critical review of heavy metal pollution of traffic area runoff: occurrence, influencing factors, and partitioning
.
Science of the Total Environment
541
,
895
919
.
https://doi.org/10.1016/j.scitotenv.2015.09.033
.
Joimel
S.
,
Cortet
J.
,
Jolivet
C. C.
,
Saby
N. P. A.
,
Chenot
E. D.
,
Branchu
P.
, Consalès, J. N., Lefort, C., Morel, J. L. &
Schwartz
C.
2016
Physico-chemical characteristics of topsoil for contrasted forest, agricultural, urban and industrial land uses in France
.
Science of the Total Environment
545
,
40
47
.
https://doi.org/10.1016/j.scitotenv.2015.12.035
.
Jung
I.-W.
,
Chang
H.
&
Moradkhani
H.
2011
Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects
.
Hydrology and Earth System Sciences
15
,
617
633
.
https://doi.org/10.5194/hess-15-617-2011
.
Kayhanian
M.
,
Stransky
C.
,
Bay
S.
,
Lau
S. L.
&
Stenstrom
M. K.
2008
Toxicity of urban high-way runoff with respect to storm duration
.
Science of the Total Environment
389
(
1
),
386
406
.
https://doi.org/10.1016/j.scitotenv.2007.08.052
.
Kelly
J.
,
Thornton
I.
&
Simpson
P. R.
1996
Urban geochemistry: a study of the influence of anthropogenic activity on the heavy metal content of soils in traditionally industrial and nonindustrial areas of Britain
.
Applied Geochemistry
11
(
1
),
363
370
.
https://doi.org/10.1016/0883-2927(95)00084-4
.
Khademi
H.
,
Gabarron
M.
,
Abbaspour
A.
,
Martinez-Martinez
S.
,
Faz
A.
&
Acosta
J. A.
2019
Environmental impact assessment of industrial activities on heavy metals distribution in street dust and soil
.
Chemosphere
217
(
1
),
695
705
.
https://doi.org/10.1016/j.chemosphere.2018.11.045
.
Kim
L. H.
2002
Monitoring and Modeling of Pollutant Mass in Urban Runoff: Washoff, Buildup and Litter
.
University of California
,
Los Angeles
.
Kim
J.
,
Lee
J.
,
Song
Y.
,
Han
H.
&
Joo
J.
2018
Modeling the runoff reduction effect of low impact development installations in an industrial area, South Korea
.
Water
10
(
8: 967
),
1
15
.
https://doi.org/10.3390/w10080967
.
Kong
X. F.
,
Tian
T.
,
Xue
S. G.
,
Hartley
W.
,
Huang
L. B.
,
Wu
C.
&
Li
C. X.
2018
Development of alkaline electrochemical characteristics demonstrates soil formation in bauxite residue undergoing natural rehabilitation
.
Land Degradation and Development
29
(
1
),
58
67
.
https://doi.org/10.1002/ldr.2836
.
Ladislas
L.
,
El-Mufleh
A.
,
Gerente
C.
,
Chazarenc
F.
,
Andres
Y.
&
Bechet
B.
2012
Potential of aquatic macrophytes as bioindicators of heavy metal pollution in urban stormwater runoff
.
Water, Air, & Soil Pollution
223
,
877
888
.
https://doi.org/10.1007/s11270-011-0909-3
.
Laurenson
G.
,
Laurenson
S.
,
Bolan
N.
,
Beecham
S.
&
Clark
I.
2013
The role of bioretention systems in the treatment of stormwater
.
Advances in Agronomy
120
(
1
),
223
274
.
https://doi.org/10.1016/B978-0-12-407686-0.00004-X
.
Lerat-Hardy
A.
,
Coynel
A.
,
Schäfer
J.
,
Marache
A.
,
Pereto
C.
,
Bossy
C.
,
Capdeville
M.-J.
&
Granger
D.
2021
Impacts of highway runoff on metal contamination including rare earth elements in a small urban watershed: case study of Bordeaux metropole (SW France)
.
Archives of Environmental Contamination and Toxicology
https://doi.org/10.1007/s00244-021-00816-4
.
Li
J.
2003
Error Theory and Measure Uncertainty Assessment
, Vol.
105
.
China Measurement Publishing Company
,
Beijing
, p.
223
.
Li
S.
&
Zhang
Q.
2010
Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques
.
Journal of Hazardous materials
176
(
1
),
579
588
.
https://doi.org/10.1016/j.jhazmat.2009.11.069
.
Li
J.
,
Pu
L.
,
Liao
Q.
,
Zhu
M.
,
Dai
X.
,
Xu
Y.
, Zhang, L., Hua, M. &
Jin
Y.
2015
How anthropogenic activities affect soil heavy metal concentration on a broad scale: a geochemistry survey in Yangtze River Delta, Eastern China
.
Environmental Earth Sciences
73
(
4
),
1823
1835
.
https://doi.org/10.1007/s12665-014-3536-7
.
Lindfors
S.
,
Österlund
H.
,
Lian
L.
&
Viklander
M.
2020
Metal size distribution in rainfall and snowmelt-induced runoff from three urban catchments
.
Science of the Total Environment
743
(
140813
).
https://doi.org/10.1016/j.scitotenv.2020.140813
.
Liu
A.
,
Goonetilleke
A.
&
Egodawatta
P.
2012
Inherent errors in pollutant build-up estimation in considering urban land use as a lumped parameter
.
Journal of Environmental Quality
41
(
5
),
1690
1694
.
https://doi.org/10.2134/jeq2011.0419
.
Liu
A.
,
Egodawatta
P.
,
Guan
Y.
&
Goonetilleke
A.
2013
Influence of rainfall and catchment characteristics on urban stormwater quality
.
Science of the Total Environment
444
(
1
),
255
262
.
https://doi.org/10.1016/j.scitotenv.2012.11.053
.
Liu
R.
,
Tan
R.
,
Li
B.
,
Song
Y.
,
Zeng
P.
&
Li
Z.
2015
Overview of POPs and heavy metals in Liao River Basin
.
Environmental Earth Sciences
73
(
1
),
5007
5017
.
https://doi.org/10.1007/s12665-015-4317-7
.
Liu
A.
,
Ma
Y.
,
Gunawardena
J. M. A.
,
Egodawatta
P.
,
Ayoko
G. A.
&
Goonetilleke
A.
2018
Heavy metals transport pathways: the importance of atmospheric pollution contributing to stormwater pollution
.
Ecotoxicology and Environmental Safety
164
(
1
),
696
703
.
https://doi.org/10.1016/j.ecoenv.2018.08.072
.
Ma
J.-S.
,
Kang
J.-H.
,
Kayhanian
M.
&
Stenstrom
M. K.
2009
Sampling issues in urban runoff monitoring programs: composite versus grab
.
Journal of Environmental Engineering
135
(
3
),
118
127
.
https://doi.org/10.1061/(ASCE)0733-9372(2009)135:3(118)
.
Maniquiz
M. C.
,
Lee
S.
&
Kim
L.-H.
2010
Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables
.
Journal of Environmental Sciences
22
(
6
),
946
952
.
https://doi.org/10.1016/S1001-0742(09)60203-5
.
Maniquiz-Redillas
M.
,
Robles
M. E.
,
Cruz
G.
,
Reyes
N. J.
&
Kim
L. H.
2022
First flush stormwater runoff in urban catchments: a bibliometric and comprehensive review
.
Hydrology
9
(
4
),
63
.
https://doi.org/10.3390/hydrology9040063
.
McQueen, A. D., Johnson, B. M., Rodgers Jr, J. H. & English, W. R. 2010
Campus parking lot stormwater runoff: physicochemical analyses and toxicity tests using Ceriodaphnia dubia and Pimephales promelas.
Chemosphere 79 (5), 561–569. https://doi.org/10.1016/j.chemosphere.2010.02.004
.
Miranda
L. S.
,
Deilami
K.
,
Ayoko
G. A.
,
Egodawatta
P.
&
Goonetilleke
A.
2022
Influence of land use class and configuration on water-sediment partitioning of heavy metals
.
Science of The Total Environment
804
,
150116
.
https://doi.org/10.1016/j.scitotenv.2021.150116
.
Mishra
S.
,
Bharagava
R. N.
,
More
N.
,
Yadav
A.
,
Zainith
S.
,
Mani
S.
,
Chowdhary
P.
,
2019
Heavy metal contamination: an alarming threat to environment and human health
. In:
Environmental Biotechnology: For Sustainable Future
(
Sobti
R.
,
Arora
N.
&
Kothari
R.
, eds).
Springer
,
Singapore
.
https://doi.org/10.1007/978-981-10-7284-0_5
.
Morselli
L.
,
Olivieri
P.
,
Brusori
B.
&
Passarini
F.
2003
Soluble and insoluble fractions of heavy metals in wet and dry atmospheric depositions in Bologna, Italy
.
Environmental Pollution
124
(
3
),
457
469
.
https://doi.org/10.1016/S0269-7491(03)00013-7
.
Müller
A.
,
Österlund
H.
,
Marsalek
J.
&
Viklander
M.
2020
The pollution conveyed by urban runoff: a review of sources
.
Science of the Total Environment
709
(
136125
).
https://doi.org/ 10.1016/j.scitotenv.2019.136125
.
Nazahiyah
R.
,
Yusop
Z.
&
Abustan
I.
2007
Stormwater quality and pollution loading from an urban residential catchment in Johor, Malaysia
.
Water Science and Technology
56
(
7
),
1
9
.
https://doi.org/10.2166/wst.2007.692
.
Niazi
M.
,
Nietch
C.
,
Maghrebi
M.
,
Jackson
N.
,
Bennet
B. R.
,
Tryby
M.
&
Massoudieh
A.
2017
Storm water management model: performance review and gap analysis
.
Journal of Sustainable Water in the Built Environment
3
(
2
),
1
32
.
https://doi.org/10.1061/JSWBAY.0000817
.
Nilsson
L.
,
Persson
P.
,
Rydén
L.
,
Darozhka
S.
&
Zaliauskiene
A.
2007
Cleaner Production Technologies and Tools for Resource Efficient Production
.
Baltic University Press, Uppsala
.
Passarii
F.
,
Pavoni
B.
&
Ugo
P.
2001
Chemical analyses of heavy metal contamination in sediments of the Venice lagoon and toxicological implication
.
Annali di Chimica – Rome
91
,
471
478
.
Pirrone
N.
,
Cinnirella
S.
,
Feng
X.
,
Finkelman
R. B.
,
Friedli
H. R.
,
Leaner
J.
,
Mason
R.
,
Mukherjee
A. B.
,
Stracher
G. B.
,
Streets
D. G.
&
Telmer
K.
2010
Global mercury emissions to the atmosphere from anthropogenic and natural sources
.
Atmospheric Chemistry and Physics
10
,
5951
5964
.
http://dx.doi.org/10.5194/acp-10-5951-2010
.
Pitt
R.
,
Field
R.
,
Lalor
M.
&
Brown
M.
1995
Urban stormwater toxic pollutants: assessment, sources, and treatability
.
Water Environment Research
67
(
1
),
260
275
.
https://doi.org/10.2175/106143095X131466
.
Reddy
K. R.
,
Dastgheibi
S.
&
Cameselle
C.
2021
Mixed versus layered multi-media filter for simultaneous removal of nutrients and heavy metals from urban stormwater runoff
.
Environmental Science and Pollution Research
28
(
1
),
7574
7585
.
https://doi.org/10.1007/s11356-020-11120-4
.
Ren
W.
,
Zhong
Y.
,
Meligrana
J.
,
Anderson
B.
,
Watt
W. E.
,
Chen
J.
&
Leung
H. L.
2003
Urbanization, land use, and water quality in Shanghai: 1947–1996
.
Environment International
29
(
5
),
649
659
.
https://doi.org/10.1016/S0160-4120(03)00051-5
.
Rossman
L. A.
&
Huber
W. C.
2016
Storm Water Management Model Reference Manual Volume III – Water Quality
.
US EPA National Risk Management Laboratory, EPA/600/R-16/093
,
Cincinnati, OH
,
USA
.
Sansalone
J. J.
&
Buchberger
S. G.
1997
Characterization of solid and metal element distributions in urban high-way stormwater
.
Water Science and Technology
36
(
1
),
155
160
.
Schueler
T.
2000
Stormwater pollution source areas isolated in Marquette, Michigan
.
Watershed Protection Technology
3
(
1
),
609
612
.
Selbig
W. R.
,
Buer
N.
&
Danz
M. E.
2019
Stormwater-quality performance of lined permeable pavement systems
.
Journal of Environmental Management
251
(
109510
).
doi: 10.1016/j.jenvman.2019.109510
.
Selin
N. E.
2009
Global biogeochemical cycling of mercury: a review
.
Annual Review of Environment and Resources
34
(
1
),
43
63
.
http://dx.doi.org/10.1146/annurev.environ.051308.084314
.
Shajib
M. T. I.
,
Hansen
H. C. B.
,
Liang
T.
&
Holm
P. E.
2019
Metals in surface specific urban runoff in Beijing
.
Environmental Pollution
248
,
584
598
.
https://doi.org/10.1016/j.envpol.2019.02.039
.
Shao
X.
,
Huang
B.
,
Zhao
Y.
,
Sun
W.
,
Gu
Z.
&
Qian
W.
2014
Impacts of human activities and sampling strategies on soil heavy metal distribution in a rapidly developing region of China
.
Ecotoxicology and Environmental Safety
104
,
1
8
.
https://doi.org/10.1016/j.ecoenv.2014.02.007
.
Shen
Z. Y.
,
Liao
Q.
,
Hong
Q.
&
Gong
Y. W.
2012
An overview of research on agricultural non-point source pollution modelling in China
.
Separation and Purification Technology
84
(
9
),
104
111
.
https://doi.org/10.1016/j.seppur.2011.01.018
.
Soltaninia
S.
,
Taghavi
L.
,
Hosseini
S. A.
,
Motamedvaziri
B.
&
Eslamian
S.
2022
The effects of antecedent dry days and land use types on urban runoff quality in a semi-arid region
.
International Journal of Urban Sciences
1
24
.
https://doi.org/10.1080/12265934.2022.2114928
.
Sörme
L.
&
Lagerkvist
R.
2002
Sources of heavy metals in urban wastewater in Stockholm
.
The Science of the Total Environment
298
(
1–3
),
131
145
.
https://doi.org/10.1016/S0048-9697(02)00197-3
.
Standardization Administration of China
2013
Gasoline for Motor Vehicles (GB 17930-2013
)
.
Su
S. L.
,
Xiao
R.
,
Mi
X. Y.
,
Xu
X. Y.
,
Zhang
Z. H.
&
Wu
J. P.
2013
Spatial determinants of hazardous chemicals in surface water of Qiantang River, China
.
Ecological Indicators
24
(
1
),
375
381
.
https://doi.org/10.1016/j.ecolind.2012.07.015
.
Sun
Z.
,
Xu
G.
,
Hao
T.
,
Huang
Z.
,
Fang
H.
&
Wang
G.
2015
Release of heavy metals from sediment bed under wave-induced liquefaction
.
Marine Pollution Bulletin
97
(
1
),
209
216
.
https://doi.org/10.1016/j.marpolbul.2015.06.015
.
Taebi
A.
&
Droste
R. L.
2004
Pollution loads in urban runoff and sanitary wastewater
.
Science of The Total Environment.
327
(
1–3
),
175
184
.
https://doi.org/10.1016/j.scitotenv.2003.11.015
.
Tiefenthaler, L. L., Stein, E. D. & Schiff, K. C. 2008
Watershed and land use–based sources of trace metals in urban storm water.
Environmental Toxicology and Chemistry: An International Journal 27 (2), 277–287. https://doi.org/10.1897/07-126R.1
.
Todd
P. A.
,
Ong
X.
&
Chou
L. M.
2010
Impacts of pollution on marine life in Southeast Asia
.
Biodiversity and Conservation
19
(
1
),
1063
1082
.
https://doi.org/10.1007/s10531-010-9778-0
.
Ukah
B. U.
,
Egbueri
J. C.
,
Unigwe
C. O.
&
Ubido
O. E.
2019
Extent of heavy metals pollution and health risk assessment of groundwater in a densely populated industrial area, Lagos, Nigeria
.
International Journal of Energy Water
3
(
1
),
291
303
.
https://doi.org/10.1007/s42108-019-00039-3
.
US-EPA 1992 Collection and use of total suspended solids data. US-EPA Office of Water Water-Quality Technical Memorandum No.2001-03. United States Environmental Protection Agency, Washington, DC
.
Villanueva
J. D.
,
Granger
D.
,
Binet
G.
,
Litrico
X.
,
Huneau
F.
,
Peyraube
N.
&
Le Coustume
P.
2016
Trace metal contribution of the runoff collector to a semi-urban river
.
Environmental Science and Pollution Research
23
(
1
),
11298
11311
.
https://doi.org/10.1007/s11356-016-6322-0
.
Wang
Z.
,
Xiao
J.
,
Wang
L.
,
Liang
T.
,
Guo
Q.
,
Guan
Y.
&
Rinklebe
J.
2020
Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map
.
Environmental Pollution
260
,
114065
.
https://doi.org/10.1016/j.envpol.2020.114065
.
World Health Organization
2011
Guidelines for Drinking-Water Quality
, 4th edn.
WHO
,
Geneva
,
Switzerland
.
Wu
J. S.
,
Allan
C. J.
,
Saunders
L.
&
Evett
J. B.
1998
Characterization and pollutant loading estimation for high-way runoff
.
Journal of Environmental Engineering
124
(
1
),
584
592
.
doi: 10.1061/(ASCE)0733-9372(1998)124:7(584)
.
Xue
H.
,
Zhao
L.
&
Liu
X.
2020
Characteristics of heavy metal pollution in road runoff in the Nanjing urban area, East China
.
Water Science and Technology
81
(
9
),
1961
1971
.
https://doi.org/10.2166/wst.2020.249
.
Yadav
A.
,
Chowdhary
P.
,
Kaithwas
G.
,
Bharagava
R. N.
,
2017
Toxic metals in the environment, threats on ecosystem and bioremediation approaches
. In:
Handbook of Metal-Microbe Interactions and Bioremediation
(
Das
S.
&
Singh
, eds).
CRC Press/Taylor & Francis Group
,
Boca Raton
.
Yang
J.
,
Liang
J.
,
Yang
G.
,
Feng
Y.
,
Ren
G.
,
Ren
C.
,
Han
X.
&
Wang
X.
2020
Characteristics of non-point source pollution under different land use types
.
Sustainability
12
(
5
),
2012
2029
.
https://doi.org/10.3390/su12052012
.
Yazdi
M. N.
,
Sample
D. J.
,
Scott
D.
,
Owen
J. S.
,
Ketabchy
M.
&
Alamdari
N.
2019
Water quality characterization of storm and irrigation runoff from a container nursery
.
Science of The Total Environment
667
,
166
178
.
https://doi.org/10.1016/j.scitotenv.2019.02.326
.
Yazdi
M. N.
,
Samaple
D. J.
,
Scott
D.
,
Wang
X.
&
Ketabchy
M.
2021
The effects of land use characteristics on urban stormwater quality and watershed pollutant loads
.
Science of The Total Environment
145358
.
doi:10.1016/j.scitotenv.2021.145358
.
Ying
G.
&
Sansalone
J.
2010
Transport and solubility of Hetero-disperse dry deposition particulate matter subject to urban source area rainfall-runoff processes
.
Journal of Hydrology
383
(
1
),
156
166
.
https://doi.org/10.1016/j.jhydrol.2009.12.030
.
Yu
Y.
,
Li
Y.
,
Li
B.
,
Shen
Z.
&
Stenstrom
M. K.
2016
Metal enrichment and lead isotope analysis for source apportionment in the urban dust and rural surface soil
.
Environmental Pollution
216
(
1
),
764
772
.
https://doi.org/10.1016/j.envpol.2016.06.046
.
Zhang
J.
,
Hua
P.
&
Krebs
P.
2017
Influences of land use and antecedent dry-weather period on pollution level and ecological risk of heavy metals in road-deposited sediment
.
Environmental Pollution
228
,
158
168
.
https://doi.org/10.1016/j.envpol.2017.05.029
.
Zhang
Y.
,
Zhang
X.
,
Bi
Z.
,
Yu
Y.
,
Shi
P.
,
Ren
L.
&
Shan
Z.
2020
The impact of land use changes and erosion process on heavy metal distribution in the hilly area of the Loess Plateau, China
.
Science of the Total Environment
718
(
137305
).
https://doi.org/10.1016/j.scitotenv.2020.137305
.
Zhao
D. Q.
,
Chen
J. N.
,
Wang
H. Z.
&
Tong
Q. Y.
2013
Application of a sampling based on the combined objectives of parameter identification and uncertainty analysis of an urban rainfall-runoff model
.
Journal of Irrigation and Drainage
139
(
1
),
66
74
.
doi: 10.1061/(ASCE)IR.1943-4774.0000522
.
Zhao
L.
,
Nan
H.
,
Kan
Y.
,
Xu
X.
,
Qiu
H.
&
Cao
X.
2019
Infiltration behavior of heavy metals in runoff through soil amended with biochar as a bulking agent
.
Environmental Pollution
254
(
pt B
),
113114
.
https://doi.org/10.1016/j.envpol.2019.113114
.
Zhu
F.
,
Cheng
Q. Y.
,
Xue
S. G.
,
Li
C. X.
,
Hartley
W.
,
Wu
C.
&
Tian
T.
2018
Influence of natural regeneration on fractal features of residue microaggregates in bauxite residue disposal areas
.
Land Degradation and Development
29
(
1
),
138
149
.
https://doi.org/10.1002/ldr.2848
.
Zhu
R.
,
Newman
G.
&
Atoba
K.
2021
Simulating the impact of land use change on contaminant transferal during flood events in Houston, Texas
.
Landscape Journal
40
(
2
),
79
99
.
doi:10.3368/lj.40.2.79
.
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