To accurately figure out how much pollution comes from urban surface runoff and take steps to protect receiving water, we needed to fully understand how road-deposited sediments (RDS) wash off. Our research indicates that particles smaller than 100 μm imparted 59–73% of the wash-off load. Two instances of natural rainfall reduced the aggregate RDS mass by approximately 27–36%. On days without rain, the RDS particle shrank in size, but it became heavier after a downpour. The results showed that the source restricted the tiny particles washed off of RDS, while transport generally restricted the heavier particles washed off. We used 39 artificial rainfall events with different particle sizes to confirm our results on RDS wash-off. When compared to the heavier particles, tiny particles have a greater wash-off percentage, and when it comes to describing the wash-off mechanism, Fw values offer an inventive and insightful assessment. It has been assessed that tiny particles were source-restricted and this mechanism occurred during the initial stage, but heavier particles were transport-restricted and it occurred during the late stage.

  • Natural rain reduces road-deposited sediment (RDS) by 27–36%, highlighting rainfall's cleansing effect on urban surfaces.

  • On rain-free days, RDS particles shrink, becoming heavier after downpours, revealing the dynamic interplay between rainfall and particle behavior in urban runoff.

Runoff from the surface is a significant contributor to the deterioration of water-body cleanliness in urban and suburban areas (Wijesiri et al. 2016). Given the grave risks that heavy metals represent to human health, the alarming rise in their concentrations in urban road dust constitutes a public health emergency (Faisal et al. 2021b, 2021a). A significant contributor to the deterioration of water quality in urban areas is the presence of surface runoff, which is responsible for the contamination of water sources all over the world (Martínez & Poleto 2014; Faisal et al. 2023). There has been a surge in both the necessity of quantifying such loads and interest in doing so as a result of the numerous studies that have shown high amounts of heavy metals in dissolved form in metropolitan water sources (Maniquiz-Redillas & Kim 2014; Faisal et al. 2022). Urban areas are replete with road-deposited sediments (RDS), which are significant pollutant carriers. Urban diffuse pollution, a consequence of RDS wash-off, has become an increasingly pressing issue in cities because of the fast industrialization and technological development in China and other developing nations (Sutherland 2003; Lu et al. 2009; Li et al. 2013). Proper regulation of RDS necessitates a thorough understanding of the mechanisms that lead to urban diffuse pollution. Heavy metals deposited into urban streams have significant impacts on ecosystem health because they are toxic and readily absorbed by living beings (Islam et al. 2015). Therefore, a primary goal of urban water management is the development of efficient methods to lessen the contamination of stormwater with heavy metals, as this poses a threat to urban aquatic ecosystems (Barbosa et al. 2012). The two main components of urban diffuse pollution, RDS accumulation on dry days and its outflow during rainstorms, are often seen as independent processes that illustrate the complex dynamics at work in the whole process of environmental pollution in cities (Vaze & Chiew 2002; Goonetilleke et al. 2009).

A thorough grasp of the development process is crucial for conducting reliable analyses and assessments about contaminant buildup in impermeable urbanized environments (Miguntanna et al. 2013). Inevitably, there are just a handful of direct measurements of RDS accumulation; the rest have been extrapolated from assessments of RDS wash-off (Vaze & Chiew 2002; Pitt et al. 2005; Shaw et al. 2006). Recent research has shown that dry spells before rainstorms and street cleaning all play a role in RDS accumulation (Deletic & Orr 2005; Tian et al. 2009; Egodawatta et al. 2013; Shen et al. 2016). Factors like land utilization, urban–rural gradients, surface characteristics, RDS particle grain size, rainfall characteristics, and many more are known to impact the wash-off process (Brodie & Rosewell 2007; Mahbub et al. 2010). The capacity to wash off and the RDS load are important considerations in identifying whether the source or transport is the cause of the wash-off limitation. Typical storms only remove a small portion of the total RDS, according to research, which implies that conveyance is the primary constraint on wash-off. Surface contamination levels return to pre-storm levels due to the rapid accumulation that occurs after a storm (Vaze & Chiew 2003). However, water quality models sometimes utilize a source-limited approach, assuming that RDS significantly decreases during storms and gradually recovers during the dry days preceding the storm (Sheng et al. 2008a).

Stormwater management model (SWMM) applications in semi-arid urban watersheds present difficulty in determining the wash-off parameters and pollution buildup. A crucial gap exists in semi-arid regions because of the large land area and fast urbanization trends. Tu & Smith (2018) highlighted this paucity of globally recognized pollutant characteristics for SWMM in semi-arid regions. They created a reliable approach to determining pollutant parameters by combining inverse modeling with historical data sources; this shed light on the processes controlling pollution accumulation and wash-off in semi-arid urban situations. Central Italy's Mediterranean coast was the subject of recent research that looked into combined sewer overflows (CSOs). Using models for dynamic rainfall–runoff simulations and water quality assessments, researchers assessed 35 spillways that were involved in CSO incidents. In particular, they aimed to determine the effects of microbiological contamination, especially Escherichia coli, on the quality of coastal bathing water and to identify important CSO spills according to flow rate and pollutant loads. The study set out to do just that – evaluate CSO pollution in all its glory – to learn what effects it has on coastal water quality (Crocetti et al. 2021). Existing simulators are unable to handle the complexity that exceeds the capabilities of RDS wash-off in stormwater quality modeling. However, differing viewpoints underscore the complexity that exceeds the capabilities of existing simulators. Current stormwater quality models' replication equations do not adequately account for the complexity of wash-off processes. Researchers need to conduct further studies to fully comprehend these processes due to their complexity. To improve stormwater quality models and make them more accurate reflections of the real-world dynamics of RDS wash-off, it is important to address the subtle complexities involved. This will advance the efficacy of stormwater management systems and environmental protection initiatives. The primary objectives were to understand how these factors influence the wash-off process and to identify parameters determining whether the process is limited by the pollution source or by transport. Simulations and field experiments were used to achieve the goals of this study.

Study area and sampling

As shown in Figure 1, Zhengzhou, the capital city of Henan Province (34° 45′ 50.4″ N, 113° 41′ 2.4″ E), is a significant economic and administrative nexus in the middle of China. The province's major city lies in the huge Central Belt metropolis. The city rests at the base of the Funiu Hills in the far north of the country. Upland regions surround it to the west, and modest and lowland terrain surrounds it to the east. It encompasses a total geographical area of 1,011.3 km2 in its entirety. Zhengzhou is a quickly expanding metropolis located in the central area of China. As a result of the city's rapid economic development and urban expansion, the city's air quality has become a serious issue. This research site was situated on an urban road surface and separated into two lanes, with one lane going in each direction. Buildings lined both sides of the road, and most of the land around them was paved over with asphalt and concrete. Our findings suggest that road sweeps occur infrequently and that rainfall, runoff characteristics, and the previous dry season have the greatest impact on RDS. Table S1 of the weather report further confirms that this road did not experience exceptionally complex rainfall patterns. We collected RDS samples from an 8.50 m2 area that had already been marked out, where cars and bicycles are the main forms of transportation and where people and traffic move at a medium flow, using a domestic vacuum cleaner. We vacuumed the area from the center of the road marking to the fence. This upright vacuum cleaner's cyclonic operation and air purification system easily removes even the smallest dust and dirt particles. The next step after leaving each RDS sample to dry at room temperature for 7 days is to weigh them using an electronic scale. After that, we used polyester sieves to separate the samples by particle size: <40, 40–60, 60–100, 100–150, 150–300, 300–500, 500–1,000, and >1,000 μm.
Figure 1

Research area.

Field observations RDS wash-off physical experiment

As part of a comprehensive strategy, we have collected 12 dust samples (Table S2). Identifying the complex correlations between RDS amount and grain size distribution, especially under varying wet and dry weather conditions, was the key objective of our endeavors. Sampling of RDS was performed 1 day before and 1 day afterward in a short rainstorm and a mild rainstorm to assess the differences in the volume and grain size composition prior to and following rainfall events. Due to the rapidity with which RDS accumulates during the dry days after a rain event, the optimal time to obtain the RDS sample for analyzing the impacts of specific rainstorms on RDS content and grain size composition is 1 day prior to and following the event (Vaze & Chiew 2002). The number and size distribution of grains were investigated during the dry and rainy seasons, employing eight subsequent RDS samples. Since RDS buildup occurs more quickly 2 days after rain events than over wetter days, we chose to explore it in that context (Vaze & Chiew 2002). We believe that the prior detection date of RDS accumulation is unlikely to impact the objective of this research, despite the fact that a number of significant studies suggest the RDS buildup approaches equilibrium after 7–9 days (Pitt et al. 2005; Goonetilleke et al. 2009).

RDS wash-off physical experiment

To get a better look at how RDS wash-off works with different particle sizes, 39 separate rain events with five different intensities (10, 46.8, 53.0, 70.4, and 77 mm/h) were used as shown in Table 1. (The range of rainfall intensities includes both low- and high-intensity events that are typical in cities. This makes it easier to get a full picture of how they affect runoff and sediment dynamics. Similarly, we selected the particle size groups to demonstrate the variety of sediment sizes present in urban runoff and to assess their contributions to sediment wash-off processes. The chosen sampling intervals also allow for a thorough look at how runoff characteristics change over time, which helps find the most important times during rain events. All of these factors work together to give us a complete picture of how rainfall characteristics, sediment properties, and sampling time intervals affect each other in urban runoff studies.)

Table 1

Factors and parameters involved in the wash-off experiment

FactorsParameters
Rainfall intensities (mm/h) 10, 46.8, 53.0, 70.4, and 77.2 
Fractions of particles size (μm) <40, 40–60, 60–100, 100–150, 150–300, 300–500, 500–1,000, and >1,000 
Sampling intervals (min) 0–3, 3–6, 6–9, 9–12, 12–15, 15–20, 20–25, 25–30, 30–35, 35–40, 40–50, 50–60, 60–80, 80–100, and 100–120 
FactorsParameters
Rainfall intensities (mm/h) 10, 46.8, 53.0, 70.4, and 77.2 
Fractions of particles size (μm) <40, 40–60, 60–100, 100–150, 150–300, 300–500, 500–1,000, and >1,000 
Sampling intervals (min) 0–3, 3–6, 6–9, 9–12, 12–15, 15–20, 20–25, 25–30, 30–35, 35–40, 40–50, 50–60, 60–80, 80–100, and 100–120 

It has been demonstrated that many significant factors affect RDS wash-off. Within the context of this simulated experiment, our primary focus was placed on the intensity and duration of the rainfall. The rain-simulating setup consisted of two separate rain simulators (Fig. S1) (Boulange et al. 2019). Each of the two rain generators' four nozzles were uniformly distributed along the 2.5-m-tall swinging nozzle boom (by a distance of 1.1 m). The rain simulators are controlled by an electrical control box, which lets them be set up for different levels of rain. The 0.04 MPa output from the nozzles (Veejet 80100) was sufficient to create raindrops of moderate size. Together, the two rain generators created a uniform rainfall field that was 1.5 m × 2.2 m in size. We decided on a plot that was 1.5 m wide and 2 m long for our wash-off test. A 1.5 m × 2 m plastic frame was used to demarcate the plot, and its edges were finished off with tiles. The plastic frame needed to be open on one end so that the catch tray could be fastened to it and utilized to collect the overflow (Faisal et al. 2022).

By comparing the amount of runoff water recovered to the amount of artificial rain that was used in the wash-off experiment, we were able to determine the runoff water retrieval efficiency. The road surface roughness was assessed at 0.624 mm, and its slope was found to be 2.37°. This road was considered for the RDS wash-off research plot since it had been recently paved with asphalt. The RDS dispersion was uniformly spread throughout the wash-off experiment plot before the test was performed. To properly wash the runoff plot before the next simulated rainstorm event, we flushed it with water. To be sure that our simulated runoff experiment was accurate, we used a diverse set of RDS samples collected from across the research area. (To get a representative sample of road dust sediments, it was necessary to collect dust from all over the study region in a methodical fashion. The first step was to choose sampling sites that are typical of various road types, traffic situations, land uses, and environmental circumstances. Establishing a consistent sampling methodology and equipping participants with the necessary gear, such as dust traps or handheld vacuum samplers, ensure consistent sample collection, handling, and storage. We used field blanks and duplicate samples as part of our quality control procedures. We annotate each sample with metadata to facilitate future analysis. The lab then analyzes the samples using various methods, including microscopy, particle size analysis, elemental analysis, and more.) The section of road used for the RDS wash-off experiment had asphalt paving installed on it around 5 years before the experiment was conducted. The wash-off process lasted for 2 h, and throughout that time, runoff samples were physically acquired at intervals of 3 min for the first 15 min, 5 min for the next 25 min, 10 min for the next 20 min, and 20 min for the last 60 min until there was no further surface runoff. The amount of RDS washed away from each grain size fraction (Fw, %) was calculated as follows (Faisal et al. 2022).

Investigative approaches

Total suspended solids in the RDS wash-off simulation experiment were determined by filtering water samples from the runoff through 0.45 μm Millipore filter paper, followed by drying and reweighing the solid particulates that remained in the filter paper (Eaton et al. 1998). The ratio of the total mass of RDS on the surface to the total mass of RDS in the runoff was used to calculate the wash-off percentage (Fw, %) of RDS on each grain size fraction. This ratio was represented as a percentage. This was determined using the formula
formula
(1)
where Fw represents the percentage of RDS size fraction that is washed off the surface (%), represents the mass of the size faction (mg), Minitial represents the initial mass of RDS with a conforming grain size on the surface (mg), C(t) represents the mass of RDS with a corresponding grain size in the surface runoff water (mg/L) at each sampling time, and Q(t) represents the surface runoff flow rate at each sampling time (m3/min).
An analogous notation was used to describe the accumulation rate (Fa, %) of RDS on each grain size fraction relative to the initial total mass of RDS, as was done for Fw. This was determined using the equation
formula
(2)
where Mbuild-up is the mass of the accumulated size fraction before an RDS sample is taken and Minitial is the initial mass of RDS with a conforming grain size on the surface.

Rainfall effects on the grain size distribution and RDS

Figures 2 and 3 demonstrate the fluctuations in RDS quantity that were detected 1 day prior to and 1 day following two natural rainfall events. The instances of the light rain event that occurred on 1 May and the moderately heavy rain event that occurred on 15 May contributed to a significant decrease in the total mass, bringing it down from 14.12 to 8.14 g/m2 and from 16.43 to 7.75 g/m2, respectively. The aggregate RDS wash-off percentages (Fw, %) seemed to be 27 and 36 throughout light and moderate rainfall events, respectively, and ascended as the rainfall intensity and volume ascended. There was a clear disparity between the impact of rainfall and the severity of rainfall on Fw values corresponding to the various grain size fractions.
Figure 2

Two different rainfall events day situations.

Figure 2

Two different rainfall events day situations.

Close modal
Figure 3

RDS changes one day before and after the rainfall event.

Figure 3

RDS changes one day before and after the rainfall event.

Close modal

In the case of the bulk RDS, the tiny particles (<100 μm) accounted for 73 and 59% of the wash-off load during light and moderate rainfall events, respectively. Fw values for heavier particles (300–500 and 500–1,000 μm) were comparatively greater throughout the moderate rain event, contrasting to the slight rain event, whereas Fw values for smaller-sized particles exhibited little change or perhaps a decline. Based on these findings, it appears that the RDS particles with a smaller size, roughly <100 μm, were able to be suitably washed away by the two rainstorm events; however, the RDS particles with a larger size, roughly >150 μm, were not. Indirect evidence from a large number of earlier studies also showed that particles with a size of 100 μm or less predominated among the suspended debris that washed off the road surface right through natural rainfall events (Brodie & Dunn 2009; Charters et al. 2015). Our findings suggest that the wash-off process of tiny particles is typically source-limited, while that of heavier particles is typically transport-limited. The wash-off process typically had limited transport capability. Rainfall occurrences also have an impact on variations in RDS particle size composition (Table S3). In particular, during both natural rain occurrences, the RDS grain size composition changed for the worse. This may have happened because tiny RDS particles had greater Fw values than heavier ones, leading to their preferential wash-off. Our findings suggest that typical rainfall events remove just a negligible fraction of the heavier particles but have the potential to remove a sizable fraction of the tiny particles.

Dry weather effects on grain size distribution

Changes in RDS concentration and grain size distribution over two dry seasons are depicted in Figure 4. A couple of days prior to the beginning of the analysis of RDS accumulation during prolonged dry periods, two rainstorms on 20 May and 16 November occurred. Both short-term dry days, which occurred for 5 days during the summer, and long-term dry days, which occurred for 11 days during the winter, resulted in a considerable increase in the total mass. Fa values varied depending on the proportion of heavier to tiny grains. The results indicate no notable differences in grain size distribution throughout the dry days that occurred during the summer, and tiny particles <100 μm comprised 60.36% of the entire amount on 11 June and 58.54% on 16 June (Table S4). However, the smaller particles <100 μm grew from 58.08% on 15 December to 70.67% on 26 December over dry days that occurred during the winter. In cities, RDS accumulation is likely to grow on dry days. Additional data are needed to confirm the impacts of dry weather periods on grain size composition, but the fact that the buildup process was not starting from zero over preceding dry days suggests inadvertently that the wash-off mechanism is transport-limited.
Figure 4

RDS changes in summer and winter over dry weather days.

Figure 4

RDS changes in summer and winter over dry weather days.

Close modal

Particle grain size effects on the RDS wash-off process

Studies from the past have shown that factors like the surface of urban roads, how the land is used, the way it rains, the size of the particles, and how often the streets are swept have a big impact on how RDS is washed away (Vaze & Chiew 2002; Sheng et al. 2008b; Miguntanna et al. 2013; Liu et al. 2014). According to research done by Vaze & Chiew (2002), the wash-off process that occurs after an average rainfall event may only remove a tiny fraction of the total RDS and has a tendency to be transport-limited, whereas phosphorus runoff has been identified as transport-limited. Liu et al. (2014) found source- and transport-limited wash-off mechanisms on concrete and asphalt surfaces. However, it was still not clear how particle size affected the wash-off process or if it was limited by the source or the transport. Numerous earlier investigations showed that in most rainstorms, 70% of the suspended solids are made up of tiny particles <100 μm (Kim & Sansalone 2008). Considering how rainstorm instances affected the amount of RDS and the distribution of grain sizes, we discovered in the present investigation that the wash-off of smaller and heavier particles typically appeared to be source- and transport-limited, respectively. The purpose of these 39 simulated rainfall events was to investigate whether particle size was a limiting factor in source- or transport-limited processes. As can be seen in Figure 5, there were significant variations in the RDS wash-off processes associated with the various features of rainfall.
Figure 5

Impacts of duration and intensity of rainfall on RDS wash-off percentage.

Figure 5

Impacts of duration and intensity of rainfall on RDS wash-off percentage.

Close modal

The Fw values are size-dependent, with larger values corresponding to smaller RDS particles. Furthermore, RDS wash-off with various grain sizes while simulating rain offers valuable knowledge about this process. In addition, the Fw values for each grain size fraction may be an indirect reflection of whether or not they are constrained by their source or their transport. The wash-off of smaller particles (<100 μm) is thus more likely to be source-limited than that of larger ones (>100 μm), which is more likely to constitute transport-limited. Although the effectiveness of each treatment approach is highly dependent on particle size, concomitant settling velocity, and hydraulic retention duration, these outcomes will aid in deciding on suitable treatment methods for sediment eradication (Li et al. 2008; Selbig & Bannerman 2011; Charters et al. 2015).

Rainfall characteristics effects on the RDS wash-off process

It is widely acknowledged that the total time it takes for the rain to wash away RDS fluctuates with the amount of rain that pours. A phenomenon known as the ‘first flush’ typically takes place near the beginning of the RDS wash-off process, which is when the concentration of pollutants is typically far higher than it will be in later times (Sansalone & Buchberger 1997; Deletic 1998; Lee et al. 2002; Li et al. 2007). It is unclear, nevertheless, whether the wash-off process is source-limited or transport-limited, and this is a factor that is affected by both rainfall duration and intensity. In the context of this research, an overall sample of 39 simulated rainstorms was generated to investigate the characteristics of RDS wash-off throughout a variety of wash-off phases. Figure 6 demonstrates that the first flush phenomenon was taking place when the concentration of suspended solids in surface runoff water was greater during the onset of the occurrence compared to subsequent times. In addition, smaller particle sizes and stronger rain resulted in a more pronounced first flush occurrence. As a result of the fact that the load of the first flush is predominantly determined by the initial RDS particles of a tiny size, this procedure may be source-limited. As a result, the frequency of the first flush may limit the amount of RDS that can be removed by typical rain events, suggesting that such events remove just a fraction of the total RDS.
Figure 6

Impacts of duration and intensity of rainfall on suspended solid concentrations.

Figure 6

Impacts of duration and intensity of rainfall on suspended solid concentrations.

Close modal

Based on these findings, the entire wash-off process that occurs during a typical rainstorm event may be broken down into two separate phases: a source-limited wash-off that occurs during the initial phase and a transport-limited mechanism that occurs during subsequent phases. On the contrary, transport and source limitations were more likely to be present for greater and lesser rain events, respectively. Larger rainfall events have more intense downpours, and as a result, more tiny particles and certain heavier particles are washed away. Rainfall models (under hypothetical configurations) ought to be compared to actual rainfall occurrences to determine any discrepancies in the RDS wash-off mechanism. For instance, there was insufficient information to establish a connection between intensity and transport ability or dispersion from the impermeable surface. Based on our site assessment, we concluded that the RDS wash-off process was predominantly a transport process, and hence the detaching process could be disregarded in the present research.

We required a thorough understanding of how RDS wash off to properly quantify the amount of pollution from urban surface runoff and to take measures to safeguard receiving water. Field studies showed that two periods of natural rain reduced the total mass of RDS by 27–36%. Tiny particles (<100 μm) made up 59–73% of the wash-off load. These findings imply that transport limits rain events on RDS, particularly those of a smaller or moderate magnitude. In addition, the grain size composition of RDS got heavier following a single rain event as well as following repeated rain events; nevertheless, these rain events only cleared a tiny percentage of the RDS load. Both short- and long-term dry periods cause RDS to accumulate more mass and result in a tiny grain size composition. Furthermore, because the buildup process has usually not begun at zero over the previous dry days, the wash-off process is somewhat limited in terms of transport. A total of 39 simulated rainstorm events were constructed to explore the impact of particle size and rainfall parameters on the wash-off process. The RDS wash-off process is affected by particle grain size, with smaller particles <100 μm being source-limited and larger particles >100 μm being transport-limited. The entire process of runoff during an ordinary rainfall event may be broken down into two phases: a source-limited phase during the first period (first flush) and a transport-limited phase during the latter phases. In addition, smaller particle sizes and stronger rain result in a more pronounced first flush event. The smaller and larger rainfall events are typically limited by transport and source, respectively. This is because stronger rainstorms have more intense rain, which makes it easier to move smaller particles and a few heavier particles away.

MF contributed to conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing the original draft, and review and editing the writing. Z-JY contributed to conceptualization, funding acquisition, project administration, resources, and supervision. MBI contributed to review and editing the writing and visualization. SA contributed to review and editing the writing and visualization. NAB contributed to review and editing the writing.

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

The authors declare there is no conflict.

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