Stormwater Runoff Pollutant Loading Distributions and Their Correlation with Rainfall and Catchment Characteristics in a Rapidly Industrialized City

Dongya Li; Jinquan Wan; Yongwen Ma; Yan Wang; Mingzhi Huang; Yangmei Chen

doi:10.1371/journal.pone.0118776

Abstract

Fast urbanization and industrialization in developing countries result in significant stormwater runoff pollution, due to drastic changes in land-use, from rural to urban. A three-year study on the stormwater runoff pollutant loading distributions of industrial, parking lot and mixed commercial and residential catchments was conducted in the Tongsha reservoir watershed of Dongguan city, a typical, rapidly industrialized urban area in China. This study presents the changes in concentration during rainfall events, event mean concentrations (EMCs) and event pollution loads per unit area (EPLs). The first flush criterion, namely the mass first flush ratio (MFFn), was used to identify the first flush effects. The impacts of rainfall and catchment characterization on EMCs and pollutant loads percentage transported by the first 40% of runoff volume (FF₄₀) were evaluated. The results indicated that the pollutant wash-off process of runoff during the rainfall events has significant temporal and spatial variations. The mean rainfall intensity (I), the impervious rate (IMR) and max 5-min intensity (I_max5) are the critical parameters of EMCs, while I_max5, antecedent dry days (ADD) and rainfall depth (R_D) are the critical parameters of FF₄₀. Intercepting the first 40% of runoff volume can remove 55% of TSS load, 53% of COD load, 58% of TN load, and 61% of TP load, respectively, according to all the storm events. These results may be helpful in mitigating stormwater runoff pollution for many other urban areas in developing countries.

Citation: Li D, Wan J, Ma Y, Wang Y, Huang M, Chen Y (2015) Stormwater Runoff Pollutant Loading Distributions and Their Correlation with Rainfall and Catchment Characteristics in a Rapidly Industrialized City. PLoS ONE 10(3): e0118776. https://doi.org/10.1371/journal.pone.0118776

Academic Editor: Roger A. Coulombe, Utah State University, UNITED STATES

Received: May 12, 2014; Accepted: January 6, 2015; Published: March 16, 2015

Copyright: © 2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research has been supported by National Natural Science Foundation of China (No. 51208206), Guangdong Provincial Department of Science (No. 2012A032300015), Guangdong Natural Science Foundation (No. S2011040000389), China Postdoctoral Science Foundation (2012M511570, 2013T60807), High-level Personnel Foundation of Guangdong Higher Education Institutions, State key laboratory of Pulp and Paper Engineering in China (201213), the Fundamental Research Funds for the Central Universities (2013ZZ0031) and the national water pollution control and management major projects (2008ZX07211-006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Many catchment areas are undergoing fast urbanization and industrialization in developing countries due to rises in population and economic growth, and these processes have significant influences on the quality of urban stormwater runoff [1, 2].

Urban stormwater runoff, which degrades streams by changing the volume, pattern and quality of flow, presents a problem that challenges dominant approaches to storm and water resource management, as well as to environmental flow assessment [3, 4]. The characteristics of stormwater runoff quality, hydrology, retention and other issues have all been examined in the literature, and it has been found that significant quantities of organics, nutrients, and heavy metals are present in stormwater runoff [5–8]. In addition, nonpoint source (NPS) pollution due to urban stormwater runoff is considered as one of the major causes of water-related adverse health impacts among urban residents [9].

It has long been recognized that the pollutant build-up and wash-off processes are influenced by rainfall and catchment characteristics [10, 11]. Stormwater runoff pollution is a very serious problem, and the temporal and spatial variations in this pollution process can be quite significant in rapidly industrialized cities, because fast urbanization and industrialization are usually characterized by an increase in the number of factories and population density, as well as the drastic changes in land-use, moving from farmland and green land to impervious surfaces [12]. Numerous efforts have been made to investigate the relationships between stormwater runoff pollution and rainfall characteristics for various catchment areas, such as residential, commercial, and industrial areas, as well as highways, parking lots, bridges and roofs [4, 13, 14]. However, it is difficult to identify the characteristics of stormwater runoff from such catchments because of the mixed land-use types, slow development of sewage treatment infrastructure, and poor waste management in rapidly industrialized cities [2]. There are thus very few studies which report the stormwater runoff characteristics of such catchments in rapidly industrialized urban areas in developing countries with a high population density (e.g., China). This lack of research means that little is known about the mechanisms underlying urban stormwater runoff pollutant transport and the influence of rainfall, as well as various catchment characteristics, on the pollutant loading of rapidly industrialized cities. Understanding these interactions would be useful for improving design criteria and strategies for controlling urban stormwater runoff pollution. There is thus a need to characterize and examine stormwater runoff quality and pollutant loading, as well as their correlations with rainfall and catchment characteristics, in a rapidly industrialized city, in order to improve management in this area.

A total of 10 rain events were surveyed at industrial, parking lot and mixed commercial and residential catchments in the rapidly industrialized Tongsha reservoir catchment in China, during the period from April 2009 to September 2011. The stormwater runoff and quality parameters were analyzed to assess the temporal characteristics of stormwater runoff with different kinds of land-use. The objectives of this study were as follows: (1) to characterize the temporal variations in pollutant wash-off during rain events and the spatial variations with regard to different land-use catchments; (2) to identify the first flush phenomena using MFFn; and (3) to use the results of this work with regard to the runoff pollution load distributions and the main factors to improve runoff management schemes in rapidly industrialized urban areas.

Materials and Methods

Study Area

This study was conducted in the Tongsha catchment in Dongguan City, Southeast China (Fig. 1), and was approved by the Dongguan Government of Guangdong Province. No specific permissions were required to study these locations, as they do not involve endangered or protected species. The GPS coordinates of the study areas are as follows: Niushan (NS) industrial zone, 22°57′23.63″N, 113°46′17.63″E. Dalingshan (DLS) mixed commercial and residential area, 22°54′09.32″N, 113°50′24.10″E. Tongsha (TS) parking lot, 22°55′55.79″N, 113°48′00.37″E."

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Fig 1. Study sites.

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The total drainage area is 100 km², including two regions of Dalingshan and East City. The climate is a typical subtropical monsoon climate, with a mean annual temperature of 22.5°C and mean annual precipitation of 1790 mm. The rain mostly occurs during the period from April to September, due to the impact of monsoons and typhoons.

As one of the largest global manufacturing bases, Dongguan City has factories that operate in many important industries. While this has helped in the development of the economy, it was led to serious water pollution, especially with regard to stormwater runoff pollutants. Tongsha catchment is located on the west bank of the Tongsha reservoir, which is currently suffering from severe eutrophication caused by the urban stormwater runoff pollution.

Tongsha reservoir catchment was selected as the study area in this work, and divided into three land-use categories (industrial, parking lot, and mixed commercial and residential catchments). Based on the land-use characteristics of the runoff watershed and the related physiographic factors, the Tongsha reservoir catchment was divided into three watersheds, namely the Niushan (NS) industrial area, the Dalingshan (DLS) mixed commercial and residential area, and the Tongsha (TS) parking lot catchment. A brief summary of the physical characteristics of these three watersheds is given in Table 1. There are seven sewer sub-catchments in Tongsha reservoir catchment, three in NS industrial catchment, two in DLS mixed commercial and residential catchment, two in TS parking lot catchment. The sampling sites were on the total sewer drains of NS, DLS and TS as shown in Fig. 1.

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Table 1. Basic characteristics of the monitoring sites.

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Runoff Sampling and Analysis

Field experiments for 16 non-rainy days and 10 storm events between April 2009 and September 2011 were conducted synchronously at the three experimental watersheds. Sampling was started at the initiation of the rain event and ended when the flow receded down to the dry weather water level. The sampling was generally done at 5 to 15 min intervals when the flow was rising, and then at 20 to 60 min intervals for the receding flow. 9 to 14 samples were collected to assess the water quality for each storm event. All the samples were collected manually by sampling at a depth of 10 cm from the water surface, and stored in 1.0 L glass sample bottles with Teflon lined screw caps. Three identical water samples were taken at each sampling to ensure precision and accuracy. The samples were refrigerated and analyzed within 8 h after collection. The typical water quality pollutant parameters were measured, including total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH₄⁺-N), total phosphorus (TP), and heavy metals (Fe, Zn, Cu). The samples were treated according to the standard procedures [15].

Rainwater was also collected for comparative analysis, because rainfall has the potential to entrain atmospheric pollutants. In addition, the flow rates and flow volumes corresponding to each sample were monitored and computed. In NS, DLS and TS, the flow rates were measured by propeller flow meters. The water stage was recorded by a water level gauge at 5 min intervals at all the monitoring sites. Recording rain gauges (MDZH4T1601) near the monitoring sites simultaneously recorded the characteristics of 10 rain events from April 2009 to September 2011. The characteristics of the monitored rainfall events are shown in Table 2. Three representative rainfall events (15/4/2009, 58.80 mm, 20/10/2009, 9.36 mm, 20/10/2010, 22.50 mm) were selected for further analysis.

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Table 2. Characteristics of the rainfall events (n = 10).

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Data Processing and Statistical Analysis

Event mean concentration (EMC) is a key analytical parameter, which refers to a flow-weighted average concentration in the whole process of a rainfall-runoff event, defined as the total pollution load mass divided by the total runoff volume [16], and this can be used to evaluate the effects of rainfall runoff on the water quality of the receiving waters. The value of EMC is expressed as: (1) Event pollution load per unit area (EPL), refers to the amount of pollutants emitted per unit area in the whole rainfall event. It can be expressed as: (2) For each storm and water quality parameter, the magnitude of the first flush can be quantified by using a mass first flush ratio (MFFn) [14]. The MFFn is a useful index, because it means that various storm and water quality data can be optimized and used as indicators in statistical analysis. MFFn can be calculated for any point in a storm, and is defined as follows: (3) Where n is the index or point in the storm, which corresponds to the percentage of the runoff, ranging from 0% to 100%; M (g) is the pollutant mass during the rainfall event; V (m³) is the runoff volume during the rainfall event; C_t (mg/L) is the pollutant concentration at time t; Q_t (m³/s) is the discharge runoff flow rate at time t; t refers to the time of total runoff; t_i is the time up to point n in the event, and △t is the interval time of sampling. Finally, A (km²) is the catchment area.

The EMCs, EPLs and MFFn of the runoff pollutants were calculated to describe the characteristics of the pollutant output process regulations. The Kolmogorov-Smirnov test was carried out on a single sample to check out the normal distributions of key parameters such as the EMCs and EPLs of the runoff pollutants and rainfall variables, and to ensure that the basic assumptions of the Pearson correlation analysis were met. The statistical analysis software packages SPSS 17.0 and Origin 8.0 were used to compute the Pearson correlation coefficients and principal component analysis to determine the correlations among the EMCs and FF₄₀ of the five pollutants and the explanatory variables of the rainfall events and catchment areas. Multiple linear regression analysis was employed to determine the relationship between the storm pollution loads of FF₄₀ (|FF₄₀| = EPLs×FF₄₀) and storm characteristics as in Eq. (4) (4) Where a is arbitrary constant and b, c, d, e and f are coefficients for each rainfall variable.

Results and Discussion

Characteristics of Pollutant Wash-Off

Ten rain events were monitored at the NS industrial, TS parking lot, and DLS mixed commercial and residential catchments during the period from 15/4/2009 to 21/9/2011. Table 2 summarizes the characteristics of the rain events and sites, such as event date, Max 5-min intensity (I_max5), mean rainfall intensity (I), rainfall duration (R_Dur), rainfall depth (R_D), Antecedent dry days (ADD), and sample number. The R_D varied from 8.40 to 88.40 mm and ADD from three to 64 days. R_Dur were measured from 52 to 260 min, and I were determined from 4.2 to 20.4 mm/h. Fig. 2A, B and C illustrates the data collected at the industrial watershed sampling sites during the three storm events of 15/4/2009 (58.80 mm), 20/10/2010 (22.50 mm) and 20/10/2009 (9.36 mm). These rainfall events differed, in that the event of 15/4/2009 had a single peak, and the event of 20/10/2009 had a bimodal peak, while the event of 20/10/2010 did not have any significant peak. A secondary flush effect phenomenon common to bimodal rainfall events occurred during the event of 20/10/2009. The pollutant concentrations peaked slightly after the rainfall intensity peak. During the event of 15/4/2009, the concentrations of COD, TSS, TN, TP, and NH₄⁺-N peaked shortly after the runoff was generated, which could be due to the fact that the relatively higher kinetic energy of high-intensity rainfall events results in more pollutants being transported [17]. Because of adequate flushing, the concentrations sharply declined to 1/3 ~ 1/5 of the peak values approximately 40 min after the runoff was generated, which is consistent with the findings of Kim (2007) [10]. However, the pollutant concentrations of the event of 20/10/2010 fell to less than 1/2 of the peak values at the end of the rainfall, and no significant first flush phenomenon was found. As shown in Fig. 2, the fall in concentrations of COD, TSS, TN, TP, and NH₄⁺-N were directly associated with the rainfall intensity during the three events. The results suggest that a higher rainfall intensity is likely to be associated with a higher first flush in the same watershed.

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Fig 2. Pollutant wash-off process curves in NS, DLS and TS.

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Fig. 2A, D and E present the variations in the COD, TSS, TN, TP, and NH₄⁺-N concentrations for the industrial, commercial and residential and parking lot areas during the same rainfall event of 15/4/2009. Most of the pollutants on the paved surfaces were washed off within 30 to 60 min of the storm beginning, although there were some variations among the five pollutant species and land-use patterns. Comparing the three watersheds, the highest pollutant concentration peaks were found in the mixed commercial and residential watershed. The pollutant concentrations decreased sharply after the initial stage of the peaks in the industrial watershed and parking lot, at about 40 and 60 min, respectively. However, the pollutant concentrations in the mixed commercial and residential watershed rose to their submaximal peak levels at 60 min, and then decreased slowly. Due to the differences in the catchment characteristics, the time intervals between the rainfall intensity peaks and pollutants concentration peaks during the three rainfall events were 10 min, 25 min, and 40 min, respectively. Furthermore, the characteristics of the wash-off process with regard to five pollutants showed some significant differences, even for the same storm events and watersheds. For example, TSS had the highest variations and peak concentrations during the storm events in the industrial watershed, whilst TP had the lowest. This means that the variability of the pollutants’ wash-off characteristics within the same storm event was significantly influenced by both pollutant species and watershed land-use.

Spatial Variation of EPLs and EMCs

EMCs and EPLs in Tables A, B, C, D, E and F in S1 File were determined using Eq. (1) and (2), with the results summarized in Table 3. Most of the constituent concentrations in the rainwater were below the detection limits, and thus the rainwater quality was not influenced by the pollutant contents in the storm, except for TN. This implies that rainfall is a significant source of nitrogen in the urban catchment area. It can be seen that the DLS mixed residential and commercial catchment has the highest median EMCs and EPLs for BOD, COD, TSS, NH₄⁺-N, and TP, followed by the NS industrial and TS parking lot catchments. The main sources of organic matter during storm events are restaurants and food stalls, especially in the commercial catchment area. The EMCs values for COD and TSS of DLS mixed commercial and residential area were much higher than those in other areas with the same watershed features [4]. Comparing the runoff water quality with regard to TN and TP, the runoff concentration values in the DLS were more than 10 times higher than those found in the earlier studies [18]. The reason for this may be that this area is in the busiest part of the city, and thus sewage and food scraps, along with other trash, are dumped by shops and restaurants on both sides of the path. In addition, this area only tends to be swept once a day, due to poor environmental management.

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Table 3. Basic statistics of runoff pollution EMCs and EPLs in all storm events.

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In contrast, the NS industrial catchment had the highest medians EMCs and EPLs for Fe, Zn and Cu. The EMCs and EPLs for metals at the NS industrial catchment were far greater than those for the mixed residential and commercial or parking catchments. The Zn could be from roofs, factories, and vehicle wear and tear in the industrial catchment. The large storm to storm variations of EMCs and EPLs, as well as the diverse kinds of land-use in urban catchments, mean that a long-term monitoring program is needed in order to better estimate the EMCs and EPLs values. The EMCs values of COD, TN, TP and NH₄⁺-N were close to those found in Lee [4]. The pollution loading levels found in the current study are also quite similar to those found for the Shiyan reservoir catchment in Shenzhen, China [1]. The average EMCs of COD, TSS, TN and TP in TS parking lot were 67.94 mg/l, 86.72 mg/l, 2.33 mg/l and 1.02 mg/l, respectively, more than two times of the values found in Japan except for TSS and TP. The values of TSS and TP are similar to the values of the study [19]. The EMCs and EPLs values of TS parking lot were much lower than that in NS and DLS, which could be the reason that grassland interception could reduce the contamination due to rainfall runoff, and the stormwater runoff pollution from grassland cannot be ignored.

First Flush Effect Analysis Using MFFn

The first flush effects of all the quality parameters were studied by plotting the MFFn against the cumulative runoff volume, as shown in Figs. 3 and 4. The first flush was observed when the data ascended above the balanced line (MFFn = 1). The balanced line (MFFn = 1) represents when the concentration of pollutants remained constant throughout the stormwater runoff. Conversely, dilution was assumed to have occurred when the data fell below the balanced line (MFFn = 1). The deviation of the cumulative pollutant mass curve from the balanced line (MFFn = 1) was used as a measure of the strength of the first flush. The deviation is positively related to the first flush coefficient MFFn.

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Fig 3. The indicator MFFn used to identify the first flush effect in NS.

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Fig 4. The indicator MFFn used to identify the first flush effect in DLS and TS.

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In the current study, most of the TSS, COD, TN, and TP MFFn curves exceeded the balanced line (MFFn = 1) in the NS industrial catchment, except for the event of 3/5/2011, meeting Geiner’s definition of a first flush [20]. The first flush is greatest for the event of 15/4/2009, although it falls short of fitting Bertrand-Krajewski et al.’s (1998) definition of a first flush (only 67% of the mass, as opposed to 80%, was discharged in the first 30% of the runoff volume) [16].

When comparing the MFFn values for the six events at the NS industrial watershed, the event of 15/4/2009 showed strongest first flush for all constituents, and the first flush relative strength of most of the constituents is in accordance with the max 5-min intensity (I_max5). This indicates that the strength of the first flush is in proportion to I_max5, I, ADD and IMR. Additionally, it is notable that the MFFn values of the pollutant species also vary based on the rainfall characteristics along the cumulative runoff volume axis, which confirms that different rainfall characteristics could lead to the different stormwater first flush characteristics of various pollutant species.

The event of 3/5/2011 had the lowest values of MFFn for all the pollutants in NS industrial zone. This is probably due to the small I_max5 (0.54 mm/min) and I (4.2 mm/h), and the limited R_D (8.40 mm) of the event. Low max 5-min and mean intensity rainfall, as within this event, typically leads to a weak first flush, because the runoff flow does not have sufficient energy to scour and mobilize the pollutants, and the limited rainfall depth may not have sufficient runoff volume to wash out the pollutants, meaning that a decline in the pollutant concentrations can be observed near the end of the storm, as explained by Kang et al. (2006) [21]. The entire runoff volume of a small storm, such as the event of 3/5/2011, can be less than the first flush volume seen with most large storms, and thus the lack of a first flush for a small storm may not be a disadvantage when using BMPs optimized for treating the first flush.

As shown in Fig. 3, the relative strength of the first flush with regard to the various pollutants is TP>TSS>TN>COD for the NS industrial watershed, while it is NH₄⁺-N>TN>COD>TSS>TP for the DLS mixed commercial and residential watershed, as shown in Fig. 4. Finally, the relative strength of the first flush with regard to the various pollutants is TSS > COD >TN> NH₄⁺-N>TP for the TS parking lot. TN and COD had larger first flushes in the DLS mixed commercial and residential watershed than in the NS industrial watershed, and this was also observed by Lee et al. (2002) [22].

Runoff Pollution Load Distributions

Based on the results of dimensionless accumulative analysis, the FF₃₀, FF₄₀, FF₅₀ and FF₆₀ of the main pollutants were calculated during the 10 events, and the results are given in Table 4. The mean values of the FF₄₀ for TSS, COD, TN, TP were 55%, 53%, 58%, 61%, respectively. More than 83% of the pollution load mass was delivered in the initial 60% of the runoff volume, which exhibited a greater first flush effect than that reported by Bertrand-Krajewski et al. (1998). This earlier study found that 80% of the total pollutant mass was transported in the first 74% of the total volume in separate sewer systems, and 80% of the total pollutant mass was transported in the first 79% of the total volume in the combined sewer system [16].

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Table 4. Statistical summary of FF₃₀, FF₄₀, FF₅₀ and FF₆₀ for TN, TP, COD and TSS.

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In addition, comparing the results from FF₆₀, FF₅₀, FF₄₀ and FF₃₀, the FF₄₀ interception of the first 40% of the total runoff volume could remove an average of 10%, 15%, 24% and 20% of the pollution loads for TSS, COD, TN and TP, respectively, more than that intercepted by 30% of the total runoff volume. An average of 58% TN and 61% TP of the EPLs could be removed by intercepting the initial 40% of the runoff volume, which is a critical measure with regard to controlling the eutrophication of urban water bodies. It is thus suggested that FF₄₀ should be adopted as the interception ratio of the initial stormwater runoff volume in the Tongsha reservoir watershed.

Correlation between Rainfall and Catchment Parameters with EMCs and FF₄₀

The relationships between the EMCs and FF₄₀ of various constituents with the various storm and watershed characteristics were analyzed by Pearson correlation analysis (Table 5) (See Table G, H and I in S1 File). The storm characteristics included I_max5, I, R_Dur, R_D and ADD [23, 24]. The watershed characteristics included the Catchment Area (CA) and IMR.

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Table 5. Correlations between EMCs, FF₄₀, rainfall and catchment characteristics.

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Based on an analysis of the correlation matrix, the most important parameters with regard to the EMCs of various pollutants are I_max5, I and IMR. The strongest correlations were found for IMR and the EMCs of COD (r = 0.66), TSS (r = 0.67), TN (r = 0.58) and TP (r = 0.48) at P <0.01 significance levels, with results that were very similar to the influence of the impervious area fraction on pollutant wash-off [11]. I was positively correlated with the EMCs of COD (r = 0.417, P<0.05), TSS (r = 0.395, P<0.05), TN (r = 0.402, P<0.05) and TP (r = 0.466, P<0.05). A larger mean rainfall intensity led to an increase in flush intensity of contaminants on the surface. Elsewhere, I was also found to be positively correlated with EMCs of stormwater runoff pollutant [1, 9, 25]. The positive correlation was also found between I_max5 and the EMCs of TN at a P<0.05 significance level, because of a complex TN source in the rapidly industrialized city due to poor litter management and bare land. It was thought that TN source depletion and pollutant dilution would not happen in the catchment. Other variables, including the rainfall characteristic parameters (ADD, R_Dur, and R_D) and CA did not display obvious correlations with the EMCs of the pollutants (P>0.05), indicating that these parameters had no apparent influence on the EMCs in all three different land use watersheds.

Based on the results of the Pearson correlation analysis (Table 5), there is no correlation between the FF₄₀ and rainfall runoff and watershed characteristics, except I_max5, ADD and R_D. The FF₄₀ of the pollutant concentrations (COD r = 0.462, TSS r = 0.486, TN r = 0.416, TP r = 0.394) (P<0.05) are positively and strongly correlated to the maximum rainfall intensity. These results are same as those of previous studies, which were also carried out in urban catchments [26, 27]. The magnitude of the first flushes of COD and TN were correlated to the ADD. The rain events with longer antecedent dry weather conditions were more likely to result in a higher first flush. Where anthropogenic factors (such as motor vehicle traffic volume, overall “wear and tear” of the drainage area between storm events and poor litter management) are available, they contribute to first flush pollutant loading (e.g., COD and TN). Non first flush pollutants (TSS and TP) do not have excess buildup of pollutants due to external factors, such as solids from bare land and tire interaction, and are driven largely by storm characteristics [28].

Previous studies concluded that EMCs and first flush are complex and site specific [9, 11, 27]. In comparison with the results for other urban catchments reported by the above studies, the magnitude of the EMCs found in the current work is bigger, but the first flush is not stronger in the Tongsha reservoir watershed. Further, the FF₄₀ in the current study does not spread over a wide range around the mean values, as seen in the standard deviation coefficients and variation coefficients. These results can be explained by the fact that Tongsha reservoir catchment is located in a wet region characterized by high intensity rain events. Bare land ready for construction and poor litter management might both have significant effects on EPLs and FF₄₀. This means that other catchment characteristics, such as urban form and environmental management, have important roles in influencing pollutant wash-off, rather than this being dominated by the impervious area fraction only. All of these factors resulted in the discharge patterns of stormwater runoff being more complex in the catchments in the rapidly industrialized city examined in this work than those from the other areas.

Fig. 5 shows the results of PCA biplots, which consist of Fig. 5-A and Fig. 5-B. Fig. 5-A displays the PC1 vs. PC2 biplot and Fig. 5-B shows the PC1 vs. PC3 biplot. Based on Fig. 5, the I_max5 vector indicates a relatively close correlation with all pollutant FF₄₀ in Fig. 5-A and Fig. 5-B, while the ADD and R_D vector only indicates a close correlation with COD and TN. These observations confirm that I_max5 can play a more influential role in the first flush than the ADD and R_D.

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Fig 5. PCA biplots for the FF₄₀ dataset.

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Table 6 presents the multiple linear regression results to estimate the pollution loads of FF₄₀. ADD shows significant relationships with the pollution loads of FF₄₀ in all the regression models. Variables I are also important for TSS and TP models in all three different land use watersheds. The model of TP is the most special parameter, which is affected by R_D, I and ADD.

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Table 6. Multiple linear regression results for the storm pollution loads of FF₄₀ (|FF₄₀|) of Tongsha reservoir watershed.

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Based on the above observations, I_max5, I and IMR are the critical parameters affecting the urban rainfall runoff pollutant event mean concentrations (EMCs). The critical parameters for FF₄₀ are I_max5, ADD and R_D. For the stormwater |FF₄₀|, the results of multiple linear regression analysis with regard to rainfall characteristics show that I_, ADD and R_D are the critical parameters.

Conclusions

The study of non-point source pollution is essential for rapidly industrialized urban areas and the successful operation of National Water Pollution Control and Management Major Projects in China. The characteristics of rainfall pollutant migration were analyzed in Tongsha reservoir catchment in the current study. Mass first flush ratio (MFFn) was used to identify the first flush effect. This study obtained new insights about the effects of rainfall characteristics on runoff pollutant wash-off, event mean concentrations (EMCs), event pollution load per unit area (EPLs) and first flush of 40% (FF₄₀), suggesting both the principal influential factors and the appropriate treatment criteria. The following conclusions are made based on the results of this work:

The pollutant wash-off process of runoff during rainfall events has significant temporal and spatial variations. The pollutant concentrations peaked about 10 to 40 min later than the rainfall intensity peak, although the exact time depended on the catchment characteristics. The variability of the pollutant wash-off characteristics within the same storm event is significantly influenced by both pollutant species and watershed land-use.

According to the results of the first flush effect dimensionless cumulative and MFFn analysis, the first flush effect appears in the order of Niushan (NS) industrial zone> Dalingshan (DLS) mixed commercial and residential area> Tongsha (TS) parking lot. The first flush effect will be more distinct when the event has greater max 5-min intensity (I_max5)_, rainfall depth (R_D) and antecedent dry days (ADD) values.

Event mean concentrations (EMCs) and event pollution load per unit area (EPLs) were widely distributed because of the various characteristics of the sites examined in this work. The Dalingsha residential and commercial catchment had the highest median EMCs and EPLs for BOD, COD, TSS, NH₄⁺-N, and TP, followed by the Niushan industrial and parking catchments. In contrast, the Niushan industrial catchment had the highest median EMCs and EPLs for Fe, Zn and Cu. The results of this work confirm that in the Tongsha catchment mean rainfall intensity (I), Impervious rate (IMR) and Max 5-min intensity (I_max5) are the critical parameters that influence EMCs, while I_max5, ADD and R_D are the critical parameters of FF₄₀, and I, ADD and R_D are the critical parameters influencing |FF₄₀|. It should be noted that the paper is limited to the analysis of temporal variations in pollutant wash-off during rain events and the spatial variations with regard to different land-use catchments, quantitative discussions in the case study are site-specific. The temporal and spatial characteristics of runoff pollution in a rapidly industrialized urban, particularly for heavy storm, require further study.

Supporting Information

S1 File. Contains the following files.

Table A. Stormwater event mean concentrations (EMCs) data for Dalingshan catchment. Table B. Stormwater event mean concentrations (EMCs) data for Niushan catchment. Table C. Stormwater event mean concentrations (EMCs) data for Tongsha catchment. Table D. Stormwater event pollution loads per unit area (EPLs) data for Dalingshan catchment. Table E. Stormwater event pollution loads per unit area (EPLs) data for Niushan catchment. Table F. Stormwater event pollution loads per unit area (EPLs) data for Tongsha catchment. Table G. Stormwater pollutant loads rate transported by the first 40% of runoff volume (FF40) data for DalingshanTongsha catchment. Table H. Stormwater pollutant loads rate transported by the first 40% of runoff volume (FF40) data for Niushan catchment. Table I. Stormwater pollutant loads rate transported by the first 40% of runoff volume (FF40) data for Tongsha catchment.

https://doi.org/10.1371/journal.pone.0118776.s001

(PDF)

Author Contributions

Conceived and designed the experiments: DYL JQW YWM YW MZH YMC. Performed the experiments: DYL JQW YWM YW MZH YMC. Analyzed the data: DYL JQW YWM MZH YMC. Contributed reagents/materials/analysis tools: DYL JQW. Wrote the paper: DYL JQW.

References

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- View Article
- Google Scholar
26. Deletic A. The first flush load of urban surface runoff. Water Res. 1998;32(8):2462–70. PubMed PMID: WOS:000075141400022.
- View Article
- Google Scholar
27. Taebi A, Droste RL. First flush pollution load of urban stormwater runoff. J Environ Eng Sci. 2004;3(4):301–9. PubMed PMID: WOS:000223649900009.
- View Article
- Google Scholar
28. Batroney T, Wadzuk BM, Traver RG. Parking Deck’s First Flush. J Hydrol Eng. 2010;15(2):123–8. PubMed PMID: WOS:000274013400005.
- View Article
- Google Scholar

[ref1] 1. Qin HP, Khu ST, Yu XY. Spatial variations of storm runoff pollution and their correlation with land-use in a rapidly urbanizing catchment in China. Sci Total Environ. 2010;408(20):4613–23. PubMed PMID: WOS:000281931500041. pmid:20667581
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Yusop Z, Tan LW, Ujang Z, Mohamed M, Nasir K. Runoff quality and pollution loadings from a tropical urban catchment. Water Sci Technol. 2005;52(9):125–32. PubMed PMID: WOS:000235103000014. pmid:16445181
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Walsh CJ, Fletcher TD, Burns MJ. Urban Stormwater Runoff: A New Class of Environmental Flow Problem. Plos One. 2012;7(9). PubMed PMID: WOS:000309388400133.
View Article
Google Scholar

[10] View Article

[11] Google Scholar

[ref4] 4. Lee JH, Bang KW. Characterization of urban stormwater runoff. Water Res. 2000;34(6):1773–80. PubMed PMID: WOS:000085867500002.
View Article
Google Scholar

[13] View Article

[14] Google Scholar

[ref5] 5. Zgheib S, Moilleron R, Saad M, Chebbo G. Partition of pollution between dissolved and particulate phases: What about emerging substances in urban stormwater catchments? Water Res. 2011;45(2):913–25. pmid:20970821
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref6] 6. Zgheib S, Moilleron R, Chebbo G. Priority pollutants in urban stormwater: Part 1-Case of separate storm sewers. Water Res. 2012;46(20):6683–92. PubMed PMID: WOS:000313459900011. pmid:22209279
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref7] 7. Davis AP, Shokouhian M, Ni SB. Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources. Chemosphere. 2001;44(5):997–1009. PubMed PMID: WOS:000170405300013. pmid:11513434
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref8] 8. Lundholm J, MacIvor JS, MacDougall Z, Ranalli M. Plant Species and Functional Group Combinations Affect Green Roof Ecosystem Functions. Plos One. 2010;5(3). PubMed PMID: WOS:000275620900011.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref9] 9. Brezonik PL, Stadelmann TH. Analysis and predictive models of stormwater runoff volumes, loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA. Water Res. 2002;36(7):1743–57. PubMed PMID: WOS:000175807400011. pmid:12044074
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref10] 10. Kim LH, Ko SO, Jeong S, Yoon J. Characteristics of washed-off pollutants and dynamic EMCs in parking lots and bridges during a storm. Sci Total Environ. 2007;376(1–3):178–84. PubMed PMID: WOS:000245776400017.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref11] 11. Liu A, Egodawatta P, Guan YT, Goonetilleke A. Influence of rainfall and catchment characteristics on urban stormwater quality. Sci Total Environ. 2013;444:255–62. PubMed PMID: WOS:000316240200028. pmid:23274244
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Chow MF, Yusop Z, Shirazi SM. Storm runoff quality and pollutant loading from commercial, residential, and industrial catchments in the tropic. Environmental Monitoring And Assessment. 2013;185(10):8321–31. PubMed PMID: WOS:000323836100029. pmid:23591675
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Chang M, McBroom MW, Scott Beasley R. Roofing as a source of nonpoint water pollution. Journal of Environmental Management. 2004;73(4):307–15. pmid:15531389
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Han YH, Lau SL, Kayhanian M, Stenstrom MK. Correlation analysis among highway stormwater pollutants and characteristics. Water Sci Technol. 2006;53(2):235–43. PubMed PMID: WOS:000236679300025. pmid:16594342
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. APHA, AWWA, WEF, WPCF. Standard Methods for the Examination of Water and Wastewater: 21th ed; 2005.

[ref16] 16. Bertrand-Krajewski JL, Chebbo G, Saget A. Distribution of pollutant mass vs volume in stormwater discharges and the first flush phenomenon. Water Res. 1998;32(8):2341–56. PubMed PMID: WOS:000075141400011.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref17] 17. Kleinman PJA, Srinivasan MS, Dell CJ, Schmidt JP, Sharpley AN, Bryant RB. Role of rainfall intensity and hydrology in nutrient transport via surface runoff. Journal Of Environmental Quality. 2006;35(4):1248–59. PubMed PMID: WOS:000239189900031. pmid:16825444
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

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View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

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View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref20] 20. Geiger WF. Flushing Effects in Combined Sewer Systems. Proceedings of the 4th international conference urban Drainage; Lausanne, Switzerland1987. p. 40–6.

[ref21] 21. Kang JH, Kayhanian M, Stenstrom MK. Implications of a kinematic wave model for first flush treatment design. Water Res. 2006;40(20):3820–30. PubMed PMID: WOS:000242988600019. pmid:17081586
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref22] 22. Lee JH, Bang KW, Ketchum LH, Choe JS, Yu MJ. First flush analysis of urban storm runoff. Sci Total Environ. 2002;293(1–3):163–75. PubMed PMID: WOS:000176589500013. pmid:12109484
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref23] 23. Gupta K, Saul AJ. Specific relationships for the first flush load in combined sewer flows. Water Res. 1996;30(5):1244–52.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref24] 24. Sansalone JJ, Cristina CM. First flush concepts for suspended and dissolved solids in small impervious watersheds. J Environ Eng-ASCE. 2004;130(11):1301–14. PubMed PMID: WOS:000224773300009.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref25] 25. Francey M, Fletcher TD, Deletic A, Duncan H. New Insights into the Quality of Urban Storm Water in South Eastern Australia. J Environ Eng-ASCE. 2010;136(4):381–90. PubMed PMID: WOS:000275659900006.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref26] 26. Deletic A. The first flush load of urban surface runoff. Water Res. 1998;32(8):2462–70. PubMed PMID: WOS:000075141400022.
View Article
Google Scholar

[88] View Article

[89] Google Scholar

[ref27] 27. Taebi A, Droste RL. First flush pollution load of urban stormwater runoff. J Environ Eng Sci. 2004;3(4):301–9. PubMed PMID: WOS:000223649900009.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref28] 28. Batroney T, Wadzuk BM, Traver RG. Parking Deck’s First Flush. J Hydrol Eng. 2010;15(2):123–8. PubMed PMID: WOS:000274013400005.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Study Area

Runoff Sampling and Analysis

Data Processing and Statistical Analysis

Results and Discussion

Characteristics of Pollutant Wash-Off

Spatial Variation of EPLs and EMCs

First Flush Effect Analysis Using MFFn

Runoff Pollution Load Distributions

Correlation between Rainfall and Catchment Parameters with EMCs and FF40

Conclusions

Supporting Information

S1 File. Contains the following files.

Author Contributions

References

Correlation between Rainfall and Catchment Parameters with EMCs and FF₄₀