Analysis of the Source Tracing and Pollution Characteristics of Rainfall Runoff in Adjacent New and Old Urban Areas

Abstract

This study aimed to examine the pollution characteristics of rainfall runoff as well as the runoff scouring effect and pollution traceability in adjacent new and old urban areas. The sampling and monitoring of rainfall runoff for different rainfall underlying surfaces were conducted for roads, green spaces, roofs, and a food market. The concentration of chemical oxygen demand (COD) and nutrients in the rainfall runoff of the market area was higher than that measured for roads, green spaces, and roofs. The initial scouring effect of each underlying surface was obvious during rainstorms, and 30% of the runoff transported nearly 50% of the pollutants. Further, 30% of the runoff transported about 30% of the pollutants under moderate and heavy rain conditions, and the overall initial scouring effect was not obvious. The results of this study showed that, as a considered underlying surface area, the market and possibly other similar service facilities had a significant impact on the calculation of runoff pollution load, and these should be included in the research scope of rainfall runoff and the impact of underlying surfaces. The purpose of this study was to provide reliable and practical information for water pollution prevention and control, especially for pollution prevention and control of rainfall runoff in areas where new and old urban parts of cities exist side by side.

1. Introduction

Urbanization has become a global trend with rapid economic development and population growth. It not only promotes social and economic progress but also adversely affects the natural environment, especially the water ecological environment. Generally, urbanization leads to an increase in impermeable surfaces, resulting in a large amount of rainwater being unable to penetrate into the soil during rainfall runoff. Rainwater that runs over impermeable surfaces washes accumulated surface pollutants into water bodies, including organic matter, nitrogen and phosphorus pollutants, heavy metals, etc., either through the urban pipe network system or directly, making the quality of the receiving water bodies worse due to the urban rainfall-runoff pollution [1]. Urban rainfall-runoff pollution has aroused widespread concern in many countries and regions due to its potential negative impact on river and lake eutrophication and water ecology [2,3,4].

It is necessary to determine the characteristics of rainfall-runoff pollution and its influencing factors to reduce urban rainfall runoff as a source of pollution and achieve high-quality sustainable development in cities. Fortunately, the issue of rainfall-runoff pollution has been resolved by many previous studies [5,6,7]. The degree of runoff pollution depends on the underlying surface types and is related to many factors, including climate, rainfall characteristics, air quality, transportation, land utilization, and so forth [8,9,10]. Most studies that explored rainwater runoff mainly focused on urban built-up areas or single underlying surface types and analyzed rainfall-runoff pollution characteristics, pollution sources, and initial scouring effects [11,12]. Studies exploring rainfall-runoff pollution in adjacent new and old urban sections where there are multiple underlying surface types are rare.

As a developing country, China has experienced a period of accelerated urbanization in recent years, resulting in the emergence of many new and old urban areas in cities. The old urban areas, such as Taizhou City in the Yangtze River Delta region of China, have existed for a long time. The ground in this region has poor water permeability, coupled with serious rain and sewage mixed flow, and more overflows at the outfall, making the river waters in the old urban area black in color during the rainy season [13]. The new urban area is built at the edge of the old urban area, with clear land type divisions and modern and effective rainwater and sewage pipeline systems. Analyzing the characteristics of rainfall-runoff pollution in the part of the city where the new and old urban areas are adjacent was of significant practical significance.

The study area has land for service facilities such as a food market. Notably, it is a high nutrient salt-load area. We considered the market area as a type of underlying surface. This study also investigated three other types of underlying surfaces, including roads, green spaces, and roofs, to explore the characteristics of rainfall-runoff water quality, initial scouring effect, and source tracing of runoff pollution. Each of the four aforementioned underlying surfaces was investigated for these characteristics specifically in the area connecting the new and old urban locations. We compared and analyzed our results with the concentration values of rainfall-runoff pollutants in different cities. We also discussed the influencing factors of the initial scouring effect and the selection criteria for the underlying surfaces when calculating the runoff pollutant to provide scientific decision-making support for preventing and controlling rainfall-runoff pollution in areas where the new and old urban sections of the city are adjacent.

2. Materials and Methods

2.1. Overview of the Study Area

The study area was located in Jiaojiang District, Taizhou City, Zhejiang Province (Figure 1). This area has a high population density and comprises residential and commercial land. The remote sensing image recognition study found that the study area was 0.23 km², with green space, roads, food market, and roof underlying surfaces accounting for 6.6%, 42.6%, 1.5%, and 49.3% of the area, respectively. Taizhou City is located in the southeast of the Yangtze River Delta region of China and north of the Wenhuang Plain. The geomorphic type is mainly coastal alluvial plain. The rainfall is abundant, with an average annual precipitation of 1563 mm. The precipitation during the plum rain period from April to June and the typhoon period from July to October accounts for approximately 75–85% of the total annual precipitation. The rainfall data for the entire year of 2018 in the region are presented in the supplementary material (Table S1). Continuous urbanization has led to the distribution of many areas connecting the old and new urban sections of the city throughout Taizhou. High daily traffic flow was observed in the study area, and the roads were found covered with dust. The roads were cleaned twice a day.

2.2. Rainfall-Runoff Monitoring and Sample Detection

In this study, seven runoff sampling points were selected: two green land sampling points (old urban and new urban), one food market sampling point, two road sampling points (roadways and sidewalks), and two roof sampling points (old urban and new urban). Further, automatic rainfall monitoring equipment (Shandong Wanxiang Environmental Technology Co., Ltd., Weifang, China) was arranged in the open space. The food market refers to an open street market. Rainfall runoff ultimately enters surface water through urban rainwater collection systems.

Before rainfall, sampling containers were arranged in the low-lying areas of green land, at the end of a rain channel of the market, at the rain grates of the road (between the old and new urban areas), and at the roof rainwater pipe openings. Samples were collected and put in clean polyethylene bottles (Thermo Fisher Scientific (China) Co., Ltd., Shanghai, China) after the start of rainwater runoff. The samples were collected once every 10 min during the first hour of rainfall, once every 30 min in the later period of the rainfall, and every 1 min toward the end of rainfall until the end of the experiment. All the collected samples were sent to the laboratory immediately, stored in a refrigerator (Zhuhai Gree Electric Appliances Co., Ltd., Zhuhai, China) at 4 °C, and analyzed within 48 h. The nitrogen and phosphorus concentrations in the river channels of the study area exceeded the water quality target. This study selected chemical oxygen demand (COD), ammonium (NH₄⁺-N), total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS) for laboratory analysis. The detailed methods and the instruments used for analysis are presented in the supplementary material (Table S2).

Rainfall characteristics were simultaneously recorded when collecting runoff, including the amount of rainfall, duration of rainfall, early drought time, background concentrations of the pollutants, and so forth. Rain from nine rainfall events was collected successively. The rainfall types in the study period included three moderate rain events, three heavy rain events based on the national standard “Precipitation Grade” issued by the National Meteorological Administration of China, and three rainstorm events. The characteristics of the rainfall events are shown in

Table 1. Characteristic parameters of rainfall events.

Serial No	Date/ YYYY-MM-DD	Rainfall/mm	Rainfall Type	Rainfall Duration /h	Average Rainfall in Tensity /(mm/h)	Early Drought Time /h	Background Concentrations of the Pollutants/(mg/L)
							COD	NH4+-N	TN	TP	TSS
1	2018-4-30	69	Hard rain	7	9.9	20	19.1	0.19	0.67	0.08	21
2	2018-5-1	5	Moderate rain	2	2.5	16	23.6	0.30	0.71	0.05	19
3	2018-5-7	22	Heavy rain	2	11.0	133	25.1	0.28	0.67	0.05	33
4	2018-5-13	9	Moderate rain	1	9.0	132	27.8	0.21	0.75	0.10	16
5	2018-5-20	47	Hard rain	3	15.7	20	18.4	0.33	0.87	0.03	23
6	2018-5-21	30	Hard rain	5	6.0	17	21.4	0.31	0.93	0.06	25
7	2018-5-22	9	Moderate rain	5	1.8	3	19.9	0.39	1.23	0.01	13
8	2018-5-23	27	Heavy rain	9	3.0	19	27.2	0.18	0.75	0.03	26
9	2018-5-26	28	Heavy rain	2	14.0	55	24.6	0.44	0.95	0.05	20

. The early drought time refers to a short drought period between rain events.

2.3. Data Analysis

2.3.1. The Average Concentration of Pollutants in Each Rainfall

The event mean concentration (EMC) of pollutants refers to the average concentration of a pollutant in surface runoff under a single rainfall [14], calculated using the equation:

$$EMC=\frac{M}{V}=\frac{{{\displaystyle \int }}_0^{t_r}{c}_t{Q}_{t}dt}{{{\displaystyle \int }}_0^{t_r}{Q}_{t}dt}\approx \frac{{{\displaystyle \sum }}_{t=1}^n{c}_t{Q}_t\Delta t}{{{\displaystyle \sum }}_{t=1}^n{Q}_t\Delta t}$$

(1)

where, M is the pollutant load, g; V is the runoff, m³; t_r is the runoff time, min; c_t is the difference between the concentration of rainfall runoff and rainwater background pollutants in t period, mg/L; Q_t is the rainfall runoff in t period, m³; n is the number of samplings in a single rainfall; and ∆t is the time interval between two samplings, min.

2.3.2. Determination of the Initial Scouring Effect

The ratio curve between the cumulative pollutant load of each rainfall and the total amount of cumulative runoff, such as the M(V) curve, was used to determine an initial scouring effect [15]. The abscissa V(t) of the M(V) curve was the dimensionless cumulative runoff, representing the percentage of the previous runoff in the total runoff. The ordinate M(t) was the dimensionless cumulative pollution load, representing the percentage of the previous pollutant load to the total pollution load. The 45° straight line across the origin in M(V), such as the equilibrium line, indicated that the pollutant concentration in the rainfall-runoff event remained stable. If the curve was on the 45°straight line, most of the pollutants in the initial runoff were transported and an initial scouring effect was observed. M(V) could be approximately expressed in the form of a power function, as follows:

$$M\left(t\right)=V{\left(t\right)}^b$$

(2)

where b is the initial scouring coefficient. The b value could be used to quantitatively measure the occurrence and intensity of the initial scour. It could be expressed by the distance between the M(V) curve and the diagonal balance line. When b < 1, initial scour occurred, and the initial scour intensity was inversely proportional to the value of b.

2.3.3. Rainfall-Runoff Pollution Load

The rainfall runoff of each underlying surface was calculated as:

$$V_s=V_T\left(S_s{R}_s/S_T{R}_T\right)$$

(3)

where V_S is the single rainfall runoff of a single underlying surface, m³; V_T is the total runoff of a single rainfall, m³; S_S and S_T are the areas of the underlying surface and the study area, respectively, m²; R_S are the runoff coefficients of the underlying surface with values of 0.25, 0.55, 0.70, and 0.85 for green spaces, roads, food markets, and roofs, respectively. R_T is the comprehensive runoff coefficient of the study area, which is 0.69. The comprehensive runoff coefficient of the study area was determined by measuring the underlying surface area.

The runoff pollutant load of each underlying surface was calculated as:

$$M_s=EM{C}_s{V}_s$$

(4)

where M_S is the single rainfall load of a single underlying surface, kg; and EMC_S is the average pollution concentration of a single underlying surface in a single rainfall process, which can be calculated using Equation (1).

2.3.4. Percentage Difference of Rainfall-Runoff Pollution Load

When analyzing the necessity of selecting the food market area as the underlying surface, the proportion of the difference in total pollutant load of rainfall runoff under the two conditions with or without the food market area as an independent underlying sur-face was compared and analyzed. Further, the total pollutant load of runoff without taking the food market area as an independent underlying surface was considered as the reference standard.

2.3.5. Correlation Analysis

This study selected rainfall, rainfall duration, average rainfall intensity, early drought time, and total runoff as factors affecting rainfall runoff, and analyze these factors using principal component analysis module of Origin 2018. In the principal component analysis chart, red indicates the type of pollutants and blue indicates the factors affecting rainfall runoff. A vector represents a variable, and its length is proportional to the variance of the corresponding variable [16]. The angle between two vectors indicates the degree of correlation between the two variables; an acute angle indicates a strong correlation.

However, the results obtained from the principal component analysis chart needed the support of the correlation matrix for better interpretation. When the absolute value of the correlation coefficient was greater than 0.8 and the included angle of the corresponding vector was less than 30°, a significant correlation between any two variables could be considered [17].

3. Results

3.1. Characteristics of Rainfall-Runoff Water Quality on Each Underlying Surface

The runoff pollutant concentration distribution of the underlying surfaces in the nine rainfall events is depicted in Figure 2. The underlying surfaces with the highest mean COD concentration belonged to the food market area (91.2 mg/L) and road (66.6 mg/L), followed by green land (43.8 mg/L). These COD concentrations indicated “inferior Class V water quality” as specified in the Chinese National Environmental Quality Standard for Surface Water. The COD concentration in roof runoff was the lowest at 35.4 mg/L. The average concentrations of NH₄⁺-N, TN, and TP in the rainfall runoff of the food market area were 1.2, 2.2, and 0.6 mg/L, respectively, which was much higher than the concentrations measured in other underlying surfaces. Additionally, these values corresponded to “inferior Class V water quality” as per the Environmental Quality Standard for Surface Water. Discharge of this water to a river directly, to some extent, negatively impacted the river water environment. The TSS concentration of road rainfall runoff was 148.9 mg/L among the four types of underlying surfaces, and the differences in other parameters were small, between 56.1 and 97.4 mg/L.

3.2. Analysis of the Initial Scouring Effect of Rainfall Runoff

The M(V) curve was used to analyze the initial scouring effect in the study area. According to the rainfall classification, each underlying surface has an obvious initial scouring effect during heavy rain. The trend of the M(V) curve of the same pollutant was consistent under the conditions of moderate and heavy rains, and it was close to the equilibrium line (Figure 3).

Further, 30% of the runoff carried about 50% of the COD pollutant load under heavy rain. The initial scouring effect of NH₄⁺-N and TN on each underlying surface was weak. TP had an obvious initial scouring effect under heavy rain. The M(V) curve developed along the equilibrium line under moderate and heavy rains, and no obvious initial scouring phenomenon was observed. TSS had an initial scouring effect under different rainfall conditions. The initial scouring effect of each underlying surface was weak under moderate and heavy rains compared with rainstorms. For the measured pollutants, the permeable underlying surface, represented by green land, had a significantly higher initial erosion pollutant concentration under heavy rain than under moderate and heavy rains. In contrast, pollutants could be quickly flushed after runoff from impermeable underlying surfaces such as roads, markets, and roofs, indicating a decrease in pollution concentration values. Permeable underlying surfaces could produce runoff only after infiltration. Therefore, when the rainfall intensity was small, the initial runoff effect in the impermeable area was not obvious.

3.3. Traceability of Rainfall-Runoff Pollution

The pollution load and contribution rate of surface runoff pollutants in each rainfall were calculated using the equation of rainfall-runoff pollutant load (Figure 4). The load of scouring pollutants increased gradually with the increase in the amount of rainfall. Taking the COD pollutant load as an example, the load of scouring pollutants of moderate rain, heavy rain, and rainstorm runoff was 62.4, 199.3, and 377.3 kg, respectively. The contribution rate of pollutant load in green land rainfall runoff was relatively low and could be ignored in general. The roof area accounted for 47% and the cumulative runoff was 59%; however, the contribution rate of pollution was only 28–58%. The contribution rate of rooftop pollutant load decreased with the increase in rainfall. The proportion of the market area was only 9%, while the average contribution rate of pollution reached 18%.

The analysis of the composition of different pollutants for each underlying surface revealed that rooftops contributed the most COD pollution load, with a stable contribution rate of more than 41%, followed by roads and the market area, with an average contribution rate of 38% and 17%, respectively. The contribution rate of the green space was the least at 3%. The rooftop nutrient pollutant load was the largest, with an average of 42%, followed by roads and the market area, with an average contribution rate of 31% and 23%, respectively. In contrast, the pollution contribution rate of green space was the smallest. Rooftop pollution contributed most of the TSS pollution load, with a contribution rate of 53%, followed by roads, with a contribution rate of 39%. The total contribution rate of green space and the market area was only 8%.

4. Discussion

4.1. Spatial Variability of Rainfall-Runoff Quality Characteristics

Comparison of the pollutant concentration of the underlying surface of rainfall runoff in this study with that of typical cities in six geographical divisions of China (

Table 2. Comparison of pollutant concentrations in rainfall runoff between this study and typical cities in six geographical regions of China.

City	Geographical Division	Pollution Index	Green Space (mg/L)	Road (mg/L)	Roof (mg/L)
Taizhou City (This study)	East China	COD	20.2–110.6 a	34.0–136.2	18.1–122.5
		NH4+-N	0.5–2.0	0.1–0.6	0.3–0.8
		TN	0.7–2.1	0.7–2.0	0.3–2.8
		TP	0.1–1.0	0.1–1.6	0.05–0.1
		TSS	28.2–86.6	78.3–226.1	48.9–254.4
Xiamen City [11]	East China	COD	37.5–137.5	/ b	/
		NH4+-N	6.3–3.75	/	/
		TSS	18.5–55.0	/	/
Beijing City [18]	North China	NH4+-N	/	1.9–5.7	2.1–7.1
		TN	/	/	5.3–12.7
		TP	/	/	0.03–0.2
Shenyang City [19]	Northeast China	COD	/	1.8 -11.0	0.2–23.6
		TN	/	0.1–3.8	0.6–9.9
		TP	/	0.2–1.1	0.1–0.2
		TSS	/	365.0–1208.1	7.0–94.3
Xi’an city [20]	Northwest China	COD	/	102.1–716.1	13.4–321.1
		NH4+-N	/	2.0–5.3	1.8–15.2
		TN	/	1.7–21.1	4.0–26.9
		TP	/	0.1–1.0	0.3–1.2
		TSS	/	82.9–640.2	17.5–241.1
Chongqing City [21]	Southwest China	COD	38.0 c	418.0	83.0
		NH4+-N	0.5	4.3	1.7
		TN	2.7	8.1	5.9
		TP	0.1	1.2	0.2
		TSS	31.0	631.0	69.0
Guangzhou City [22]	South Central China	TN	1.9–3.5	2.4–10.0	2.4–5.0
		TP	0.01–0.1	0.04–0.2	0.1–0.5

Notes: The superscripted “a” indicates the minimum to maximum; the superscripted “b” indicates missing data; and the superscripted “c” indicates an average.

) revealed that the pollutant concentration of rainfall runoff in Taizhou City was equivalent to that in Xiamen City in East China. The nutrient concentration of road rainfall runoff in the study area was generally lower than that in other cities, which might be because this study separated out the market area, a special underlying surface, from the road. Furthermore, the frequent road cleaning, combined with the high rainfall frequency, made it impossible to fully accumulate pollutants. The concentrations of COD and TSS of rooftop runoff in this study were higher than those in most cities, but the nutrient pollutants were lower. This might be because the study area was located in old and new urban areas, and the aging of roof asphalt resulted in high COD and TSS concentrations in the rooftop runoff. It might also be related to different atmospheric deposition caused by the different trajectories of the air masses, and other parameters such as wind, roof slope, and heating. The pollutant concentration of green land runoff was equivalent to that in other cities. The pollutant concentrations in this study were within the ranges reported by existing studies. The collected data indicated that the monitoring and calculation methods used accurately reflected the pollutant concentration of runoff on each underlying surface included in this study.

4.2. Identification of Factors Influencing the Initial Scouring Effect of Rainfall Runoff

The M(V) curve qualitatively analyzed the initial scouring, and the b value quantitatively analyzed the strength of the initial scouring effect of rainfall runoff. The smaller the b value, the more obvious the initial scouring effect [15]. Figure 5 shows that the b value during a rainstorm was generally small, indicating that the initial scouring effect was strong. The b value for moderate and heavy rain was generally greater than 0.8, even up to 1.4, indicating that the initial scouring effect was weak or failed to produce the initial scouring effect. Besides the impact of rainfall, the early drought time was short, and the pollutants were not fully accumulated, which might also lead to the poor scouring effect of moderate rain and heavy rain in the initial stage. From the perspective of pollutants, COD and nutrient pollutants generally followed the rule that the stronger the rainfall, the smaller the b value. However, for TSS, the b value of heavy rain in the market area, roads, and rooftops was greater than that of moderate rain. This might be because the market had a high nutrient load and a small catchment area, combined with low daily cleaning frequency and human factors, resulting in a rapid accumulation rate of pollutants. This led to a greater rain scouring effect in the market than in heavy rain. Ash was easily deposited on roads due to the impact of vehicles and pedestrians [23]. The aforementioned analysis of road rainfall-runoff quality characteristics also confirmed the maximum TSS concentration on the road, therefore, it could exert a strong scouring effect even in moderate rain. For rooftops, the effect might be related to the roof material [24]; the roof material falling off and roof ash were washed away at the beginning of rainfall during moderate rain. In summary, the order of runoff scouring intensity of pollutants was TP, COD, TN, TSS, and NH₄⁺-N, and the b values were 0.78, 0.80, 0.83, 0.84, and 0.89, respectively, which was similar to the findings of Lee et al. [25].

The initial scouring of rainfall runoff was affected by many factors, such as rainfall intensity, permeability of the area, early drought time, and so forth [8,9,10]. The principal component analysis results are depicted in Figure 6. The correlation matrix between variables is presented in

Table 3. Correlation coefficient between EMC of rainfall runoff on each underlying surface and impact factors.

Underlying Surface	Pollutant Index	RF	RD	ARI	EDT	TR
Green space	COD	0.587	0.314	0.470	−0.264	−0.473
	NH4+-N	0.479	0.429	−0.044	−0.849 **	0.404
	TN	0.757 *	0.153	0.517	−0.086	−0.095
	TP	−0.015	0.036	−0.043	−0.075	0.207
	TSS	−0.036	0.072	0.070	0.187	−0.351
Food market	COD	0.200	0.120	−0.171	−0.610	0.725 *
	NH4+-N	0.548	0.233	0.178	−0.449	−0.188
	TN	0.511	0.150	0.432	−0.244	−0.129
	TP	0.150	0.062	0.155	−0.199	−0.333
	TSS	0.292	−0.173	0.362	−0.251	0.455
Road	COD	−0.107	−0.083	0.235	0.411	−0.699 *
	NH4+-N	0.254	−0.120	0.340	0.087	0.194
	TN	0.350	0.242	0.382	0.400	−0.769 *
	TP	0.272	0.000	0.324	0.250	−0.007
	TSS	0.202	0.035	0.193	−0.038	−0.678 *
Roof	COD	−0.396	−0.291	−0.131	0.010	0.144
	NH4+-N	0.231	0.392	0.196	−0.205	−0.274
	TN	0.740 *	0.184	0.432	−0.167	−0.184
	TP	−0.238	0.318	−0.654	−0.751 *	0.752 *
	TSS	−0.308	−0.221	0.173	0.771 *	−0.417

Notes: ** The correlation was significant at 0.01 level (double tail). * The correlation was significant at 0.05 level (double tail).

. The correlation coefficient between the ammonium EMC of green land rainfall runoff and the early drought time was −0.849, but the corresponding angle of the two vectors exceeded 30°. Therefore, the early drought time could not be regarded as a variable significantly related to the ammonium EMC of green land rainfall runoff. It was reported that the pollutant accumulation on the underlying surface might tend to be stable if the current drought period was more than 7 days, and the pollutant concentration in the runoff can be significantly affected by rainfall intensity [20]. The drought time in the early stage of this study was less than 7 days, which might lead to no significant correlation between the aforementioned two items. Perera et al. [17] used machine learning algorithms such as random forests to predict and rank the importance of the main driving factors of the initial scouring effect of rainfall runoff. Total rainfall was the most important variable affecting the initial scouring effect of rainfall runoff, followed by the maximum rainfall intensity, rainfall duration, runoff depth, runoff peak value, and average intensity. The score rankings of the previous dry period and effective impervious area were low. The concentration time and event average concentration were the least important variables. Zhang et al. [26] found the rainfall intensity at the initial stage of a rainfall event crucial to the runoff scouring effect. The duration of rainfall and the early drought time also affected the scouring of pollutant load in the middle and final stages of the rainfall event. In this study, no influencing factors were significantly related to the EMC of rainfall-runoff pollutants on each underlying surface. This finding was different from those of previous studies, which might be due to the differences in pollutant accumulation, urban terrain, runoff generation, and concentration mechanisms in adjacent new and old urban areas, as well as lower monitoring frequency. In follow-up studies on the rainfall-runoff scouring effects in this study area, we believe that more rainfall characteristics, runoff hydrological characteristics, other relevant influencing factors, and the combination of variables and their interactions should be considered, which might produce better results.

4.3. Reason for Selecting the Food Market as an Underlying Surface

In previous studies, only roads, green spaces, and roofs were selected as underlying surfaces [27,28,29,30]. The present study included adjacent new and old urban areas, and the food market was a typical underlying surface threatened by the coupling of human activities and urban non-point source pollution. Therefore, the market data were separated from the road data in this study. The average concentrations of COD, NH₄⁺-N, and TP in the rainfall runoff of the market were higher than those of other underlying surfaces, which was because the market sold numerous products such as food items, melons and fruits, fish and shellfish, poultry and eggs, meats and meat products, and other agricultural and related products. As these products have high nitrogen and phosphorus contents, the rainfall runoff from the market was also high in nitrogen and phosphorus contents and led to potential eutrophic conditions in water bodies that received this runoff.

The results indicated that when the market was used as an independent underlying surface, the COD and nutrient loads in the rainfall-runoff pollution were greater than those when the market was not used as the underlying surface. In particular, total pollution loads of TP and TN were 18% and 12% (

Table 4. Percentage difference of total runoff pollutant load with or without considering the market as an underlying surface.

Rainfall Type	COD (%)	NH4+-N (%)	TN (%)	TP (%)	TSS (%)
Moderate rain	8.08	8.36	11.85	18.76	2.63
Heavy rain	8.84	9.07	11.27	16.87	3.20
Hard rain	10.41	9.20	12.09	18.05	3.31

). This was because t, the difference in EMC of rainfall-runoff pollutants, was high in the market area and on roads. The concentration of COD, nitrogen, and phosphorus nutrients in the rainfall runoff of the market was about twice that of the road, and the difference even reached 218.6%. No significant change was observed in TSS irrespective of whether or not the market was considered as the underlying surface.

Pollutants are largely concentrated on the surface and washed into the pipeline system with rainfall runoff daily when it rains. Eutrophication is inevitable if this pollution is discharged directly into a natural water body. As the COD and nutrient pollution of the market itself were higher than those of roads, this factor could not be ignored and was taken into account while calculating the pollutant load of rainfall runoff from adjacent new and old urban areas. Ignoring markets with vegetable and food products may cause large errors in assessing and quantifying pollution in an area. A special type of underlying surface with a large urban area can be considered for the analysis of runoff based on the actual conditions so as to be close to the real situation and reduce the resulting error.

5. Conclusions

In this study, the source tracing and pollution characteristics of rainfall runoff in the area connecting the old and new urban areas of Taizhou were analyzed. The concentration of COD and nutrients in the rainfall runoff of the food market was higher than that on the other underlying surfaces. Each underlying surface had an obvious initial scouring effect under heavy rain conditions, with an average b value of 0.62. Furthermore, 30% of the runoff transported nearly 50% of the pollutants. The initial scouring effect was not obvious in the correlation between rainfall washout in moderate and heavy rains, and rainfall event parameters were quite different because of the differences in urban environments. The selection of the underlying surfaces significantly impacted the total pollutant load of runoff. The total pollutant load of TP and TN was 18% and 12%, respectively, when the market was considered as an independent underlying surface. Because the food market in this study impacted the pollutant load in rainfall runoff quite significantly, we suggest that the land for service facilities such as markets should be included in the research scope of investigating rainfall runoff and underlying surfaces in future studies examining adjacent new and old urban areas.