Spatio-temporal Patterns of Total Nitrogen Pollution Source

Composition in the Ru River Basin

C Y Z Yu

1, 2

, L Tan

1, 2

, R M He

, Z Y Zhao

and X Y Yang

1, 2,*

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering，

Nanjing Hydraulic Research Institute, Nanjing 210029, China

Department of Environmental Science & Engineering, Fudan University, Shanghai

200433, China

Corresponding author and e-mail: X Y Yang, xiaoying@fudan.edu.cn

Abstract. Water pollution in China is usually caused by multiple pollution sources.

Understanding the contribution of each pollution source and its spatio-temporal patterns is

crucial to the development of effective watershed pollution control programs. In this study,

Soil and Water Assessment Tool (SWAT) was used to simulate the processes of nitrogen

discharge, migration, and transformation in the Ru River Basin. The SWAT simulation results

were used to estimate total nitrogen (TN) load contributions from different pollution sources

in each sub-basin. Clustering analysis results showed that TN loads were mainly from sewage

treatment plants and industries in the north and some areas in the middle, septic tanks and

crop production in the south and some regions in the middle, and crop production in the east

and west. The distinct spatial disparity in TN pollution source composition underlined the

necessity of formulating region-specific watershed pollution control measures.

1. Introduction

Nitrogen (N) is one of the most important biogenic elements that affects primary productivity and

species diversity in both aquatic and terrestrial ecosystems [1-2]. Nevertheless, excessive inputs of N

can cause the degradation of surface water quality [3]. Discharge of N contaminants into water

bodies driven by anthropogenic activities is a main issue at the river basin scale [4-5].

Located in the Upper Huai River Basin, Ru River is one of the most seriously polluted rivers in

China. Excessive N inputs have caused water quality deterioration in the Ru River. The N pollution

source composition of the Ru River Basin is complex with a mix of point and non-point sources.

Meanwhile, the climate of the region is highly seasonal. Disparity in seasonal rainfall may lead to

seasonal differences in water pollutant transport and transformation processes. Therefore,

understanding of the contribution of each pollution source and its spatio-temporal patterns is crucial

to the development of effective watershed pollution control programs in the region.

The Soil and Water Assessment Tool (SWAT) is a physically-based model that has been widely

used to simulate the hydrological and water quality processes of complex large basins [6-10].

However, past SWAT applications have mostly focused on simulating the discharge and transport of

pollutants from agricultural pollution [11-13]. Unlike previous studies, this study used SWAT to

simulate the discharge of N and its subsequent migration and transformation from all known

Yu, C., Tan, L., He, R., Zhao, Z. and Yang, X.

Spatio-temporal Patterns of Total Nitrogen Pollution Source Composition in the Ru River Basin.

In Proceedings of the International Workshop on Environmental Management, Science and Engineering (IWEMSE 2018), pages 94-101

ISBN: 978-989-758-344-5

anthropogenic sources including industries, municipal sewage treatment plants, concentrated animal

feedlot operations, crop production, scattered small-scale animal feedlot operations, and rural

households in the Ru River Basin. Through scenario analysis, SWAT simulation results were used to

estimate total nitrogen (TN) load contributions from each pollution source. K-means clustering

analysis was further conducted to study the spatio-temporal patterns of TN pollution source

composition in the four seasons. This study aims to provide a framework to understand the patterns

of regional pollution load attributions and inform the development of effective local watershed

pollution control programs.

2. Materials and methods

2.1. Study region

Ru River is one main tributary to the upper reaches of the Huai River. The study region is the

upstream contribution area (113°18'-114°28' E and 32°33'-33°26' N) of the Shakou hydrological

station with an area of 5803 km

(Figure1). The entire region is completely located in Zhumadian

City, Henan Province [14]. Located in the southern part of the warm temperate zone, the study region

is characterized by both subtropical and warm temperate climate. Its annual average temperature is

15 °C, and its annual rainfall is about 920 mm mainly concentrated in May to September.

Figure 1. The map of the study region.

2.2. SWAT model

Inputs to SWAT include DEM, land use, soil type, river network, weather, pollution data, and

agricultural management practices. Using a threshold area of 8000 ha, a total of 55 sub-basins and

394 hydrological response units (HRUs) were defined in the study region. Model calibration and

validation were performed using the Sequential Uncertainty Fitting Version-2 (SUFI-2) routine built

in Soil and Water Assessment Tool Calibration and Uncertainty Procedure (SWAT-CUP) [15].

Spatio-temporal Patterns of Total Nitrogen Pollution Source Composition in the Ru River Basin

After the warming-up period from 2001 to 2004, the SWAT model was calibrated from 2005 to

2007 and validated from 2008 to 2011 based on the daily streamflow records at the Lixin, Luzhuang,

and Shakou stations. In addition, the SWAT model was calibrated between 2006 and 2011 based on

monthly TN loads at the Shakou station. The SWAT model driven by hourly precipitation was able

to satisfactorily simulate the monthly streamflow and TN loads in the Ru River Basin with both

Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R

) above 0.8 [16]. Detailed

descriptions of the SWAT models for the simulation of hydrological processes and N pollution

processes in the Ru River basin could be found in Yang et al. (2016a) [14] and Yang et al. (2016b)

[17].

2.3. Pollution source composition estimation

TN pollution source composition of each of the 55 sub-basins was estimated through scenario

analysis. As the baseline scenario, SWAT was ﬁrst run without any pollution source except fertilizer

applications to estimate the TN load from crop production. For the other ﬁve pollution sources,

different scenario runs were then carried out to estimate the combined TN loads from crop

production and each individual source, whose difference from the baseline scenario was calculated as

the load from individual source. The percentage of TN load contribution by each pollution source

was then calculated.

2.4. Clustering analysis

Clustering analysis is the process of grouping a collection of objects into multiple classes of similar

objects. Clustering analysis has been previously widely used to characterize the spatio-temporal

patterns of water quality conditions based on the field pollutant concentration monitoring results [18-

20]. The k-means clustering algorithm divides a set of n samples into k disjoint clusters with samples

more similar within each cluster. It first arbitrarily selects k objects from n samples as the initial

cluster centres. Using distance for measuring similarity, it then assigns every remaining sample to its

most similar cluster centre. New cluster centres are calculated as the average of all samples in the

clusters. The above algorithm repeats until the cluster centres don’t change significantly.

In this study, we used the squared Euclidean distance as the measure of similarity and set the k

value to be 4. With proportions of TN loads from six major pollution sources at the outlet of each

sub-basin as the variables and the 55 sub-basins as the samples, the k-means clustering analysis was

performed for all four seasons to identify the spatio-temporal patterns in the distribution of TN

pollution source composition.

3. Results and discussion

3.1. Source apportionment of TN load

Average annual TN loads and seasonal TN loads from 2006 to 2011 at the outlet of the Ru River

basin were calculated and compared in Table 1. Annual TN loads were mainly contributed by non-

point pollution sources, with the largest contributor - crop production accounting for 46.20%. On the

other hand, point sources only contributed 34.83% of total TN load in the study region.

Seasonally, TN load was the highest in summer, followed by autumn, winter, and spring.

Contributions by septic tanks, industries, concentrated feedlots and sewage treatment plants all

reached a maximum in summer and dropped to a minimum in winter. Unlike the others, contributions

from crop production in summer and autumn were much larger than those in spring and winter.

3.2. Clustering analysis of TN pollution source composition

Table 2 compared the four identified seasonal clusters of TN source composition, while Figure 2

showed their spatial distributions. In spring, cluster 1 included 11 sub-basins in the west, 4 sub-basins

in the middle, sub-basin 1 and 6 in the north, sub-basin 15, 16 and 28 in the east, and sub-basin 55 in

IWEMSE 2018 - International Workshop on Environmental Management, Science and Engineering

the south. Since there are generally large areas of farmland in these sub-basins, TN loads are mainly

from non-point sources dominated first by crop production and then by septic tanks. Cluster 2

included sub-basin 33, 35, 44 in the middle and sub-basin 2 in the north. Due to industrial activities

and the presence of sewage treatment plants in these regions, TN loads mainly originated from point

sources dominated by industries. Cluster 3 included 13 sub-basins in the north and in the middle as

well as sub-basin 3 and 8 in the north. Similar to the cluster 1 regions, TN loads in cluster 3 regions

were mostly from non-point sources. However, septic tanks acted as the primary non-point pollution

source. Cluster 4 consisted of sub-basin 50 and 54 in the southwest, sub-basin 19 in the middle, 6

sub-basins in the east, and 6 sub-basins in the north-east. In these regions, TN load contributions

from crop production, septic tanks, and concentrated feedlots all fell in the range between 20% and

30%, much more than those from the other three sources.

Table 1. Average annual TN loads and seasonal TN loads in Ru River Basin.

TN sources

Spring Summer Autumn Winter Whole year

TN load

(ton)

Ratio

(%)

TN load

(ton)

Ratio

(%)

TN load

(ton)

Ratio

(%)

TN load

(ton)

Ratio

(%)

TN load

(ton)

Ratio

(%)

Crop production 275.27 31.31

812.48

43.90

893.68

58.23

404.42

44.97

2385.85

46.20

Septic tanks 195.45 22.23 388.86 21.01 218.55 14.24 159.90 17.78 962.77 18.64

Scattered feedlots

1.64

0.19

7.50

0.41

5.31

0.35

2.53

0.28

16.99

0.33

Industries 103.54 11.77 168.00 9.08 104.88 6.83 83.39 9.27 459.81 8.91

Concentrated

feedlots

149.56

17.01

216.13

11.68

154.70

10.08

130.71

14.53

651.09

12.61

Sewage treatmen

plants

153.83 17.49 257.68 13.92 157.52 10.27 118.47 13.17 687.51 13.31

Total 879.29 100 1850.65 100 1534.64 100 899.42 100 5164.02 100

Table 2. Cluster centres of TN pollution source composition in the four seasons.

Cluster centres Crop

production

Septic

tanks

Scattered feedlots Industries

Concentrated

feedlots

Sewage treatment

plants

Spring

59.95% 33.25% 0.66% 0.21% 4.72% 1.20%

17.41% 21.29% 0.13% 18.37% 9.89% 32.91%

3 20.15% 72.48% 0.26% 0.14% 6.97% 0.00%

25.44% 29.28% 0.23% 8.34% 26.21% 10.50%

Summer

91.66% 5.62% 0.66% 0.08% 1.41% 0.56%

2 21.51% 17.11% 0.28% 22.64% 5.48% 32.98%

37.17% 52.50% 1.36% 0.11% 7.76% 1.10%

49.73% 25.45% 1.01% 4.92% 12.09% 6.80%

Autumn

1 84.91% 11.15% 0.78% 0.09% 2.64% 0.43%

20.97% 6.10% 0.24% 32.67% 1.04% 38.98%

43.02% 56.64% 0.34% 0.00% 0.00% 0.00%

4 56.52% 20.43% 0.44% 4.74% 10.24% 7.62%

Winter

83.04% 14.25% 0.96% 0.11% 1.63% 0.00%

18.46% 9.67% 0.18% 27.92% 5.14% 38.64%

3 32.40% 60.17% 0.23% 0.09% 7.12% 0.00%

47.85% 25.74% 0.42% 5.13% 12.28% 8.59%

Spatio-temporal Patterns of Total Nitrogen Pollution Source Composition in the Ru River Basin

In summer, Cluster 1 included 11 sub-basins in the west, 4 sub-basins in the middle, sub-basin 1

in the north and sub-basin 15 and 28 in the east. TN loads in cluster 1 regions were mostly

contributed by crop production whose contributions increased significantly from spring to summer.

Cluster 2 included sub-basin 33 in the middle and sub-basin 2 in the north. In these regions, TN loads

were mainly from point sources dominated first by sewage treatment plants and then by industries.

Cluster 3 included 9 sub-basins in the south-east, sub-basin 14, 32 and 42 in the middle, and sub-

basin 3 in the north. TN loads in these regions were primarily from non-point sources with septic

tanks being the largest contributor. Cluster 4 included sub-basin 50, 54 and 55 in the south-west, 6

sub-basins in the middle, 5 sub-basins in the east, and 8 sub-basins in the north. In these regions, TN

loads mainly originated from non-point sources with crop production contributing nearly half of TN

loads.

(a) (

)

Figure 2. Spatial distribution of four TN pollution source composition clusters in the Ru River

Basin in four seasons: (a) Spring; (b) Summer; (c) Autumn; (d) Winter.

IWEMSE 2018 - International Workshop on Environmental Management, Science and Engineering

In autumn, Cluster 1 included 11 sub-basins in the west, 11 sub-basins in the middle, 4 sub-basins

in the north and sub-basin 54 and 55 in the south-west. In these regions, crop production was the

predominant contributor to TN loads. Cluster 2 only contained sub-basin 33 in the middle. Cluster 3

included sub-basin 42 in the middle and sub-basin 47, 51 and 52 in the south. The majority of TN

loads in these regions resulted from non-point sources. Among them, septic tanks and crop

production were the major contributors, while point sources contributed little. Cluster 4 included 7

sub-basins in the north, 11 sub-basins in the east, sub-basin 19 and 32 in the middle and sub-basin 53

and 55 in the south. In these regions, the primary TN source was crop production, followed by septic

tanks.

In winter, Cluster 1 included 11 sub-basins in the west, sub-basin 1, 6 and 8 in the north and sub-

basin 11, 16 and 17 in the middle. In these regions, TN loads contributed by crop production were

much higher than other pollution sources. Cluster 2 included sub-basin 2 in the north and sub-basin

33 in the middle. In these regions, sewage treatment plants were the largest contributor to TN loads,

followed by industries. Cluster 3 included 8 sub-basins in the south and sub-basin 32 and 42 in the

middle. TN loads in these regions were mainly from non-point sources dominated first by septic

tanks and then by crop production. Cluster 4 included sub-basin 50 and 54 in the south-west, 7 sub-

basins in the middle, 9 sub-basins in the east and 7 sub-basins in the north. In these regions, TN loads

primarily originated from non-point sources dominated by crop production.

In summary, TN load in the west was mainly contributed by crop production throughout the year

while point sources only contributed a small amount of TN loads. At sub-basin 33 in the middle, TN

loads were mainly from point sources dominated first by sewage treatment plants and then by

industries. In the south as well as the sub-basin 42 in the middle, the largest contributors of TN loads

were non-point sources dominated first by septic tanks and then crop production. Similar to the

western part, the main TN sources in the east as well as some sub-basins in the north and south were

non-point sources with crop production being the largest contributor. Nevertheless, proportions of

TN load contributions by crop production in these regions were fewer than those sub-basins in the

west. In addition, the patterns of TN pollution source composition in some sub-basins changed with

seasons. For instance, TN loads in sub-basin 35 and 44 in the middle were mainly from point sources

in spring but non-point sources in the other three seasons. At sub-basin 14 in the middle, septic tanks

contributed most TN loads in spring and summer while crop production contributed the most in

autumn and winter.

4. Conclusions

A well-performed SWAT model was used to estimate TN pollution source composition in the Ru

River basin. K-means clustering analysis was then performed to characterize the spatial patterns of

the pollution source composition of TN loads in the four seasons. Non-point sources remained the

main sources of TN loads in the study region throughout the year, accounting for 65.17% of the total.

Clustering analysis on the TN pollution source composition at the 55 sub-basins showed that TN

loads in the west were primarily contributed by crop production all year around with small

contributions from point sources. TN loads in the north and some areas in the middle were mainly

from point sources. In the south and some regions in the middle, the majority of TN loads originated

from non-point sources with the largest contribution from septic tanks followed by crop production.

In the east and some regions in the south and north, TN loads were mainly from non-point sources

with crop production the primary contributor.

The revealed distinct spatial disparity in the TN pollution source composition underlined the

necessity of formulating region-specific water pollution control programs that target the main

pollution sources within the region to achieve the best pollution control effects. In the future, the

effectiveness of different pollution control measures under various configurations could be evaluated

with SWAT to help optimize the watershed pollution control program.

Spatio-temporal Patterns of Total Nitrogen Pollution Source Composition in the Ru River Basin

Acknowledgments

This work was supported by Open Foundation of State Key Laboratory of Hydrology-Water

Resources and Hydraulic Engineering (2016490411).

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