Statistical Analysis of Wastewater Discharge in Yunnan Province

2015-2020

Na Dou

, Zhong Sun

, Yao Zhao

, Shuwei Zhang

and Guozhong He

1,*

School of Public Health, Kunming Medical University, China

School of Medicine and Health, Universiti Putra Malaysia, Malaysia

Keywords: Yunnan Province, Wastewater Discharge, Domestic Sewage, Industrial Wastewater, Factor Analysis, Cluster

Analysis.

Abstract: Yunnan Province is endowed with abundant water systems and resources. In recent years, with urbanization,

human population, social and economic progress fastening, water environment problem is becoming

increasingly serious. Therefore, this study proposes the coordinated development of social economy and water

environment in Yunnan Province. According to the situation of wastewater discharge in Yunnan Province,

the main sources of wastewater discharge and the main factors affecting urban wastewater discharge in recent

years are analyzed. Statistical analysis results show that wastewater discharge in Yunnan Province in the past

five years mainly came from domestic wastewater (8,891,023,700 tons), followed by industrial sewage

(1,984,587,500 tons). Through cluster analysis, 16 cities in Yunnan Province were finally divided into four

categories, with Kunming as category 1, Qujing as category 2, Lijiang, Nujiang, Diqing, Baoshan, Chuxiong,

Lincang, and Xishuangbanna as category 3, and other cities category 4. In recent years, wastewater discharge

in Kunming and Qujing City has been on the rise, probably due to large population density and high demand

for domestic water, the concentration of high water-consuming industries such as mining and metallurgy, and

defects in wastewater and sewage treatment technology.

1 INTRODUCTION

There are many rivers and lakes in Yunnan Province.

The water resources developed are about 153.381

billion cubic meters, accounting for 5.46% of the

national water resources in China. However, affected

by geographical and natural environment, water

resources distribution is uneven

[1]

, Worse still, as

urbanization increases, urban population grows,

pollution intensifies, water quality deteriorates, river

runoff decreases, lakes shrink, which worsens the

water environment

[2]

. Since 1992, Yunnan Province

has been committed to water environment protection,

and the overall water quality has improved year by

year. However, as Yunnan Province is located in

inland China, the waste water discharge mainly

depends on inland lakes

[3]

, and the self-purification

capacity of water is very limited, there is a relatively

serious water pollution problem. Therefore, with a

view to achieving the coordinated development of

social economy and water environment in Yunnan

https://orcid.org/0000-0002-1887-5417

Province, this study plans to adopt multiple statistical

factor analysis and cluster analysis methods to study

how to reasonably recycle water resources, analyze

the main factors affecting wastewater discharge in

cities, find solutions to effectively improve

wastewater treatment, and propose effective

suggestions for water environment protection and

wastewater discharge treatment in Yunnan Province.

2 DATA SOURCES AND

SELECTION OF POLLUTION

INDICATORS FOR

WASTEWATER DISCHARGE

2.1 Data Sources

The wastewater discharge data in this study comes

from the “Yunnan Statistical Yearbook” and the

Dou, N., Sun, Z., Zhao, Y., Zhang, S. and He, G.

Statistical Analysis of Wastewater Discharge in Yunnan Province 2015-2020.

DOI: 10.5220/0011930000003536

In Proceedings of the 3rd International Symposium on Water, Ecology and Environment (ISWEE 2022), pages 123-128

ISBN: 978-989-758-639-2; ISSN: 2975-9439

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

123

water quality evaluation index data from “Yunnan

Province Environmental Status Bulletin” and “

Yunnan Province Plateau Lake Water Quality

Monthly Report”.

2.2 Selection of Pollution Indicators for

Wastewater Discharge

According to the Comprehensive Sewage Discharge

Standard implemented by China in 1998, a total of 6

wastewater discharge pollution targets were selected:

X1 total wastewater discharge, X2 domestic sewage

discharge, X3 COD discharge from domestic sewage,

X4 industrial wastewater discharge, X5 chemical

oxygen demand (COD) emissions, and X6 urban

sewage discharge.

3 RESEARCH METHODOLOGY

According to the selected 6 wastewater discharge

pollution indexes, the wastewater discharge in

Yunnan Province is analyzed by SPSS26.0.

3.1 Factor Analysis

Factor analysis was used to reduce the dimension of

multiple sewage discharge standards. In other words,

through the study of the correlation between a set of

indicators, a linear model is synthesized into a few

comprehensive common factors, and the original

variables are represented by these comprehensive

factors. So, the variables and factors representing

most of the index information are easy to study

[4]

3.2 Cluster Analysis

Cluster analysis is to classify uncategorized

observation units or variables with similar

characteristics according to the principle of “object

by object clustering”. Normally, the Ward’s method

[5]

can classify the observation units or variables based

on the factor analysis.

4 RESULTS

4.1 Factor Analysis Results

According to the model and principle of factor

analysis, factor analysis of main pollution indexes of

wastewater discharge in 16 cities of Yunnan Province

was performed by SPSS26.0. Factor analysis

hypothesis test was carried out on the collected

variable data. First, the KMO test value of variable

data is 0.746>0.5. Bartlett ’ s Approximate Chi-

Square is 2201.565, If the df (degree of freedom) is

15, the significant difference is 0. Therefore, when

α=0.05, the original hypothesis is rejected. It can be

considered that the correlation coefficient matrix is

significantly related to the unit matrix. There is a

positive linear correlation between variables. Factor

analysis of variable data can be carried out (see table

1, table 2 for detail).

Table 1: KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of

Sam

lin

Ade

uac

0.746

Bartlett’s Test of

Sphericity

Approximate Chi-

Square

2201.565

df 15

. 0.000

Table 2: Correlation matrix.

Total

wastewater

discharge (X1)

Domestic sewage

discharge (X2）

COD discharge

from domestic

sewage (X3)

Industrial

wastewater

discharge (X4)

COD (X5）

Urban sewage

discharge

(X6)

Correlation

Total wastewater discharge (X1) 1.000 0.993 0.914 0.847 0.882 0.993

Domestic sewage discharge （X2）

0.993 1.000 0.886 0.779 0.833 0.990

COD discharge from domestic

sewage (X3)

0.914 0.886 1.000 0.877 0.963 0.891

Industrial wastewater discharge(X4) 0.847 0.779 0.877 1.000 0.940 0.823

COD (X5) 0.882 0.833 0.963 0.940 1.000 0.860

Urban sewage discharge (X6) 0.993 0.990 0.891 0.823 0.860 1.000

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The ratio of common factor variance extracted is

shown in table 3. The information extraction of six

evaluation indexes is above 90%, and the effective

information of variable data is retained, which

indicates that the evaluation of wastewater discharge

in Yunnan Province greatly reduces the complexity of

the original data. The two common factors extracted

are the total amount of wastewater discharge and the

discharge of domestic sewage. The contribution rates

of factor variance before rotation is 91.537%, 6.162%,

50.877%, and 46.822%, respectively, and the

cumulative contribution rate is 97.699% (see table 4

for details)

Table 3. Proportion of variance of common factor.

Communalities

Total wastewater discharge (X1) Initial Extraction

Domestic sewage discharge （X2） 1 0.997

COD discharge from domestic sewage (X3) 1 0.999

Industrial wastewater discharge(X4) 1 0.951

COD (X5) 1 0.982

Urban sewage discharge (X6) 1 0.991

Extraction Method: Principal Component Analysis

As shown in figure 1, the horizontal coordinate

represents the number of principal components and

the vertical coordinate the eigenvalues, from which

we can see that the contribution rate of different

indicators is greater than 1, and the eigenvalue of the

second index is between 0-1.

Figure 1: Scree plot of wastewater and major pollutants in

Yunnan Province.

Table 4. Total variance explained.

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total of Variance% Cumulative% Total of Variance% Cumulative% Total of Variance% Cumulative%

1 5.492 91.537 91.537 5.492 91.537 91.537 3.053 50.877 50.877

2 0.370 6.162 97.699 .370 6.162 97.699 2.809 46.822 97.699

3 0.111 1.858 99.557

4 0.019 0.320 99.877

5 0.007 0.123 100.000

6 5.690E-006 9.483E-005 100.000

Extraction Method: Principal Component Analysis.

The factor load matrix is shown in table 5. The

factor load matrix reflects the total amount of

wastewater discharge, the total amount of munici

pal sewage discharge, the COD discharge from

domestic sewage, and the discharge from COD,

and industrial wastewater in Yunnan Province. T

heir respective expressions can be written as foll

ows:

total wastewater discharge=×component1-0.184×c

omponent2;

0.981 Municipal sewage discharge=×component1-

0.228×component2;

0.969 COD discharge=0.963×component1+0.120×

component2;

domestic sewage discharge=×component1-0.291×c

omponent2;

0.956COD=0.954×component1COD=0.269×compo

nent1COD=component2;

wastewater discharge=0.916×component1+0.335×c

omponent2.

Table 5. Factor load matrix.

Com

onent

1 2

Total wastewater discharge (X1) 0.981 -0.184

Domestic sewage discharge （X2） 0.969 -0.228

COD discharge from domestic sewage

(

)

0.963 0.120

Industrial wastewater discharge(X4) 0.956 -0.291

COD (X5)

0.954 0.269

Urban sewage discharge (X6)

0.916 0.335

Statistical Analysis of Wastewater Discharge in Yunnan Province 2015-2020

125

The factor load matrix after maximum variance

rotation is shown in table 6. The information of

each variable is extracted more fully after the m

aximum variance rotation. The total amount of

wastewater discharge and domestic sewage disch

arge are extracted as common factors. Compone

nt1 mainly explains the domestic sewage dischar

ge, urban sewage discharge, and wastewater disc

harge, while component2 mainly explains the ind

ustrial wastewater discharge, COD, and domestic

sewage discharge.

Table 6. Rotated component Matrix 6a.

Component

1 2

Total wastewater discharge

(X1)

0.893 0.449

Domestic sewage discharge

（X2）

0.858 0.504

COD discharge from

domestic sewa

(

)

0.837 0.544

Industrial wastewater

discharge(X4)

0.432 0.874

COD (X5)

0.504 0.853

Urban sewage discharge

(

)

0.614 0.752

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 3 iterations.

The principal component conversion matrix is shown

in table 7. The principal component conversion

matrix reflects the relationship between the principal

components before and after rotation, so the principal

components before and after rotation can be

converted by transforming the matrix.

Table 7: Component transformation matrix.

Component 1 2

1 0.724

0.690

2 -0.690

0.724

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

The standardized factor score coefficient matrix is

shown in table 8. The linear combination of principal

components about each variable can be given by

standardized factor score coefficient matrix, and

further clustering analysis is carried out. F



0.179X



+ 0.174X



+ 0.167X



+ 0.175X



0.174X



+ 0.176X



Table 8. Component score coefficient matrix.

Component

Total wastewater discharge

(

)

0.179

Domestic sewage discharge （

X2）

0.174

COD discharge from domestic

sewa

(

)

0.167

Industrial wastewater

dischar

(

)

0.175

COD (X5)

0.174

Urban sewage discharge (X6)

0.176

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization

4.2 Cluster Analysis Results

The total amount of wastewater discharge, domestic

sewage discharge, domestic sewage COD discharge,

urban sewage discharge, industrial wastewater

discharge and chemical oxygen demand (COD)

discharge were numbered in the systematic cluster

analysis according to six wastewater pollution

discharge indexes in Yunnan Province. After 15

steps, it was finally merged into a class (see table 9

for details).

Table 9. Agglomeration schedule.

Sta

Cluster

Combine

Coefficients

Stage Cluster

First A

ears

Sta

Cluste

Cluster

2 Cluster 1 Cluster 2

1 5 7 0.043 0 0 10

2 6 15 0.108 0 0 6

3 4 9 0.193 0 0 5

4 3 14 0.326 0 0 10

5 4 8 0.458 3 0 8

6 6 16 0.639 2 0 11

7 11 13 0.972 0 0 9

8 4 12 1.446 5 0 11

9 10 11 2.347 0 7 12

10 3 5 3.870 4 1 12

11 4 6 7.294 8 6 13

12 3 10 13.120 10 9 13

13 3 4 24.600 12 11 14

14 2 3 41.866 0 13 15

15 1 2 90.000 0 14 0

According to figure 2, 16 cities in Yunnan Province

can be divided into the following four categories

according to six wastewater discharge pollution

indicators: ① Kunming; ② Qujing; ③ Lijiang,

Nujiang, Diqing, Baoshan, Chuxiong, Lincang,

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Xishuangbanna; ④ Zhaotong, Pu ’ er, Yuxi,

Dehong, Wenshan, Dali, Honghe.

Figure 2. Cluster analysis tree map of wastewater and major

pollutants in Yunnan Province.

5 CONCLUSION

The factor analysis results show that the total amount

of wastewater discharge has been increasing year by

year, among which the proportion of public domestic

sewage continues to increase, becoming the main

source of water resources pollution in Yunnan

Province. The reason may be that since the reform

and opening-up, Yunnan, bordering south and

southeast Asia, has been committed to economic and

social development. Its total population increased

from 46,016,300 in 2010 to 48,583,000 in 2020; its

urban population rose from 11,111,300 to

14,310,730; its population density from 116.6 to

123.3 persons per square kilometer. Among the six

wastewater discharge pollution indicators, the total

wastewater discharge and domestic wastewater

emissions have the greatest impact on water

environmental pollution, followed by industrial

sewage and other pollutants. The reason may be due

to the weak development of state-run enterprises in

Yunnan Province and rapid growth of highly polluted

industries discharging more industrial sewage and

requiring large water consumption.

Based on the cluster analysis method, the total

amount of wastewater discharge and domestic

sewage discharge obtained from factor analysis were

classified into 16 cities in Yunnan Province. The first

category is Kunming, which has a large population

density and produces more domestic wastewater and

industrial sewage than other cities. The second

category is Qujing City

[6]

. There are many industrial

and mining enterprises and industrial parks, most of

which are heavily polluted and high-water-

consuming industries such as thermal power

generation, coal, machinery, chemical industry,

metallurgy, and so on. The third category is Lijiang,

Nujiang, Diqing, Baoshan, Chuxiong, Lincang,

Xishuangbanna, and the fourth category is Zhaotong,

Pu’er, Yuxi, Dehong, Wenshan, Dali, Honghe. The

population density in these areas is relatively

balanced, and the domestic sewage and industrial

wastewater produced are less than that in Kunming

and Qujing. The main source of wastewater discharge

is domestic sewage, which has a trend of yearly

increase, followed by industrial wastewater. The

discharge of industrial wastewater in 2018 appeared

in a significant decline mode, and then began to grow

slowly.

With the continuous urban expansion in Yunnan

Province, urban population, discharge of domestic

wastewater and industrial sewage produced by cities,

as well as total wastewater and main pollutants

increase. It mainly results from the lack of effective

planning and reasonable control of urban domestic

sewage, the low wastewater treatment rate of

factories, and excessive discharge or discharge. It

then leads to the continuous deterioration of water

environment and continuous pollution of water

resources

[7].

At present, China’s own core technology and

complete set equipment for efficient wastewater

treatment are still mainly dependent on imports. What

’s more, the research and development of research

institutes, and wastewater treatment technology

evaluation system is not perfect, leading to a lack of

universality in evaluation technology and standards.

6 RECOMMENDATIONS

First, the rapid development of social economy, the

improvement of public living standards and the

acceleration of the industrialization process will all

increase the urban wastewater discharge and pose

some threats to water resources protection. Secondly,

the water environmental pollution control in Yunnan

Province mainly relies on the self-purification

capacity of water bodies. However, limited by

geographical factors, the self-purification capacity of

some river basins began to diminish. Worse still, the

lack of surrounding environmental infrastructure

construction and the imperfect urban drainage pipe

network, the rivers entering the lake have become the

main sewage channel for wastewater discharge.

Therefore, focusing on the source to control

Statistical Analysis of Wastewater Discharge in Yunnan Province 2015-2020

127

wastewater and pollution discharge, and striking a

balance between urban economic development and

environmental protection is one of the key approaches

for water resources and environmental protection.

According to the latest statistics, as of 2019, 194

enterprises in Yunnan Province have obtained sewage

treatment qualifications. Among them, the number of

wastewater treatment facilities in industrial

enterprises increased by 1.39 times; the number of

urban sewage treatment plants increased by 4.67

times. The treatment method of wastewater and

pollutants has changed from the most basic physical

method to the use of microorganisms to remove

dissolved organic matter and colloidal substances in

sewage and improve the discharge of public domestic

wastewater and enterprises to the standard rate.

However, due to the high treatment cost, difficulty in

ensuring the effect, and unstable operation, the

treatment of high-concentration pollutants and

organic wastewater is still relatively difficult.

Therefore, it is suggested that Yunnan Province

should adopt professional and strong treatment level

technical methods for enterprises with large industrial

sewage discharge and introduce high-end and fine

automatic wastewater treatment integration and

control technology, to ensure it matches the process

and equipment operation. Special technical

evaluation indicators should be formulated for some

regions and enterprises with difficult processing. The

close combination between the overall environmental

protection strategy and environmental science and

technology development strategy should be done to

meet the requirements of environmental supervision

and achieve scientific and technological progress and

the development of environmental protection

industry.

Government departments should improve the

supervision capacity of water environment

protection, beef up environmental monitoring team

and standardization construction, improve

fundamental conditions for environmental

monitoring, upgrade environmental supervision

agency equipment, increase energy conservation and

emission reduction, promote water protection

publicity, improve public environmental awareness,

encourage the public to maximize the recycling of

domestic water

[8]

, reduce domestic sewage

discharge, and formulate corresponding sewage

discharge indicators and regulations to ensure that

plant production and public sewage discharge do not

exceed environmental capacity.

In promoting the development of prefectures and

cities in Yunnan Province, we must pay attention to

the protection of the ecological environment. We

should increase the government ’ s capital

investment in the field of water conservancy

construction and water environment protection,

mobilize enterprises to strengthen their pollution

prevention and control efforts, strengthen the

construction of safe water network and water

pollution prevention and control in key river basins,

prefectures and cities, optimize the water pollution

prevention and control system by using the system

engineering, and recycle urban waste water resources

as much as possible, We also call for strengthening

the formulation and implementation of relevant laws

and regulations for key polluting enterprises, and

advocating scientific governance by introducing

artificial intelligence and big data into the

environmental monitoring platform – a water

environment protection and management platform

integrating the province ’ s water environment

quality testing and water environmental pollution

early warning.

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