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RESEARCH PAPER

Association discovery and outlier detection of
air pollution emissions from industrial
enterprises driven by big data

Zhen Peng1†, Yunxiao Zhang2, Yunchong Wang2, Tianle Tang2

1China University of Geosciences Beijing, School of Economics and Management, Beijing 100038, Chine

2Beijing Institute of Petrochemical Technology, School of Economics and Management, Beijing 102617, Chine

Mots clés: Air Pollution Emissions of Enterprises; Outlier detection based on clustering; Association Rule Mining;

Grey Association Analysis; Big data

Citation: Peng, Z., Zhang, Y.X., Wang, Y.C., Tang, T.L.: Association discovery and outlier detection of air pollution emissions

from industrial enterprises driven by big data. Data Intelligence 5(2), 438-456 (2023). est ce que je: https://doi.org/10.1162/dint_a_00205

Submitted: Décembre 5, 2022; Revised: Février 2, 2023; Accepté: Février 15, 2023

ABSTRAIT

Air pollution is a major issue related to national economy and people’s livelihood. At present, the researches
on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole, and is a
lack of attention to enterprise pollutant emissions from the micro level. Limited by the amount and time
granularity of data from enterprises, enterprise pollutant emissions are still understudied. Driven by big data
of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei, the data mining of
enterprises pollution emissions is carried out in the paper, including the association analysis between
different features based on grey association, the association mining between different data based on
association rule and the outlier detection based on clustering. The results show that: (1) The industries
affecting NOx and SO2 mainly are electric power, heat production and supply industry, metal smelting and
processing industries in Beijing-Tianjin-Hebei; (2) These districts nearby Hengshui and Shijiazhuang city in
Hebei province form strong association rules; (3) The industrial enterprises in Beijing-Tianjin-Hebei are
divided into six clusters, of which three categories belong to outliers with excessive emissions of total VOCs,
PM and NH3 respectively.

auteur correspondant: Zhen Peng, China University of Geosciences Beijing (E-mail: pengzhen@cugb.edu.cn; ORCID:

†
0000-0003-3907-7200).

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
driven by big data

1. INTRODUCTION

Air pollution is a major environmental problem that seriously threatens human life, health, production
and living. The World Health Organization and the United Nations Environment Organization state that air
pollution has become an unavoidable fact of life in cities around the world. Over the past ten years, along
with rapid economic development and urbanization, the intensification of industrial development and
energy consumption have increased greatly, and air environment situation is particularly severe. According
to the Bulletin of China’s Ecological Situation released by the Ministry of Ecology and Environment, le
average concentration of PM2.5 in Beijing-Tianjin-Hebei and its surrounding areas in 2019 was about 1.6
times higher than China’s second-level standard for atmospheric environment, and heavy atmospheric
pollution in regional areas occurred from time to time. Air pollution emissions from industrial enterprises
have become the main influencing factors of regional air pollution, among which SO2, NOx, etc.. are the
important sources of the air pollution [1, 2].

Dans 2013, China introduced the ‘Atmosphere Ten’ Five-Year Action Plan to comprehensively launch the
tough battle of pollution prevention and control focusing on tackling haze. Dans 2018, the Three-Year Action
Plan of “Blue Sky Defense War” was launched successively, and a decisive battle against PM2.5 is underway
in key areas such as Beijing-Tianjin-Hebei. Alors, The State Council issued the “Opinions on Deepening
the Battle against Pollution” in November 2021. It has set a goal of continuously improving the ecological
environment and decreasing the total emission of major pollutants by 2025. It is clear that the current air
pollution prevention and control is a long-term and persistent battle. Although the Beautiful China has
achieved preliminary goals, the air quality of Chinese cities is still not out of the “meteorological influence
type” on the whole, and there is still a long way to go in atmospheric governance.

The main cause of air pollution is man-made pollutants, which mainly come from fuel combustion and
large-scale production emissions of industrial enterprises. SO2, NOx and so on emitted by industrial
enterprises are important sources of air pollution, which are the inevitable products of energy consumption.
Only the pollution source is located in the most fundamental unit of the enterprise, can air pollution control
be provided a reasonable direction for energy efficiency improvement and industrial structure adjustment
from the view of micro, which make it possible that urban air quality can be improved fundamentally.

Cependant, the existing researches mainly focus on the characteristics and impacts of air pollution emissions
in industries or regions, and there are few researches on the view of enterprise to discover the internal rules
of air pollution emissions of industrial enterprises.

With the strengthening of national environmental supervision, the development of Internet of Things
technology and the digitalization of enterprise emissions, it is possible to explore the rules of air pollution
emissions from industrial enterprises, and make air pollution control more precise and reliable. Donc,
based on big data and information technology, this paper will analyze and discuss the rules of air pollution

 https://www.unep.org/news-and-stories/press-release/un-environment-and-world-health-organization-agree-major

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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emission of enterprises and micro-regions. The significance and value of this article is mainly reflected in
the following two points:

(1)

(2)

It provides an important data base for air pollution control in Beijing-Tianjin-Hebei from the micro
enterprise level.
It provides information methods and decision-making suggestions for micro-region and enterprise
air pollution control from the perspective of data.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature. Section 3
and Section 4 present theoretical basis, data and methods. Section 5 analyzes the results and discussion.
Enfin, Section 5 reveals the conclusions and provides policy suggestions.

2. LITERATURE REVIEW

In view of the air pollution emissions, most studies have focused on the characteristics of pollutants and
the influence of pollutants on the atmosphere in different industries or regions by using different methods.

2.1 Specific Industry-Oriented Air Pollution Analysis

There are many industry-specific studies focusing on the impact of industrial production on air pollution,
the assessment of emissions, spatial and temporal distribution of air pollution. The industry involves the
construction, transportation, or steel industry accompanied by serious pollution.

For the construction-related industry, considering the cross-regional transfer and capacity replacement
of the cement industry, Li et al. used spatial econometric methods to explore the impact of China’s cement
production on air pollution and the spatial spillover effect [3]; Zhang et al. evaluated the energy saving
and emission reduction potential of CO2 and air pollutants in cement industry of Jiangsu by using the energy
model based on Geographic Information System [4]; Qu et al. analyzed the spatial distribution of the
potential maximum emissions of VOCs and other pollutants during the whole life cycle of asphalt mixture
by establishing appropriate laboratory testing conditions and methods [5]; Giunta integrated the CALPUFF
model of air quality and the meteorological CALMET model to study PM10 concentration prediction and
diffusion near the construction site [6].

In the transportation industry, Guo et al. analyzed the technological development path, emission path
and energy structure adjustment path of the transportation industry, as well as the synergistic benefits of
pollutants and carbon emission reduction, based on a multi-stage bottom-up mathematical model of
automobile development [7]; Masiol et al. analyzed various sources of ground pollution discharged by civil
aviation industry and their contributions to ground air quality [8].

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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For the steel industry, Tang et al. used the comprehensive air quality model (CAMx) to estimate the unit
level of pollution sources in China’s iron and steel industry and explored their contributions to air quality [9];
Gao et al. analyzed the spatial and temporal characteristics of air emissions from the steel industry and the
emission trends of different pollutants based on the air inventory pollutants from China’s steel industry [10].

In short, the research on industrial air pollution is mainly oriented towards a specific industry, lequel
indicates that there is a certain relationship between air pollution and industry, but the relationship is still
unclear.

2.2 Region-Oriented Air Pollution Analysis

For a specific region, the studies focus on the impact of regional industrial structure and regional industrial

emissions.

The impact of industrial structure on air quality was usually studied from a national macro perspective.
Par exemple, Fan et al. adopted the Dynamic Spatial Durbin Model (DSDM) to find that industrial structure
upgrading, clean energy promotion and technological innovation were the driving forces for haze reduction
in China [11]. Zheng et al. constructed a panel threshold model between air pollution and industrial
structure in China, and found that industrial structure could change the impact of economic development
on air pollution [12].

There are studies on the impact of industrial pollution emissions of administrative provinces and cities.
Based on the pollution panel data of the Beijing-Tianjin-Hebei region from 2013 à 2017, Meng et al. used
Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) to fit the relationship
between pollution emission levels and relevant socio-economic indicators, and found that industrial exhaust
emissions played a decisive role in the level of atmospheric pollution [13]. Sun et al. used Thermal Anomaly
Radiative Power (CTRP) to evaluate the spatio-temporal pattern of industrial pollution emission and its
impact on air quality in Beijing-Tianjin-Hebei region from 2012 à 2018. They observed that the spatial
distribution of CTRP was unbalanced in the Beijing-Tianjin-Hebei region, and Tangshan, Tianjin, Handan,
Xingtai and Shijiazhuang were high-density pollution areas, the CTRP of Tangshan and Handan was higher
than that of other cities, and the CTRP was positively correlated with industrial energy consumption [2].

It can be seen that the industrial pollution emissions are the main sources affecting regional air quality
and all these regions are provinces or cities. The certain correlation between different micro-regions needs
further study.

2.3 Industrial Enterprises-Oriented Air Pollution Analysis

The research on atmospheric emissions from enterprises has been emerging. Wu et al. analysed the
correlation between the power consumption of industrial enterprises and the total industrial output and
between the total industrial output and the direct emissions of major pollutants, estimated and predicted
the direct pollution emissions of industrial enterprises using power grid big data [14]. En outre, Xiao et al.

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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constructed three spatial emission characteristic indexes of Industrial Pollutant Emission Intensity (IPEI),
Industrial Pollution Concentration Emission Intensity (IPCEI) and Density of Waste Gas Monitoring Enterprise
(DWGME) based on the emission monitoring data of 37.123 million enterprises in 31 cities in China [15].
And based on the data the correlation degrees between different pollutants and between cities and industrial
air pollution in Beijing-Tianjin-Hebei are found.

In summary, limited by the availability, amount or time granularity of enterprise data, there are a few
researches on atmospheric emission of enterprises, which are not detailed enough. Donc, il faut
to deeply study the rules of enterprise pollution emission, districts or counties, pollutants and industries
from enterprise emission data.

Taking this as the entry point, this paper collects the unstructured data of atmospheric emissions of
monitored enterprises in Beijing-Tianjin-Hebei in the past seven years. On the basis of data processing, le
information of industrial enterprises and their air pollution is integrated, clustered, and associated and so
sur. The relationship between pollutants and their industries, as well as the relationship between districts
or counties region is analyzed. The abnormal clusters are mined, which seriously impact on air quality. Il
not only has important reference significance for the emission reduction of enterprises in Beijing-Tianjin-
Hebei, but also provides research approach for other regional pollution, which has important reference and
practical significance.

3. THEORETICAL BASIS

The theoretical basis of this research is the theory of corporate social responsibility, air basin theory and

environmental informatics.

The theory of corporate social responsibility focuses that enterprise is the main body of emissions and
should bear the social responsibility of environment [16]. The air basin theory points out that the atmosphere
is a whole and there are no boundaries that prevent the flow of air. Cependant, pollutants discharged to the
atmosphere from a local source do not immediately mix uniformly around the globe and generally only
pollute the air in local areas [17]. The environmental informatics means that the collection and effective
processing of environmental information is the basis of environmental management, planning and emergency
plan decision-making [18].

Donc, from the perspective of data, it is necessary and of great significance to study the rules of
pollution emissions in enterprises and different micro-regions through the collection, processing and mining
of enterprise data for air pollution decision-making and management of regions or enterprises.

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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4. DATA AND METHODS

4.1 Research Framework

This paper constructs an association discovery and outlier detection research framework, as shown in

Chiffre 1. The specific steps are as follows:

(1)

(2)

(3)

To acquire and pre-process the big data of air pollution of industrial enterprises. It mainly includes
two kinds of data. One is the characteristic data of the enterprise itself, and the other is the data of
the enterprise’s air pollution emissions. Data pre-processing refers to unstructured processing,
l'intégration, unification, classification, and standardization, etc..
To conduct data mining on the processed data. It mainly includes outlier detection based on
clustering, association mining between data and between features, and evaluation.
To analyse the results of (2). It includes the relationship analysis between air pollution and the
industries, the relationship analysis between pollution districts or counties, the abnormal analysis
of enterprise air pollution emissions, and put forward control measures and suggestions.

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Chiffre 1. Research framework.

4.2 Data Sources and Data Processing

The data source is the monitoring information disclosure platform of ecological environment departments
and government, which includes Beijing Municipal Bureau of ecological environment, Tianjin Ecological
Environment Bureau, Hebei Provincial Department of ecological environment, National pollution source
monitoring information management and sharing platform.

From the above official platforms, the emission data of monitored enterprises in Beijing-Tianjin-Hebei
depuis 2013 à 2019 were collected and collated. The main types of data are Web text data, PDF data and
other unstructured forms. The collected data includes 35 enterprises in Beijing, 52 enterprises in Tianjin,
et 1349 enterprises in Hebei Province, totaling nearly 300,000 pieces. The data obtained include two
categories of documents. One is the description of the characteristics of the industrial enterprise itself
including the enterprise name, industry, region, and county name, etc.. The other is pollution data. The fields

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
driven by big data

of pollutant discharge data include enterprise name, monitored year, monitored quarter, monitored date,
pollution type, monitored point, emission concentration, emission unit, etc..

Alors, the data are cleaned and processed through the following steps.

(1)

(2)

(3)

(4)

(5)

(6)

Uns tructured transformation. The government publishes the pollutant discharge monitored data of
enterprises in PDF format. Since it is difficult to extract information automatically from PDF files, it
is converted to CSV format by some technical means.
Multi-source data integration. It is the integration of the characteristic data of the enterprise and the
air pollution emission data of the enterprise. The integration of the two is based on the same
enterprise name. Cependant, there are some cases of enterprises changing their names. Whether an
enterprise has changed its name and industry can be queried through Tianyan website (https://www.
tianyancha.com).
Unity of feature names and merging of similar features. The unification of feature names is to
standardize the features with different names but the same meaning, such as ammonia, ammonia
gas and ammonia (escape), which are uniformly named ammonia. Jinghai County and Jinghai
District are unified as Jinghai District according to the latest administrative planning. Some pollutants
are merged into one category. Par exemple, Volatile organic compounds include TVOC, benzene,
benzene series, etc., and heavy metals and their compounds include lead, mercury, chromium, etc..
Unification of data granularity. At present, there are semi-annual emission data, quarterly emission
data, and air pollution emission data of enterprises with the specific time. If we unify these data
into semi-annual data, the amount of data is too few to data mining. If we unify these data into data
with specific time, the accuracy of the data will be greatly reduced. Donc, the “middle” principle is
the best way to deal with them. In order to facilitate the subsequent data mining and maintain the
accuracy, all data are refined or expanded into quarterly data. The semi-annual emission data are
converted to quarterly data according to its change rate of pollution emission. The time-specific data
of air pollution are averaged to represent quarterly data.
Data classification. Il y a 492 enterprise types in the data. For the convenience of research, it
can be summarized into the following ten types based on industry tags from enterprise search
website and expert knowledge. They are power and heat production and supply, nonmetal mineral
manufacturing, electrical machinery and equipment manufacturing, ferrous metal smelting and
rolling processing, chemical raw materials and chemical products, metal products, science and
technology promotion and application service, crude oil processing and petroleum products
manufacturing, nonferrous metal smelting and rolling processing, other manufacturing and
traitement.
Standardization of data. Due to the inconsistent of different magnitude, the differences between
values of different features may be large. In order to eliminate the impact of different magnitude, it
is necessary to standardize data into the same interval.

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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4.3 Grey Association Mining Between Different Features

Grey correlation analysis is a method used to quantitatively describe the degree of correlation between
different features according to the similarity between their development trends [19, 20]. In the process of
system development, if the change trend of two features is consistent, c'est, the degree of synchronous
change is high, the degree of correlation between them is high.

In this study, industry of enterprise and pollutants emitted by enterprise are different features, so grey
correlation analysis can be used for the association analysis between the industry and one pollutant
according to the data change trend of these two features. The data of the industry refer to the average
concentration of different pollutants in the industry at different times, which are taken as the reference
sequence a0. The average value of one pollutant emission concentration at the different times are taken as
the comparison sequence ai.

The correlation coefficient between the industry and the pollutant at the kth time can be got according

to Eq. (1).

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( )
k

d
je

un
min
0
( )
−
un
k
0

−

un
je

+
un
maximum
je
( )
+
k
maximum

un
0
un
0

−
un
je
−
un
je

r
r

(1)

where di(k) represents the grey correlation coefficient, r is the resolution coefficient, 0 < r < 1. The comprehensive correlation degree ri from the kth time to the yth time is calculated according to each correlation coefficient as shown in Eq. (2): = r i 1 − k y ∑ d y ik ( ) k (2) The associations between one comparison sequence and the reference sequence are arranged in descending order reflecting the degree of their relevance. 4.4 Association Rule Mining Between Different Data Association rule mining is usually used to discover the association between different data [21, 22], which is suitable for association discovery between different districts or counties with industrial pollution emissions. For any A → B, A is the antecedent, B is the consequent, and A and B are mutually exclusive. It forms an association rule, which means that A will lead to B, if and only if Eq. (3) and (4) are true. e d u d n / i t / l a r t i c e - p d f / / / / 5 2 4 3 8 2 0 8 9 7 7 9 d n _ a _ 0 0 2 0 5 p d . t / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 ( sup A ) → = B ( P A B ∪ ) = ) s ∪ A B ( N >

min

sup

conf A

(

)
→ =
B

(
P B A
|

)

(

∪
P A B
)
(
P A

s
(

)

∪
A B
s
UN
)
(

min

conf

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(4)

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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where the support of sup(A → B) is the probability of A ∪ B occurring at the same time, representing the
frequency of both occurrences; the confidence of conf(A → B) refers to the probability of the occurrence
of the consequent B under the condition of the antecedent A. Only when the support is greater than the
minsup threshold and the confidence is greater than the minconf threshold, A → B can be called an association
rule. De plus, for any rule, the confidence must be greater than the support.

4.5 Outlier Detection Based on Clustering

Outlier detection based on clustering uses outlier factor to determine outliers on the basis of clustering
résultats [23, 24]. The clustering mainly includes three steps [20]. The first is to determine the optimal number
of clusters; the second is to cluster data; the third is to evaluate the clustering results.

Elbow method based on SSE (Sum of Square Error) as Eq. (5) is often used to determine the optimal
number of clusters. When the number of clusters k is less than the real number of clusters, the increase of
k will greatly increase the degree of aggregation in each class, that is, the decline range of SSE will be
grand. After k reaches the real number of clusters, the decline of SSE will be slower. Donc, the relationship
between SSE value and cluster number k forms an elbow shape, and the k of this elbow is the real cluster
number.

SSE

∑ ∑

(

−
m x
je
toi

∈
u C

(5)

where k represents the number of clusters, Ci represents the ith cluster, u represents a sample point in Ci, xu
represents the value of the sample u, and mi represents the centroid of the cluster Ci. How to determine
the centroid is the key. The best centroid is to minimize the partial derivative of SSE with respect to the
centroid mi as Eq. (6). The partial derivative is set to 0 as Eq. (7), where the best center of mass is the mean
value of all points in the cluster.

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∂
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m
je

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(

−
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je
toi

)

∈
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∑ ∑

∂

∈
u C

∂

m
je

(

−
m x
je
toi

)

∑

(

−
m x
je
toi

)
,

∈
u C

∑

∈
u C

(

−
m x
je
toi

)

= ⇒
0

m C
|
je

=
|

∑

⇒ =
x m
je
toi

∈
u C

∑

1
C

X
toi

∈
u C

Where |Ci| represents the number of all sample points in Ci.

(6)

(7)

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This paper uses multiple clustering methods (based on distance, hierarchy and density) to cluster industrial
enterprises according to their emitted pollutants, compares these clusters and analyzes the characteristics
of the clusters.

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Dans cette étude, Silhouette Coefficient and Calinski-Harabasz Index are applied to estimate the clustering
effect. un(p) is how similar an object p is to its own cluster. b(p) represents the similarity between an object
p and other clusters. The Silhouette Coefficient takes both cohesion a(p) and separation b(p) into account.
The value range is [-1, 1] as Eq. (8). The larger the value, the better the clustering effect.

(
S p

)

(
b p
maximum{

)
−
a p
)
(
)
(
a p b p
,

(

)}

(8)

Calinski-Harabasz Index is another clustering evaluation method, which is much faster than Silhouette

Coefficient.

( )
CH k

trB k
(
trW k
(

−
k
) / (
1)
−
n k
) / (

)

(9)

Where n represents the number of training sample sets, B(k) is the covariance matrix between clusters, W(k)
is the covariance matrix of data within clusters, and tr represents the trace of the matrix as Eq. (9). Pour
clustering, smaller within-cluster variance is better, and larger between-cluster variance is better. So the
higher Calinski-Harabasz index indicates the better clustering effect.

Enfin, the outlier factor OF1(p) of object p is defined as the weighted average of the spacing between

all clusters as Eq. (10).

(
OF p
1

)

= ∑

C
D=

d p C
,

(

)

(10)

Where |D| is the total amount of samples, d(p, Cj) is the distance between the sample p and the cluster Ci.

When Eq. (11) is satisfied, the object p is an outlier.

(
OF p
1

)

≥

Ave OF
_

Dev OF
_

(
1

≤ ≤
b

)
2

(11)

where OF1(p) is the outlier factor, Ave_OF is the average, and Dev_OF is the standard deviation of all in
dataset D.

5. RESULTS AND DISCUSSION

5.1 Association Between Air Pollution and Industries of Enterprises

Based on the grey correlation model, we study the correlation between ten kinds of industries and the

emissions of NOx and SO2 pollutants of Beijing-Tianjin-Hebei region. The results are shown in Table 1.

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Tableau 1. Association between industries and pollutants.

Industries

Crude oil processing and petroleum products manufacturing
Nonferrous metal smelting and rolling processing
Chemical raw materials and chemical products manufacturing
Power and heat production and supply
Nonmetal mineral manufacturing
Science and technology promotion and application service
Electrical machinery and electric equipment manufacturing
Other manufacturing
Ferrous metal smelting and rolling processing
Metal products

NOx

0.61
0.69
0.64
0.69
0.6
0.62
0.6
0.58
0.69
0.69

SO2

0.58
0.63
0.64
0.69
0.62
0.61
0.65
0.65
0.66
0.64

From the association ranking of industries, power and heat production and supply, ferrous metal smelting
and rolling processing, metal products and nonferrous metal smelting and rolling processing are high
correlation with NOx emissions, and power and heat production and supply is most closely related to SO2
emissions in Beijing-Tianjin-Hebei.

Donc, the power and heat production and supply industry have the highest association degree with
NOx and SO2. And the association degrees of ferrous metal smelting and rolling processing industries are
generally higher. These results indicate that the reduction of emissions from these industries will effectively
impact on the overall emission trend of Beijing-Tianjin-Hebei.

5.2 Association Rules of Districts or Counties With the Industry Enterprises

Taking the air pollutant NOx with the highest emission as the object, based on the association rule
mining, in the frequent item sets the high confidence between districts with NOx emission is shown in
Chiffre 2. Among them, Taocheng District→Hengshui Economic Development Zone, and Jingxing county
→Xingtang County form strong association rules.

Taocheng District is the urban area of Hengshui City. Hengshui economic development zone is located
in the suburb of Hengshui City, which is geographically close to Taocheng District, indicating that for
Hengshui City, the air pollution in the urban area has a great impact on the suburbs. Xingtang County is
located in the north of Shijiazhuang city and Jingxing County is located in the west of Shijiazhuang city.
Fait intéressant, the two counties are not adjacent, but there is a strong correlation. The strong correlation
between the two counties shows that there is similar NOx emission trend in the industrial enterprises of the
two counties. The possible reason is that it is influenced by other factors, such as meteorological conditions.

In addition, this study finds that the areas with high confidence are Shijiazhuang, Tangshan, lequel
indicates that there is more NOx emitted by industrial enterprises in these areas, and the frequency and
activity of the production in these areas are also relatively high. So for Beijing, we should pay attention to
the influence of these industrial enterprises in surrounding areas and strengthen their management.

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Chiffre 2. High conﬁ dence between districts of NOx emission.

5.3 Outliers of Air Pollution Emission from Industrial Enterprises

Through the statistical analysis of air pollutants emitted by industrial enterprises in Beijing-Tianjin-Hebei,
it is found that six kinds of pollutants including NOx, SO2, VOCs, MP, heavy metals and their compounds,
and NH3 occur the most frequently in the enterprises.

According to the elbow rule in the clustering principle, it can be judged that the optimal number of
clustering for the data set is 6. Taking c = 6 as the parameter, three clustering methods are implemented
including K-means based on distance, Agglomerative based on hierarchy and DBSCAN based on density.

TSNE dimensionality reduction method is used to visualize the clustering results, which reduces the
distribution dimension of the original data in high-dimensional space to two-dimensional space, as shown
in Figure 3 (un), (b), et (c).

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(un) K-means result

(b) Agglomerative result

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Chiffre 3. The results of three clustering algorithms.

The evaluation results are shown in Table 2. It can be seen that K-means algorithm has the best clustering

results for industrial pollution emissions in Beijing-Tianjin-Hebei.

Tableau 2. Evaluation results of three clustering methods.

Clustering Method

Silhouette Coefﬁ cient

Calinski-Harabasz Index

K-means
Agglomerative
DBSCAN(the best: eps=0.5, min_Samples=3)

0.6725
0.6561
0.5195

566.065
358.386
46.300

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Chiffre 4. Clustering results of enterprisepollution emissions.

As shown in Figure 4, there are six clusters in industrial enterprise pollution emission based on K-means.

According to Eq. (11) with b = 1.5, the Cluster 1, Cluster 2 and Cluster 5 are determined as outliers.

Cluster 1 is Hebei ** Energy Technology Development Co., Ltd in Shijiazhuang City, Hebei Province,
belonging to abnormal cluster. The enterprise belongs to the crude oil processing and petroleum products
manufacturing industry, and the VOCs emission concentration seriously exceeds the standard.

Cluster 2 is Hebei ** bio Power Generation Co., Ltd. in Shijiazhuang City, Hebei Province, which belongs
to the power and heat production and supply industry. The emission concentration of NOx is high and the
emission concentration of PM seriously exceeds the standard. It is also an abnormal cluster.

The enterprise in Cluster 5 is Hebei ** Chemical Co., Ltd. in Cangzhou city, Hebei province, lequel
belongs to the manufacturing industry of chemical raw materials and chemical products. The NH3 emission
concentration of the enterprise seriously exceeds the standard and the SO2 emission concentration is too high.

6. CONCLUSIONS AND SUGGESTIONS

It can be seen that the remarkable achievements have been attained in atmospheric pollution control of

Beijing-Tianjin-Hebei in recent years, but it still needs improvement in the following aspects.

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(1) In Beijing-Tianjin-Hebei, on the whole, the power and heat production and supply industries contribute

the most to the main pollutants NOx and SO2, followed by smelting and rolling processing industries.

These industrial enterprises need strengthen the desulfurization and denitrification treatment of waste
gas, especially in urban heating, accelerate the construction of regional central heating, improve efficiency
and reduce the emission of pollutants.

(2) The main air pollutants emitted by all types of enterprises in the production and manufacturing
process are NOx, SO2, PM and smoke. The industrial enterprises with high association degree are located
in Hebei, and the association between counties near Hengshui and Shijiazhuang is greater. Ces résultats
show that the air pollutant in these places is not spread diffusely so that there exists a serious accumulation.

It is suggested that the geographical distribution of industrial enterprises should be adjusted avoiding the

aggregation of enterprises in Hebei Province if possible, especially in Hengshui and Shijiazhuang.

(3) There are a large number of enterprises in Beijing-Tianjin-Hebei, especially in Hengshui, Tangshan,
Baoding, Xingtai et al, where there are three outliers. The first outlier is the emission concentration of VOCs.
Most of VOCs not only have pungent smell, but also are key substances that produce ozone. The second
outlier has high NOx and PM emission concentration. These two pollutants are the main pollutants in air
qualité. The NH3 and SO2 emission concentration is the third anomaly. NH3 will react with SO2 in the air
to form granular substances.

The regulatory authorities should strengthen the management of these areas and urge their enterprises
to upgrade and transform waste gas treatment facilities. The above three abnormal enterprises should be
ordered to stop production.

REMERCIEMENTS

This work is supported by the National Natural Science Foundation of China [numéro de subvention 72271033];
the Beijing Municipal Education Commission and Beijing Natural Science Foundation [grant number
KZ202110017025]; the National Undergraduate Innovation and Entrepreneurship Plan Project (2022J00244).

COMPETING INTERESTS

We declared that we have no conflicts of interest to this work. We declare that we do not have any
commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Association discovery and outlier detection of air pollution emissions from industrial enterprises
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CONTRIBUTIONS DES AUTEURS

Zhen Peng (pengzhen@cugb.edu.cn, 0000-0003-3907-7200): Conceptualisation, Méthodologie, Validation,
En écrivant, Review & édition. Yunxiao Zhang (yunxiaozhang2022@163.com): Conceptualisation, Méthodologie,
Validation, En écrivant. Yunchong Wang (yunchongwang2022@163.com): Data Curation, Méthodologie, Validation,
En écrivant. Tianle Tang (tangtianle20@163.com): Data Curation, Met hodology, Validation.

DATA AVAILABILITY

Please refer to Beijing Municipal Bureau of ecological environment http://sthjj.beijing.gov.cn/, Tianjin
Ecological Environment Bureau http://sthj.tj.gov.cn/, Hebei Provincial Department of ecological environment
http://hbepb.hebei.gov.cn/, National pollution source monitoring information management and sharing
platform http://123.127.175.61:6375/.

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AUTHOR BIOGRAPHY

Zhen Peng received respectively her bachelor degree and master degree in
computer science and computer application technology from Shandong
University, Chine, her Ph.D in computer application technology from Beijing
University of Science and Technology, Chine. Now she is a professor in
department of management science and engineering, China University of
Geosciences Beijing. Her research interests include data mining, game theory
and environmental information.
E-mail: pengzhen@cugb.edu.cn, zhen_peng1981@163.com
ORCID: 0000-0003-3907-7200

Yunxiao Zhang has obtained engineering degree of engineering management
from Shandong Jianzhu University in 2019. Starting in 2021, she is currently
studying at school of economics and management of Beijing Petrochemical
Institute and specializes. Her research interests include environmental
governance and carbon emissions.
E-mail: yunxiaozhang2022@163.com

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Yunchong Wang is an undergraduate majoring in management and
application of big data at Beijing Institute of Petrochemical Technology. Son
interests include big data, environmental information, IT, AI.
E-mail: 13241840707@163.com, yunchongwang2022@163.com

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Tianle Tang received his bachelor degree in information management and
information system from Beijing Institute of Petrochemical Technology. Son
research interests include big data processing and analysis, data. And he is
good at writing programs.
E-mail: tangtianle20@163.com

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