Factor diagnosis and future governance of dangerous goods accidents in China’s ports

Journal Pre-proof Factor diagnosis and future governance of dangerous goods accidents in China's ports Jihong Chen, Huiying Zheng, Ling Wei, Zheng Wan, Ruisi Ren, Jie Li, Haoqiang Li, Wentao Bian, Manjia Gao, Yun Bai PII:

S0269-7491(19)34505-1

DOI:

https://doi.org/10.1016/j.envpol.2019.113582

Reference:

ENPO 113582

To appear in:

Environmental Pollution

Received Date: 10 August 2019 Revised Date:

4 November 2019

Accepted Date: 4 November 2019

Please cite this article as: Chen, J., Zheng, H., Wei, L., Wan, Z., Ren, R., Li, J., Li, H., Bian, W., Gao, M., Bai, Y., Factor diagnosis and future governance of dangerous goods accidents in China's ports, Environmental Pollution (2019), doi: https://doi.org/10.1016/j.envpol.2019.113582. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

1

Factor Diagnosis and Future Governance of Dangerous Goods

2

Accidents in China's Ports

3

Jihong Chena，Huiying Zhenga, Ling Weib,c,*, Zheng Wana, Ruisi Renb,c, Jie Lid,

4

Haoqiang Lia, Wentao Biana，Manjia Gaoa, Yun Baie

5

a

，

201306, China

7

b

8

c

9

710127, China

College of Transport and Communications, Shanghai Maritime University, Shanghai

School of Mathematics, Northwest University, Xi'an, 710127, PR China Institute of Concepts, Cognition and Intelligence, Northwest University, Xi'an,

10

d

11

Engineering, Shanghai Maritime University, China

12

e

13

Piscataway, NJ 08854, USA

14

Abstract

Department of Safety Science and Engineering, School of Ocean Science and

Center for Advanced Infrastructure and Transportation, Rutgers University,

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Dangerous goods are particularly hazardous, as they can be flammable, explosive,

1，

and toxic. These characteristics make them vulnerable to accidents, and such mishaps

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during port operations can lead to massive economic losses and even deaths. It is,

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therefore, necessary and important to analyze and study the dangerous goods

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accidents at ports, so as to identify major factors and prevent them. Formal concept

20

analysis (FCA) is a powerful tool for rule extraction. This paper introduces FCA along

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with relevant documents and case studies to analyze the dangerous goods accidents at

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China’s ports, building a concept lattice model of dangerous goods accidents at

*Corresponding author at Northwest University, Xi'an, 710127, PR China. E-mail addresses: [email protected] (J. Chen), [email protected] (H. Zheng), [email protected](L. Wei), [email protected] (Z. Wan), [email protected] (R. Ren), [email protected] (J. Li), [email protected] (H. Li) [email protected] (W. Bian), [email protected] (M. Gao), [email protected] (Y. Bai)

1

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China’s ports, and reduces the condition attributes to come up with three key

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attributes of dangerous goods accidents at China’s ports: warehousing management ,

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facilities and equipment, goods registration and extract four effective diagnostic rules

2，

for dangerous goods accidents at ports. This paper proposes corresponding

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governance strategies to the rules of dangerous goods accidents, which can

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significantly prevent and manage dangerous goods accidents at China’s ports in the

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future. In the future, the concept scale can be introduced to study the problem that the

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influencing factor is multi-valued attribute so as to expand the scope of research.

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Keywords: dangerous goods; port accidents; marine pollution; factor diagnosis;

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future governance

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1. Introduction

3，

The development and practice of green ports are a dominant trend in the current

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port industry in the world. How to reduce port pollution and protect the port

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environment is a shared goal and task for all maritime countries (Chen, et al., 2019a;

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Chen, et al., 2019b). Dangerous goods have particular hazardous properties, such as

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being inflammable, explosive, and toxic (Tanackov, et al., 2018). For this reason, safe

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operations of dangerous goods at ports should receive more attention to the global

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port industry. With the boom in the maritime transportation industry, the storage

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capacity and throughput of dangerous goods at ports are rising and the types of

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hazardous materials are expanding, leading to an increased risk of safe production at

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ports (Gheorghe and Vamanu, 2002). Dangerous goods accidents at ports can result in

4，

serious damages and consequences, including high casualties in addition to economic

47

losses.

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China is a major producer, user, and transporter of hazardous chemicals, and the

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chemical sector is also one of the most risky industries in the country (Schauder,

50

2010). With the rapid development of China's port and shipping industry, dangerous 2

51

goods operations at ports and the maritime traffic volume continue to grow, and many

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dangerous goods accidents have occurred at ports. A particularly typical one took

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place on August 12, 2015. A fire followed by an explosion happened at the dangerous

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goods warehouse of Ruihai International Logistics Co., Ltd. at Tianjin Port, Tianjin

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Binhai New Area in Tianjin city, causing a number of casualties and property losses.

5，

(Fu, et al., 2016). It is therefore important to identify key factors for the purpose of

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launching effective accident prevention and control measures, so as to reduce

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dangerous goods incidents at China’s ports. In the fields of dangerous goods

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transportation and port operations, scholars have carried out studies and research on

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standard management of dangerous goods operations, causes of dangerous goods

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accidents at ports, and governance measures for dangerous goods at ports.

，2

In terms of standard management of operations, Wang, et al. (2018) provided

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useful evidence and suggestions for safety management in the hazardous chemical

，4

industry at home and abroad. Inc (2013) provided an information guide on the safety

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management of chemical substances, detailing safety measures for transporting

，，

chemicals through various means, such as by road, rail, air, and sea. Paulauskas (2000)

，7

pointed out that different countries have different regulations and requirements for the

，8

transportation of dangerous goods. Most of the existing literature offers suggestions

，9

on the specifications of operations for different means of transport for dangerous

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goods and does not specifically target the operations of dangerous goods at ports.

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In terms of analysis method, AHP theory was used in Santarremigia, et al., (2018)

72

and Molero, et al., (2017), the former designed the layout of ITDGs involved in rail

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transport through a hierarchy of container handling equipment (CHE) and the latter

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analyzed the criteria used for the design of safe, secure, cost-efficient and greener

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ITDGs. However, AHP model needs the support of the expert system. If the index

7，

given is unreasonable, the result obtained will be inaccurate. The Bayesian model

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needs to know the prior probability, and the prior probability depends on the

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hypothesis in many cases, so in some cases, the prediction effect will be poor due to 3

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the prior model of the hypothesis.

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Focusing on the management of accidents at ports and in response to the frequent

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dangerous goods accidents at ports, Hamidou, et al. (2014) suggested improving the

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configuration of container terminals to enhance safety. Zhang(2018) proposed

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competent departments should establish a mature and comprehensive risk assessment

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method for fires at ports. Bernechea and Viger (2013) proposed a new storage tank

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optimization design that can minimize the risk of such tanks. Over the years, many

8，

authors have proposed measures to prevent dangerous goods accidents at ports, but

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most of them focused on the management level. In fact, there’s a need for

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improvement at all levels during the operations of dangerous goods at ports.

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Current studies manifest a good research basis for studies on operation

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specifications and safety insurance for dangerous goods at ports. However, dangerous

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goods accidents at ports are small sample events overall from the perspective of

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national governance, but the threats posed by such small sample events are enormous

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(Chen, et al., 2011; Baalisampang et al., 2018). Therefore, it is necessary to introduce

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a new method for analyzing the cause of dangerous goods accidents at ports that

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possess small sample attributes, so as to establish a well-designed and effective

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accident management policy. Formal concept analysis (FCA) does not rely on

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domain-specific rules to extract concepts and their relationships from accidents and

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their influencing factors.It has certain superiority in rule extraction and fault diagnosis

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for small sample events, and the method is primarily applied to accident diagnosis and

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rule extraction (Hu, et al., 2000).So, This paper applies FCA to the diagnosis of

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dangerous goods accidents at China’s ports, trying to establish the concept lattice for

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the formal decision context of dangerous goods accidents at ports, thereby building a

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model which extracts diagnostic rules for the purpose of locating major causes of

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dangerous goods accidents at ports. And on that basis, combined with domestic and

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foreign literature, put forward policy recommendations. This approach is

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forward-looking and research-worthy to some extent. 4

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The rest of the paper is structured as follows. Section 2 introduces the research

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methods, describes the theoretical bases of FCA, including definitions of formal

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decision context, reduction of conditional attributes after formal decision context is

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generated, and the principle and algorithm for the extraction of diagnostic rules,

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followed by the proposed FCA-based diagnosis framework and steps for dangerous

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goods at ports. Section 3 elaborates on the model building for the concept lattice of

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dangerous goods accidents at ports. In this section, we first summarize the factors of

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accidents through case studies and applicable documents and builds the concept

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lattice. Then attributes are reduced and diagnostic rules of dangerous goods accidents

11，

are extracted to obtain several important factors with their corresponding governance

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policies proposed. Section 4 draws the conclusion to summarize the whole paper.

118

Section 5 makes a prospect and points out the direction for further studies

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2. Methodology

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2.1 Theoretical basis for formal concept analysis

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2.1.1 Basic notion of formal concept analysis

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Formal concept analysis (FCA) is a powerful data analysis tool, which bases on the

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formal context for data analysis and extraction of various rules. FCA, as a method to

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obtain the concepts, can discover the hierarchical relationships between concepts, and

125

eventually builds the concept lattice of the formal context. Today, FCA has received

12，

extensive applications for event cause analysis and rule extraction in engineering,

127

social science and management sectors (Messai, et al., 2011). FCA is also used in data

128

mining (Faïd, et al., 2010), data analysis (Jiang, et al., 2007), information retrieval

129

(Harms and Deogun, 2004), source code error correction, machine learning,

130

construction of classification and ontology, etc. (Kuznetsov, SO., 2004).

131

The data of FCA is represented as a formal context. Each row represents an object

132

in the domain of interest, and each column represents an attribute. The elements input

133

into the formal context can be assumed to be Boolean values, that is, an object has or 5

134

does not have a specific attribute. All concepts of a formal context are derived through

135

a pair of derivative operators defined by Wille (1982) between subsets of objects and

13，

subsets of attributes, and then FCA gives a hierarchical relationship of all established

137

concepts in the form of a Hasse graph which is called the concept lattice (Wille, 1982).

138

The core structure of FCA is the concept lattice, which can reflect the instantiation

139

and generalization between concepts (Yan, et al., 2015). Nodes represent

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corresponding concepts, and each node has two parts, namely intent and extent. The

141

extent of the concept is a set of practical examples, such as fault events. The intent of

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the concept contains the characteristic attributes of the objects in the extent.

143

When applying FCA to data analysis, we need to construct a formal context as the

144

basis for analysis, which is the first step. Definitions 1 to 14 and theorem 1 use

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relevant definitions about concept lattices by Wille (1982), Zhang, et al. (2005) and

14，

Wei, et al. (2008).

147 148 149 150 151

Definition 1: we consider (U, A, I) as a formal context, and U = {x1, …, xn} is the

object set, each xi (i ≤ n) is called an object. A = {a1, …, am} is an attribute set, and each aj (j ≤ m) is seen as an attribute. I is the binary relationship between U and A, and I ⊆ U × A. If (x, a) ∈ I, then we say x has an attribute a, and is written as xIa.

In this paper, 1 is used to express (x, a) ∈ I, 0 is used to express (x, a) ∉ I, and the

152

formal context is expressed as a table with 0 and 1 only. For example, Table1 in

153

Appendix 1 is a typical formal context (U, A, I), in which U = {G, W, L, N}, A= {E, J,

154

K, M}.

155 15， 157 158

In the formal context (U, A, I), if X ⊆ U and B ⊆ A, there are a pair of derivative

operators:

X * = {a | a ∈ A, ∀x ∈ X, xIa}

B * = {x | x ∈ U, ∀a ∈ B, xIa}

(1) (2)

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X * represents a collection of attributes shared by all objects in the object subset

1，0

X, B * represents a collection of objects that have all the attributes in the attribute ，

1，1

subset B. As shown in Table 1, for the object set X = {G, W}, the set of attributes

1，2

shared by X is X * = { E }. Similarly, for the attribute set B = {E, K}, B * = { W } is

1，3

the set of objects that have all the attributes in .

1，4

Definition 2: We consider (U, A, I) is a formal context, in which (X, B) is a formal

1，5

concept, when X * = B and X = B *, X is proved to be the extent and B the intent of (X,

1，，

B). (Zhang, et al., 2005)

1，7

The concepts of a formal context (U, A, I) are ordered by (X1, B1) ≤ (X2, B2) ⟺ X1 ⊆ X2 (B1 ⊇ B2)

1，8 1，9 170 171 172 173

Definition 3: Formal decision context is expressed as (U , A , I , C , J ), when (U ,

A , I ) and (U , C , J ) are both contexts, plus A ∩ C = ∅, then A is called a condition attribute set, C is called a decision attribute set. (Wei, et al. ,2008)

Definition 4: We consider L (U, A, I) is a concept lattice, and the collection that consists all the extents of concepts is expressed as

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LU (U, A, I) = {X | (X , B) ∈ L(U , A , I)}. (Wei, et al. ,2008)

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Definition 5: We consider L(U , A1 , I1) and L(U , A2 , I2) are two concept lattices,

17， 177 178 179 180 181 182 183

(X , B) ∈ L(U , A2 , I2), (X ’ , B ’) ∈ L(U , A1 , I1). If there exists X ’ ⊆ X, then we say (X ’ , B ’) implicates (X , B), and can be written as (X ’ , B ’) ⇒ (X , B).

If (X ’ , B ’) ⇒ (X , B), further we can conclude the implication B ’⇒ B. (Wei, et

al. ,2008)

Definition 6: We consider L (U , A1 , I1) and L(U , A2 , I2) are two concept lattices.

If an injection f : L(U , A2 , I2) → L(U , A1 , I1) exists and plus satisfying (1) f ((U , ∅)) = (U , ∅),

f ((∅ , A2)) = (∅ , A1)

(2) ∀(X , B) ∈ L(U , A2 , I2),

f ((X , B)) ⇒ (X , B),

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Then we can say f is an implication mapping from L(U , A2 , I2) to L(U , A1 , I1).

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The extents of concepts concerning the injection f can be written in the form: 7

 (U, A1, I1) = {X ’ | (X ’ , B ’) = f ((X , B)), (X , B) ∈ L(U , A2 , I2)}. 

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As it was defined, we can conclude that  (U, A1, I1) ⊆ LU (U, A1, I1). (Wei, et 

al. ,2008)

189

Definition 7: We consider L(U , A , I) and L(U , C , J) are two concept lattices. If an

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implication mapping from L(U , C , J) to L(U , A , I) exists, then we conclude that

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L(U , A , I) is weakly finer than L(U , C , J), and can be expressed in the following

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form:

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L(U , A , I) ≦ L(U , C , J). (Wei, et al. ,2008)

194

Definition 8: We consider (U , A , I , C , J ) is a formal decision context. If L(U , A ,

195 19，

I) ≦ L(U , C , J ), then (U , A , I , C , J ) can be defined to be weakly consistent; otherwise, inconsistent. (Wei, et al. ,2008)

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2.1.2 Attribute reduction and rule acquisition for weakly-consistent formal

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decision context

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Working out all the concepts in a large-scale database often takes a lot of time for

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computation. On the one hand, there are many attributes describing an accident, and

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on the other hand, not all attributes are used in one problem. This renders the removal

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of inactive attributes necessary, namely attribute reduction (Wille, 1982, Zhang, et al.,

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2005, Wei et al., 2008, Qi, 2009). Attribute reduction of a formal context can get a

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minimum subset of attributes at the hierarchy of concepts with all concepts

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distinguished, which makes the expression of knowledge hidden in data more accurate

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and concise (Zhang, et al., 2005). In the diagnosis of accident causes, redundant

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attributes are removed to build a model with fewer conditional attributes for the

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purpose of achieving the same diagnostic effect as before the reduction, but the

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diagnostic rules obtained after reduction can be more concise and clear-cut. At present,

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many important achievements have been made in attribute reduction theory. For 8

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example, the discernibility attribute matrix method (Zhang, et al., 2005) can be

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introduced to the concept lattice to identify and calculate the attribute reduction of the

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concept lattice.

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Definition 9: We consider (U , A , I , C , J ) is a weakly consistent formal decision

context, the injection f : L(U , C , J ) → L(U , A , I) is an implication mapping. If a

condition attribute subset D ⊆ A such as  (U , A , I) ⊆ LU(U , D , ID) exists, we 

say D is a consistent set of (U , A , I , C , J ) with respect to f. If ∀d ∈ D,  (U , A ,

I) ⊆  (U , D-{d} , ID-{d}) is inconsistent, then we say D is a reduct of (U , A , I , C , J ) with respect to f. (Wei, et al. ,2008)

Definition 10: We consider (X1, B1) and (X2, B2) are two concepts of concept lattice

L(U , A , I). If (X1, B1) ≤ (X2, B2), then we say (X1, B1) is a subconcept of (X2, B2),

and (X2, B2) is a superconcept of (X1, B1). (X1, B1) ≤ (X2, B2) and (X1, B1) ≠ (X2, B2) can be combined as (X1, B1) < (X2, B2). If (X1, B1) < (X2, B2), and there is no concept (Y, C) satisfying (X1, B1) <

and is expressed as (X1, B1) < (X2, B2). (Wei, et al. ,2008)

Definition 11: We consider (U , A , I , C , J ) is a weakly consistent formal decision

context, the injection f : L(U , C , J ) → L(U , A , I) is an implication mapping, (Xi , Bi) , (Xj , Bj) ∈ L(U , A , I). Then the discernibility set between (Xi , Bi) and (Xj , Bj) with respect to f is expressed as

232

233

Besides,

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(Y, C) < (X2, B2), under this condition we can

say (X1, B1) is a child-concept of (X2, B2), and (X2, B2) is a parent-concept of (X1, B1),

  ((Xi

234



, Bi) , (Xj , Bj)) = 

 −  ， ∈  !, #, $,  ,  $ ≺ & ,  '， 

∅，otherwise

  ⋀ = ( ((Xi , Bi) , (Xj , Bj)) , (Xi , Bi) , (Xj , Bj) ∈ L(U , A , I))

is a discernibility matrix of (U , A , I , C , J ) with respect to f.

9

（3）

(4)

23， 237 238

Definition 12: We suppose (U , A , I , C , J ) is a weakly consistent formal decision

context, and the injection f : L(U , C , J ) → L(U , A , I) is an implication mapping. Then

φ&⋀ ' = ⋀7∈⋀: ⋁6∈7 ℎ$ 

239 240 241

89

(5)

is the discernibility function with respect to f. (Wei, et al. ,2008) For further application, we can transform the f discernibility function φ&⋀ ' into 

242

a minimum disjunction paradigm. That‘s to say, the reduction of formal background

243

attribute of weak coordinated decision is the set of all minimal elements that contain

244

the reduction of mapping attribute. All components of this minimum disjunction

245

paradigm are all reductions about f.

24， 247 248 249 250 251 252 253 254 255 25， 257 258 259

Definition 13: We suppose (U, A, I, C, J) is a weakly consistent formal decision

context. If a subset D ⊆ A exists and makes (U, D, ID, C, J) weakly consistent, then we say D is a consistent collection of (U, A, I, C, J). Besides, if ∀ d ∈ D, (U, D-{d}, ID-{d}, C, J) is inconsistent, then we say D is a reduct of (U , A , I , C , J ). (Wei, et al. ,2008) Theorem 1: We consider (U, A, I, C, J) is a weakly consistent formal decision

context. D ⊆A, D≠ ∅, when D is a minimum element in reducts with respect to all implication mapping of (U , A , I , C , J ), we can conclude that D is a reduct of the decision context. (Wei, et al. ,2008) Definition 14: We suppose L (U , A1 , I1) and L (U , A2 , I2) are two concept lattices,

L (U, A1, I1) ≦ L (U , A2 , I2). If for Y ≠U , ∅ , the following relationship exists (X , B) ∈ L(U , A1 , I1) , (Y , C) ∈ L(U , A2 , I2)

and X ⊆ Y, we say "B → C" is a proposition, and express it as "If B, then C". If B1 ⊇ B, C1 ⊆ C, we say "B → C" implies "B1 → C1".

2，0 10

2，1

2.2 Framework and steps of diagnostic rules extraction for dangerous goods

2，2

accidents at China’s ports

2，3

Based on the FCA theory and algorithm as well as the above discussions, this paper

2，4

builds an FCA-based dangerous goods operation analysis model at China’s ports. The

2，5

overall framework and operation steps are as follows. Step 1 Literature review of dangerous goods accidents in ports in Asia, Europe and USA

Identify accident factors

Collect historical data

Build the diagnostic formal context

Step 2 Generate concept lattice Step 3 Reduce condition attributes Step 4

2，， 2，7

Extract diagnostic rules

Figure 1 FCA Framework of Dangerous Goods Operation Accidents at China’s Ports

2，8

Step 1: Establish a formal context for dangerous goods accidents at China’s ports.

2，9

Do a literature review of dangerous goods accidents in ports in Asia, Europe and USA,

270

determine dominant factors of dangerous goods accidents at ports based on relevant

271

documents, collect and analyze the data on historical dangerous goods accidents at

272

ports, and extract the set of patterns of dangerous goods accidents at ports to establish

273

the formal decision context for dangerous goods accident at ports (U , A , I , C , J ).

274

Step 2: construct the concept lattice of dangerous goods accidents at China’s ports. 11

275

According to the formal context (U , A , I , C , J ) of dangerous goods accidents at

27，

ports, build the corresponding condition concept lattice L(U , A , I ) and decision

277

concept lattice L(U , C , J ).

278

Step 3: conduct attribute reduction for the concept lattices of dangerous goods at

279

China’s ports. Based on the theories of attribute reduction, find the reducts of

280

condition attributes.

281

Step 4: extract the diagnostic rules of dangerous goods accidents at China’s ports.

282

Based on the obtained attribute reducts, the conditional concept lattice is constructed

283

and IF-THEN rules are obtained.

284 285

3. Empirical evidence and future governance of dangerous goods accidents at

28，

China's ports

287

3.1 Formal context construction of dangerous goods accidents at China's ports

288

This paper collects all the eight dangerous goods accidents at China's ports from

289

2003 to 2018.The detailed case study of the eight accidents in this paper is shown in

290

Table2 in Appendix 2.

291

From past accidents, we can summarize dangerous goods accidents at China's ports

292

in the past 16 years into three major types, namely fires, explosions and leakage and

293

divided the factors into nine categories:

294




Personnel professionalism: It refers to the professional qualifications of

295

operators, including possession of corresponding work permits, possession of

29，

qualifications to carry out dangerous goods operations, and work experience.

297

(Antes, et al., 2019; Petukhov and Steshina, 2015; Wurm-Schaar, 2015)

298




Operation specifications: It refers to whether the staff members are working

299

according to relevant regulations. (Anwer, et al.,2018; Li, 2007; Molina, et

300

al.,2019) 12

301




Warehousing management: It refers to the management of the warehouse and

302

the materials stored in the warehouse. (Fichtinger, et al., 2015; Makaci, et al.,

303

2017; Tejesh, & Neeraja, 2018)

304




Enterprise operations: It refers to the part of an organization devoted to the

305

storage of dangerous goods. (Chang, et al., 2018; Chavarria-Barrientos, et al.,

30，

2017; Thompson, 2002)

307




Supervision and management: It refers to whether relevant administrative

308

departments of the ports have performed their supervision and management

309

functions according to law. (Gordon, et al.,2019; Jin, et al., 2011; Sommerville,

310

2007)

311




Facilities and equipment: It refers to whether dangerous goods enterprises meet

312

requirements in terms of hardware equipment. (Bueger, 2015; De Salas, 1997;

313

Nolan, 2014)

314




315 31，

Emergency management: It refers to whether accidents can be handled in a timely and effective manner.(Cunha, et al., 2015; Elbanna, et al., 2019)




Goods registration: It refers to a requirement for operating enterprises, that is,

317

whether the data on dangerous goods operations is placed on record. (Fennelly,

318

2012; Lewandowski, 2016; Xu, et al., 2017)

319




Natural factors: It refers to primarily natural accidents and difficult to

320

control(Alexander, 2017; Baalisampang, et al., 2018; Deegan, et al.,1984;

321

Mohajerani, et al., 2019;)

322 323

(Further explanation and study of the above 9 factors can be seen in Table3 in Appendix 3.)

324

Based on the information collected, the formal context of the dangerous goods

325

operation accidents at China's ports can be established. The eight dangerous goods

32，

operation accidents serve as the object set of the formal decision context. The nine 13

327

factors of dangerous goods accidents serve as a set of conditional attributes in the

328

formal context. The types of accidents serve as a set of decision attributes in the

329

formal decision contex. Then we discretize the qualitative attributes of the accidents

330

and represent them in the form of a two-dimensional table to build a formal context

331

(U , A , I , C , J ), where the object set is U = {1,2,3,4,5,6,7,8}, the conditional

332

attribute set is A = {a1 , a2 , … , a9}. 0 means that the requirements are met or

333 334

qualified, and 1 means that the requirements are not met or unqualified.  is the

binary relationship between the object set and decision attributes C = {c1, c2, c3},

335

where 1 means the case is positive or abnormal, 0 means the case is negative or

33，

normal. J is the binary relationship between the object set and decision-making

337

attribute set, and the formal context of dangerous goods at ports that it constructs is

338

shown in Table 1.

339

Table 1

Collection of Typical Dangerous Goods Accidents at China's Ports and

340

Factors Accident No. U

Factors (A/C) ;< ;= ;> ;? ;@

relationship (I/J)

Personnel

1

2

3

4

5

6

7

8

1

0

1

1

1

0

1

0

1

0

1

1

1

0

1

0

0

0

0

0

0

1

1

0

0

1

1

1

1

0

1

0

1

0

0

1

1

1

1

0

professionalism Operation specifications Warehousing management Enterprise operation Supervision and management 14

;A ;B ;C ;D E< E= E>

Facilities and

0

0

0

0

1

0

1

0

0

1

0

1

1

1

1

1

0

0

0

1

1

1

0

0

Natural factors

0

0

0

0

0

0

1

0

Fire

1

0

1

1

0

0

1

0

Explosion

1

0

1

1

1

0

1

0

Leakage

0

1

0

0

1

1

0

1

equipment Emergency management Goods registration

341

( 0 ------ Requirements Qualified / Accidents unhappened

342

1 ------ Requirements Unqualified / Accidents happened)

343 344

3.2 Concept lattice construction of dangerous goods accidents at China's ports

345

Now we can construct the concept lattice L(U , A , I) and L(U , C , J) as shown in

34，

Figure 2(A) and Figure 2(B) . Each node in the figure represents a concept. The extent

347

of the concept is composed of accident cases and the intent of the concept is

348

composed of the influencing factors or accident types of the accident, for example in

349

Figure 2(A)

350

happened the influence factor 1,2 which is fire and explosion. Furthermore, the line in

351

Figure 2(A) and Figure 2(B) also showed some relation between each concepts, the

352

concept at the top of the line is the super concept of the concept at the bottom the line,

353

that’s to say the concept at the bottom the line is the subconcept of the the concept at

354

the top of the line. For example, FC14 is the superconcept of FC10, and FC10 is the

355

subconcept of FC14. The concepts which don’t have a line to connect means they

35，

don’t have the above relationships, such as FC14 and FC12.

the concept

(13457 , a1a2)

15

means in accident case 1,3,4,5,7

357 (U, ∅) FC18

(23457, a4)

(14567, a5)

(245678, a7)

FC14

FC15

FC16

FC17

(3457, a1a2a4)

(1457, a1a2a5)

(2457, a4a7)

(4567, a5a7)

(13457, a1a2)

(U, ) FC11

FC10

FC12

FC13

(457, a1a2a4a5a7)

(456, a5a7a8)

(67, a3a5a7)

FC7

FC8

FC9

(57, a1a2a4a5a6a7) FC5

(5, a1a2a4a5a6a7a8)

(13457, c2)

(2568, c3)

(1347, c1c2)

(5, c2c3) ( , C)

(45, a1a2a4a5a7a8) FC6

(7, a1a2a3a4a5a6a7a9)

FC2

FC3

(6, a3a5a7a8) FC4

(∅, A) FC1

358 359

Figure 2 (A) Concept Lattice L(U , A , I) of Dangerous Goods Accident Operations

3，0

at China's Ports. (B) Concept Lattice L(U , C , J) of Dangerous Goods Operations at

3，1

China's Ports

3，2 3，3 3，4 3，5 3，， 3，7

From the two concept lattices above, we can construct the implication mapping f1 as follows: f1: (U, ∅)→FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457,

c2) →FC14, (∅, C) →FC1

Therefore, the formal decision context (U , A , I , C , J ) of the dangerous goods accidents at ports is weakly consistent.

3，8 3，9

3.3 Attribute reduction of concept lattice of dangerous goods operation accidents

370

at China's ports

371

As in Section 3.2 we found that China's ports dangerous goods accident factors are

372

a weakly-consistent formal decision context, and in this paper, the main purpose is to 1，

373

find the most critical factors affecting dangerous goods accidents at China's ports, so

374

as to propose more targeted and effective countermeasures. So this paper conduct

375

attribute reduction on the base of the weakly consistent between condition concept

37，

lattice and decision concept lattice.

377

To FC3 (7, a1a2a3a4a5a6a7a9) and FC9 (67, a3a5a7) in figure 2, they satisfy

378 379 380 381 382

a1a2a3a4a5a6a7a9 ⊇ a3a5a7, so  ((7, a1a2a3a4a5a6a7a9) , (67, a3a5a7)) = 

a1a2a3a4a5a6a7a9 - a3a5a7 = a1a2a4a6a9;

To FC3 (7, a1a2a3a4a5a6a7a9) and FC8 (456, a5a7a8) they don’t satisfy a1a2a3a4a5a6a7a9 ⊇ a5a7a8, so it’s a ∅;

The others can be concluded in the same way.

383

So for implication mapping f1, the Discernibility attribute matrix about f1 is

384

constructed as shown in table 2.

385

Table 2 Discernibility Matrix for Formal Decision Context for Dangerous Goods

38，

Operation Accidents at China's Ports FC5

FC6

FC2

a8

a6

FC3

a3 a9

FC1

FC2

FC3

FC4

a3 a9

a8

a1a2a4a6a9

FC8

388 389

a8 a1 a2

FC14 387

FC18

a1a2a4a6a9 a3

FC4

FC9

(To make it intuitive to see the discernibility set between each concept, we omitted some concepts between which the discernibility set is a ∅)

According to Formula (5) and the discernibility matrix in Table 2, we can obtain that 17

 φ&⋀ ' = (a1 ⋀ a3 ⋀ a6 ⋀ a8) ∨ (a2 ⋀ a3 ⋀ a6 ⋀ a8)

390

391 392

That is, the formal decision context has two reductions about f1 : {a1 , a3 , a6 , a8} , {a2 , a3 , a6 , a8}.

393

In the form decision context, in addition to implication mapping f1, there are also

394

another five implication mappings, repeat the above calculation process,

395 39， 397 398 399 400 401 402

f2: (U, ∅)→ FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457, c2) →FC10, (∅, C) →FC1.

The two reductions about f2 are {a1, a3, a4, a6, a8}, {a2, a3, a4, a6, a8}

f3: (U, ∅)→FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457, c2) →FC11, (∅, C) →FC1

The two reductions about f3 are {a1, a3, a5, a6, a8}, {a2, a3, a5, a6, a8}

f4: (U, ∅)→FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457, c2) →FC7, (∅, C) →FC1

403

The three reductions about f4 are {a5, a3, a4, a6, a8}, {a1, a3, a7, a6, a8}, {a2, a3, a7, a6,

404

a8 }

405 40， 407 408 409 410 411 412

f5: (U, ∅)→FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457, c2) →FC5,(∅, C) →FC1

The reduction about f5 is {a3, a6, a8}

f6: (U, ∅)→FC18, (5, c2c3) →FC2, (1347, c1c2) →FC3, (2568, c3) →FC4, (13457, c2) →FC6, (∅, C) →FC1

The three reductions about f6 are {a1, a3, a6, a8}, {a2, a3, a6, a8}, {a4, a3, a6, a8}; we can finally find that the most attribute reduction for weakly consistent formal decision context should be {a3, a6, a8}.

413 414

3.4 Extraction of diagnostic rules for dangerous goods operation accidents at

415

China's ports

41，

Through attribute reduction, we can get the reduction of dangerous goods accidents 18

417

at China's ports B = {a3, a6, a8}, then the Hasse diagram of L (U , B , IB) can be drawn,

418

as shown in Figure 3.

419 ∅

∅

420

Figure 3 Concept Lattice L (U , B , IB) of Dangerous Goods Operation Accidents at

421

China's Ports

422 423 424 425 42， 427 428 429 430 431

432

As elaborated in the preceding part, in the formal decision context of dangerous

goods operation accidents at China's ports, the diagnostic rule here: "B → D " means

that if the factors of dangerous goods accidents in the conditional attribute set do not meet the requirements or are unqualified, it can be judged that the type of accident in the attribute set  will occur.

We suppose R (B , C) is the diagnostic rule set of the formal decision context (U , A , I , C , J ). The specific steps are as follows: Initialize R (B , C) = ∅. Traverse all the (Y , D) ∈ L(U , C , J). If (X , B) ∈ L(U ,

B , IB) exists which results in X ⊆ Y, add r : B → D to R (B , C) , and eventually obtain the following diagnostic rules set:

HI : KL → MN HN : KO KP → MO R (B , C) = GH : K K → M M R O O L I N HQ : KL KP → MN MO

433

From the above diagnostic rules, we can know that the three conditional attributes

434

after reduction can complete the diagnosis tasks of the original nine conditional 19

435

attributes, which greatly simplifies the rules and can find the most important actors of

43，

dangerous goods accidents. This has great guiding significance for the

437

implementation of subsequent preventive and control policies.

438 439

3.5 Governance policies of dangerous goods accidents at China's ports

440

Analysis of the above-mentioned diagnosis results of dangerous goods accidents at

441

China's ports shows that the key factors of accidents focus on three aspects:

442

warehouse management, goods registration, and facilities and equipment.

443

In terms of warehouse management safety, based on the chapter 2 of the

444

Regulations on the Control over Safety of Dangerous Chemicals in China, ports

445

should allocate the appropriate areas to be specialized for the manufacture and storage

44，

of dangerous chemicals in accordance with the principle of ensuring safety. In this

447

appropriate area emphasized supervision is implemented. Ports should select an

448

appropriate management mode tailored to the characteristics of port enterprises by

449

innovating the warehousing management mode for goods and materials, so as to

450

ensure safer warehousing and smooth production and transportation of the enterprises

451

(Johnstone and Hai, 2012). Meanwhile, a port-wide safety management and

452

accountability system for dangerous goods should be established and improved, with

453

the employees' awareness of responsibility strengthened to ensure safe operations and

454

maximize the economic benefits of enterprises.

455

In terms of goods registration and safety systems of port enterprises, based on the

45，

chapter 6 of the Regulations on the Control over Safety of Dangerous Chemicals in

457

China, port enterprises should, in compliance with dangerous goods management laws

458

and regulations and their own situations and special properties of their operated

459

dangerous goods, formulate strict management systems for handling, transferring and

4，0

warehousing such goods and detailed safety operation procedures and emergency

4，1

measures and plans. (Corrigan, et al., 2018) They should also file the information for 20

4，2

the record on the entire operational process in a systematical, comprehensive and

4，3

scientific manner, government can build a the dynamic risk management system of

4，4

dangerous chemicals based on the information technology (Liu, X., Li, J., & Li, X.

4，5

2017) which can record all related data in real-time so that to provide safety

4，，

information disclosure for dangerous goods operations at ports and warnings against

4，7

operating accidents.

4，8

In terms of equipment and facilities safety, facilities and equipment for dangerous

4，9

goods operations at ports should be improved with the focus laid on three basic

470

factors: type, quantity, and maintenance. Facilities and equipment types should be in

471

line with the characteristics of the terminals, meet the needs of production operations,

472

and have the emergency response capacity after accidents, which can refer to the

473

article Molero, et al., (2017). Alarm facilities, fire protection facilities, and pollution

474

prevention facilities should be equipped. When a port has the required facilities and

475

equipment in place, it must also meet the quantity requirements to prevent accidents

47，

to the maximum extent and to quickly bring dangerous situations under control once

477

an accident occurs. (Di Vaio, et al., 2019). When a port has the required quantity of

478

facilities and equipment, it also needs to carry out regular maintenance of such

479

facilities and equipment to ensure that they are always in good conditions. Such

480

maintenance includes repair, safe use, and periodical updates. Unavailability of

481

facilities and equipment due to lack of maintenance will bring potential safety hazards

482

to the port.

483

Based on the diagnostic rules obtained and the governance policies above, we can

484

put forward prevention and control measures and suggestions on the three types of

485

dangerous goods accidents in China’s ports: fire, explosion, and leakage. in the

48，

prevention of the occurrence of fire accidents, we can refer to the suggestions to

487

equipment and facilities safety. And the prevention of the occurrence of leakage needs

488

to improve warehousing management and goods registration. When it comes to the

489

concurrence of fire and explosion, warehousing management and equipment and 21

490

facilities safety is of great importance. Last but not least, ways to prevent the

491

concurrence of explosion and leakage are stricter equipment and facilities safety and

492

goods registration.

493 494

4. Conclusions and future research

495

With the help of the FCA, this paper analyzes the dangerous goods accidents at

49，

China's ports, and summarizes nine factors of dangerous goods accidents at ports by

497

studying the related documents and eight dangerous goods cases at China's ports:

498

personnel professionalism, operation specifications, warehousing management,

499

enterprise operation, supervision and management, equipment and facilities,

500

emergency management, goods registration, and natural factors, and three major types

501

of accidents: leakage, fire and explosion. It builds the concept lattice model of

502

dangerous goods accidents at ports and conducts attribute reduction of the accident

503

factors to obtain three major accident factors: warehouse management, goods

504

registration, and facilities and equipment.

505

This paper extracts four effective diagnostic rules as following:

50，




unqualified facilities and equipment will lead to explosions;

507




unqualified warehousing management and goods registration will lead to

508 509

leakage;


510 511 512

unqualified warehouse management and facilities and equipment will lead to fires and explosions;




unqualified facilities and equipment and goods registration will lead to explosions and leakage.

513

It also offers an effective reference from the above aspects for the prevention of

514

future accidents at China's ports and for the adoption of emergency measures, so as to

515

achieve safe operations of dangerous goods at ports. 22

51，

5. Future research

517

Applying the formal concept analysis to cause analysis of dangerous goods

518

operation accidents at ports is an innovative attempt. There are still some limitations

519

in this paper. First, this paper only considers the dangerous goods operation accidents

520

at ports. In the future, the formal concept analysis may be applied to the analysis of

521

operations of various dangerous goods, rather than being limited to the operations at

522

ports. Second, different types of dangerous goods may lead to different accidents, and

523

the types and factors of dangerous goods may be further distinguished in the future.

524

Third, methodologically, the factors of dangerous goods accidents considered in this

525

paper are single-valued attributes. In the future, the concept scale can be introduced to

52，

study the problem that the influencing factor is multi-valued attribute so as to expand

527

the scope of research.

528

Acknowledgments

529

The authors gratefully acknowledge support from the National Natural Science

530

Foundation of China (Grant No.51879156, 61772021, 71704103 and 51409157), and

531

Shanghai Pujiang Program(17PJC053). However, the authors are solely responsible

532

for all the views and analyses in this paper.

533

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29

Highlights 

Dangerous goods accidents at ports are small sample events.

 Dangerous

goods accidents in China’s ports are divided into three types.



Rules exist between dangerous goods accidents and influence factors.



Targeted measures can be taken to reduce dangerous goods accidents.

Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.