Major Industrial Accidents in Korea: The Characteristics and Implication of Statistics 1996–2011

Shin, In Jae

doi:10.1002/prs.11551

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…All these studies have enhanced our understanding of the nature of the problems related to environmental incidents. In particular, statistical methods have been widely employed in these studies (Glickman and Golding, 1992;Shin, 2013;Uth, 1999).With regard to China's environmental incidents, these studies have mainly concentrated on the analysis of characteristics (Ding et al, 2015;Hou and Zhang, 2009;Lu et al, 2012;Yao et al, 2016), the evaluation of damage loss (Li et al, 2008;Xue and Zeng, 2011) as well as the evaluation of influencing factors (Li et al, 2008;Yang et al, 2013) based on the public statistics. Some studies have focused on a specific type of material or sector, such as industrial incidents (Chan et al, 2015;Wei and Lu, 2015), hazardous chemical incidents (Duan et al, 2011;He et al, 2011;Zhang and Zheng, 2012), and traffic incidents (Yang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Environmental incidents in China: Lessons from 2006 to 2015

Cao

Yang

Liu

et al. 2018

Science of The Total Environment

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Environmental incidents are among the most significant environmental challenges in China. Hundreds of environmental incidents occur every year, endangering human health and ecosystems. In this paper, we conducted an analytical study of environmental incidents from 2006 to 2015 in China. We first examined the spatiotemporal characteristics of the total 5213 incidents based on the statistical data collected from the China Statistical Yearbook on Environment. We then examined the characteristics of the sources of risk, causes of harm and resulting damage of environmental incidents based on first-hand data from 1369 cases collected by the Ministry of Environmental Protection (MEP) of China, which obtains detailed incident information. The results show that (1) there was a significant downward trend in the overall number of environmental incidents between 2006 and 2015, and developed eastern regions were high incidence areas; (2) hazardous chemicals were the main risk stressors; (3) production safety accidents and traffic accidents were the two major causes, and (4) most of these incidents resulted in polluted water and air. This paper is the first to provide a longitudinal analysis of the full scope of environmental incidents across the different regions of China, which has useful implications for policy-making and environmental management.

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Section: Introductionmentioning

confidence: 99%

Environmental incidents in China: Lessons from 2006 to 2015

Cao

Yang

Liu

et al. 2018

Science of The Total Environment

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“…Environmental incidents have tendency to occur suddenly and cause major influence to the environment and society, hence there are a significant number of studies related to this phenomena in different industries and countries. Also, different statistical methods are used in analyses of environmental incidents in the current literature (Shin, 2013). However, the focus of this study is on the environmental incidents on construction sites.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Environmental Incidents on Construction Sites in Australia: The Causal Factors, Environmental Impact and Their Relations

Andrić

Wang

Mahamadu

et al. 2019

Journal of Civil Engineering and Management

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The construction sites are prone to environmental incidents. In this paper, the root causes of incidents and immediate actions taken after events are identified through qualitative analysis, while the environmental impacts, the cost of mitigation, time of incident occurrence, as well as the relationship between the causes of incidents and immediate actions and the causes and environmental impacts were quantitatively analysed. In total, 499 environment incidents occurred over an 8-year period on the construction sites in Australia was examined in this study. The results show that the most common causes of incidents were equipment and plant failure, oil spillage, and fuel spillage, while the most expensive incidents in terms of mitigation were flooding, poor weather, and process failures. Further, the most common actions taken immediately after incidents were cleaning and clearing, shut down of operation, plant and equipment and notifications. More than 50% of incidents resulted in the contamination of land and groundwater, while the majority of incidents occurred between 10:00 am and 12:00 am. We propose strategies for project managers and environmental managers to better understand potential environmental hazards.

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“…2 Cross section of real-time industrial accidents occurrence patterns Sources: (Kher and Yerpude 2015;Okoh and Haugen 2014;Shin 2013;Docstoc 2013) j ¼ 1; 2; 3; . .…”

Section: Identification Of Data Variation Principal Component Patternmentioning

confidence: 99%

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

2017

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Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under timeinvariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzyMarkov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

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Major Industrial Accidents in Korea: The Characteristics and Implication of Statistics 1996–2011

Cited by 23 publications

References 5 publications

Environmental incidents in China: Lessons from 2006 to 2015

Environmental incidents in China: Lessons from 2006 to 2015

Understanding Environmental Incidents on Construction Sites in Australia: The Causal Factors, Environmental Impact and Their Relations

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

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