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Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management

The accidents caused by hazardous material during road transportation may result in catastrophic losses of lives and economics, as well as damages to the environment. Regarding the deficiencies in the information systems of hazmat transportation accidents, this study conducts a survey of 371 acciden...

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Autores principales: Zhou, Li, Guo, Chun, Cui, Yunxiao, Wu, Jianjun, Lv, Ying, Du, Zhiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215458/
https://www.ncbi.nlm.nih.gov/pubmed/32316693
http://dx.doi.org/10.3390/ijerph17082793
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author Zhou, Li
Guo, Chun
Cui, Yunxiao
Wu, Jianjun
Lv, Ying
Du, Zhiping
author_facet Zhou, Li
Guo, Chun
Cui, Yunxiao
Wu, Jianjun
Lv, Ying
Du, Zhiping
author_sort Zhou, Li
collection PubMed
description The accidents caused by hazardous material during road transportation may result in catastrophic losses of lives and economics, as well as damages to the environment. Regarding the deficiencies in the information systems of hazmat transportation accidents, this study conducts a survey of 371 accidents with consequence Levels II to V involving road transportation in China from 2004–2018. The study proposes a comprehensive analysis framework for understanding the overall status associated with key factors of hazmat transportation in terms of characteristics, cause, and severity. By incorporating the adaptive data analysis techniques and tackling uncertainty, the preventative measures can be carried out for supporting safety management in hazmat transportation. Thus, this study firstly analyzed spatial–temporal trends to understand the major characteristics of hazmat transportation accidents. Secondly, it presented a quantitative description of the relation among the hazmat properties, accident characteristics, and the consequences of the accidents using the decision tree approach. Thirdly, an enhanced F-N curve-based analysis method that can describe the relationship between cumulative probability F and number of deaths N, was proposed under the power-law distribution and applied to several practical data sets for severity analysis. It can evaluate accident severity of hazmat material by road transportation while taking into account uncertainty in terms of data sources. Through the introduction of the as low as reasonably practicable (ALARP) principle for determining acceptable and tolerable levels, it is indicated that the F-N curves are above the tolerable line for most hazmat accident scenarios. The findings can provide an empirically supported theoretical basis for the decision-makers to take action to reduce accident frequencies and risks for effective hazmat transportation management.
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spelling pubmed-72154582020-05-22 Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management Zhou, Li Guo, Chun Cui, Yunxiao Wu, Jianjun Lv, Ying Du, Zhiping Int J Environ Res Public Health Article The accidents caused by hazardous material during road transportation may result in catastrophic losses of lives and economics, as well as damages to the environment. Regarding the deficiencies in the information systems of hazmat transportation accidents, this study conducts a survey of 371 accidents with consequence Levels II to V involving road transportation in China from 2004–2018. The study proposes a comprehensive analysis framework for understanding the overall status associated with key factors of hazmat transportation in terms of characteristics, cause, and severity. By incorporating the adaptive data analysis techniques and tackling uncertainty, the preventative measures can be carried out for supporting safety management in hazmat transportation. Thus, this study firstly analyzed spatial–temporal trends to understand the major characteristics of hazmat transportation accidents. Secondly, it presented a quantitative description of the relation among the hazmat properties, accident characteristics, and the consequences of the accidents using the decision tree approach. Thirdly, an enhanced F-N curve-based analysis method that can describe the relationship between cumulative probability F and number of deaths N, was proposed under the power-law distribution and applied to several practical data sets for severity analysis. It can evaluate accident severity of hazmat material by road transportation while taking into account uncertainty in terms of data sources. Through the introduction of the as low as reasonably practicable (ALARP) principle for determining acceptable and tolerable levels, it is indicated that the F-N curves are above the tolerable line for most hazmat accident scenarios. The findings can provide an empirically supported theoretical basis for the decision-makers to take action to reduce accident frequencies and risks for effective hazmat transportation management. MDPI 2020-04-17 2020-04 /pmc/articles/PMC7215458/ /pubmed/32316693 http://dx.doi.org/10.3390/ijerph17082793 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Li
Guo, Chun
Cui, Yunxiao
Wu, Jianjun
Lv, Ying
Du, Zhiping
Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title_full Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title_fullStr Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title_full_unstemmed Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title_short Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management
title_sort characteristics, cause, and severity analysis for hazmat transportation risk management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215458/
https://www.ncbi.nlm.nih.gov/pubmed/32316693
http://dx.doi.org/10.3390/ijerph17082793
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