Analysis of Relationship between Construction Accidents and Particulate Matter using Big Data

  • Lee, Minsu (Department of Safety Engineering, Seoul National University of Science and Technology) ;
  • Jeong, Jaewook (Department of Safety Engineering, Seoul National University of Science and Technology) ;
  • Jeong, Jaemin (Department of Safety Engineering, Seoul National University of Science and Technology) ;
  • Lee, Jaehyun (Department of Safety Engineering, Seoul National University of Science and Technology)
  • Published : 2022.06.20

Abstract

Because construction work is conducted outdoors, construction workers are affected by harmful environmental factor. Especially, Particulate Matter (PM10) is one of the harmful environmental factors with a diameter of 10㎍/m3 or less. When PM10 is inhaled by human, it can cause fatal impact on the human. Contrary to the various analyses of health impact on PM10, the research on the relationship between construction accidents and PM10 are few. Therefore, this study aims to conduct the relative frequency analysis which find out the correlation between construction accidents and PM10, and the modified PM10 grade is suggested to expect accidents probability caused by PM10 in the construction industry. This study is conducted by four steps. i) Establishment of the database; ii) Classification of data; iii) Analysis of the Relative Frequency of accidents in the construction industry by PM10 concentration; iv) Modified PM10 groups to classify the impact of PM10 on accident. In terms of frequency analysis, the most accidents were occurred in the average concentration of PM10 (32㎍/m3). However, we found that the relative frequency of accident was increased as the concentration of PM10 increased. This means the higher PM10 concentration can cause more accidents during construction. In addition, PM10 concentration was divided as 6 groups by the WHO, but the modified PM10 grade by the relative frequency on accident was suggested as 3 groups.

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Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22CTAP-C163805).