Factors Associated with Housing Damage Caused by an EF4 Tornado in Rural Areas of Funing, China

Int J Environ Res Public Health. 2022 Oct 31;19(21):14237. doi: 10.3390/ijerph192114237.

Abstract

Rural areas are vulnerable to natural disasters and tend to suffer severe losses. An EF4 tornado occurred in Funing on 23 June 2016, killing 99 people, injuring at least 846 people, and destroying more than 2000 houses. Using a multinomial logistic regression model, this study explored the influencing factors between housing damage and variables of building conditions, tornado intensity, and village environmental factors. The results show that 2-story houses and masonry houses were more likely to be slightly damaged or be in a dangerous state. Furthermore, the building area was positively related to houses in two categories: slight damage (SD) and dangerous and requiring immediate repair (DR), indicating that the larger or taller the house, the more severe the damage. In terms of tornado intensity, houses classified as SD were more likely to be hit by EF4 tornados than by EF3 tornados, and houses were damaged more by EF1 or EF2 tornados. This finding demonstrates that the level of housing damage was not strongly correlated with the tornado intensity. Slightly damaged houses exhibited the highest correlation with environmental factors. The proportion of slightly damaged houses was positively correlated with the water area in the village, unlike the proportion of houses in the DR and unable to be repaired (UR) categories. Moreover, the larger the water area of a village, the less housing damage it suffered. These findings provide new insights into minimizing housing damage in wind disasters to improve disaster prevention planning in rural areas.

Keywords: disaster prevention; emergency management; housing damage; tornado; wind disaster.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China / epidemiology
  • Disasters*
  • Housing
  • Humans
  • Tornadoes*
  • Water

Substances

  • Water

Grants and funding

The National Natural Science Foundation of China (Grant No. 52108053); the Social Science Foundation of Jiangsu Province (Grant No. 20ZZC001); the Natural Science Foundation of Jiangsu Province (Grant No. BK20200762); the University Social Science Research Project of Jiangsu Province (Grant No. 2020SJA0098); and the University Natural Science Research Project of Jiangsu Province (Grant No. 20KJB560024).