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Risk-Based Assessment of 132 kV Electric Distribution Substations and Proximal Residential Areas in the Mangaung Metropolitan Region.

Phoka Caiphus RathebeSetlamorago Jackson Mbazima
Published in: International journal of environmental research and public health (2023)
Annually, an estimate of 2.3 million workers die prematurely due to occupational injuries and illnesses. In this study, a risk assessment was conducted to evaluate the compliance of 132 kV electric distribution substations and proximal residential areas with the South African occupational health and safety Act 85 of 1993. Data were collected from 30 electric distribution substations and 30 proximal residential areas using a checklist. Distribution substations of 132 kV were assigned an overall compliance value of ≥80%, while a composite risk value of < 0.5 was assigned to individual residential areas. The Shapiro-Wilk test was used to check for data normality before multiple comparisons and the Bonferroni adjustment was applied. Non-compliances in electric distribution substations were as a result of poor housekeeping and inappropriate fencing conditions. Ninety-three percent of the electric distribution substations (28/30) scored < 75% compliance on housekeeping and 30% (7/30) were non-compliant (<100%) on fencing. Conversely, there was compliance in the proximal residential areas concerning the substations. Statistically significant differences were found when substation positioning and surrounding infrastructure ( p < 0.00), electromagnetic field sources ( p < 0.00) and maintenance/general tidiness ( p < 0.00) were compared. A peak risk value of 0.6 was observed when comparing the substation positioning with proximal electromagnetic field sources in the residential area. Housekeeping and fencing in the distribution substations must be improved to prevent occupational incidents such as injuries, fire outbreaks, theft and vandalism.
Keyphrases
  • air pollution
  • risk assessment
  • healthcare
  • public health
  • mental health
  • electronic health record
  • high frequency
  • heavy metals
  • human health
  • artificial intelligence
  • image quality
  • deep learning