Systematically quantifying the dynamic characteristics of PM2.5 in multiple indoor environments in a plateau city: Implication for internal contribution

Environ Int. 2024 Apr:186:108641. doi: 10.1016/j.envint.2024.108641. Epub 2024 Apr 9.

Abstract

People generally spend most of their time indoors, making a comprehensive evaluation of air pollution characteristics in various indoor microenvironments of great significance for accurate exposure estimation. In this study, field measurements were conducted in Kunming City, Southwest China, using real-time PM2.5 sensors to characterize indoor PM2.5 in ten different microenvironments including three restaurants, four public places, and three household settings. Results showed that the daily average PM2.5 concentrations in restaurants, public spaces, and households were 78.4 ± 24.3, 20.1 ± 6.6, and 18.0 ± 4.3 µg/m3, respectively. The highest levels of indoor PM2.5 in restaurants were owing to strong internal emissions from cooking activities. Dynamic changes showed that indoor PM2.5 levels increased during business time in restaurants and public places, and cooking time in residential kitchens. Compared with public places, restaurants generally exhibit more rapid increases in indoor PM2.5 due to cooking activities, which can elevate indoor PM2.5 to high levels (5.1 times higher than the baseline) in a short time. Furthermore, indoor PM2.5 in restaurants were dominated by internal emissions, while outdoor penetration contributed mostly to indoor PM2.5 in public places and household settings. Results from this study revealed large variations in indoor PM2.5 in different microenvironments, and suggested site-specific measures for indoor PM2.5 pollution alleviation.

Keywords: Dynamic characteristics; Indoor air; Microenvironments; PM(2.5); Source contributions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution, Indoor* / analysis
  • Air Pollution, Indoor* / statistics & numerical data
  • China
  • Cities
  • Cooking
  • Environmental Monitoring*
  • Humans
  • Particle Size
  • Particulate Matter* / analysis
  • Restaurants / statistics & numerical data

Substances

  • Particulate Matter
  • Air Pollutants