Examining the joint effect of air pollution and green spaces on stress levels in South Korea: using machine learning techniques

  • Ashraf, Khadija; 
  • Park, Yoo Min; 
  • Browning, Matthew H. E. M.; 
  • Wang, Jue; 
  • Wang, Ruoyu; 
  • ... Lee, Kangjae
Citations

WEB OF SCIENCE

4
Citations

SCOPUS

5

초록

This study investigates the joint effect of air pollution and different types of green spaces (e.g. mixed forests) on stress levels in South Korea. Two periods were examined: before the COVID-19 pandemic (2017-2019) and during the COVID-19 pandemic (2020-2022). We used 16 total parameters for our Random Forest model. Stress was the dependent variable, and 15 other variables were independent parameters. Our focused independent parameters were PM10 and green spaces (forest types). Our findings show that mixed forests reduce stress, particularly when pollution levels are low. In addition, is associated with increased stress levels, and this relationship became stronger during the COVID-19. These findings indicate that protecting mixed forests and improving air quality may improve people's mental health. This study provides insights into how cities can be made healthier and happier places to live, particularly during challenging periods such as a pandemic.

키워드

Air quality; green space; mental health; GIS; machine learning; South Korea; MENTAL-HEALTH; EXPOSURE; COVID-19; BENEFITS; DISORDER; ACCESS
제목
Examining the joint effect of air pollution and green spaces on stress levels in South Korea: using machine learning techniques
저자
Ashraf, Khadija; Park, Yoo Min; Browning, Matthew H. E. M.; Wang, Jue; Wang, Ruoyu; Lee, Kangjae
DOI
10.1080/17538947.2024.2372321
발행일
2024-12-31
유형
Article
저널명
International Journal of Digital Earth
권
17
호
1