Effects of Landscape Patterns on the Concentration and Recovery Time of PM 2.5 in South Korea

Sungsoo Yoon, Youngdae Heo, Chan-Ryul Park and Wanmo Kang ()
Additional contact information
Sungsoo Yoon: Ecological Information Team, National Institute of Ecology, 1210 Geumgang-ro, Seocheon-gun 33657, Republic of Korea
Youngdae Heo: AI Platform Team, Data and AI Division, JOBKOREA, 74 Seochodae-ro, Seocho-gu, Seoul 06620, Republic of Korea
Chan-Ryul Park: Urban Forests Division, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea
Wanmo Kang: Department of Forest Environment and Systems, College of Science and Technology, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

Land, 2022, vol. 11, issue 12, 1-13

Abstract: Landscape and urban planning efforts aimed at mitigating the risk of PM 2.5 exposure have been hindered by the difficulties in identifying the effects of landscape factors on air pollutants. To identify interactions between PM 2.5 and landscape elements, this study explored the contributions of landscape variables at multiple scales to the mean hourly PM 2.5 concentration and the duration of high PM 2.5 levels in South Korea. We found that the hourly mean PM 2.5 concentration was significantly correlated with landscape variables that explained the spatial processes contributing to fluctuations in air pollutants on a regional level while controlling the spatial autocorrelation of regression residuals. On the other hand, a constant, high PM 2.5 level was related to landscape patterns that explained relatively independent spatial processes on local levels; these processes include vegetation’s ability to reduce PM 2.5 dispersion rates and the influence of transient human activities in local buildings or heavy traffic on roadways on the emission of air pollutants. Our results highlight that urban planners looking to establish design priorities and leverage landscape factors that could reduce the negative impact of PM 2.5 on citizens’ health should consider both the more general PM 2.5 patterns that exist at regional levels as well as local fluctuations in PM 2.5 .

Keywords: air pollution; landscape effect; ordinary least squares; PM 2.5; spatial regression (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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