Effects of Forest Types and Landscape Factors on PM 2.5 Concentrations

Heejung Nam, Jina Jeong, Wanmo Kang () and Chan-Ryul Park ()
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Heejung Nam: Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea
Jina Jeong: Creative & Communications Consulting Company, 59 Hongik-ro 5-gil, Mapo-gu, Seoul 04039, 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
Chan-Ryul Park: Livable Urban Forests Research Center, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea

Land, 2025, vol. 14, issue 11, 1-13

Abstract: Particulate matter (PM), particularly PM 2.5 , is a major urban air pollution concern globally. While temporary mitigation measures are generally implemented during high-pollution periods, sustainable solutions focusing on forest landscape management are crucial. This study examines the effects of forest landscape types and environmental variables on PM 2.5 concentrations during the high-pollution period (January–March 2022) in South Korea, using data from 40 national air quality monitoring stations. GIS and Fragstats were used to construct spatial variables and landscape indices. Stepwise multiple linear regression analyses were then conducted to identify significant factors affecting PM 2.5 concentrations. The aggregated forest model (i.e., without distinguishing between forest types) explained 72.9% of the variance in PM 2.5 concentrations. Forest percent cover (within 5000 m) and distance from the China national border were found to negatively affect PM 2.5 levels, while population size (within 5000 m) and urbanized area patch density (within 5000 m) had positive effects ( p < 0.05). By incorporating forest types as variables, the forest type model improved explanatory power to 83.4%. Specifically, mixed forest percent cover (within 5000 m), mixed forest patch density (within 3000 m), and broad-leaved forest percent cover (within 1000 m) were negatively correlated with PM 2.5 , while population size and urbanized area patch density (within 5000 m) showed positive effects ( p < 0.05). These results highlight the importance of considering forest types, along with anthropogenic environmental variables, when assessing the mitigating effects of forests on PM 2.5 , as both showed scale-dependent relationships with pollution levels. This study informs urban planning and long-term environmental management strategies for reducing PM 2.5 pollution.

Keywords: forest type; landscape factor; land use regression; particulate matter (PM 2.5 ) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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