Land Use Impacts on Particulate Matter Levels in Seoul, South Korea: Comparing High and Low Seasons

Abstract

Seoul, a city in South Korea, experiences high particulate matter (PM) levels well above the recommended standards suggested by the World Health Organization. As concerns about public health and everyday lives are being raised, this study investigates the effects of land use on PM levels in Seoul. Specifically, it attempts to identify which land use types increase or decrease PM10 and PM2.5 levels and compare the effects between high and low seasons using two sets of land use classifications: one coarser and the other finer. A series of partial least regression models identifies that industrial land use increases the PM levels in all cases. It is also reported that residential and commercial land uses associated with lower density increase these levels. Other uses, such as green spaces and road, show mixed or unclear effects. The findings of this study may inform planners and policymakers about how they can refine future land use planning and development practice in cities that face similar challenges.

Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions

“…Local land-use planning is considered the key factor affecting economic development and the spatial distribution of air pollutants. , We collect general land-use and building morphology data from the NSDI (National Spatial Data Infrastructure Portal, ) by the Ministry of Land, Infrastructure and Transport. Digital elevation data and normalized difference vegetation index (NDVI) commonly used in LUR studies are also included (Figure S2c, SI).…”

“…Several regression methods including multiple linear regression, ,, supervised forward regression, ,, partial least-squares regression methods have been applied to build and develop LUR models for air pollutant estimation. Although LUR models have high adaptability and wide applicability, high multicollinearity is the main problem, and general statistical formulas cannot fully capture the complicated relationships between air pollutant concentration and potential predictor variables .…”

“…Mitigating air pollution in city environments requires precise and detailed air quality prediction models for exposure and health assessments as well as emission control policy formulation . Different air quality prediction methods, such as air dispersion models, atmospheric chemical transport models, and statistical methods, have been developed and implemented into air pollution prediction in previous research. − Among these assessment methods, land-use regression (LUR) models are commonly employed to predict the spatial distribution of air pollutants. , Regarding methods, multiple linear regression, , supervised forward linear regression, and partial least-squares regression methods have been well applied to build LUR models for air pollutant prediction. Moreover, detailed real building morphology and dynamic population data are not always available in previous LUR model studies.…”

“…9−11 Among these assessment methods, land-use regression (LUR) models are commonly employed to predict the spatial distribution of air pollutants. 5,12 Regarding methods, multiple linear regression, 13,14 supervised forward linear regression, 15 and partial least-squares regression methods 16 have been well applied to build LUR models for air pollutant prediction. Moreover, detailed real building morphology and dynamic population data are not always available in previous LUR model studies.…”

Differences in the Influence of Microclimate on Pedestrian Volume According to Land-Use

“…In addition, many studies on the effect of climate on outdoor activities, including walking, have been conducted in the public health field [20][21][22]. In recent years, as climate-adaptive cities have become an issue, interest in the impact of microclimate factors on walking in urban spaces is also increasing in the urban planning field [16,23,24]. However, research on the interaction of the urban physical environment and the influence of microclimate on PV is insufficient.…”