Spatial Regression Modeling Approach for Assessing the Spatial Variation of Air Pollutants

Atmosphere

Dongwon Ko¹  Seunghoon Park² 

[1] Department of Urban Planning, Dankook University, Yongin 16890, Korea;School of Urban Planning and Real Estate Studies, Dankook University, Yongin 16890, Korea;

关键词: air pollutants;    built environment;    spatial regression model;    PM₁₀;    PM_2.5;    NO₂;   

DOI  :  10.3390/atmos12060785

来源: DOAJ

【 摘 要 】

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM₁₀, PM_2.5), NO₂, CO, and SO₂ concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM_2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO₂ concentration was higher, that of PM_2.5 was higher in neighborhoods with more bus stops, and those of CO and PM_2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.

【 授权许可】

Unknown