Jung, Meen Chel; Park, Jaewoo; Kim, Sunghwan. (2019). Spatial Relationships between Urban Structures and Air Pollution in Korea. Sustainability, 11(2).
Abstract
Urban structures facilitate human activities and interactions but are also a main source of air pollutants; hence, investigating the relationship between urban structures and air pollution is crucial. The lack of an acceptable general model poses significant challenges to investigations on the underlying mechanisms, and this gap fuels our motivation to analyze the relationships between urban structures and the emissions of four air pollutants, including nitrogen oxides, sulfur oxides, and two types of particulate matter, in Korea. We first conduct exploratory data analysis to detect the global and local spatial dependencies of air pollutants and apply Bayesian spatial regression models to examine the spatial relationship between each air pollutant and urban structure covariates. In particular, we use population, commercial area, industrial area, park area, road length, total land surface, and gross regional domestic product per person as spatial covariates of interest. Except for park area and road length, most covariates have significant positive relationships with air pollutants ranging from 0 to 1, which indicates that urbanization does not result in a one-to-one negative influence on air pollution. Findings suggest that the government should consider the degree of urban structures and air pollutants by region to achieve sustainable development.
Keywords
Land-use Regression; Particulate Matter Concentrations; Nitrogen-dioxide; Temporal Variations; Smart City; Quality; Health; Pm10; Fine; Pollutants; Urban Structure; Air Pollution; Moran's I; Bayesian Spatial Model; Motivation; Population; Urbanization; Nitrogen Oxides; Urban Structures; Emissions; Regression Analysis; Regression Models; Sulfur; Spatial Dependencies; Environmental Impact; Outdoor Air Quality; Metropolitan Areas; Economic Growth; Photochemicals; Industrial Areas; Urban Areas; Industrial Plant Emissions; Particulate Emissions; Particulate Matter; Data Analysis; Bayesian Analysis; Sustainable Development; Sulfur Oxides; Regions; Mathematical Models; Cities; China