Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics
| Autor: | Xiaobai Angela Yao, Liding Chen, Ye Tian |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Land use Ecological Modeling Geography Planning and Development Population 0211 other engineering and technologies 021107 urban & regional planning Regression analysis 02 engineering and technology Vegetation Spatial distribution Atmospheric sciences Urban Studies Megacity Urbanization Environmental science education Air quality index 021101 geological & geomatics engineering General Environmental Science |
| Zdroj: | Computers, Environment and Urban Systems. 75:35-48 |
| ISSN: | 0198-9715 |
| DOI: | 10.1016/j.compenvurbsys.2019.01.003 |
| Popis: | Due to the worldwide trend of industrialization and urbanization, air pollutants were emitted heavily on a global scale particularly in developing countries, which produces adverse effects on human health by causing health problems such as respiratory and lung diseases. Many regression models based on land use types and urban fabrics have been built to analyze the spatiotemporal distribution of air pollutants, however, few of them examined the relationship between urban morphological characteristics and the distribution of air pollutants in a megacity. This study investigates such relationships for six types of air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and a composite AQI (Air Quality Index) based on hourly data at 35 monitoring stations in Beijing in 2016, with morphological characteristics (Morphological building index), meteorological factors (Land Surface Temperature, LST), land use (vegetation, road length, gas station and industry point data), and population distribution data. We also analyzed the results with spatiotemporal regression and SSH (Spatial Stratified Heterogeneity) models respectively. According to the spatiotemporal regression model, the morphological building index (MBI) shows a strong correlation with the dispersion of PM2.5 (R2 = 0.81) and AQI (R2 = 0.80) in the warm season and this finding was reinforced through the Leave-one-out-cross-validation (LOOCV) analysis. From the SSH analysis, the road length in a large proximal region impacts air pollutants the most, especially for O3; and population density significantly affects PM 2.5, AQI, SO2, and NO2 in the cold season. From an integrated interpretation, distance to nearest industry impacts the spatial distribution of NO2 in cold season, while it impacts that of PM2.5 and AQI in both warm and cold seasons. The research finds that these two models supplement each other well and together help to give us a better understanding of how air quality is affected in the urban landscape. |
| Databáze: | OpenAIRE |
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