Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data

• Published in: ISPRS international journal of geo-information 2020-09, Vol.9 (9), p.500
• Main Authors: Wu, Dong, Gong, Jianhua, Liang, Jianming, Sun, Jin, Zhang, Guoyong
• Format: Article
• Language: English
• Subjects: Accuracy; Air pollution; Air pollution measurements; Air quality indexes; Analysis; Atmospheric particulates; Buildings; City planning; Data transmission; deep learning; Diffusion; Digital map services; Distribution; Dust storms; Environmental aspects; Green buildings; Green infrastructure; Greening; Impact analysis; Nitrogen dioxide; Outdoor air quality; Particle size; Particulate matter; Pollutants; Pollution control; Pollution dispersion; Pollution index; Pollution monitoring; Pollution sources; Regression analysis; Roads & highways; Semantics; Simulation; street buildings; street greening; street view image data; Streets; Sustainable living; Teaching methods; Urban areas; Urban environments; Vertical distribution; Vertical profiles
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi9090500

Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning.