Abstract
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.
Highlights
Air pollutants such as nitrogen dioxide (NO2), fine particulate matter (PM2.5, particle size ≤ 2.5 μm), and inhalable particles (PM10, particle size ≤ 10 μm) in the urban central area are greatly affected by traffic emissions and regional transmission in the summer
As the street view image data reflect the vertical structure of the street greening and buildings, by using the street view image data, we found that the increase of the vertical distribution of street greening can effectively reduce the summertime air pollution from the streets
We found that the vertical distribution of street buildings within 1 km was positively associated with the NO2 index of air pollution (p = 0.077), and the model with the street buildings variables added (Model a2) has lower Akaike information criterion (AIC) and Bayesian information criterion (BIC) scores than the models that only have street length variables (Model a1)
Summary
Air pollutants such as nitrogen dioxide (NO2), fine particulate matter (PM2.5, particle size ≤ 2.5 μm), and inhalable particles (PM10, particle size ≤ 10 μm) in the urban central area are greatly affected by traffic emissions and regional transmission in the summer. Transport emissions and street dust are important pollution sources in the summer [1]. Reasonable use of street greening can form efficient urban pollutant filters and continuously improve street air quality in dense urban areas [5]. The current research methods for street greening and buildings regarding the diffusion of street pollution include numerical simulation [7,8,9], wind tunnel experiments and field measurements [10]. With the development of computer technology, numerical simulation has become widely used in the research of street pollution diffusion [11,12]. Most numerical simulation studies [13] lack the support of measured data
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