Big data-based urban greenness in Chinese megalopolises and possible contribution to air quality control.

Urban greenness is essential for people's daily lives, while its contribution to air quality control is unclear. In this study, Streetview big data of urban greenness and air quality data (Air Quality Index, PM, PM, SO, NO, O, CO) from 206 monitoring stations from 27 provincial capital cities in China were analyzed. The national averages for the sky, ground and middle-level (shrub and short trees) view greenness were 5.4%, 5.5%, and 15.4%, respectively, and the sky:ground:middle ratio was 2:2:6. Street-view/bird-view greenness ratio averaged at 1.1. Large inter-city variations were observed in all the greenness parameters, and the weak associations between all street-view parameters and bird-eye greenspace percentage (21%-73%) indicate their representatives of different aspects of green infrastructures. All air quality parameters were higher in winter than in summer, except O. Over 90% of air quality variation could be explained by socioeconomics and geoclimates, suggesting that air quality control in China should first reduce efflux from social economics, while geoclimatic-oriented ventilation facilitation design is also critical. For different air quality components, greenness had most significant associations with NO, O and CO, and street-view/bird-view ratio was the most powerful indicator of all greenness parameters. Pooled-data analysis at national level showed that street-view greenness was responsible for 2.3% of the air quality variations in the summer and 3.6% in the winter; however, when separated into different regions (North-South China; East-West China), the explaining power increased up to 16.2%. Increased NO was accompanied with decreased O, indicating NO titration effect. The higher O aligned with the higher street-view greenness, showing the greenness-related precursor risk for O pollution. Our study manifested that big internet data could identify the association of greenness and air pollution from street view scale, which can favor urban greenness management and evaluation in other regions where street-view data are available.