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

Wenjie Wang; Panli Tian; Jinghua Zhang; Evgenios Agathokleous; Lu Xiao; Takayoshi Koike; Huimei Wang; Xingyuan He

doi:10.1016/j.scitotenv.2022.153834

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

Sci Total Environ. 2022 Jun 10:824:153834. doi: 10.1016/j.scitotenv.2022.153834. Epub 2022 Feb 11.

Authors

Wenjie Wang¹, Panli Tian², Jinghua Zhang², Evgenios Agathokleous³, Lu Xiao⁴, Takayoshi Koike⁵, Huimei Wang², Xingyuan He⁶

Affiliations

¹ State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China; Northeast Institute of Geography and Agroecology, Chinese Acadamy of Science, Changchun, China. Electronic address: wwj225@nefu.edu.cn.
² Key Lab. of Forest Plant Ecology (Ministry of Education), Key Lab. of Forest Active Substance Ecological Utilization (Heilongjiang Province), Northeast Forestry University, Harbin, China.
³ Department of Ecology, School of Applied Meteorology, Nanjing University of Information Science & Technology (NUIST), Nanjing, China.
⁴ Northeast Institute of Geography and Agroecology, Chinese Acadamy of Science, Changchun, China.
⁵ Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan.
⁶ Northeast Institute of Geography and Agroecology, Chinese Acadamy of Science, Changchun, China; University of Chinese Academy of Sciences, Beijing, China.

PMID: 35157858
DOI: 10.1016/j.scitotenv.2022.153834

Abstract

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_2.5, PM₁₀, SO₂, NO₂, O₃, 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 O₃. 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 NO₂, O₃ 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 NO₂ was accompanied with decreased O₃, indicating NO titration effect. The higher O₃ aligned with the higher street-view greenness, showing the greenness-related precursor risk for O₃ 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.

Keywords: Air pollution; Bird-view greenness; Geographical variation; Redundancy ordination; Street-view greenness; Variation partitioning.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Big Data
China
Cities
Environmental Monitoring
Humans
Nitrogen Dioxide / analysis
Particulate Matter / analysis
Quality Control

Substances

Air Pollutants
Particulate Matter
Nitrogen Dioxide