Quantifying spatially varying impacts of public transport on NO[Formula: see text] concentrations with big geo-data

• Published in: Environmental monitoring and assessment Vol. 195; no. 6; p. 702
• Main Authors: Wang, Han, Zhou, Xiao, Guo, Hao, Dong, Quanhua, Huang, Zhou
• Format: Journal Article
• Language: English
• Published: Netherlands 20.05.2023
• Subjects: Air Pollution; Climate Change; Environmental Monitoring; Humans; Spatial Regression; Spatial analysis; Spatial heterogeneity; Air quality; Geographical weighted regression; NO emissions; Public transport supply and demand
• ISSN: 1573-2959
• DOI: 10.1007/s10661-023-11289-4

Anthropogenic NO[Formula: see text] concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO[Formula: see text] emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO[Formula: see text] concentrations. This study first applies a two-stage interpolation model to generate a high-resolution urban NO[Formula: see text] concentration map originating from satellite measurement products. Then, we formulate 12 explanatory indicators derived from a fusion of massive big geo-data including smart card data and point of interest information, to represent the specific degree of public transport supply and citizens' demand. Furthermore, a geographically weighted regression is applied to quantify the spatial variation in the effect of these indicators on the urban NO[Formula: see text] concentrations. The result shows that public transportation coverage, frequency, and capabilities as public transport supply indicators in metropolitan and suburban areas have a two-way influence on the NO[Formula: see text] emissions. However, among public transport demand indicators, the economic level has a significant positive impact in most areas. Our findings can provide policy implications for public transportation system optimization and air quality improvement.