Quantifying spatially varying impacts of public transport on $NO_2$ concentrations with big geo-data
Abstract
Anthropogenic $NO_2$ concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to $NO_2$ emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution $NO_2$ concentrations. This study first applies a two-stage interpolation model to generate a high-resolution urban $NO_2$ 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 $NO_2$ 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 $NO_2$ 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.
Xiao Zhou
Postdoctoral Researcher
2021 - 2023
My research interests include sustainable development, marine spatial planning, and environmental policy analysis
Hao Guo
Grad Student
2020 - present
My research interests lie in the general area of spatial analysis theory and methods, including spatial statistics, GeoAI, and spatial optimization.
