Evaluating Air Quality Status in Chicago: Application of Street View Imagery and Urban Climate Sensors

Cities worldwide have initiated the installation of urban climate sensors to monitor air quality in real time and take proactive measures against the growing threat of climate change. This study focuses on the city of Chicago and utilizes Microsoft’s recently launched Project Eclipse sensors to evaluate air quality status. We extracted surrounding land use features near the installed sensors, integrating street view images from Google Street View (GSV) with conventional land use extraction toolkits. Principal component analysis (PCA) was conducted to decompose spatial information and evaluate the unique characteristics of using street view imagery. Additionally, we integrated XGBoost machine learning regression analysis and SHapley Additive exPlanations (SHAP) value calculation to investigate the impact of determinants on air quality. Analysis results indicated that the measured air pollution exposure from the sensors was consistent with the city’s predefined values, except for the Northern intersection of West Belmont Avenue, which reported critical air quality issues. The regression analysis and SHAP calculation revealed significant differences in the impact of land use on air quality between the intersection of West Belmont Avenue and random observations. The city and local government agencies should address the existing built environment and land use conditions in the North to mitigate potential harm.