Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy

Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO2, PM2.5, and PM10, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m). Additionally, three typical machine learning algorithms were compared with SVI-based land-used regression (LUR) model to explore their performances. Generally, machine learning methods outperform linear LUR, with the ranking: random forest > XGBoost > neural network > LUR. Averaging strategy is an effective method to avoid bias of insufficient feature capture. Therefore, the optimal sampling strategy is to integrating multiple viewing angles at a 100-m buffer, which achieved absolute errors mostly less than 2.5 μg/m3 or ppb. Besides, overexposure, blur, and underexposure led to image misjudgments and incorrect identifications, causing an overestimation of road features and underestimation of human-activity features. These findings enhance understanding and offer valuable support for developing image-based air quality models and other SVI-related research.