Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.
Journal:
Environmental science & technology
PMID:
39879134
Abstract
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, NO, PM, and PM, 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/m 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.