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How to Predict On-road Air Pollution Based on Street View Images and Machine Learning: a Quantitative Analysis of the Optimal Strategy by Zhong, Hui; Chen, Di; Wang, Pengqin; Wang, Wenrui; Shen, Shaojie; Liu, Yonghong; Zhu, Meixin is a scholarly article available to read on EtoBox.

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On-road air pollution exhibits substantial variability over short distances due to emission sources, dilution, and physicochemical processes. 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 dynamically and sampled corresponding SVIs, aiming to develop a reliable strategy. We extracted SVI features from ~ 382,000 streetscape images, which were collected at various angles (0{\deg}, 90{\deg}, 180{\deg}, 270{\deg}) and ranges (buffers with radii of 100m, 200m, 300m, 400m, 500m). Also, three machine learning algorithms alongside the linear land-used regression (LUR) model were experimented with to explore the influences of different algorithms. Four typical image quality issues were identified and discussed. Generally, machine learning methods outperform linear LUR for estimating the four pollutants, with the ranking: random forest > XGBoost > neural network > LUR. Compared to single-angle sampling

Author

Zhong, Hui; Chen, Di; Wang, Pengqin; Wang, Wenrui; Shen, Shaojie; Liu, Yonghong; Zhu, Meixin

Published

2024

Language

EN