Integrating Audiovisual Data for Short-Term Particulate Matter Concentration Prediction on Urban Sidewalks.

Urban air pollution severely impacts pedestrians on sidewalks, yet traditional fixed-site monitoring lacks the spatial coverage needed for fine-scale exposure assessment due to high costs. This study proposes a novel machine-learning framework to predict short-term sidewalk PM2.5 and PM1 concentrations using multimodal audiovisual features extracted from self-collected street-view videos, alongside meteorological and background pollution data. Based on a mobile monitoring campaign in Shenzhen, China, we evaluated multiple models (linear regression, XGBoost, and LightGBM) across different temporal resolutions (10 s and 1 min) and validation strategies. LightGBM achieved the best performance, yielding R2 values of 0.64-0.65 for 10 s predictions and 0.80 for 1 min predictions under random cross-validation. Under rigorous spatial cross-validation, the model maintained moderate generalizability, with R2 reaching 0.41-0.48 at the 1 min resolution. Furthermore, developing a hybrid model that incorporated static geospatial context further improved the overall predictive accuracy. Variable interpretation revealed that while background PM and meteorology were dominant predictors, dynamic audio-derived features and visual indicators provided substantial additional predictive power. These findings demonstrate that integrating multimodal audiovisual sensing with ancillary data enables scalable, high-resolution estimation of street-level PM, effectively complementing conventional monitoring for urban air-quality management.