Urban Air Pollution Data Collection, Mapping, and Prediction Using Mobile Sensors Installed on Courier Trucks

Particulate matter with a diameter of 2.5 microns or less (PM2.5) has significant impacts on human health, making it essential to understand its spatial and temporal variations. This study focuses on developing land use regression (LUR) models and improving their performance in predicting PM2.5 concentrations in an urban setting. In this study, air quality data were collected using a sensor on a courier truck in downtown Toronto. Extreme gradient boosting (XGBoost), a machine learning algorithm, was employed to address limitations in traditional linear regression based LUR models, incorporating predictors such as land use, meteorology, and emissions to build robust models. A total of 27 models were trained, with varying road segment lengths, predictors, and outlier treatment thresholds. Three models tested the impact of road segment length on model predictions. Eight models examined the effect of removing outliers with different thresholds, revealing that appropriate thresholds improve accuracy. Ten models assessed the addition of emission and traffic data, which did not enhance performance, likely because of overlapping effects with other predictors. In six models, time-variant predictors such as time of day, month, humidity, wind speed, temperature, and pollutant concentrations from stationary stations were included. Adding these predictors significantly improved model performance, highlighting the complex relationships in LUR models for PM2.5 predictions and offering valuable insights for air quality assessment.