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Land use regression (LUR) has been extensively used to capture the spatial distribution of air pollution. However, regional background and non-linear relationships can be challenging to capture using linear approaches. Machine learning approaches have recently been used in air quality prediction. Using data from a mobile campaign of fine particulate matter and black carbon in Toronto, Canada, this study investigates the boundaries of LUR approaches and the potential of two different machine learning models: Artificial Neural Networks (ANN) and gradient boost. In addition, a moving camera was used to collect real-time traffic. Models developed for fine particulate matter performed better than those for black carbon. For the same pollutants, machine learning exhibited superior performance over LUR, demonstrating that LUR performance could benefit from understanding how explanatory variables were …