The spatial resolution of third party traffic data is not adequately describing the variation of air pollution exposure along the travelled routes of bicycle commuters. In prior work, a city-wide mobile noise mapping methodology was proposed to predict Black Carbon exposure for random bicycle trips, including meteorological variability. In a proof-of-concept pilot, funded by the National Institutes of Environmental Health Sciences (NIEHS), this method is examined in the context of a commuter study in New York City. An independent measurement campaign sampled for noise, Black Carbon and Ultrafine Particles in NYC. We focus on the spatiotemporal analysis of the preliminary data. NYC has different fleet composition compared to Ghent (i.e. less diesel, more hybrids) and different geography. Additional parameters are identified to improve the model in comparison to the prior European work. The validity, feasibility and applicability of the methodology are positively evaluated. Sampling exposure across all seasons during rush hours couldn't be reached within the pilot. Adding noise levels meters to the protocol of the commuter study can supply the missing data with minimal investments. When a full year of data becomes available, the commuter study can be retro-actively attributed with meteorology independent exposure for BC and UFP.
Keywords: Air pollution; Mobile mapping; Noise; Spatiotemporal models.