Abstract
Crowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal resolution. Our goals were to examine associations between (1) radar-based measurements of traffic and congestion colors displayed on Google Traffic (GT) maps and (2) black carbon (BC) levels and radar/GT data. At an off-ramp of an interstate and a small one-way street in a mixed-use area in New York City, we used radar devices to obtain vehicle speeds and flows (hourly counts) for cars and trucks. We downloaded in real time GT maps and assigned an ordinal variable GCC to the Google congestion colors (GCC increased with vehicle speed). BC levels were monitored with aethalometers. Flow was highest for intermediate speeds and GCC. Relationships between GCC, vehicle speed and flow were different for the two road types indicating local calibration of GT data is needed. Regression analyses showed that BC levels increase with vehicle flow. At the off-ramp, BC depended additionally on vehicle speed, which was negatively associated with BC levels. Time-dependent BC levels can be inferred from time-dependent GCC data and average vehicle flow (radar-derived in our case). Use of inexpensive crowd-sourced traffic data holds great promise for use in air pollution modeling.
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