A new approach for inferring traffic-related air pollution: Use of radar-calibrated crowd-sourced traffic data. (June 2019) - Hilpert, Markus; Johnson, Mychal; Kioumourtzoglou, Marianthi-Anna; Domingo-Relloso, Arce; Peters, Anisia; Adria-Mora, Bernat; Hernández, Diana; Ross, James; Chillrud, Steven N.
Abstract: Background: Crowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal and spatial resolution. Objectives: To examine associations (1) of radar-based traffic measurements with congestion colors displayed on crowd-sourced traffic data maps and (2) of black carbon (BC) levels with radar and crowd-sourced traffic data. Methods: 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. At these radar sites and at an additional non-radar equipped site at a 2-way street, we monitored BC levels using aethalometers in the summer and early fall of 2017. At all three sites, free-flow traffic conditions typically did not occur due to the nearby presence of traffic lights and forced turns. We also downloaded real-time traffic maps from a crowd-sourced traffic data provider and assigned an ordinal integer congestion color code CCC to the congestion colors, ranging from 1 (dark red) to 5 (gray). Results: CCC increased with vehicle speed. Traffic flow was highest for intermediate speeds and intermediate CCC. Regression analyses showed that BC levels increased with either segregated or total vehicle flows. At the off-ramp, time-dependent BC levels can be inferred from time-dependent CCC and radar-derived mean vehicle flow data. A unit decrease in CCC for a mean traffic flow of 100 vehicles/h was associated with a mean (95% CI) increase in BC levels of 0.023 (0.028, 0.018) μg/m 3 . At the small 1-way and the 2-way street, BC levels were also negatively associated with CCC, though at a >0.05 significance level. Conclusions: Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies. Time-dependent traffic-related primary air pollution levels may be inferred from radar-calibrated crowd-sourced traffic data, in our case radar-derived mean traffic flow and widely available CCC data. However, at some locations mean traffic flow data may already be available. Highlights: Crowd-sourced traffic data can be calibrated by traffic-radar measurements Traffic-related black carbon levels depend on traffic volume but may also depend on vehicle speed Traffic-related black carbon levels can be associated with congestion colors displayed on crowd-sourced traffic maps Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies