ArticleEnvironment international2019
A new approach for inferring traffic-related air pollution: Use of radar-calibrated crowd-sourced traffic data.
Article in Environment international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Strengthening Community Science: Engaging Youth to Build an Open-Source Educational Air Monitoring Tool.Environmental justice (Print) · 2025Article
- Variation in the Impact of New York on Pause on Traffic Congestion by Racialized Economic Segregation and Environmental Burden.GeoHealth · 2024Article
- Spike in pollution to ignite the bursting of COVID-19 second wave is more dangerous than spike of SAR-CoV-2 under environmental ignorance in long term: a review.Environmental science and pollution research international · 2022Review
- Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City.Environmental science & policy · 2022Article
- Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data.The Science of the total environment · 2021Article
- Social Determinants of Health and Diabetes: A Scientific Review.Diabetes care · 2020Review
- Opening a Large Delivery Service Warehouse in the South Bronx: Impacts on Traffic, Air Pollution, and Noise.International journal of environmental research and public health · 2020Article
- Urban health: an example of a "health in all policies" approach in the context of SDGs implementation.Globalization and health · 2019Article
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Authors and funding
9 authors.
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Abstract
backgroundCrowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal and spatial resolution.
objectivesTo 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.
methodsAt 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).
resultsCCC 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
conclusionsUse 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.
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