ArticleEnvironmental science & technology2017
Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles.
Article in Environmental science & technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Integrating traffic pollution dispersion into spatiotemporal NOThe Science of the total environment · 2024Article
- Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site.Research report (Health Effects Institute) · 2022Article
- Seasonal source identification and source-specific health risk assessment of pollutants in road dust.Environmental science and pollution research international · 2022Article
- Enhancing Models and Measurements of Traffic-Related Air Pollutants for Health Studies Using Dispersion Modeling and Bayesian Data Fusion.Research report (Health Effects Institute) · 2020Article
- Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants.Atmospheric environment (Oxford, England : 1994) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.
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Registered trials
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