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Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Patton, AP, Milando, C, Durant, JL and Kumar, P (2016) Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles Environmental Science and Technology.

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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 non-highway 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.

Item Type: Article
Subjects : Environmental Engineering
Authors :
AuthorsEmailORCID
Patton, APUNSPECIFIEDUNSPECIFIED
Milando, CUNSPECIFIEDUNSPECIFIED
Durant, JLUNSPECIFIEDUNSPECIFIED
Kumar, PUNSPECIFIEDUNSPECIFIED
Date : 28 November 2016
Identification Number : https://doi.org/10.1021/acs.est.6b04633
Copyright Disclaimer : Copyright © 2016 American Chemical Society
Depositing User : Symplectic Elements
Date Deposited : 29 Nov 2016 11:17
Last Modified : 29 Nov 2016 11:17
URI: http://epubs.surrey.ac.uk/id/eprint/812975

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