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

Overview of attention for article published in Environmental Science & Technology, December 2016
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Article details
Title
Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles
Published in
Environmental Science & Technology, December 2016
DOI 10.1021/acs.est.6b04633
Pubmed ID
Authors
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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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 16 23%
Researcher 15 21%
Student > Master 8 11%
Lecturer 4 6%
Other 3 4%
Other 10 14%
Unknown 14 20%
Readers by discipline
Readers by discipline Count As %
Environmental Science 19 27%
Engineering 12 17%
Medicine and Dentistry 6 9%
Chemical Engineering 2 3%
Mathematics 2 3%
Other 6 9%
Unknown 23 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 June 2018.
All research outputs
#11,075,031
of 33,738,238 outputs
Outputs from Environmental Science & Technology
#12,015
of 25,992 outputs
Outputs of similar age
#146,911
of 456,682 outputs
Outputs of similar age from Environmental Science & Technology
#127
of 253 outputs
Altmetric has tracked 33,738,238 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 25,992 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.0. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 456,682 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 253 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.