Estimating near-road pollutant dispersion: A model inter-comparison

Heist, David; Isakov, Vlad; Perry, Steven G.; Snyder, Michelle; Venkatram, Akula; Hood, Christina; Stocker, Jenny; Carruthers, David; Arunachalam, Saravanan; Owen, R. C.

doi:10.1016/j.trd.2013.09.003

Cited by 78 publications

(52 citation statements)

References 18 publications

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“…31–39 We found that our tested models generally underestimated PNC relative to measurements, consistent with previous studies that reported underestimation of traffic-related air pollution by dispersion models for conditions of atmospheric instability, wind direction perpendicular to the highway, or low concentrations. 11,36,37,45 However, our results were different from studies that reported overestimation of traffic-related air pollution during stable or parallel wind conditions, and when concentrations were relatively high. 11,27,36,44,45,64 Differences between our study and those reporting overestimations of concentrations could be related to model characteristics (e.g., the importance of aerosol chemistry or other primary and secondary PNC sources) and uncertainty in the emission factor inputs.…”

Section: Resultscontrasting

confidence: 99%

“…In different European studies of NO 2 , one component of traffic exhaust, performance ( R 2 and standard error) of dispersion models was similar, 31–33 worse than 34,35 or better than 36 LUR performance relative to measurements. At different traffic sites, LUR and dispersion models either underestimated 31,32 or overestimated 37 air pollutant concentrations. Within most single studies, correlation coefficients ( R 2 ) between NO 2 predicted by dispersion models and LUR, or two dispersion models, ranged from 0.55 to 0.90.…”

Section: Introductionmentioning

confidence: 95%

“…39 In the near-road environment, slight improvements were obtained by modeling plume meander and vehicle- and road-induced turbulence. 37,40 One near-road study using both a dispersion model (QUIC) and LUR to model PNC reported R 2 = 0.8 between QUIC and LUR, although model performance was not evaluated. 24 To our knowledge, there are no studies in the literature comparing performance of dispersion and regression models of PNC near roads.…”

Section: Introductionmentioning

confidence: 99%

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

Patton

Milando

Durant

et al. 2016

Environ. Sci. Technol.

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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.

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Section: Resultscontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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

Patton

Milando

Durant

et al. 2016

Environ. Sci. Technol.

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“…Badania porównawcze modeli dyspersji zanieczyszczeń powietrza pochodzących z transportu drogowego, opracowane na podstawie dwóch eksperymentów polowych wskazują, że istnieją obecnie znacznie lepsze modele niż CALINE4. Należą do nich: R-LINE, ADMS, AERMOD [18].…”

Section: Wstępunclassified

Modelowanie Dyspersji Zanieczyszczeń Powietrza W Kanionie Ulicznym Na Przykładzie Alei Krasińskiego W Krakowie

Bogacki¹,

Rzeszutek²,

Heba³

2016

ZNPRzBiS

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“…Many previous studies have used Gaussian plume dispersion models to predict ambient air concentrations (Heist et al 2013). These models are useful for calculating the exposure to directly emitted pollutants near individual point sources; however these models simplify the atmospheric chemistry for species and do not typically consider long-range transport.…”

Section: Introductionmentioning

confidence: 99%

Toxic volatile organic air pollutants across Canada: multi-year concentration trends, regional air quality modelling and source apportionment

Stroud¹,

Zaganescu²,

Chen

et al. 2015

J Atmos Chem

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A Unified Regional Air-quality Modelling System, AURAMS, was expanded to predict six toxic volatile organic compounds (VOCs) within a continental domain and two nested domains covering eastern and western Canada. The model predictions were evaluated against Environment Canada's National Air Pollution Surveillance (NAPS) data set to assess the predictive capability of the model at daily and seasonal time scales. The predictions were also evaluated with satellite-derived column total maps for formaldehyde, carbon monoxide, and nitrogen dioxide. In general, the model showed fair to good predictive skill in terms of both correlation (R) and normalized mean bias (NMB) for benzene (R = 0.53 NMB = 26 %), formaldehyde (R = 0.73, NMB = −15 %) and acetaldehyde (R = 0.55, NMB = 29 %). For the other toxics VOCs, the model showed less predictive skill in the order 1,2,4-trimethylbenzene (R = 0.50, NMB = −41 %), 1,3-butadiene (R = 0.26, NMB = 40 %) and acrolein (R = 0.052, NMB = −51 %). The goal of this study was to apply an air quality model to assess the contribution of mobile sources to ambient levels of toxic VOCs at urban locations across Canada. The mobile source contribution varied in a complex manner for each species for different regions. For benzene and 1,2,4-trimethylbenzene, the mobile source contribution was in the range 40-65 % for major Canadian cities. The model predicted considerably lower

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Estimating near-road pollutant dispersion: A model inter-comparison

Cited by 78 publications

References 18 publications

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

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

Modelowanie Dyspersji Zanieczyszczeń Powietrza W Kanionie Ulicznym Na Przykładzie Alei Krasińskiego W Krakowie

Toxic volatile organic air pollutants across Canada: multi-year concentration trends, regional air quality modelling and source apportionment

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