Literature DB >> 19921893

Modeling near-road air quality using a computational fluid dynamics model, CFD-VIT-RIT.

Y Jason Wang1, K Max Zhang.   

Abstract

It is well recognized that dilution is an important mechanism governing the near-road air pollutant concentrations. In this paper, we aim to advance our understanding of turbulent mixing mechanisms on and near roadways using computation fluid dynamics. Turbulent mixing mechanisms can be classified into three categories according to their origins: vehicle-induced turbulence (VIT), road-induced turbulence (RIT), and atmospheric boundary layer turbulence. RIT includes the turbulence generated by road embankment, road surface thermal effects, and roadside structures. Both VIT and RIT are affected by the roadway designs. We incorporate the detailed treatment of VIT and RIT into the CFD (namely CFD-VIT-RIT) and apply the model in simulating the spatial gradients of carbon monoxide near two major highways with different traffic mix and roadway configurations. The modeling results are compared to the field measurements and those from CALINE4 and CFD without considering VIT and RIT. We demonstrate that the incorporation of VIT and RIT considerably improves the modeling predictions, especially on vertical gradients and seasonal variations of carbon monoxide. Our study implies that roadway design can significantly influence the near-road air pollution. Thus we recommend that mitigating near-road air pollution through roadway designs be considered in the air quality and transportation management In addition, thanks to the rigorous representation of turbulent mixing mechanisms, CFD-VIT-RIT can become valuable tools in the roadway designs process.

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Year:  2009        PMID: 19921893     DOI: 10.1021/es9014844

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  9 in total

1.  Part 1. Assessment of carcinogenicity and biologic responses in rats after lifetime inhalation of new-technology diesel exhaust in the ACES bioassay.

Authors:  Jacob D McDonald; Melanie Doyle-Eisele; JeanClare Seagrave; Andrew P Gigliotti; Judith Chow; Barbara Zielinska; Joe L Mauderly; Steven K Seilkop; Rodney A Miller
Journal:  Res Rep Health Eff Inst       Date:  2015-01

2.  Modeling spatial variations of black carbon particles in an urban highway-building environment.

Authors:  Zheming Tong; Yan Jason Wang; Molini Patel; Patrick Kinney; Steven Chrillrud; K Max Zhang
Journal:  Environ Sci Technol       Date:  2011-11-30       Impact factor: 9.028

3.  On-Road Chemical Transformation as an Important Mechanism of NO2 Formation.

Authors:  Bo Yang; K Max Zhang; W David Xu; Shaojun Zhang; Stuart Batterman; Richard W Baldauf; Parikshit Deshmukh; Richard Snow; Ye Wu; Qiang Zhang; Zhenhua Li; Xian Wu
Journal:  Environ Sci Technol       Date:  2018-04-03       Impact factor: 9.028

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

Authors:  Allison P Patton; Chad Milando; John L Durant; Prashant Kumar
Journal:  Environ Sci Technol       Date:  2016-12-14       Impact factor: 9.028

5.  Factors associated with NO2 and NOX concentration gradients near a highway.

Authors:  J Richmond-Bryant; M G Snyder; R C Owen; S Kimbrough
Journal:  Atmos Environ (1994)       Date:  2017-11-21       Impact factor: 4.798

6.  Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data.

Authors:  Jennifer Richmond-Bryant; R Chris Owen; Stephen Graham; Michelle Snyder; Stephen McDow; Michelle Oakes; Sue Kimbrough
Journal:  Air Qual Atmos Health       Date:  2017-06       Impact factor: 3.763

7.  QUIC Transport and Dispersion Modeling of Vehicle Emissions in Cities for Better Public Health Assessments.

Authors:  Michael J Brown; Michael D Williams; Matthew A Nelson; Kenneth A Werley
Journal:  Environ Health Insights       Date:  2016-11-14

8.  Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

Authors:  Jiangshe Zhang; Weifu Ding
Journal:  Int J Environ Res Public Health       Date:  2017-01-24       Impact factor: 3.390

9.  Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment.

Authors:  Hai-Ying Liu; Erik Skjetne; Mike Kobernus
Journal:  Environ Health       Date:  2013-11-04       Impact factor: 5.984

  9 in total

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