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Review of modelling air pollution from traffic at street-level - The state of the science

Journal Article


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Abstract


  • Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Publication Date


  • 2018

Citation


  • Forehead, H. & Huynh, N. (2018). Review of modelling air pollution from traffic at street-level - The state of the science. Environmental Pollution, 241 775-786.

Scopus Eid


  • 2-s2.0-85049357347

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1273&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/246

Number Of Pages


  • 11

Start Page


  • 775

End Page


  • 786

Volume


  • 241

Place Of Publication


  • United Kingdom

Abstract


  • Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Publication Date


  • 2018

Citation


  • Forehead, H. & Huynh, N. (2018). Review of modelling air pollution from traffic at street-level - The state of the science. Environmental Pollution, 241 775-786.

Scopus Eid


  • 2-s2.0-85049357347

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1273&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/246

Number Of Pages


  • 11

Start Page


  • 775

End Page


  • 786

Volume


  • 241

Place Of Publication


  • United Kingdom