Literature DB >> 29908501

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

H Forehead1, N Huynh2.   

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.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Agent-based model; Exposure; Health; ITS; Open-source; microsimulation

Mesh:

Substances:

Year:  2018        PMID: 29908501     DOI: 10.1016/j.envpol.2018.06.019

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  6 in total

1.  Light at night and the risk of breast cancer: Findings from the Sister study.

Authors:  Marina R Sweeney; Hazel B Nichols; Rena R Jones; Andrew F Olshan; Alexander P Keil; Lawrence S Engel; Peter James; Chandra L Jackson; Dale P Sandler; Alexandra J White
Journal:  Environ Int       Date:  2022-09-02       Impact factor: 13.352

2.  Robust empirical Bayes approach for Markov chain modeling of air pollution index.

Authors:  Yousif Alyousifi; Kamarulzaman Ibrahim; Wei Kang; Wan Zawiah Wan Zin
Journal:  J Environ Health Sci Eng       Date:  2021-01-26

3.  How to choose healthier urban biking routes: CO as a proxy of traffic pollution.

Authors:  L Bertrand; L Dawkins; R Jayaratne; L Morawska
Journal:  Heliyon       Date:  2020-06-18

4.  Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping.

Authors:  Huanfeng Shen; Man Zhou; Tongwen Li; Chao Zeng
Journal:  Int J Environ Res Public Health       Date:  2019-10-24       Impact factor: 3.390

5.  From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development.

Authors:  Tiago Veiga; Arne Munch-Ellingsen; Christoforos Papastergiopoulos; Dimitrios Tzovaras; Ilias Kalamaras; Kerstin Bach; Konstantinos Votis; Sigmund Akselsen
Journal:  Sensors (Basel)       Date:  2021-05-05       Impact factor: 3.576

6.  A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength.

Authors:  Shivam Gupta; Albert Hamzin; Auriol Degbelo
Journal:  Sensors (Basel)       Date:  2018-10-25       Impact factor: 3.576

  6 in total

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