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Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)

This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of...

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Detalles Bibliográficos
Autor principal: Shin, Hyesop
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005962/
https://www.ncbi.nlm.nih.gov/pubmed/35433289
http://dx.doi.org/10.1016/j.mex.2022.101673
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author Shin, Hyesop
author_facet Shin, Hyesop
author_sort Shin, Hyesop
collection PubMed
description This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model’s main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city’s air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster. • An ODD protocol is used to document the agent-based model TRAPSim. • Sensitivity experiments and calibration are explained. • The step-by-step codes and annotations are attached in the protocol and HPC sections.
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spelling pubmed-90059622022-04-14 Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM) Shin, Hyesop MethodsX Method Article This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model’s main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city’s air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster. • An ODD protocol is used to document the agent-based model TRAPSim. • Sensitivity experiments and calibration are explained. • The step-by-step codes and annotations are attached in the protocol and HPC sections. Elsevier 2022-03-25 /pmc/articles/PMC9005962/ /pubmed/35433289 http://dx.doi.org/10.1016/j.mex.2022.101673 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Method Article
Shin, Hyesop
Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_full Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_fullStr Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_full_unstemmed Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_short Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_sort quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (abm)
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005962/
https://www.ncbi.nlm.nih.gov/pubmed/35433289
http://dx.doi.org/10.1016/j.mex.2022.101673
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