IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2018-14-2.html
   My bibliography  Save this article

An Agent-Based Assessment of Health Vulnerability to Long-Term Particulate Exposure in Seoul Districts

Author

Abstract

This study presents a proof-of-concept agent-based model (ABM) of health vulnerability to long-term exposure to airborne particulate pollution, specifically to particles less than 10 micrometres in size (PM10), in Seoul, Korea. We estimated the differential effects of individual behaviour and social class across heterogeneous space in two districts, Gwanak and Gangnam. Three scenarios of seasonal PM10 change (business as usual: BAU, exponential increase: INC, and exponential decrease: DEC) and three scenarios of resilience were investigated, comparing the vulnerability rate both between and within each district. Our first result shows that the vulnerable groups in both districts, including those aged over 65, aged under 15, and with a low education level, increased sharply after 5,000 ticks (each tick corresponding to 1 day). This implies that disparities in health outcomes can be explained by socioeconomic status (SES), especially when the group is exposed over a long period. Additionally, while the overall risk population was larger in Gangnam in the AC100 scenarios, the recovery level from resilience scenarios decreased the risk population substantially, for example from 7.7% to 0.7%. Our second finding from the local-scale analysis indicates that most Gangnam sub-districts showed more variation both spatially and in different resilience scenarios, whereas Gwanak areas showed a uniform pattern regardless of earlier prevention. The implication for policy is that, while some areas, such as Gwanak, clearly require urgent mitigating action, areas like Gangnam may show a greater response to simpler corrections, but aggregating up to the district scale may miss particular areas that are more at risk. Future work should consider other pollutants as well as more sophisticated population and pollution modelling, coupled with explicit representation of transport and more careful treatment of individual doses and the associated health responses.

Suggested Citation

  • Hyesop Shin & Mike Bithell, 2019. "An Agent-Based Assessment of Health Vulnerability to Long-Term Particulate Exposure in Seoul Districts," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-12.
  • Handle: RePEc:jas:jasssj:2018-14-2
    as

    Download full text from publisher

    File URL: https://www.jasss.org/22/1/12/12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jennifer Boyd & Rebekah Wilson & Corinna Elsenbroich & Alison Heppenstall & Petra Meier, 2022. "Agent-Based Modelling of Health Inequalities following the Complexity Turn in Public Health: A Systematic Review," IJERPH, MDPI, vol. 19(24), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    2. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    3. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    4. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    5. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    6. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    7. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    8. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    9. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.
    10. Lorscheid, Iris & Meyer, Matthias, 2016. "Divide and conquer: Configuring submodels for valid and efficient analyses of complex simulation models," Ecological Modelling, Elsevier, vol. 326(C), pages 152-161.
    11. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    12. Meli, Mattia & Auclerc, Apolline & Palmqvist, Annemette & Forbes, Valery E. & Grimm, Volker, 2013. "Population-level consequences of spatially heterogeneous exposure to heavy metals in soil: An individual-based model of springtails," Ecological Modelling, Elsevier, vol. 250(C), pages 338-351.
    13. Groeneveld, Jürgen & Johst, Karin & Kawaguchi, So & Meyer, Bettina & Teschke, Mathias & Grimm, Volker, 2015. "How biological clocks and changing environmental conditions determine local population growth and species distribution in Antarctic krill (Euphausia superba): a conceptual model," Ecological Modelling, Elsevier, vol. 303(C), pages 78-86.
    14. Henzler, Julia & Weise, Hanna & Enright, Neal J. & Zander, Susanne & Tietjen, Britta, 2018. "A squeeze in the suitable fire interval: Simulating the persistence of fire-killed plants in a Mediterranean-type ecosystem under drier conditions," Ecological Modelling, Elsevier, vol. 389(C), pages 41-49.
    15. Kanapaux, William & Kiker, Gregory A., 2013. "Development and testing of an object-oriented model for adaptively managing human disturbance of least tern (Sternula antillarum) nesting habitat," Ecological Modelling, Elsevier, vol. 268(C), pages 64-77.
    16. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.
    17. Claudia Dislich & Elisabeth Hettig & Jan Salecker & Johannes Heinonen & Jann Lay & Katrin M Meyer & Kerstin Wiegand & Suria Tarigan, 2018. "Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    18. Dur, Gaël & Won, Eun-Ji & Han, Jeonghoon & Lee, Jae-Seong & Souissi, Sami, 2021. "An individual-based model for evaluating post-exposure effects of UV-B radiation on zooplankton reproduction," Ecological Modelling, Elsevier, vol. 441(C).
    19. Bauduin, Sarah & Grente, Oksana & Santostasi, Nina Luisa & Ciucci, Paolo & Duchamp, Christophe & Gimenez, Olivier, 2020. "An individual-based model to explore the impacts of lesser-known social dynamics on wolf populations," Ecological Modelling, Elsevier, vol. 433(C).
    20. Zhai, Xueting & Zhong, Dixi & Luo, Qiuju, 2019. "Turn it around in crisis communication: An ABM approach," Annals of Tourism Research, Elsevier, vol. 79(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2018-14-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.