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

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 micrometres in size (PM), in Seoul, Korea. We estimated the di erential e ects of individual behaviour and social class across heterogeneous space in two districts, Gwanak and Gangnam. Three scenarios of seasonal PM 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 , aged under , and with a low education level, increased sharply a er , ticks (each tick corresponding to 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 AC scenarios, the recovery level from resilience scenarios decreased the risk population substantially, for example from. % to. %. Our second finding from the local-scale analysis indicates that most Gangnam sub-districts showed more variation both spatially and in di erent 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.

Agent-Based Modelling of Health Inequalities following the Complexity Turn in Public Health: A Systematic Review

“…Of the three models that focused on physical health outcomes, one examined the incidence of severe neonatal morbidity and deaths per 1000 live births averted [ 33 ], one looked at the health status and care need [ 34 ], and the other investigated the impact of an exposure to air pollution on the health status [ 35 ]. Two of the papers modelled the effect of potential interventions on the physical health outcomes [ 33 , 34 ].…”

“…In one, the intervention was the alteration of the eligibility criteria for government-funded social care, in the other increasing the responsibilities and coverage of community health workers. All the models used a different measure of the socioeconomic position including wealth quintile [ 33 ], approximated social grade [ 34 ], and educational attainment [ 35 ].…”

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

“…The agent’s health will decline on the assumption that it encounters over 100µg/m 3 at which they are currently located. According to Shin and Bithell [37] , H max denotes an agent’s health status at the beginning. H(t) is the current health status.…”

“…While the equation above is equivalent to that of Shin [37] , there are several measurements from which the application differs. First, the infiltration ratio, often termed as the I/O ratio, is used to estimate indoor exposure of individual agents.…”

An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

“…Our rationale for this is partially driven by computational constraints when running simulations with large numbers of agents within complex geographical environments. This is quite common in the ABM literature, representing the population with a representative sample population (e.g., Jumadi, Malleson, Carver, & Quincey, 2020; Malik, Crooks, Root, & Swartz, 2015; Shin & Bithell, 2019; Shirzadi Babakan & Alimohammadi, 2016). The argument is that the dynamics of a reduced system are likely to be identical to those of a larger system (Parry & Bithell, 2012) especially if other factors (parameters) are scaled accordingly.…”