Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation

This study aims to investigate the exposure to air pollution in Seoul and the consequent health effects in Seoul South Korea, and suggest possible solutions using agent-based modelling (ABM). ABM is a useful technique that can simulate pollution generation and exposure, mobility patterns of unique individuals, and explore future scenarios. The first study compared Universal Kriging and Generalised Additive Models to spatially interpolate pollution station data over Seoul. A new method was discovered to enhance the accuracy of NO2 on roads. Next, ABM was used to evaluate potential health loss for a set of demographic groups after being cumulatively exposed to particulates (PM10), with a nominal heath impact threshold of 100µg/m3. Finally, a traffic simulation examined the coupled problem of non-exhaust emissions and behaviour and estimate exposure to PM10 for groups of drivers and pedestrians in central Seoul. Having tested the sensitivity to calibrated parameters, scenarios of traffic restriction and modification of pedestrian behaviour to avoid polluted areas was investigated. With less difference between interpolation methods, the result showed a remarkable contrast between roadside and background NO2 as well as a daily cycle, while PM10 had a small variance between hours but had greater seasonal oscillation. The first ABM study showed that disparities in health may arise as a result of differences in socioeconomic status, especially when the group was exposed over a long period, and road proximity caused additional health loss. In the traffic simulation study, extreme PM10 was found along roadways, but although drivers were exposed to extreme values, longer exposure for pedestrians led to higher health risks. Despite the absence of reliable data linking exposure to actual health effects, it is possible to make progress with ABM. In addition, pollution exposure can vary by commuting patterns and the urban development of one’s location. Scenarios can be advantageous for healthcare policy – to aid the most vulnerable groups and districts

Benefits of open research in social simulation: an early-career researcher's perspective

No abstract available

Assessing the Accuracy of Pathfinding Algorithms for Scottish Children's Home-to-School Commutes: A Comparison with GPS Trajectories

Walking to and from school has significant implications for children's physical and mental well-being. This study aims to investigate the accuracy of routing engines (Google Maps, Mapbox, and OSRM) in replicating GPS trajectories and explore potential associations with gender and socioeconomic status. The study analysed GPS data from 227 children aged 10-11 years old in Scotland. The results indicated that OSRM exhibited the highest accuracy with a mean GPS track overlap of 56%. However, no substantial differences were found between the routing engines. Additionally, the accuracy of the engines did not vary based on gender or socioeconomic status. These findings provide reassurance that potential biases do not arise when using these navigation tools, as their accuracy remains consistent across different demographic groups

How agent-based models offer insights on strategies to mitigate soil degradation in North Korea: A conversation with Dr. Yoosoon An

No abstract available

TRAPSim: an agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul

Non-exhaust emissions (NEEs) from brake and tyre wear cause detrimental health effects, yet their relationship with mobility has not been examined rigorously. We constructed an agent-based traffic simulator to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 for groups of drivers and subway commuters in Seoul CBD. Having calibrated the parameters, the results regarding the air quality revealed that roughly 25–30% of the roadside PM10 was significantly higher than the background PM10. Additionally, compared to intra-urban cars, pedestrians who commuted for longer periods of time and were exposed to more ambient particles suffered significant health losses; however, drivers only became aware of the health risk when PM10 levels were consistently high for a few days. Compared to the business-as-usual scenario of vehicle entry, a 90% vehicle restriction was able to reduce PM10 by 18–24% and cut the percentage of resident drivers who were at risk. However, it was not effective for subway commuters. Using an agent-based traffic simulator in a health context can provide insights into how exposure and health effects can vary depending on the time of exposure and the form of transportation

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. Results Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics

How simulation techniques can be used to measure population exposure to high levels of PM2.5: a case study of London

No abstract available

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 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 section

How computer simulation techniques can be used to measure population exposure to high levels of PM2.5 in London

No abstract available

An agent-based assessment of health vulnerability to long-term particulate exposure in Seoul districts

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