ObjectivesCohort 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.MethodsWe 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.ResultsMean 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.ConclusionsIn 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.
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.
Non-exhaust emission (NEE) from brake and tyre wear cause deleterious effects on human health, but relationship with mobility has not been thoroughly examined. We construct an in silico agent-based traffic simulator for Central Seoul to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 (particles less than 10 microns in size) for groups of drivers and subway commuters. The results show that significant extra particulates relative to the background exist along roadways where NEEs contributed some 40% of the roadside PM10. In terms of health risk, 88% of resident drivers had an acute health effect in late March but that kind of emergence rarely happened. By contrast, subway commuters' health risk peaked at a maximum of 30% with frequent oscillations whenever the air pollution episodes occurred. A 90% vehicle restriction scenario reduced PM10 by 18-24%, and reduced the resident driver's risk by a factor of 2, but not effective for subway commuters as the group generally walked through background areas rather than along major roadways. Using an agent-based traffic simulator in a health context can give insights into how exposure and health outcomes can depend on the time of exposure and the mode of transport.
Early career researchers (ECRs) encounter distinctive opportunities (and challenges) within the neoliberal academy. In this commentary, we reflect on issues common to ECR experiences in quantitative human geography. Our discussion is inspired by and develops conversations from a panel at the Royal Geographical Society-Institute of British Geographers (RGS-IBG) postgraduate forum, with panellists from across the subfield. While many aspects of the ECR experience transcend sub-disciplinary boundaries, the quantitative subfield presents unique dynamics for ECRs to navigate. ECRs in quantitative geography are steeped in 'data science', which changes relations between academia and industry, with the growth of our field increasing the size and the scope of what ECRs might be expected to know and do. Bringing together reflections from the panel, we highlight the variation in pathways experienced by ECRs, reflecting on opportunities, uncertainties, and mentorship, in the hope of offering insights and advice for prospective and current ECRs and their mentors. This commentary reflects on the perspectives and experiences of early career
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