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

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

Оцінка ризиків для здоров’я населення міста Івано-Франківська у зв’язку з забрудненням атмосферного повітря

Робота публікується згідно наказу ректора від 21.01.2020 р. №008/од "Про перевірку кваліфікаційних робіт на академічний плагіат у 2019-2020 навчальному році". Керівник роботи: доцент кафедри екології, к.т.н., Радомська Маргарита МирославівнаObject of research – Impacts of air pollution. Subject – Relationship between air pollutants and ailments Aim оf work – To investigate the possible health risks caused by air pollution among the population in the study area Methods of research: Statistical Data Analysis, Air Quality Evaluation

Оцінка ризиків для здоров’я населення міста Івано-Франківська у зв’язку з забрудненням атмосферного повітря

Робота публікується згідно наказу ректора від 21.01.2020 р. №008/од "Про перевірку кваліфікаційних робіт на академічний плагіат у 2019-2020 навчальному році". Керівник роботи: доцент кафедри екології, к.т.н., Радомська Маргарита МирославівнаObject of research – Impacts of air pollution. Subject – Relationship between air pollutants and ailments Aim оf work – To investigate the possible health risks caused by air pollution among the population in the study area Methods of research: Statistical Data Analysis, Air Quality Evaluation

Оцінка ризиків для здоров’я населення міста Івано-Франківська у зв’язку з забрудненням атмосферного повітря

Робота публікується згідно наказу ректора від 21.01.2020 р. №008/од "Про перевірку кваліфікаційних робіт на академічний плагіат у 2019-2020 навчальному році". Керівник роботи: доцент кафедри екології, к.т.н., Радомська Маргарита МирославівнаObject of research – Impacts of air pollution. Subject – Relationship between air pollutants and ailments Aim оf work – To investigate the possible health risks caused by air pollution among the population in the study area Methods of research: Statistical Data Analysis, Air Quality Evaluation

Natural and Technological Hazards in Urban Areas

Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

Risk Exposure to Particles – including Legionella pneumophila – emitted during Showering with Water-Saving Showers

The increase in legionellosis incidence in the general population in recent years calls for a better characterization of the sources of infection, such as showering. Water-efficient shower systems that use water atomization technology may emit slightly more inhalable bacteria-sized particles than traditional systems, which may increase the risk of users inhaling contaminants associated with these water droplets. To evaluate the risk, the number and mass of inhalable water droplets emitted by twelve showerheads—eight using water-atomization technology and four using continuous-flow technology— were monitored in a shower stall. The water-atomizing showers tested not only had lower flow rates, but also larger spray angles, less nozzles, and larger nozzle diameters than those of the continuous-flow showerheads. A difference in the behavior of inhalable water droplets between the two technologies was observed, both unobstructed or in the presence of a mannequin. The evaporation of inhalable water droplets emitted by the water-atomization showers favored a homogenous distribution in the shower stall. In the presence of the mannequin, the number and mass of inhalable droplets increased for the continuous-flow showerheads and decreased for the water-atomization showerheads. The water-atomization showerheads emitted less inhalable water mass than the continuous-flow showerheads did per unit of time; however, they generally emitted a slightly higher number of inhalable droplets—only one model performed as well as the continuous-flow showerheads in this regard. To specifically assess the aerosolisation rate of bacteria, in particular of the opportunistic water pathogen Legionella pneumophila, during showering controlled experiments were run with one atomization showerhead and one continuous-flow, first inside a glove box, second inside a shower stall. The bioaerosols were sampled with a Coriolis® air sampler and the total number of viable (cultivable and noncultivable) bacteria was determined by flow cytometry and culture. We found that the rate of viable and cultivable Legionella aerosolized from the water jet was similar between the two showerheads: the viable fraction represents 0.02% of the overall bacteria present in water, while the cultivable fraction corresponds to only 0.0005%. The two showerhead models emitted a similar ratio of airborne Legionella viable and cultivable per volume of water used. Similar results were obtained with naturally contaminated hoses tested in shower stall. Therefore, the risk of exposure to Legionella is not expected to increase significantly with the new generation of water-efficient showerheads

GIS and Health: Enhancing Disease Surveillance and Intervention through Spatial Epidemiology

The success of an evidence-based intervention depends on precise and accurate evaluation of available data and information. Here, the use of robust methods for evidence evaluation is important. Epidemiology, in its conventional form, relies on statistics and mathematics to draw inferences on disease dynamics in affected populations. Interestingly, most of the data used tend to have spatial aspects to them. However, most of these statistical and mathematical methods tend to either neglect these spatial aspects or consider them as artefacts, thereby biasing the resultant estimates. Thankfully, spatial methods allow for evidence evaluation and prediction in epidemiologic data while considering their inherent spatial characteristics. This, thus, promises more precise and accurate estimates.This thesis documents and illustrates the contribution spatial methods and spatial thinking makes to epidemiology through studies carried out in two countries with different heath-data quality realities, Uganda and Sweden. To be able to use spatial methods for epidemiology studies, proper spatial data need to be available, which is not the case in Uganda. Consequently, this study had two main aims: (1) It proposed and implemented a novel way of spatially-enabling patient registry systems in settings where the existing infrastructures do not allow for the collection of patient-level spatial details, prerequisites for fine-scale spatial analyses; (2) Where spatial data were available, spatial methods were used to study associative relationships between health outcomes and exposure factors. Spatial econometrics approaches, especially spatially autoregressive regression models were adopted. Also, consistent with location-specific epidemiologic intervention, the advantages of using spatial scan statistics, Geographically Weighted (Poisson) Regression and local entropy maps to distil model parameter estimates into their inherent spatial heterogeneities were illustrated. Our results illustrated that through the use of mobile and web technologies and leveraging on existing spatial data pools, systems that enable recording and storage of geospatially referenced patient records can be created. Also, spatial methods outperformed conventional statistical approaches, giving refined and more accurate parameter estimates. Finally, our study illustrates that the use of local spatial methods can inform policy and intervention better through the identification of areas with elevated disease burden or those areas worth additional scrutiny as illustrated by our study of HIV-TB coinfection areas in Uganda, the areas with high CVD-air pollution associations in Sweden, and areas with consistently high joint mortality burden for CVD and cancer among the Swedish elderly.Overall, the incorporation of spatial approaches and spatial thinking in epidemiology cannot be overemphasized. First, by enabling the capture of fine-scale personal-level spatial data, our study promises more robust analyses and seamless data integration. Secondly, associative analyses using spatial methods showed improved results. Thirdly, identification of the areas with elevated disease burden makes identifying the primary drivers of the observed local patterns more informed and focused. Ultimately, our results inform healthcare policy and strategic intervention as the most affected areas can easily be zoned out. Therefore, by illustrating these benefits, this study contributes to epidemiology, through spatial methods, especially in the aspects of disease surveillance, informing policy, and driving possible effective intervention