A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Guak, Sooyoung; Lee, Sang Kyu; An, Jaehoon; Lee, Hunjoo; Lee, Kiyoung

doi:10.1016/j.envpol.2021.117406

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…This last parameter is required for the personal exposure computation, so it is necessary to define these I/O ratios accurately. For this reason, we use the I/O ratios proposed in this paper for several buildings and activities [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Illueca Fernández,

Fernández Llatas,

Jara Valera

et al. 2023

PLoS ONE

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The World Health Organization has estimated that air pollution will be one of the most significant challenges related to the environment in the following years, and air quality monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on mortality risk. Thus, generating a methodology that supports experts in making decisions based on exposure data, identifying exposure-related activities, and proposing mitigation scenarios is essential. In this context, the emergence of Interactive Process Mining—a discipline that has progressed in the last years in healthcare—could help to develop a methodology based on human knowledge. For this reason, we propose a new methodology for a sequence-oriented sensitive analysis to identify the best activities and parameters to offer a mitigation policy. This methodology is innovative in the following points: i) we present in this paper the first application of Interactive Process Mining pollution personal exposure mitigation; ii) our solution reduces the computation cost and time of the traditional sensitive analysis; iii) the methodology is human-oriented in the sense that the process should be done with the environmental expert; and iv) our solution has been tested with synthetic data to explore the viability before the move to physical exposure measurements, taking the city of Valencia as the use case, and overcoming the difficulty of performing exposure measurements. This dataset has been generated with a model that considers the city of Valencia’s demographic and epidemiological statistics. We have demonstrated that the assessments done using sequence-oriented sensitive analysis can identify target activities. The proposed scenarios can improve the initial KPIs—in the best scenario; we reduce the population exposure by 18% and the relative risk by 12%. Consequently, our proposal could be used with real data in future steps, becoming an innovative point for air pollution mitigation and environmental improvement.

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Section: Methodsmentioning

confidence: 99%

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Illueca Fernández,

Fernández Llatas,

Jara Valera

et al. 2023

PLoS ONE

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“…This government-grade air monitoring station provides relatively accurate data but is expensive to run due to high installation and maintenance costs [19]. A total of 522 "Air Korea" stations are installed across South Korea as national air quality monitoring stations (AQMS) [20], with generally one station in each urban district [10]. In Seoul, one Air Korea station covers about 15.5 km 2 on average, which generates wide holes between different monitoring stations and hampers the localized assessment of dynamic trends of air pollutants within the region [21].…”

mentioning

confidence: 99%

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Seo

Min²,

Preston

et al. 2022

Sustainability

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Despite ample evidence that high levels of particulate matter (PM) are associated with increased emergency visits related to respiratory diseases, little has been understood about how prediction processes could be improved by incorporating real-time data from multipoint monitoring stations. While previous studies use traditional statistical models, this study explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared the predictive accuracy of the models based on PM datasets collected between 1 December 2019 and 31 December 2021 from a single but more accurate air monitoring station in each district (Air Korea) and multiple but less accurate monitoring sites (Korea Testing & Research Institute; KTR) within Guro District in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.

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“…7) 따라서 PM 2.5 농도값을 포함한 공기오염물질의 노출을 정확하 게 평가하기 위해서 시간활동양상(time-activity pattern)을 이 용한 실내•외 환경 농도는 노출평가 및 예측모델에서 유용하게 이용할 수 있다. 8) Guak 등(2021) 9) 의 연구에 따르면 서울의 인구집단은 하 루 중 60% 이상을 집에서 보내며 PM         (μg/m 3 )…”

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Evaluation of PM_2.5 Exposure Contribution Using a Microenvironmental Model

Shin

Choe

Min

et al. 2022

J Environ Health Sci

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Background: Since people move through microenvironments rather than staying in one place, they may be exposed to both indoor and outdoor PM 2.5 concentrations. Objectives:The aim of this study was to assess the exposure level of each sub-population group and evaluate the contribution rate of the major microenvironments.Methods: Exposure scenarios for sub-population groups were constructed on the basis of a 2019 Time-Use survey and the previous literature. A total of five population groups were classified and researchers wearing MicroPEM simulated monitoring PM 2.5 exposure concentrations in real-time over three days. The exposure contribution for each microenvironment were evaluated by multiplying the inhalation rate and the PM 2.5 exposure concentration levels.Results: Mean PM 2.5 concentrations were 33.0 µg/m 3 and 22.5 µg/m 3 in Guro-gu and Wonju, respectively.When the exposure was calculated considering each inhalation rate and concentration, the home showed the highest exposure contribution rate for PM 2.5 . As for preschool children, it was 90.8% in Guro-gu, 94.1% in Wonju. For students it was 65.3% and 67.3%. For housewives it was 98.2% and 95.8%, and 59.5% and 91.7% for office workers. Both regions had higher exposure to PM 2.5 among the elderly compared to other populations, and their PM 2.5 exposure contribution rates were 98.3% and 94.1% at home for Guro-gu and Wonju, respectively. Conclusions:The exposure contribution rate could be dependent on time spent in microenvironments.Notably, the contribution rate of exposure to PM 2.5 at home was the highest because most people spend the longest time at home. Therefore, microenvironments such as home with a higher contribution rate of exposure to PM 2.5 could be managed to upgrade public health.

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A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Cited by 9 publications

References 30 publications

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Evaluation of PM_2.5 Exposure Contribution Using a Microenvironmental Model

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A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Cited by 9 publications

References 30 publications

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Evaluation of PM2.5 Exposure Contribution Using a Microenvironmental Model

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Evaluation of PM_2.5 Exposure Contribution Using a Microenvironmental Model