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

SungChul Seo, Choongki Min, Madeline Preston, Sanghoon Han, Sung-Hyuk Choi, So Young Kang and Dohyeong Kim
Additional contact information
SungChul Seo: Department of Nano, Chemical and Biological Engineering, Seokyeong University, 124 Seokyeong-ro, Seongbuk-gu, Seoul 02713, Korea
Choongki Min: Waycen, 9 Yeongdong-daero 75-gil, Gangnam-gu, Seoul 06182, Korea
Madeline Preston: School of Economic, Political and Policy Sciences, University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX 75080, USA
Sanghoon Han: Waycen, 9 Yeongdong-daero 75-gil, Gangnam-gu, Seoul 06182, Korea
Sung-Hyuk Choi: Department of Emergency Medicine, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea
So Young Kang: Department of Police Science, Konkuk University, 268 Chungwon-daero, Chungju 27478, Korea
Dohyeong Kim: School of Economic, Political and Policy Sciences, University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX 75080, USA

Sustainability, 2022, vol. 14, issue 5, 1-8

Abstract: 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.

Keywords: particulate matters; emergency visits; deep learning; multi-point monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/5/2703/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/5/2703/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:5:p:2703-:d:758660

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().