The impact of air pollution alert services on respiratory diseases

Generalized additive modeling study in South Korea

Bibliographic Data

ID15550434
AuthorsYeankyoung Hahm (0000-0003-3112-8413, Seoul National University), Heeyeun Yoon (0000-0003-4446-7692, Seoul National University, corresponding author)
Year2021
Volume16
Issue6
Pages064048-064048
Publication date2021-05-11
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac002f
OpenAlexW3161573267
LanguageEN
Citations received3
References cited44

To reduce human exposure to particulate matter (PM), governments have enacted various preventive measures, to which a warning system is central. To the best of our knowledge, we are the first to assess the effectiveness of mobile-based warning systems on respiratory health outcomes, examining two types of P M 2.5 (particles less than 2.5 μ m in diameter) alerts via text messaging systems: Wireless Emergency Alert (WEA) and Air Quality Information Text (AIT) as employed in South Korea from January 2015 to October 2019. We used a generalized additive model to control the non-linear relationship between the P M 2.5 level and the number of hospital visits and admissions for four respiratory sicknesses—chronic obstructive pulmonary disease, respiratory tract infection, asthma, and pneumonia—while deciphering how such visits and admissions are reduced by the warning systems. Our results found that both systems reduced the number of new patients with the four sicknesses at a 5% statistical significance level. Of the two, WEA was found to be more effective than AIT. The former reduced the number of new patients by 16.4%, while the latter did so by 2.8%. WEA is for everyone with a cell phone connection. By sending simple and direct alerts to a broader range of people, WEA would help people to reduce the chance of short-term exposure to PM in general. The findings provide evidence with policy implications regarding air pollution adaptation

Algorithm · Machine learning · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · Urban Transport and Accessibility · Artificial Intelligence

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