Neo, En Xin and Hasikin, Khairunnisa and Mokhtar, Mohd Istajib and Lai, Khin Wee and Azizan, Muhammad Mokhzaini and Razak, Sarah Abdul and Hizaddin, Hanee Farzana (2022) Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review. Frontiers in Public Health, 10. ISSN 2296-2565, DOI https://doi.org/10.3389/fpubh.2022.851553.
Full text not available from this repository.Abstract
Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education through the Malaysian Research University Network (MRUN) MR001-2019, Universiti Malaya Research University Grant (SATU) entitled "Climate-Smart-Mitigation and Adaptation: Integrated Climate Resilience Strategy for Tropical Marine Ecosystem" ST065-2021 |
| Uncontrolled Keywords: | Federated learning; Health hazard; Deep learning; Machine learning; Air pollution |
| Subjects: | Q Science > QP Physiology R Medicine > RA Public aspects of medicine |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department Faculty of Engineering > Department of Chemical Engineering Faculty of Science > Institute of Biological Sciences |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Oct 2023 19:21 |
| Last Modified: | 13 Oct 2023 19:21 |
| URI: | http://eprints.um.edu.my/id/eprint/42182 |
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