Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review

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 co...

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Main Authors: Neo, En Xin, Hasikin, Khairunnisa, Mokhtar, Mohd Istajib, Lai, Khin Wee, Azizan, Muhammad Mokhzaini, Razak, Sarah Abdul, Hizaddin, Hanee Farzana
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Published: Frontiers Media 2022
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Online Access:http://eprints.um.edu.my/42182/
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spelling my.um.eprints.421822023-10-13T19:21:10Z http://eprints.um.edu.my/42182/ Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review Neo, En Xin Hasikin, Khairunnisa Mokhtar, Mohd Istajib Lai, Khin Wee Azizan, Muhammad Mokhzaini Razak, Sarah Abdul Hizaddin, Hanee Farzana QP Physiology RA Public aspects of medicine 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. Frontiers Media 2022-05 Article PeerReviewed 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 <https://doi.org/10.3389/fpubh.2022.851553>. 10.3389/fpubh.2022.851553
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QP Physiology
RA Public aspects of medicine
spellingShingle QP Physiology
RA Public aspects of medicine
Neo, En Xin
Hasikin, Khairunnisa
Mokhtar, Mohd Istajib
Lai, Khin Wee
Azizan, Muhammad Mokhzaini
Razak, Sarah Abdul
Hizaddin, Hanee Farzana
Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
description 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.
format Article
author Neo, En Xin
Hasikin, Khairunnisa
Mokhtar, Mohd Istajib
Lai, Khin Wee
Azizan, Muhammad Mokhzaini
Razak, Sarah Abdul
Hizaddin, Hanee Farzana
author_facet Neo, En Xin
Hasikin, Khairunnisa
Mokhtar, Mohd Istajib
Lai, Khin Wee
Azizan, Muhammad Mokhzaini
Razak, Sarah Abdul
Hizaddin, Hanee Farzana
author_sort Neo, En Xin
title Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
title_short Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
title_full Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
title_fullStr Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
title_full_unstemmed Towards integrated air pollution monitoring and health impact assessment using federated learning: A systematic review
title_sort towards integrated air pollution monitoring and health impact assessment using federated learning: a systematic review
publisher Frontiers Media
publishDate 2022
url http://eprints.um.edu.my/42182/
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score 13.211869