Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review

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.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

“…DL is an important subset of AI and a subset of ML that demonstrates how machines can mimic humans in learning certain types of information and accumulating situational awareness ( Akhtar et al, 2023 ; Brownlee, 2017 ; Huang et al, 2023 ; Mehrjardi et al, 2023 ; Rastall & Green, 2022 ; Tanveer et al, 2023 ; Usuga-Cadavid et al, 2022 ; Velichko et al, 2023 ; Zhao et al, 2020a ; Zhou et al, 2023 ). DL techniques such as long short-term memory (LSTM), deep neural network (DNN), and other DL architectures were used ( Neo et al, 2022 ). DNNs are formed by stacking these layered networks ( Brownlee, 2017 ).…”

“…Unsupervised learning is typically applied to clustering issues and looks for hidden patterns or intrinsic structures in the input data ( Abd-Elrazek et al, 2021 ). ML techniques such as naive Bayes, random forest, support vector machine (SVM), decision tree, and regression models are the most widely used AI approaches ( Khairuddin et al, 2022 , 2023 ; Neo et al, 2022 ), artificial neural network (ANN), and decision trees ( Hosamo et al, 2022 ) and ML is also used for Autoregressive Integrated Moving Average (ARIMA) ( Yang et al, 2021 ). From the review, 19 studies use ML approaches for PdM using DT ( Altun & Tavli, 2019 ; Avornu et al, 2022 ; Das et al, 2021 ; Harries et al, 2023 ; Heim et al, 2020 ; Hosamo et al, 2022 , 2023 ; Hu et al, 2023a ; Luo et al, 2020 ; Mourtzis, Tsoubou & Angelopoulos, 2023 ; Mubarak et al, 2022 ; Panagou et al, 2022a , 2022b ; Rajesh et al, 2019 ; Rossini et al, 2020 ; Siddiqui, Kahandawa & Hewawasam, 2023 ; Singh et al, 2023 ; Wang et al, 2022 ; Werner, Zimmermann & Lentes, 2019 ).…”

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

“…A review from Neo, E.X., et al [55] imposes a framework on the basis of data from environmental, health, and AQI prediction for integrated environmental and health impact assessment systems, as well as big data learning and prediction abilities of AI and FL. In the framework of early health care service preparedness prediction and hospital administration, it is also crucial for efficient medical services.…”

Artificial intelligence-assisted air quality monitoring for smart city management

“…The study indicated that the effect is adversely impacted by the quality of air surrounding us, and therefore having clean air is very important in extending the life span. The low quality of air has led to several health complications such as respiratory diseases, cardiorespiratory diseases, various types of cancers, and pregnancy and birth complications ( Neo et al, 2022 ).…”