Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review

TL;DR

This systematic review examines how machine learning and IoT are used for outdoor air pollution monitoring and prediction, highlighting current methods, limitations, and future research directions in this vital environmental health area.

Contribution

It provides a comprehensive analysis of existing IoT and machine learning applications for air pollution prediction, identifying gaps and proposing future research directions.

Findings

Identification of three prediction methods: time series, feature-based, spatio-temporal

Highlighting high-cost monitoring versus low-cost IoT solutions

Major limitations include data coverage, diversity, and context-specific features

Abstract

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review's findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific features. This review proposes directions for future research and underlines practical implications in healthcare, urban planning, global synergy and smart cities.