Applications of machine learning and IoT for Outdoor Air Pollution
  Monitoring and Prediction: A Systematic Literature Review
Ihsane Gryech Chaimae Assad Mounir Ghogho Abdellatif Kobbane
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.