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.

该研究回顾了使用机器学习和物联网（IoT）进行室外空气污染预测以及监测传感器和输入特征的组合的应用，发现了预测的三种方法：时间序列、基于特征和时空。该回顾还提出了未来研究的方向，并强调了在医疗保健、城市规划、全球协同和智能城市方面的实际意义。