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。

机器学习和物联网在户外空气污染监测和预测中的应用：一项系统文献综述

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

Calculating an Air Quality Index (AQI) typically uses data streams from air
quality sensors deployed at fixed locations and the calculation is a real time
process. If one or a number of sensors are broken or offline, then the real
time AQI value cannot be computed. Estimating AQI values for some point in the
future is a predictive process and uses historical AQI values to train and
build models. In this work we focus on gap filling in air quality data where
the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario
is where one or a number of air, weather and traffic sensors are offline and
explores prediction accuracy under such situations. The work is part of the
MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the
DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI,
weather and CCTV traffic images for air pollution prediction.

使用多模数据进行空气污染预测，关注在空气质量数据的间隔填充方面的预测准确性，是 MediaEval'2022 Urban Air: Urban Life 和空气污染任务的 DCU-Insight-AQ 团队提交的一部分。