  The increasing air pollution poses an urgent global concern with far-reaching
consequences, such as premature mortality and reduced crop yield, which
significantly impact various aspects of our daily lives. Accurate and timely
analysis of air pollution is crucial for understanding its underlying
mechanisms and implementing necessary precautions to mitigate potential
socio-economic losses. Traditional analytical methodologies, such as
atmospheric modeling, heavily rely on domain expertise and often make
simplified assumptions that may not be applicable to complex air pollution
problems. In contrast, Machine Learning (ML) models are able to capture the
intrinsic physical and chemical rules by automatically learning from a large
amount of historical observational data, showing great promise in various air
quality analytical tasks. In this article, we present a comprehensive survey of
ML-based air quality analytics, following a roadmap spanning from data
acquisition to pre-processing, and encompassing various analytical tasks such
as pollution pattern mining, air quality inference, and forecasting. Moreover,
we offer a systematic categorization and summary of existing methodologies and
applications, while also providing a list of publicly available air quality
datasets to ease the research in this direction. Finally, we identify several
promising future research directions. This survey can serve as a valuable
resource for professionals seeking suitable solutions for their specific
challenges and advancing their research at the cutting edge.
