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.

通过机器学习模型，本文提供了一份综合的调研报告，涵盖了空气质量分析的各个方面，从数据采集到预处理，并包括污染模式挖掘、空气质量推断和预测等分析任务。同时，给出了对现有方法和应用的系统分类和总结，并提供了一份公开可用的空气质量数据集列表，以便在这个方向上进行研究。最后，我们确定了一些有前景的未来研究方向。这份调研报告可以作为一个宝贵的资源，帮助专业人士找到适合解决自己特定挑战的解决方案，并推动他们在前沿领域的研究。