With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.

随着经济的飞速发展和空气污染事件的频繁发生，空气污染问题已成为全民关注的热点问题。空气质量大数据通常具有多源异质性，动态变异性和时空相关性的特征，通常在数据融合后使用大数据技术进行空气质量分析。近年来，已经提出了使用大数据技术的各种模型和算法。为了总结这些空气质量研究方法，本文首先将通过大数据技术进行的空气质量监测分为三类，即空间模型，时间模型和时空模型。其次，我们将通过大数据技术将空气质量预测所需的典型方法归纳为三个方面，统计预测模型，深度神经网络模型和混合模型，在某些方面展现了代表性的场景。第三，我们详细分析和比较了一些代表性的空气污染可追溯性方法，将其分为两类：结合大数据技术的传统模型和数据驱动模型。最后，我们对空气质量分析的未来进行了展望，并提出了一些有前途和挑战性的想法。