Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario.