This chapter highlights the importance of air quality monitoring to public health, environmental sustainability, and economic development, as well as its key role in environmental protection and policymaking. It further examines the current landscape of data science in environmental monitoring, acknowledging the inherent complexity of air quality data and the challenges associated with its collection. Based on this, this chapter outlines the current application of data science in air quality monitoring and points out the achievements and shortcomings of existing research. This chapter aims to explore this field in more depth by focusing on the key issues faced by data science in air quality monitoring, including data preprocessing, data decomposition, data identification, data clustering, data prediction, and data interpolation. For each key issue, this chapter discusses its role and importance in air quality monitoring, analyzes the challenges each faces, and outlines the commonly used methods. Finally, this chapter briefly introduces the scope of this book, laying the foundation for the content of subsequent chapters.