This chapter provides a comprehensive overview of data interpolation techniques in air quality monitoring, focusing on two key dimensions: temporal and spatial interpolation. In the domain of temporal interpolation, it details the principles and model construction for four widely used methods: linear interpolation, polynomial interpolation, spline interpolation, and interpolation based on statistical models. For spatial interpolation, it explores the theoretical foundations and model design of four classic approaches: nearest neighbor interpolation, inverse distance weighting interpolation, Kriging interpolation, and radial basis function interpolation. To conclude, the chapter presents experimental comparisons of the four temporal and four spatial interpolation methods, comparing the characteristics, performance, and application scenarios of different methods. Additionally, the distinct characteristics and appropriate application scenarios between temporal interpolation and spatial interpolation are highlighted in the chapter.