This chapter explores the application of data decomposition techniques in air quality monitoring, focusing on wavelet decomposition and modal decomposition methods. These advanced techniques are particularly effective for analyzing the nonlinear, non-stationary, and multi-scale characteristics inherent in air quality data. By employing multi-scale decomposition, researchers can extract valuable insights, including long-term trends, seasonal variations, and random fluctuations. Such detailed analysis not only enhances the accuracy of air quality assessments but also provides robust data support for policy formulation, environmental management, and pollution control strategies. These methods play a crucial role in addressing complex environmental challenges.