Numerous real-world systems, whether they are of natural or human origin, display complex interactions and dynamics evolving over time. As well known, transforming time series data into a complex network representation helps to gain a deep understanding of the dynamics underlying the data. In this talk, I will provide an introduction about converting time series to complex networks and then introduce a new approach in the context of physics for converting spatial-temporal series data into a complex network. And as an example study, I will discuss its application to the air quality of various regions in Istanbul, the largest metropolitan city in Turkiye with a population of approximately 16 million.