Monitoring the status of urban environmental phenomena is of great significance for urban research and management. While The monitoring sites are often insufficient and unevenly, interpolation values vary in urban spaces non-linearly. It is difficult to find a method that fulfills the requirements of accuracy, robustness, and flexibility for various types of phenomena. In this paper, we present a new kind of deep learning driven spatial interpolation method which works on the grid data that can be applied on the unevenly distributed sites. To generate better accurate spatial continuous data, we design the S2 attention structure and incorporate it with the GAN to turn it into SI-AGAN which can model spatial dependencies across different regions via sparsely and unevenly distributed sampling. It can directly learn an end-to-end mapping between low- and high-quality environmental signals without in-depth knowledge of the phenomenon. Experiments on two real-world air-pollution datasets demonstrate that our training strategy effectively makes the GAN work for the interpolation of uneven data and our proposed SI-AGAN significantly outperforms previous state-of-the-art spatial interpolation methods.