Understanding transport and mixing in environmental flows requires computational tools capable of extracting coherent structures from large-scale numerical simulations. In this talk, I will present several Lagrangian methods for analyzing transport processes in atmospheric and oceanic flows, with an emphasis on their computational formulation and applicability to complex geophysical datasets. The presentation will focus on three complementary approaches. First, I will introduce JetLag, a computational framework that identifies jet streams as maxima of isentropic displacement, providing robust estimates of their position and variability without the need for ad hoc parameter tuning. Second, I will present a generalized spectral clustering method that identifies coherent structures from trajectory data by combining spatial proximity with physical variables such as density, temperature, or salinity, enabling the detection of dynamically and thermodynamically coherent regions. Finally, I will discuss retention volume diagnostics, which quantify finite-time trapping and provide a computational measure of transport barriers and mixing efficiency. These methods are designed to extract transport information from large trajectory datasets generated by numerical models and illustrate how computational Lagrangian analysis can complement traditional Eulerian diagnostics. The examples demonstrate applications to atmospheric and oceanic flows, highlighting the role of coherent structures in governing transport and mixing across a wide range of environmental fluid dynamics problems.