There are several phenomena where the description by mathematical models is in terms of differential equations. Among these phenomena, we can find (1) the diffusion of pollution particles, (2) the growth of metal nanoparticles, and (3) the displacement of foam within porous media. In this paper we present some solutions for models that depict the two first processes of interest mentioned above, which were programmed using libraries for PythonTM, and explore the possibility of applying the programming framework to the third application. The diffusion of air pollutants, specially particulate matter, can be modelled with an advection-diffusion equation. This equation makes it possible to consider the phenomena that describe the change in concentration of particulate matter (PM10 and PM2.5) with time and also the effects of wind and rain. The chosen method to tackle the solution for the advection-diffusion equation is the finite-volume method, and its respective algorithm makes use of a triangular grid. On the other hand, the growth of copper nanoparticles on silicon surfaces can be approached from different perspectives. Until now we have explored curve fitting to obtain equations that approximate experimental data. In addition, the Hamilton-Jacobi equation has been also used to describe this process. Both, curve fitting and the solution of Hamilton-Jacobi equation have been implemented in Python. Finally, the flow of a foam front inside a porous medium can be described using a simplified model for bubble films known as the pressure-driven growth m odel. In this application, different formulations can be used as well. In particular, an Eulerian model can make use of the so-called Eikonal equation to address its solution using triangular meshes. Implementations of the algorithms to solve the models for the applications of interest use libraries for Python, mainly NumPy, OpenMesh©, Matplotlib, pandas, and scikit-image.