**1. Introduction**

Recurrent neural networks (RNN), especially Long Short Term Memory (LSTM), have proved their efficiency in working on time-dependent values by (as its names indicate) the use of memory (sequences) gives enough information to the network to work properly finding patterns and trends in the values, which are not so obvious at first glance. Also, the gaussian processes are a useful statistical technique that allows the hyperparameters of the network since it has shown that the processing time is reduced and, at the same time, the accuracy may be improved [1]. Afterward, there is airborne pollution, which is a complex system that affects billions of people worldwide, especially in a metropolis such as Hotan, China. Shanghai, China, Ghaziabad, India, or in the case of this study, Mexico City, Mexico. Also, there we have a lot of

types of particles interacting with each other in chemical, biological, and physical ways. The pollutants that are monitored by the SEDEMA's network in the City of Mexico are nitrogen dioxide (*NO*2), Ozone (*O*3), sulfur dioxide (*SO*2) and particulate matter (*PM*2*:*5, and *PM*10). Hence, we propose the use of an RNN-LSTM and optimizing its hyperparameters using Gaussian processes to increase the accuracy in the forecast of airborne pollution instead of the use of the current method.
