Nowadays, during the colder season, a number of cities in central and southern Chile are affected by a problem of air pollution. This problem is associated to the topography of the central valley, high emissions of domestic wood-burning stoves, and other meteorological characteristics such as the lack of rain or when the prevailing winds are calmed. Although, numerous studies have been undertaken for predicting the air quality in Santiago city, other medium-sized cities have seldom been studied. The present work focuses on the problem of air quality in Talca city (35°26'S; 71°44'W), a medium sized city located in Central Chile. The objective of the study is to predict, with a day in advance, the particulate matter with a diameter less than or equal to 2.5 micrometers (PM2.5). For this purpose we have used a deep learning neural network. The algorithm learns from historical records of air quality pollution as well as meteorological information at three monitoring stations. Unlike state-of-the-art methods that require intensive computational power to simulate the weather conditions, our proposed solution uses only pollutants measures of the stations in the city. We use exactly 24 records for a particular day, one for each hour. Our study focuses on the autumn-winter season for 3 years, including data for 612 days, i.e., 151 days per year (from April 1st, until August 31st). Our results prove the high capacity of RNN as a predictor algorithm for environmental emergency episodes in the three monitoring stations of the city. As a result, the model is an alternative for local authorities because it would improve the current forecast system of the city.