Monitoring and Forecasting of Air Emissions with IoT Measuring Stations and a SaaS Cloud Application

Environmental pollution is a significant problem in densely populated cities due to increased respiratory diseases in the population. The pollution level is used to inform the public to take extraordinary measures based on a pollution scale. Particulate matter (PM), precisely
$PM_{10}$
and
$PM_{2.5}$
, is used to estimate the degree of pollution. The monitoring and analysis of these two variables have attracted the research community's attention, particularly in the design of measuring stations, forecasting, and software infrastructure to host user applications. This paper proposes a modular and ready-to-use architecture for a SaaS cloud application for air emissions forecasting using IoT measuring stations. The SaaS cloud application is connected to the IoT measuring stations located in the densely populated cities of El Salvador. The data is used to create deep learning models, to make forecasts for the next day.