Air pollution modelling and forecasting
Final assignment on the subject Machine Learning with the data from air pollution in Rio de Janeiro.
Air quality is a growing concern and area of research because most cities worldwide have been facing problems with it in the past few decades. The rapid increase of the urban population and the development of the centers is causing environmental pollution, which can give rise to damage to human health.
The emission and transmission of air pollutants, such as, for example, Nitrogen dioxide (NO$_2$), Carbon monoxide (CO), Ozone (O\(_3\)) and, Particulate Matter (PM), result in ambient air pollution and are caused by different factors. The World Health Organization (WHO) explained that PM, O\(_3\), and NO\(_2\) have, respectively, the heaviest effects on human health.
In Rio de Janeiro, the city hall recognized the problem and created the Program MonitorAr-Rio in 2008. The objective was to monitor the air quality, verify the degree of exposure of the population to the pollutants, and inform the community of the results. Eight fixed stations monitor the pollutants defined in the legislation, and some meteorological conditions, such as, for example, temperature, relative humidity, solar radiation, and wind.
It is fundamental to have up-to-date knowledge and accurate predictions of air pollutants to assist in the formulation of public health and environmental policies. This study proposes models for hourly air quality forecasting for the city of Rio de Janeiro. For more details consult these report.