Abstract
Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.
Highlights
Pollution represents probably the most important topic in the last decade, an issue that is constantly discussed in the media, in government meetings and environment activities.For the last decades, air pollution has seemed to be impossible to manage and control, but this can be combated with the help from advancements in technology
In order to determine the exact value of Air Quality Index (AQI) and to detect which air pollutants are responsible for this disaster, various sensors from several categories currently available could be used; for example, electrochemical sensors that are based on a chemical reaction between gases in the air and the electrode in a liquid inside a sensor, or photo ionization detectors, optical particle counters or even optical sensors [4]
The algorithms used for the analysis are the linear regression algorithm, support vector machines with Gaussian kernel and Gaussian process regression (GPR) using an exponential kernel
Summary
Pollution represents probably the most important topic in the last decade, an issue that is constantly discussed in the media, in government meetings and environment activities. In order to determine the exact value of AQI and to detect which air pollutants are responsible for this disaster, various sensors from several categories currently available could be used; for example, electrochemical sensors that are based on a chemical reaction between gases in the air and the electrode in a liquid inside a sensor, or photo ionization detectors, optical particle counters or even optical sensors [4] By placing such sensors across urban areas, in combination with weather detection sensors and creating a connection algorithm between them, one can create live reports of AQI and determine potentially dangerous zones.
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