PMs concentration forecasting using ARIMA algorithm

Air pollution is a key environmental and social issue and it is also a complex problem posing multiple challenges in terms of management and mitigation of pollutants. The evaluation of the status of air quality is based mainly on ambient air measurements. Although the emissions of principal air pollutants are highly regulated, there is a lack of information about the real extent of emissions generated by the traffic and made difficult the quantification of the effects of policies and measures to reduce air pollution. To tackle these challenges, local air pollution measurements near main streets, based on small IoT devices became necessary. The aim of the paper is to present the way in which low-cost sensors in combination with Artificial Intelligence algorithms could be used for prediction of PM
₁₀
and PM
_-2.5
concentration. The data were collected using IoT devices based on Optical Particle Counter technologies, statistically analyzed and corrected (using a specific algorithm) to reduce the influence of the air humidity. Comparison with measurement from reference station were presented. For PM
₁₀
and PM
_-2.5
concentration forecasting was developed an ARIMA algorithm which was tested for time series registered in Bucharest. The results show that in 89% of cases the predicted values are within the accepted uncertainty limit, while the Pearson correlation coefficients have significant values.