Machine learning model for air pollution prediction in Skopje, North Macedonia

Abstract

The low quality of air, especially high concentration of particulate matter that have significant negative effect on human health and environment, is a global problem in urban areas. Thus, early air pollution prediction is an urgent need in Skopje, North Macedonia with highly increased concentration of particulate matter especially during the winter months. The objective of this paper is to develop machine learning model for predicting the air pollution in Skopje. The methods are based on processing the collected data from different measurement locations in Skopje, generating numerous weather and pollution features, and choosing the optimal parameters (hyperparameters) for the model. The information for the various pollutants were provided from the measurement stations located near the Faculty of Electrical Engineering and Information Technologies building. The measured data are gathered from the three sensor nodes that are collecting data for following parameters: particulate matter with 10 or less micrometres (PM10), particulate matter with 2.5 or less micrometres (PM2.5), CO and NO2, and sending these data to a server for online monitoring or off-line analysis. The pollution data, together with the weather information for temperature, humidity, wind speed, and wind direction were combined to train the prediction model. The results show that the weather information is correlated with the air pollution, which allows to train a model that predicts the air pollution based on the weather data and the historical data about the pollution. The experimental evaluation showed that the best performing model, XGBoost, achieves Mean Absolute Error for PM10 values of 6.8, 9.7, and 12.4 for the nodes 3, 2, and 1 respectively, and for PM2.5 values 6.36, 8.81 and 8 for nodes 3, 2 and 1 respectively.