Abstract:

Particulate matter (PM) pollution causes significant environmental problems. The adverse effects of PM pollution have become a common problem due to its risks to living health. Due to all these negative effects of PM pollution and its complex interaction in the atmosphere, it is important that it be the subject of more studies. In particular, studies on monitoring and estimating PM pollution are important. In recent years, studies on estimating PM pollu-tion have increased by considering meteorological factors. Especially with machine learning methods, PM pollution estimating has accelerated. In this study, PM10 pollution is estimated with various machine learning algorithms considering meteorological factors. The meteorological data used in the study were obtained from the Ankara Regional Station of Turkish State Meteorological Service (latitude: 39,9727, longitude: 32,8637, altitude: 891 m.). PM10 pollution data were obtained from the Ministry of Environment, Urbanization and Climate Change Ankara Keçiören-Sanatorium air quality station (latitude: 39,999, longitude: 32,856, altitude: 1009 m.). In the machine learning phase, different machine learning (decision tree regression, support vector regression, lasso regression and neural network) were used, considering temperature, dew point temperature, precipitation, relative humidity, wind speed, pressure, cloud cover and PM10 measurements of the previous day. Algorithms were studied separately and the consistencies of these algorithms were compared. Various statistical metrics were used to examine their con-sistency. As a result, considering the test section, the determination coefficient was found to be ̴0,6, root mean square error ̴18, and mean absolute error ̴12 for artificial neural network algorithm, and it was seen that the artifi-cial neural network algorithm gave better results than other algorithms.