Abstract
Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM 2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM 2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent, and convolutional neural networks.
Highlights
The industrial revolution and modernization have led us to a new era of science and technology.On the one hand, it has opened new horizons for transportation, trade, mining, agriculture, and urbanization
The second part covers the performance of each model at all sites after including community multiscale air quality (CMAQ) features
We estimate the hourly values of PM2.5 concentration by applying various well-known machine learning models
Summary
The industrial revolution and modernization have led us to a new era of science and technology. It has opened new horizons for transportation, trade, mining, agriculture, and urbanization. It has become a vital factor in polluting air, soil, and water. In the last two decades, many environmental researchers have been monitoring the quality of ambient air. Particulate matter (PM) is found to be the most dangerous kind of air pollution among various other air pollutants. After a study done by the World Health Organization (WHO) and the International
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