Feature extraction and prediction of fine particulate matter (PM

The concentrations of fine particulate matter (PM 2.5 ) constituents, which are very important and essential information for the identification of air pollution sources, were predicted at three sites (Seoul, Ulsan, Baengnyeong) in South Korea between 2016 and 2018 using four machine learning (ML) models: generative adversarial imputation network (GAIN), fully connected deep neural network (FCDNN), random forest (RF), and k-nearest neighbor (kNN). 3 PM 2.5 constituent groups, namely 8 ions, 2 carbons, and 15 trace elements, were targeted for prediction. The latest hyperparameter optimization techniques were used to learn air pollution characteristics from ambient PM 2.5 -related information, such as time, meteorology, and air pollutant concentrations. We compared the feature extraction abilities of the four models. The prediction accuracy identified by the coefficient of determination (R 2 ) between prediction and observation was highest in GAIN, followed by FCDNN and RF or kNN. On availability of data on the time, air pollutant concentrations, and/or meteorology, simultaneously missed 20 % data of all PM 2.5 constituent groups were predicted, with R 2 = 0.897, 0.861, 0.785, and 0.801 by the GAIN, FCDNN, RF, and kNN, respectively. As missing ratios (20 %, 40 %, 60 %, 80 %) of input data increased, prediction accuracy decreased in the four models and was predominantly more noticeable in GAIN and kNN. As the available period of data increased, prediction accuracy increased in GAIN and FCDNN. Trace elements were predicted with the lowest R 2 in all models among the target constituent groups. Study sites with more emission sources showed lower prediction accuracy, resulting in the highest R 2 in Baengnyeong island and the lowest in Ulsan. According to the current findings, ML models can be used to evaluate various air pollution issues for which data is missing.