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. A deep recurrent neural network system based on a longshort-term memory (LSTM) model was developed for daily PM 10 andPM 2.5 predictions in South Korea. The structural and learnableparameters of the newly developed system were optimized from iterative modeltraining. Independent variables were obtained from ground-basedobservations over 2.3 years. The performance of the particulate matter (PM)prediction LSTM was then evaluated by comparisons with ground PMobservations and with the PM concentrations predicted from two sets of 3-Dchemistry-transport model (CTM) simulations (with and without dataassimilation for initial conditions). The comparisons showed, in general,better performance with the LSTM than with the 3-D CTM simulations. Forexample, in terms of IOAs (index of agreements), the PM prediction IOAs wereenhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with theLSTM-based model. The deep LSTM-based PM prediction system developed atobservation sites is expected to be further integrated with 3-D CTM-basedprediction systems in the future. In addition to this, further possibleapplications of the deep LSTM-based system are discussed, together with somelimitations of the current system.

atmospheric chemistry and physics

Development of a daily PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; and PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; prediction system using a deep long short-term memory neural network model