## 1 Introduction  
Particulate matter (PM) is a global concern that is emitted locally and moves intercontinentally via long-range transport [1, 2]. PM\({}_{2.5}\) (particulate matter with an aerodynamic diameter smaller than 2.5 \(\upmu\)m) can penetrate deep into the lungs and enter the bloodstream [3, 4], which majorly affects the cardiovascular and respiratory systems [5] but also affects other organs. Exposure to air pollutants, including PM\({}_{2.5}\), reportedly causes 7 million premature deaths every year [6], and can cause lung function decline [7, 8], respiratory infections [9, 10], and asthma [11, 12] in vulnerable groups, such as children. In developing countries, air quality improvement is urgently required because of the unprotected use of fossil fuels [13, 14]. In 2013, the International Agency for Research on Cancer (IARC) classified outdoor air pollution and PM\({}_{2.5}\)t as carcinogens [15]. In addition, in September 2021, the World Health Organization (WHO) lowered the new air quality guideline for PM\({}_{2.5}\) to an annual average of 5 \(\upmu\)g/m\({}^{3}\) from 10 \(\upmu\)g/m\({}^{3}\), considering the effects of the particles on the human body due to long-term exposure [16]. An accurate PM\({}_{2.5}\) daily prediction model help people plan their outdoor activities according to PM\({}_{2.5}\). It will contribute to reducing the incidence of diseases caused by PM2.5 concentration. Therefore, a PM\({}_{2.5}\) daily prediction model is developed in this study. PM\({}_{2.5}\) is mainly emitted directly by fuel combustion in various sectors including transportation, energy, household, industry, and agriculture [17; 18]. It is also emitted in the form of nitrogen oxide (NOx) or sulfur oxide (SOx) during the gas phase reaction with ammonia (NH\({}_{3}\)) [19]. It is very difficult to control because it generates ammonium nitrate and ammonium sulfate, which are secondary PMs. Korea grapples with the effects of long-range transport as well as domestic emissions [20]. Although the concentration of PM\({}_{2.5}\) in Korea is decreasing, remarkable long-lasting pollution episodes often occur, but remain difficult to predict. The National Institute of Environmental Research has been using a community multiscale air quality (CMAQ) model to predict the concentration of PM\({}_{2.5}\) for air quality forecasting [21; 22]. In the CMAQ model, meteorological and emission information are used as input data, although the accuracy of domestic and foreign emission information requires further improvement [23]. Korea forecasts PM\({}_{2.5}\) by classifying it into four stages (low, moderate, high, and very high), and in 2018, the accuracy of the one-day and two-day forecasts were 73% and 68%, respectively; thus, uncertainty regarding these forecasts remains [24]. In recent years, air pollution prediction studies using artificial neural networks (ANN) have been widely conducted to improve the uncertainty of air quality prediction [25; 26]. ANNs are used to accurately predict pollutant concentrations because they can build complex nonlinear models [27; 28; 29; 30]. Various studies have been undertaken to predict dust concentrations. A study was conducted to forecast PM2.5 concentration by building a prediction model based on the radial basis function (RBF) neural network and comparing it with the existing back propagation (BP) network model [31]. Another study used particle swarm optimization (PSO) and fuzzy neural network as a more optimized method to predict PM2.5 concentration using meteorological factors and chemical composition of PM2.5. A case in which ANN technology was successfully applied to predict the concentration of PM2.5 in the air in a complex road traffic network was used to predict the concentration at a specific point [32]. To compensate for the disadvantages of CMAQ, [PERSON] et al. conducted a study that grafted a recurrent neural network (RNN) to CMAQ [24; 33]. However, most existing studies optimize the model or predict the concentration in a specific situation.