## 4 Conclusions  
In this study, the correlation between the daily averages and daily variances of air pollutants and meteorological factors was analyzed with next-day PM\({}_{2.5}\) concentrations. Through this, it was confirmed that next-day PM\({}_{2.5}\) concentration was not only influenced by the daily average of air pollutant data and meteorological data but also by the daily variances.  
The data were divided into two types: NW data and administrative district data (16 regions). Subsequently, daily averages and variances of air pollutant data (PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), CO, and PM\({}_{2.5}\)) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) were calculated. A prediction model that determined next-day PM\({}_{2.5}\) was developed using an ANN. Factorial design was used to find the best factor level (month, hidden layer layout, and threshold) for the ANN, and the optimal model was selected based on ANOVA. The absolute error in 1 \(\sigma\) of the PM\({}_{2.5}\) NW prediction model is 91% (in-sample 91%, out-of-sample 91%), and that for AD is 86% (in-sample 88%, out-of-sample 84%). Assuming that the weather forecast was accurate, the accuracy of these prediction models (absolute error in 1 \(\sigma\)) increased up to 96% (in-sample) and 92% (out-of-sample) for NW and 91% (in-sample) and 89% (out-of-sample) for the AD.  
Our developed model has a significant advantage over traditional predictive models in that it uses the air pollutions and meteorological data of the current day to forecast the PM\({}_{2.5}\) for the next day. This is more convenient than conventional models that rely on statistical analysis or air pollution modeling, such as CMAQ. Regions such as South Korea, where haze events are common, experience daily PM\({}_{2.5}\) concentration fluctuations of more than three times during the event [19]. As a result, it is crucial to have a model that can provide people with an action plan to prepare for these daily changes. Our study confirmed the accuracy of the NW model, and we plan to conduct an optimization study of the AD model, which is relatively inaccurate, in future research.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13063575/s1](https://www.mdpi.com/article/10.3390/app13063575/s1)]([https://www.mdpi.com/article/10.3390/app13063575/s1](https://www.mdpi.com/article/10.3390/app13063575/s1)), Table S1. Concentration ranges of air pollutants, comprehensive air quality index in South Korea. Table S2. Number of data and % of each category for PM2.5. categorized by comprehensive air quality index in South Korea.  
Conceptualization, S.P.; methodology, J.-W.H.; software, J.-W.H. and P.-M.P.; validation, J.-W.H. and J.-S.Y.; formal analysis, J.-W.H.; investigation, K.-J.J.; resources, S.P.; data curation, S.P.; writing--original draft preparation, J.-W.H. and J.-S.Y.; writing--review and editing, S.P. and K.-J.J.; visualization, J.-S.Y.; supervision, S.P. and K.-J.J.; project administration, S.P. and K.-J.J.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a 2021 Research Grant from Kangwon National University. This work was also supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2021-03-03-007).  
Not applicable.  
Thankful for support by a 2021 Research Grant from Kangwon National University and grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea.  
The authors declare no conflict of interest.