# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 4 Conclusions  
Predicting real-time air pollution concentrations in a city using historical air pollution concentration data is a very important but difficult task in all industrialized countries. The reason is that too many factors are involved and some of the air pollutants cross the border and flow into the local city, making the air pollution concentration in that city very high and the air pollution concentration also depends on the weather conditions of the city itself. In this study, we developed multivariate regression models for the real-time prediction of air quality at the current time before, during and after the yellow sand periods in Kangnung using the 3 h-earlier PM and gas concentration and meteorological parameter data from the city and the 2-days-earlier PM and gas concentration data from Beijing. Our research results are summarized as follows. The correlation coefficient between the predicted and measured real-time PM\({}_{10}\) and PM\({}_{2.5}\) values calculated by the model were 0.919-0.957 and 0.906-0.945, and the prediction abilities during the event were the maximum. On the other hand, the correlation coefficients for NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and CO were 0.866-0.902, 0.795-0.917, 0.892-0.932 and 0.864-0.887, respectively, which were slightly lower than those for the PM values. However, their predicted values well reflect the measured ones and the predictive performance of our models was slightly better than other scholars' multivariate regression models. Before the Yellow Sand event, the PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), SO\({}_{2}\) and CO concentrations in Kangnung were not affected by the PM concentrations in Beijing, but some of them were affected by the gas concentrations in Beijing. During and after the yellow sand event, all PM and gas concentrations, except for O\({}_{3}\), in Kangnung City were affected by all PM and gas concentrations in Beijing City, except for O\({}_{3}\). The contribution of meteorological parameters to air pollutant concentrations in Kangnung was relatively smaller than the effect of PM and gas concentrations in Beijing City. The significance levels of all correlation coefficients were less than 0.001, indicating that they were very significant. Generally, to predict air pollution concentrations, numerical models, multivariate regression statistical models, neural network models and meteorological satellite image interpretation methods are used. Since each method has advantages and disadvantages and the methods complement each other, I think that there is no need to analyze which method is superior. Rather, there is a need to improve each process to improve the prediction ability. Further studies of air pollution prediction in urban areas need to obtain a deeper, more comprehensive understanding the physical processes leading to the observed differences between local cities and trans-border cities for solving the nationwide air quality problem via useful models. Conceptualization, S.-M.C. and H.C.; methodology, S.-M.C., H.C. and W.P. ; software, S.-M.C. and H.C.; validation, S.-M.C., H.C. and W.P. ; formal analysis, S.-M.C. and H.C.; investigation, S.-M.C. and H.C.; resources, S.-M.C. and H.C.; data curation, S.-M.C. and H.C.; writing--original draft preparation, S.-M.C. and H.C.; writing--review and editing, H.C. and S.-M.C. ; visualization, S.-M.C. ; supervision, H.C. and W.P. ; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.