# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Definition of Variables in the Multivariate Regression Model
The predicted CO_K(N) is positively influenced by the measured CO_K (0.769; highest), NO2_K (0.418; 2\({}^{\rm nd}\) high), SO2_K, RH_K, PM10_K and PM25_K in Kangnung but is a little influenced by the NO2_C in Beijing; the other variables have a negative influence. Among the six real-time predicted variables, five (except for O3_K(N)) in Kangnung were negatively influenced by all of the PM in Beijing; however, they were negatively or positively influenced by the gases in Beijing. In Table 6, during the yellow sand event, the predicted PM10_K(N) is highly positively influenced by the measured PM10_K (0.884; highest), PM25_K (0.749; second highest), O3_K, SO2_K, CO_K, WIND_K and TEMP_K in Kangnung but is a little influenced by the PM10_C, SO2_C, NO2_C, PM25_C and CO_C in Beijing. The other variables had a negative influenced on it. Thus, all of the PM and gas values in Beijing influenced it, except for O3_C. The predicted PM25_K(N) is highly positively influenced by the measured PM25_K (0.932; highest), SO2_K (0.797; second highest), PM10_K, CO_K, O3_K, NO2_K, TEMP_K and WIND_K in Kangnung but is significantly influenced by the PM10_C, PM25_C, SO2_C, CO_C and NO2_C in Beijing. In contrast, O3_C and RH_K affected it negatively. All of the PM and gas values in Beijing influenced it, except for O3_C. The predicted O3_K(N) is positively influenced by the measured O3_K (0.829; highest), WIND_K (0.607; second highest), PM10_K, TEMP_K, PM25_K and SO2_K in Kangnung and the PM10_C, O3_C in Beijing; the other variables affected it negatively. The predicted CO_K(N) is positively affected by the measured CO_K (0.826; highest), SO2_K (0.667; second highest), NO2_K, PM25_K, RH_K and PM10_K in Kangnung and the CO_C, PM25_C, SO2_C, NO2_C and PM10_C in Beijing. However, O3_C, O3_K, WIND_K and TEMP_K affect it negatively. Five real-time predicted variables (except for O3_K(N)) in Kangnung are most positively influenced by all of the PM and gas (except for O3_C) values in Beijing. Therefore, the PM and gas (except for O3_C) values in Beijing highly affected the predicted PM and gas values in Kangnung. In Table 7, after the yellow sand event, the predicted PM10_K(N) is positively influenced by the measured PM10_K (0.888; highest), PM25_K (0.606; second highest), TEMP_K, NO2_K, SO2_K, O3_K, WIND_K and CO_K in Kangnung and the NO2_C, PM25_C, CO_C and PM10_C in Beijing, with a high effect on the predicted PM10_K(N). The other variables affected it negatively. The predicted PM25_K(N) is positively influenced by the measured PM25_K (0.921; highest), SO2_K, PM10_K, NO2_K, TEMP_K and CO_K in Kangnung and the PM25_C (0.819; second highest), NO2_C, CO_C, SO2_C and PM10_C in Beijing. Beijing's measured PM and gas greatly contributed to the increase in the predictedPM\({}_{2.5}\)_K(N). The other variables affected it negatively. Thus, the PM and gas in Beijing (except for O3_C) greatly contributed to the increase in both PM\({}_{10}\)_K(N) and PM\({}_{2.5}\)_K(N). The predicted NO\({}_{2}\)_K(N) is positively influenced by the measured NO\({}_{2}\)_K (0.855; highest), PM\({}_{2.5}\)_K, SO\({}_{2}\)_K, TEMP\(\_\)K, CO\(\_\)K, PM\({}_{10}\)_K and RH\(\_\)K in Kangnung and the NO\({}_{2}\)_C (0.564; second highest), PM\({}_{2.5}\)_C, SO\({}_{2}\)_C, CO\(\_\)C and PM\({}_{10}\)_C in Beijing; O\({}_{3}\)_K, WIND\(\_\)K and O\({}_{3}\)_C affect it negatively. The measured PM and gas (except for O\({}_{3}\)_C) in Beijing greatly contributed to the increase in the predicted NO\({}_{2}\)_K(N). O\({}_{3}\)_K contributes negatively to it through NO\({}_{x}\) cycle processes. The predicted SO\({}_{2}\)_K(N) is positively influenced by the measured SO\({}_{2}\)_K (0..751; highest), CO\(\_\)K, PM\({}_{2.5}\)_K, NO\({}_{2}\)_K, PM\({}_{10}\)_K, TEMP\(\_\)K and RH\(\_\)K in Kangnung and the PM\({}_{2.5}\)_C (0.698; second highest), CO\(\_\)C, NO\({}_{2}\)_C, SO\({}_{2}\)_C and PM\({}_{10}\)_C in Beijing; the other variables have a negative influence.