# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Definition of Variables in the Multivariate Regression Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{11}{c}{**Person Correlation Coefficient (t) during the Yellow Sand event**} \\ \hline
**Item** & **PM\({}_{\text{Ks}}\),KNO** & **PM\({}_{\text{Ks}}\),K** & **PM\({}_{\text{Ks}}\),K** & **TTMP\({}_{\text{K}}\)** & **WND\({}_{\text{K}}\)** & **RI\({}_{\text{K}}\)** & **SO\({}_{\text{S}}\),K** & **CO\({}_{\text{K}}\)** & **O\({}_{\text{K}}\)** & **NO\({}_{\text{S}}\),K** & **PM\({}_{\text{Ks}}\),C** & **SO\({}_{\text{S}}\),C** & **CO\({}_{\text{S}}\),C** & **O\({}_{\text{S}}\),C** & **NO\({}_{\text{S}}\),C** & **NO\({}_{\text{S}}\),C** \\ \hline
**PM\({}_{\text{Ks}}\),KNO** & 1.000 & 0.748 & 0.932 & 0.066 & 0.065 & \(-\)0.044 & 0.970 & 0.522 & 0.331 & 0.332 & 0.624 & 0.399 & 0.328 & 0.213 & \(-\)0.208 & 0.229 \\ \hline
**Person Correlation Coefficient (t) during the Yellow Sand event** & & & & & & & & & & & & & & & \\ \hline
**Item** & **NO\({}_{\text{Ks}}\),KNO** & **PM\({}_{\text{Ks}}\),K** & **PM\({}_{\text{Ks}}\),K** & **TTMP\({}_{\text{K}}\)** & **WND\({}_{\text{K}}\)** & **RI\({}_{\text{K}}\)** & **SO\({}_{\text{S}}\),K** & **CO\({}_{\text{K}}\)** & **O\({}_{\text{S}}\),K** & **NO\({}_{\text{S}}\),K** & **PM\({}_{\text{Ks}}\),C** & **PM\({}_{\text{Ks}}\),C** & **SO\({}_{\text{S}}\),C** & **CO\({}_{\text{S}}\),C** & **NO\({}_{\text{S}}\),C** & **NO\({}_{\text{S}}\),C** \\ \hline
**NO\({}_{\text{Ks}}\),KNO** & 1.000 & 0.480 & 0.279 & 0.476 & 0.607 & 0.665 & 0.004 & 0.428 & 0.829 & 0.437 & 0.420 & \(-\)0.137 & \(-\)0.286 & \(-\)0.483 & 0.285 & \(-\)0.292 \\ \hline
**Person Correlation Coefficient (t) during the Yellow Sand event** & & & & & & & & & & & & & & & \\ \hline
**Item** & **CO\({}_{\text{Ks}}\)** & **PM\({}_{\text{Ks}}\),K** & **PM\({}_{\text{Ks}}\),K** & **TTMP\({}_{\text{K}}\)** & **WND\({}_{\text{K}}\)** & **RI\({}_{\text{K}}\)** & **SO\({}_{\text{S}}\),K** & **CO\({}_{\text{S}}\),K** & **NO\({}_{\text{S}}\),K** & **PM\({}_{\text{Ks}}\),C** & **PM\({}_{\text{Ks}}\),C** & **PM\({}_{\text{Ks}}\),C** & **SO\({}_{\text{S}}\),C** & **CO\({}_{\text{S}}\),C** & **NO\({}_{\text{S}}\),C** \\ \hline
**CO\({}_{\text{Ks}}\)** & 1.000 & 0.124 & 0.671 & \(-\)0.251 & \(-\)0.343 & 0.477 & 0.667 & 0.826 & \(-\)0.365 & 0.585 & 0.341 & 0.517 & 0.697 & 0.607 & \(-\)0.487 & 0.488 \\ \hline \end{tabular}
\end{table} 
Table 6: _Curt._In Table 5, a partial correlation coefficients matrix before the yellow sand period, predicted that the current PM10_K(N) in Kangnung is positively influenced by not only the PM10_K (0.910; highest), PM25_K (0.591; 2\({}^{\rm nd}\) high), NO2_K, CO_K, TEMP_K and SO2_K in Kangnung, which had relatively high contributions, but also both SO2_C and NO2_C in Beijing, which had a little contribution. A variable with a minus sign implies negatively contributing to the PM10_K(N) concentration. PM25_C, PM10_C, CO_C and O3_C contributed to the predicted PM10_K(N) negatively. The predicted PM25_K(N) is positively influenced by the measured PM25_K (0.826; highest), PM10_K (0.701; 2\({}^{\rm nd}\) high), CO_K, RH_K, NO2_K and O3_K in Kangnung. However, it is a little influenced by the SO2_C and NO2_C values in Beijing city. The others negatively influenced it. The predicted NO2_K(N) is highly positively influenced by the measured NO2_K (0.842; highest), SO2_K (0.379; 2\({}^{\rm nd}\) high), TEMP_K, CO_K and PM10_K in Kangnung but is a little influenced by the O3_C and NO2_C in Beijing; the other variables negatively influence the predicted NO2_K(N). The predicted SO2_K(N) is positively influenced by the measured SO2_K (0.704; highest), CO_K (0.557; 2\({}^{\rm nd}\) high), NO2_K, TEMP_K and PM10_K in Kangnung and the NO2_C in Beijing. The other variables affect it negatively. The predicted O3_K(N) is positively influenced by the measured O3_K (0.899; highest), WIND_K, PM25_K and TEMP_K in Kangnung and SO2_C (0.425; 2\({}^{\rm nd}\) high), PM10_C, PM25_C, CO_C and O3_C in Beijing; the other variables have a negative influence. Only O3_K(N) is more positively influenced by the PM and gas in Beijing.