# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 2 Study Area and Data Analysis
### Measured Material and Analysis of Data  
Hourly averaged PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), CO, O\({}_{3}\) and NO\({}_{2}\) concentrations measured at the Ockheondong observation point of Kangnung city were obtained from the homepage (Air Korea) of the Korea Environment Corporation (website address: [[https://www.airkorea.or.kr/web/](https://www.airkorea.or.kr/web/)]([https://www.airkorea.or.kr/web/](https://www.airkorea.or.kr/web/)), accessed on 1 February 2023) and hourly meteorological data (air temperature (\({}^{\circ}\)C; TEMP), wind speed (m/s; WIND) and relative humidity (%; RH)) measured at the Ockheondong observation point of Kangnung city by the Gangwon Regional Meteorological Administration (GRMA) were obtained through a website ([[https://data.kma.go.kr/](https://data.kma.go.kr/)]([https://data.kma.go.kr/](https://data.kma.go.kr/)), accessed on 1 February 2023). Hourly PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), CO, O\({}_{3}\) and NO\({}_{2}\) values measured at the Yongdingmen observation point in Beijing, China, were also obtained ([[https://quotsoft.net/air/](https://quotsoft.net/air/)]([https://quotsoft.net/air/](https://quotsoft.net/air/)), accessed on 20 January 2021).  
[PERSON] et al. [16], [PERSON] and [PERSON] [17] and [PERSON] et al. [18] explained clearly that the yellow dust particles raised from the Gobi Desert under strong northwesterly wind over 10 m/s were transported toward downwind regions such as Beijing and they further combined with local pollutants emitted from the cities. Then, those particles moved to the further downwind regions such as the Korean peninsula including Kangnung city and the islands of Japan. It takes approximately two days (i.e., 48 h) for the particles to travel the over 1400 km distance between Beijing and Kangnung. This means the current concentrations of particulate matter and gases at Kangnung City should be affected by the two-days-earlier air pollutant concentrations in Beijing.  
Thus, in this study to make current-time predictions of air pollutant concentrations, such as PM (PM\({}_{10}\) and PM\({}_{2.5}\)) and gas (SO\({}_{2}\), CO, O\({}_{3}\) and NO\({}_{2}\)), at Kangnung City, we used input data sets of not only three-hours-earlier data of PM, gas and meteorological parameters (air temperature, wind speed and relative humidity) at Kangnung city but also two-days-earlier data of PM and gas in Beijing.  
The correlation coefficients and regression equations between the observed and predicted PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), CO, O\({}_{3}\) and NO\({}_{2}\) concentrations at Kangnung City affected by the PM and gases from Beijing city (China) in the yellow sand route between 18 and 27 March were divided into three periods: before the inflow of yellow dust (00:00 LST, 18 March 2015-00:00 LST 21 March 2015), during its inflow (01:00 LST, 21 March 2015-00:00 LST, 23 March 2015) and after its inflow (01:00 LST, 23 March 2015-00:00 LST, 27 March 2015). Thus, multivariate regression prediction models were obtained via multiple regression analyses using SPSS-v27 statistical software. The predicted value of each pollutant was compared with the one later observed to test the goodness of its predicted value.