# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 1 Introduction  
The rapidly increasing consumption of fossil fuels, such as gasoline and coal, due to industrial development in each country and rural urbanization in northeast and southeast Asia results in worse ambient air pollution, which is mainly composed of particulate matter and gases emitted from the vehicles on the road, industries, forest fires, heating and cooking boilers, trash burning and the yellow dust raised from the desert and arid areas [1; 2; 3; 4; 5; 6]. Various pollutants are very harmful to human health through respiratory diseases, are a multi-faceted health threat to outdoor physical activity and cause worse visibility during long hazy weather [7; 8; 9; 10; 11; 12].  
[PERSON] et al. [13], [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [16] and [PERSON] and [PERSON] [17] insist that yellow dust, which is also called various names such as Asian dust, dust storm, yellow sand and KOSA is easily caused under a relative humidity of less than 40% near the ground surface of wide arid areas and the desert in northern China and a wind velocity of over 10 m/s for the dust mobilization in spring.  
[PERSON] et al. [18] carried out lidar observation to investigate the large depolarization ratio of free tropospheric aerosols over the Taklamakan Desert. [PERSON] et al. [19], [PERSON] [20], [PERSON] et al. [21] and [PERSON] et al. [22] reported on dust storm frequency in spring and dust deposition on the Chinese Loes Plateau. As a strong northwesterly wind could remove several hundred thousand tons of both sand and dust from the desert and arid areas and those substances were uplifted, about 50% of the dust was accumulated in the desert area's vicinity and the rest was transported to downwind areas such as Korea and depositedto its ground surface, causing low visibility due to a higher PM concentration [23; 24]. [PERSON] et al. [25] insisted that Asian dust particles transported concurrently with anthropogenic pollutants contributed to an elevated PM\({}_{2.5}\) concentration, through aerosol lidar and optical particle counter measurements.  
Using numerical models, [PERSON] [26] showed the transport of yellow sand to Taiwan and [PERSON] et al. [27] explained the long-range transport of soil dust from Asia to the tropical North Pacific. [PERSON] et al. [28], [PERSON] et al. [29] and [PERSON] et al. [30] indicated the long-range transport of air pollutants and Asian dust to southern California and western Canada and further analyzed the chemical composition of aerosols.  
Many researchers have conducted statistical and artificial neural network modeling research to predict dust concentrations. The previous statistical analyses on the impact of yellow dust in Korea comprised simple regression models. [PERSON] and [PERSON] [31] explained the fractional ratio and correlations of temporal PM\({}_{10}\), PM\({}_{2.5}\) and PM\({}_{1}\) at Gangneung that was affected by the transported dust from the Gobi Desert. [PERSON] and [PERSON] [32] showed the meteorological effect on particulate matters (PM) in Ahmedabad. Similarly, [PERSON] et al. [33], [PERSON] et al. [34], [PERSON] et al. [35] and [PERSON] [36] explained the relationship of PM with meteorological elements in China using multiple regression models without a gas effect.  
[PERSON] [37], [PERSON] [38], [PERSON] [39] and [PERSON] and [PERSON] [40] performed air quality prediction using artificial neural network and multivariate regression models in Korea. The predictions by two models with numerical models were very accurate. Instead of a complicated numerical model, a multivariate regression model was devised for real-time prediction of PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\) and CO at Kangnung (Korea), using 3 h-earlier pollution and meteorological data and 2-days-earlier pollution data from Beijing (China).