The PM10 concentration in the underground areas should be monitored to protect the health of the commuters in the underground subway system. The purpose of this work is to study the reliability of the instruments using light scattering method to measure the PM10 concentrations continuously. A linear regression analysis method is used to improve the performance of the instruments using light scattering method. Some experimental results show that a linear regression technique would be very helpful for the performance improvement of light scattering instruments such as Air test PM2500 and HCT 4103. 

Millions of citizens in Seoul use the subway as a mean of transportation to go to work or move from someplace to the other per a day. There are high emissions of air pollutants from cars and some other air-pollutant resources in Seoul. Since many individuals usually spend most of their working hours indoors, the ambient air quality refers to indoor air quality. In particular, PM10 concentration in the underground areas should be monitored to preserve the health of commuters and workers in the subway system. Seoul Metro measures several air pollutants regularly. In this study, the possibility of the installing cost reduction will be shown through using the linear regression method to be improved in the accuracy between the measuring data of Airtest PM2500 and Grimm. 

The linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. A linear regression analysis method is used to improve the performance of the instruments using light scattering method. The data measured by these instruments have to be converted to actual PM10 concentrations using some factors. These findings propose that the instruments using light scattering method can be used to measure and control the PM10 concentrations of the underground subway stations. In this study, the reliability of the instrument for PM measurement using light scattering method was improved with the help of a linear regression analysis technique to continuously measure the PM10 concentrations in the subway stations. In addition, the linear regression analysis technique was also applied to calibrate a radon counter with the help of an expensive radon measuring apparatus.