| Keywords |
Gas consumption; Filterable particulate matter; Condensable particulate matter; Dust emission prediction; Artificial neural network |
| Abstract |
The TSP, PM10, PM2.5, which is calculated by NIER(National institute of environmental research)’s clean air policy support system(CAPSS), targets only filterable particulate matter(FPM) consisting of 2.5 to 10μm particles.
However, it should be considered together as total particulate matter(TPM) emitted from workplaces, cars, heating and cooling will emit not only filterable PM but also condensable particulate matter(CPM). Therefore, in this study, filterable PM and condensable PM generated by the burning of LNG in buildings were calculated for each building use, using the emission factors announced by NIER. In addition, for the purpose of predicting the effects of PM by buildings, the prediction model of PM emissions was established and the case analysis was performed using machine learning.
As a result, particulate matter per unit area of buildings was generally analyzed high in residential and commercial buildings and particularly high in the winter months due to increased LPG use caused by heating. And, since about 98% of the total PM was analyzed to be condensable PM, prevention and reduction measures for condensable PM should be expanded. |