Daily PM_2.5 Estimation using Multiple Linear Regression and Artificial Neural Networks Before 2015

다중선형회귀와 인공신경망을 이용한 2015년 이전 PM_2.5 일일 평균 수치 추정 방법론 제안

Since 2015, the PM_2.5 measurement data has been publicly available nationwide in South Korea, but its use is restricted to after 2015, unlike other air pollutants. To overcome this limitation, multiple linear regression and artificial neural network models were developed to predict the daily average PM_2.5 values in South Korea before 2015. The daily data of air pollution measurement(SO₂, CO, O₃, NO₂, PM₁₀) and meteorological observation data (temperature, humidity, wind speed, atmospheric pressure, precipitation, snowfall) were used as input variables to develop regional prediction models for five regions(Seoul, Incheon, Gwangju, Daejeon, Ulsan) and a national prediction model. The models were developed and validated using the air pollution measurement data after 2015, and applied to predict PM_2.5 values before 2015. The multiple linear regression model showed R² values of 0.80 nationwide, 0.73 in Seoul, and 0.67 in Incheon, which enabled estimation of daily average PM_2.5 values before 2015. The artificial neural network model showed good prediction power with R2 values of 0.79 in Gwangju, 0.81 in Daejeon, and 0.72 in Ulsan. The regional prediction models showed good prediction power in most regions, and both the multiple linear regression and artificial neural network models showed good prediction power.