Multivariate Regression Modeling for Coastal Urban Air Quality Estimates

Abstract

Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data of PM and gas from Beijing (China)). Multiple correlation coefficients between the predicted and measured PM10, PM2.5, NO2, SO2, CO and O3 concentrations were 0.957, 0.906, 0.886, 0.795, 0.864 and 0.932 before the yellow sand event at Kangnung, 0.936, 0.982, 0.866, 0.917, 0.887 and 0.916 during the event and 0.919, 0.945, 0.902, 0.857, 0.887 and 0.892 after the event. As the significance levels (p) from multi-regression analyses were less than 0.001, all correlation coefficients were very significant. Partial correlation coefficients presenting the contribution of 15 input variables to 6 output variables using the models were presented for the three periods in detail. Scatter plots and their hourly distributions between the predicted and measured values showed the quite good accuracy of the modeling performance for the current time forecasting of six output values and their high applicability.