An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan

Accurate spatial and temporal prediction of PM2.5 ambient concentration is crucial to appropriate exposure assessment. We develop a spatiotemporal land use regression model by integrating a random forests (RF) technique and the Community Multiscale Air Quality (CMAQ) modeling system to accurately estimate daily PM2.5 levels in the Kansai region of Japan, which is affected by long-range transport in the Asian continent and by local pollution. The most important advantage of RF is that it captures nonlinearity among the target air pollutants and the predictor variables including land-use variables, meteorological variables, and CMAQ-estimated PM2.5 concentration. We compare the predicting performances of the land use random forests (LURF) models with and without CMAQ variables to determine their effectiveness. A cross-validation (CV) technique that calculates the coefficient of determination (R-2) and root mean square error (RMSE) is performed to evaluate their prediction performances through spatial and temporal CVs. The performance of the with-CMAQ LURF model was superior to that of the without-CMAQ LURF model. Moreover, we evaluated the PM2.5 prediction performances of the with-CMAQ LURF and the with-CMAQ land use linear regression (LULR) models via CV to determine the efficiency of the non-linear model. Accordingly, the with-CMAQ LURF model is preferable for PM2.5 estimation compared to that of the with-CMAQ LULR model. In addition, the with-CMAQ LURF model exhibits higher PM2.5 predictability than the CMAQ model, as indicated by the higher model-R-2 and lower model-RMSE values. Our findings demonstrate that the CMAQ-simulated PM2.5 level integrated into the LURF is advantageous in accurately estimating PM2.5 concentration, which is influenced by long-range transport and local pollution.

Automated summary

The study developed a spatiotemporal land use regression model using random forests and CMAQ to estimate PM2.5 levels in Japan's Kansai region. Results showed that the model with CMAQ variables performed better than the model without CMAQ variables in predicting PM2.5 concentrations.