Cite this article: Araki, S., Yamamoto, K. and Kondo, A. (2015). Application of Regression Kriging to Air Pollutant Concentrations in Japan with High Spatial Resolution.
Aerosol Air Qual. Res.
15: 234-241. https://doi.org/10.4209/aaqr.2014.01.0011
The application of regression kriging at a spatial resolution of 1 km was examined.
The prediction performance assessed by cross validation was satisfactory.
Regression kriging outperformed the conventional linear regression method.
The prediction performance was comparable to those reported in previous studies.
Regression kriging can be applied to predict air pollutant distributions in Japan.
The application of regression kriging to air pollutants in Japan was examined for the purpose of providing a practical method to obtain a spatial distribution with sufficient accuracy and a high spatial resolution of 1 × 1 km. We used regulatory air monitoring data from the years 2009 and 2010. Predictor variables at 1 × 1 km resolution were prepared from various datasets to perform regression kriging. The prediction performance was assessed by indicators, including root mean squared error (RMSE) and R2, calculated from the leave-one-out cross validation results, and was compared to the results obtained from a linear regression method, often referred to as land use regression (LUR). Regression kriging well-explained the spatial variability of NO2, with R2 values of 0.77 and 0.78. Ozone (O3) was moderately explained, with R2 values of 0.52 and 0.66. The reason for this difference in performance between NO2 and O3 might be the characteristics of these pollutants - primary or secondary. Regression kriging outperformed the linear regression method with regard to RMSE and R2. The performance of regression kriging in this work was comparable to that found in previous studies. The results indicate that regression kriging is a practical procedure that can be applied for the prediction of the spatial distribution of air pollutants in Japan, with sufficient accuracy and a high spatial resolution.
Keywords: Spatial distribution; Geostatistics; Air quality; Ozone; NO2