New Spatial Interpolation Algorithm for Sparse AQI Based on Extended Field Intensity Model

The monitoring stations for air quality index (AQI) are sparsely distributed, and spatial interpolations are less accurate from the existing methods. A new algorithm is proposed based on the extended field intensity model. The single parameter model controls intensity attenuation by parameter c, while the optimal c value is computed from the relationship between c and deviation data with binary search method. The double parameters model adjusts intensity range by additional parameter k, while the optimal c and k are computed from the relationship among c, k and deviation data with iterative bilinear interpolation method. The 50 monitored sets of AQI value are taken as experimental data from Beijing, Tianjin, Wuhan and Zhengzhou Between August 2014 and April 2015. Based on cross validation and evaluation criteria RMSE, AME, PAEE, both single parameter model and double parameters model are implemented with their optimal parameters, then the extended field intensity model is compared with Kriging and inverse distance weighted methods. Experimental results prove that the precision of AQI interpolation from our algorithm is higher, while double parameters model obtains the highest precision. Our algorithm is suitable for spatial interpolation of sparse data with fixed number and locations, and can be used for spatial data with other types and dimensions.