A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models

Missing or incomplete data in short or long intervals is a common problem in measuring air pollution. Severe issues may arise when dealing with missing data for time-series prediction schemes or mean analysis. This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System (TAPFS). The proposed framework was designed based on three types of features including measurements of other stations, WRF and CAMx physical models. In this framework, elastic net and neuro-fuzzy networks were efficiently combined in a two-layer structure. The framework was applied on Tehran's air pollution monitoring network. The hourly imputing results of the suggested method were seen to be superior to existing methods according to statistical criteria such as RMSE, MAE and R-values. Average R-values of 0.88, 0.73, 0.76 and 0.79 were obtained for O3, NO, PM2.5 and PM10, respectively. The measurements of other stations had the main predictive power with a modest increase for the two physical models. The benefit of the models was somewhat higher for stations on boundaries of monitoring network. In addition, the central stations had better performance than the boundary stations and an approximately 0.05 increase was obtained in average R-value.