An Improved Geographically and Temporally Weighted Regression for Surface Ozone Estimation From Satellite-Based Precursor Data

It is very essential to resolve the issues of atmospheric ozone (O₃) pollution and health impact evaluation with high spatial resolution and accurate near-surface O₃ concentration. Nevertheless, the existing remotely sensed O₃ products could not meet the demands of high spatial resolution monitoring. For this purpose, this study using surface O₃ precursor (the surface nitrogen dioxide concentration and formaldehyde concentration) data developed an improved geographically and temporally weighted regression (IGTWR) method to estimate the surface O₃ concentration. This method calculated a generalized distance between sample points in that multidimensional space constructed using the longitude, latitude, day, and normalized difference vegetation index (NDVI). Next, the surface O₃ precursor data were used as independent variables to retrieve the daily O₃ concentrations. The contribution of the proposed model is that the NDVI data were introduced as the underlying factor to explain the heterogeneity of underlying conditions and indicate O₃ concentration more accurately to improve the estimation accuracy. Then, the ground station observations were used to validate the estimated ground-level O₃ concentration results. Based on the cross-validation results of all test data, the model estimated the root mean squared error and the correlation coefficient of surface O₃ to be 9.456 μg/m³ and 0.983, respectively. The results demonstrate that it is feasible to estimate surface O₃ concentrations using data from the TROPOMI sensor and an improved geographically weighted regression model.