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

Abstract

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.