## 6 Conclusions  
In the scope of this research, ground-based surface ozone measurements from the National Air Quality Monitoring Network of China, recorded between 2019 and 2021 in the NCP, were employed. The observational data were enhanced with multiple data sources that included 7 km \(\times\) 3.5 km resolution NO\({}_{2}\) and HCHO data from the Sentinel-5 TROPOMI satellite, corresponding data from the CAMS, CAMS VOCs dataset, NO\({}_{X}\) emissions data from CAMS, meteorological readings, and data derived from the MODIS, NDVI, DEM, POPU, and LULC details. The integration of these diverse datasets facilitated the development of an extensive model for estimating the surface ozone levels in the NCP. In the NCP, a set of four models for estimating the surface ozone concentrations was crafted, merging data from the Sentinel-5 TROPOMI satellite monitoring system with reanalysis data from the CAMS. This integration aimed to enhance the accuracy of the XGBoost-based models. The \(R^{2}\) values, which indicate the site-based verification results for the four MDA8 models, were consistently observed to be above 0.856. Concurrently, the \(RMSE\) values  
Figure 6: Temporal variations in the MDA8 exceedances ratio in each month and the accumulated MDA8 exceedances in each province from 2019 to 2021. (**a**) indicates the MDA8 exceedances ratio in each month in 2019, 2020, and 2021. (**b**) represents the accumulated MDA8 exceedances in each province in 2019, 2020, and 2021.  
for these models were reliably kept under 19.48 ug m\({}^{-3}\), demonstrating a high level of accuracy in the model predictions. The _MPE_ value was kept under 14.38 ug m\({}^{-3}\), and the _RPE_ value was limited to less than 20.15%. Given that the coverage of CAMS reanalysis data is more extensive than that of the S5P-TROPOMI satellite monitoring data, the estimation coverage using the combination of CAMS-NO\({}_{2}\) and CAMS-HCHO was the most extensive, followed by the combination of S5P-NO\({}_{2}\) and CAMS-HCHO. The outcomes of the MDA8 estimations, derived from the pair of models in question, were synthesized by taking their average values. This synthesis process led to the creation of the ultimate surface ozone estimation product. Utilizing this method, the daily gridded MDA8 data at a resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) for the NCP, covering the years 2019 to 2021, were deduced accordingly. This comprehensive approach ensured a robust and accurate representation of the spatiotemporal distribution of surface ozone in the region. Utilizing the surface ozone XGBoost estimation model, a quantitative assessment of the principal factors influencing ozone was conducted in this study. The findings disclosed that VOCs and NO\({}_{2}\), recognized as critical ozone precursors, were responsible for the most substantial contributions to ozone pollution, with their impacts amounting to 23.34% and 7.35%, respectively. Furthermore, the role of HCHO was significant, with its contribution surpassing 5.64%. Meteorological elements such as wind direction, wind speed, temperature, relative humidity, and ultraviolet radiation were also identified as having a notable effect on ozone pollution, each with a contribution greater than 3.31%. The spatiotemporal distribution of the estimated MDA8 concentrations indicated that the convergence area of Hebei, Shandong, and Henan Provinces is the primary region grappling with severe O\({}_{3}\) pollution in the NCP, with summer being pinpointed as the peak season for ozone pollution, particularly in June. Further analysis of the spatiotemporal distribution patterns revealed that severe ozone pollution in the NCP predominantly occurs at the tri-province junction of Hebei, Shandong, and Henan, with June and May being the months with the highest frequency of ozone pollution exceedances.