## 4 Results  
### Evaluation of the Accuracy of the MDA8 Estimation Model  
In this study, a quartet of models for estimating surface ozone concentrations was developed, each underpinned by the XGBoost algorithm. These models incorporated different datasets of ozone precursors, specifically focusing on the following pairings: data from SSP NO\({}_{2}\) alongside S5P HCHO, S5P NO\({}_{2}\) with CAMS HCHO, CAMS NO\({}_{2}\) with S5P HCHO, and finally, CAMS NO\({}_{2}\) with CAMS HCHO. This diverse integration of datasets aimed to enhance the accuracy and reliability of the ozone estimations. The accuracy and spatial coverage of the MDA8 estimation models, derived from these four schemes, were then compared. The two methods that yielded the highest model accuracy and the most comprehensive spatial coverage were selected. Finally, the average near-surface ozone estimation of the NCP was determined by averaging the MDA8 grid products estimated by the two methods.  
Figure 2 presents the site-based accuracy verification results of four MDA8 estimation methodologies. In the course of model training, initially, 5% (7 out of 133) of the clustered sites within the NCP were randomly selected and constituted the site-based validation dataset, followed by the selection of an additional 20% of the remaining 95% of sites for the sample-based validation dataset. The results indicated that all four XGBoost models for MDA8 demonstrated a high verification accuracy, with a strong correlation existing between the models' estimated MDA8 concentrations and the ground-based MDA8 observations. The \(R^{2}\) values for the four models exceeded 0.856, the root mean square error was maintained below 19.48 \(\upmu\)g m\({}^{-3}\), the _MPE_ (mean prediction error) was kept under 14.38 \(\upmu\)g m\({}^{-3}\), and the _RPE_ (relative prediction error) was limited to less than 20.15%. Among the quartet of MDA8 estimation models, those employing SSP NO\({}_{2}\) and CAMS HCHO as well as CAMS NO\({}_{2}\) and CAMS HCHO exhibited superior prediction accuracy, with both \(R^{2}\) values surpassing 0.857. Furthermore, the spatial coverage of the estimated MDA8 results from both models was the most extensive, each exceeding 96%. Consequently, the MDA8 estimation models based on these two input schemes were chosen for the ultimate estimation of the MDA8 product. To strengthen the reliability of the MDA8 estimation model, a total of 50 separate training experiments were executed independently. This approach was implemented to ensure a comprehensive validation of the model's predictive capabilities and to confirm the consistency of its performance across various datasets. Ultimately, 100 independent training iterations of these two models were employed, and the average of the model estimation outcomes was adopted as the definitive daily MDA8 concentration estimation product for the 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid in the NCP from 2019 to 2021.  
Figure 2: For the site-based validation of the four MDA8 estimation models. (**a**) displays a comparative analysis at a selection of sites (representing 5% of the total), where the observed surface O\({}_{3}\) levels are  
juxtaposed with the O\({}_{3}\) estimates that were generated using SSP NO\({}_{2}\) and SSP HCHO data. (**b**) displays the validation findings for the model that incorporates SSP NO\({}_{2}\) and CAMS HCHO. (**c**) illustrates the results for the model utilizing CAMS NO\({}_{2}\) and SSP HCHO, and finally, (**d**) provides the validation data for the model that employs both CAMS NO\({}_{2}\) and CAMS HCHO. Each figure elucidates the performance of the respective model in predicting the surface O\({}_{3}\) concentrations compared to the ground-based measurements. The red dotted line represents the fitting results.