## 3 Method
### Statistical Indicators for Model Accuracy Evaluation  
After the XGBoost algorithm model had been utilized to construct a surface ozone estimation model, the accuracy of the estimation model was evaluated and combined with relevant statistical indicators to ascertain the performance of the model. To thoroughly evaluate the accuracy of the model's surface ozone estimations, a suite of performance indicators was applied. This set included the root mean square error (_RMSE_) to measure the deviation of predictions, the mean prediction error (_MPE_) to gauge the overall bias, the relative percentage error (_RPE_) to assess errors relative to the observed values, and the coefficient of determination (_R_2) to quantify the model's explanatory power. Among them, _RMSE_ is a standard indicator for measuring the prediction error of the model and is more sensitive to large errors. The smaller the _RMSE_ value, the better the prediction performance of the model. MPE is a measure of the average percentage error of the predicted value relative to the actual value, which can reflect the direction of the prediction deviation. _RPE_ is used to measure the relative error of the predicted value relative to the actual value and is used for the error calculation of a single sample. \(R\)2 is an indicator to measure the degree of variation of the actual value explained by the model. The closer the value is to 1, the better the model fits. These metrics provided a multifaceted assessment of the model's predictive capabilities concerning ozone levels. These evaluation metrics served not only to quantify the model's overall efficacy, but to also expose its performance across various dimensions, offering valuable insights for further refinement and enhancement. Through this systematic accuracy evaluation, the feasibility and effectiveness of the XGBoost model for surface ozone estimation could be more reliably adjudged, thus augmenting the precision and practical utility of such estimations. The evaluation of XGBoost models typically encompasses a multitude of indicators, with some of the common evaluation indicators being mathematically represented as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}} \tag{2}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum\limits_{i=1} ^{n}(y_{i}-\overline{y})^{2}} \tag{3}\]  
\[MPE=100\%\times\frac{1}{n}{\sum\limits_{i=1}^{n}\frac{(y_{i}-\hat{y}_{i})}{y _{i}}} \tag{4}\]  
\[RPE=100\%\times\frac{1}{n}{\sum\limits_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{y _{i}}} \tag{5}\]  
Among them, \(y_{i}\) is the observed value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}\) is the mean of the observed value, and n is the sample number. In the evaluation and comparison of the predictive accuracy of the four combinations models, using different combinations of NO\({}_{2}\) and HCHO from the CAMS reanalysis datasets and S5P-TROPOMI observations, the models were selected for their higher prediction precision and higher extensive spatiotemporal coverage in the estimation results of MDA8. This combined model was determined as the final combined model for surface ozone estimation. Subsequently, the identified models for estimating surface ozone were then utilized to calculate the daily gridded MDA8 values. The mean outcomes derived from these models were treated as the definitive daily gridded MDA8 datasets, characterized by a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), representing the NCP for the period spanning from 2019 to 2021. Furthermore, the research delved into a detailed examination of the spatial and temporal dynamics of MDA8 within the NCP and scrutinized the distribution and trends of high ozone pollution episodes, specifically instances where MDA8 exceeded 160 ug m\({}^{-3}\). This study aimed to comprehensively understand the spatiotemporal characteristics of surface ozone and the potential impact of explanatory variables and external factors on ozone pollution.