## 3 Method  
### Statistical Modeling Methods  
XGBoost, a gradient boosting tree model, was designed to construct a robust prediction framework by amalgamating various weak learners, typically decision trees. The fundamental concept underlying XGBoost involves the application of ensemble learning to enhance the overall model's performance through the amalgamation of predictions from multiple models. \"Gradient boosting\" refers to an iterative process in which each iteration is designed to correct for the residuals of the previous model, leading the model closer to the actual predicted value. The term \"gradient boosting\" refers to the iterative process where each iteration aims to rectify the residuals from the preceding model, thereby guiding the model incrementally closer to the actual predicted values. XGBoost employs decision trees as its base learner, with each iteration training a new tree model based on the residuals of the prior model, subsequently aggregating the predictions of this new tree with those of the previous model. To mitigate overfitting, regularization terms were integrated into XGBoost including L1 and L2 regularization of leaf node weights. Additionally, XGBoost incorporates the notion of a learning rate, which governs the weight of the new model in each iteration, thus modulating the rate of model update. By judiciously choosing the learning rate and number of iterations, XGBoost is able to significantly enhance the model's stability and generalization capabilities. The model boasts efficient implementation, leveraging optimized data structures and algorithms, which facilitates faster training on extensive datasets. Before using XGBoost to build a surface ozone estimation model, it is necessary to collect relevant data including data on various explanatory variables of ozone and monitoring data on surface ozone concentration sites. The construction of a surface ozone estimation model employing XGBoost commenced with the collection of pertinent data, which included the procurement of surface ozone concentration readings and a variety of explanatory variables. The dataset was then divided into a training set, a test set, and a validation set, and the XGBoost regression model was subsequently initialized and trained. By iteratively optimizing the decision tree through gradient boosting, XGBoost gradually revealed and expressed the complex interaction between the surface ozone and influencing factors. After the model was trained, the test set and the validation set were used to evaluate the accuracy of the model, and the accuracy of the model was evaluated by metrics such as the mean square error. Post the training phase, the test set was utilized to evaluate the model, with accuracy assessed using indicators such as the mean square error. By integrating NO\({}_{2}\) and HCHO data obtained from satellite-based monitoring (SSP-TROPOMI) and re-analysis sources (CAMS), four distinct XGBoost models for estimating the surface ozone levels in the NCP were developed, utilizing varied datasets of NO\({}_{2}\) and HCHO, as outlined below:  
\[\text{surface-O}_{3}\text{-f (SSP-NO}_{2}\text{/CAMS-NO}_{2}\text{, }\text{SP-HCHO/CAMS-HCHO, CAMS-O}_{3}\text{, VOCs, CAMS-NO}_{X}\text{, BLH,} \tag{1}\]  
Among them, surface-O\({}_{3}\) denotes the ground-based MDA8 observational data, while SSP-NO\({}_{2}\) and SSP-HCHO correspond to the column densities of tropospheric NO\({}_{2}\) and total HCHO, respectively. The terms BLH, RH, T, TCC, TP, SP, UV, WS, and WD are indicative of the boundary layer height, 1000 hPa relative humidity, surface 2-m temperature, total cloud cover, total precipitation, surface pressure, ultraviolet radiation, wind speed, and wind direction. In the construction of the surface ozone estimation model, a collection of six individual datasets from the CAMS VOC inventory was integrated.