## 1 Introduction
Empirical models, based on statistical principles, involve analyzing historical observational data and environmental conditions to establish mathematical models that forecast surface ozone concentrations. Models such as land use regression (LUR), Bayesian maximum entropy (BME), generalized additive model (GAM), and geographically weighted regression (GWR) are all statistical models. Statistical models are relatively simple to implement, but these models are often relatively low in accuracy and are limited by data quality and reliability. Artificial intelligence including machine learning and deep learning is popular for its powerful data mining capabilities. It facilitates the extraction of valuable insights from vast datasets, leading to more accurate parameter estimation through various techniques such as neural networks, random forests (RF), and extreme gradient boosting (XGBoost). Machine learning and deep learning models are simpler than the implementation of deterministic models, and the accuracy of parameter estimation is higher than that of statistical models. Compared with previous studies, this paper utilized high spatial resolution NOx and HCHO observations derived from S5P satellite. These S5P-NO2 and S5P-HCHO datasets have been used sparingly in previous studies and have higher integrity than other satellite observations. In addition, the spatial resolution of the ozone estimation results in most studies was 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) or coarser, and only a few studies have estimated surface ozone products with a resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), therefore, it is necessary to estimate O3 concentration products with a higher resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). In recent years, as machine learning models have seen rapid development, the XGBoost model, recognized for its efficiency as an integrated learning algorithm, has been demonstrated to possess robust predictive capabilities and has been projected to have extensive applications in the fields of data mining and predictive modeling. The NCP, which is characterized by a developed economy, dense population, and high degree of urbanization, is also considered to be one of the most polluted areas in China, especially in terms of ozone pollution, so it was selected as the study area for the study. Consequently, an XGBoost-based model was developed to estimate the concentration of ozone in the NCP. Once the model was constructed, its precision was validated. Subsequently, an assessment was conducted to determine the relative significance and the extent of the contribution of various factors affecting near-surface ozone pollution. Subsequently, it was utilized to calculate the gridded daily maximum daily 8-h average (MDA8) concentration data at a resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) for the NCP over the period from 2019 to 2021. Utilizing the MDA8 estimation outcomes, insights into the spatial and temporal patterns of O\({}_{3}\) pollution were uncovered. Additionally, the study provided a further analysis of the spatiotemporal distribution of ozone pollution in the region, considering the frequency of ozone pollution exceedances. These studies have been instrumental in enhancing our comprehension of ground-level O\({}_{3}\) pollution and in the development of targeted environmental protection policies.