Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) $R^2$ of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

估算地面臭氧浓度对于研究臭氧暴露对健康的不利影响以及更好地了解地面臭氧对生物多样性和植被的影响至关重要。然而，很少有研究尝试使用卫星检索到的臭氧作为指标，因为它们在边界层的敏感性较低。使用对流层监测仪 (TROPOMI) 的总臭氧柱和臭氧监测仪 (OMI) 检索到的臭氧剖面信息，由于 TROPOMI 臭氧剖面产品尚未发布，我们开发了一个机器学习模型来估计每日最大 8 - 加利福尼亚州 10 公里空间分辨率下的小时平均地面臭氧浓度。除了卫星参数，我们将来自高分辨率快速刷新 (HRRR) 系统的 3 公里分辨率的气象场和土地利用信息作为预测因子。我们的模型实现了整体 10 倍交叉验证（CV）$R^2$为 0.84，均方根误差 (RMSE) 为 0.0059 ppm，表明模型预测与观测值吻合良好。模型预测显示，洛杉矶都会区郊区的臭氧含量最高，而湾区和太平洋沿岸的臭氧含量最低。南加州和中央山谷东侧的臭氧水平也很高。与没有它的类似模型相比，TROPOMI 数据改进了对极值的估计。我们的研究证明了在地面臭氧浓度时空表征中使用 TROPOMI 数据的可行性和价值。