Abstract

Surface ozone (O3) is a crucial ambient pollutant gas that poses substantial risks to both human health and ecosystems. Nonetheless, there is a scarcity of high-spatial-resolution hourly surface O3 data, particularly during the day when this information is needed due to the strong diurnal variations of O3. We thus determined a best-performing artificial intelligence model to derive 24-hourly 1-kmresolution surface O3 concentrations in China from a large array of satellite and surface data, which can portray well the diurnal variations of O3 concentration. The overall sample-based crossvalidated coefficients of determination (root-mean-square error) are 0.89 (15.74 μg/m 3), 0.91 (14.91 μg/m 3), and 0.85 (16.31 μg/m 3) during the full day (00:00-23:00 local time, or LT), daytime (08:00-17:00 LT), and nighttime (18:00-07:00 LT), respectively. The surface O3 level generally rises from sunrise, around 07:00 LT, reaching a peak at ~15:00 LT, then continuously declining overnight. The magnitude of the diurnal variation amounts to 180% relative to its diurnal mean level. During daytime, solar radiation in the ultraviolet and shortwave spectral bands, along with temperature, explain more than half (32% and 24%) of the diurnal variations using the interpretable SHapley Additive exPlanations (SHAP) method, while nighttime O3 levels are dominated by temperature (31%) and relative humidity (16%). In 2018, approximately 59%, 93%, and 100% of populated areas were susceptible to O3 exposure risk for at least one day, with the maximum daily average 8-h O3 levels surpassing the World Health Organization's recommended daily air quality standards of 160 µg/m³, 120 µg/m³, and 100 µg/m³, respectively. Approximately 65%, 70%, and 99% of vegetated areas in China exceed the minimum critical levels for O3 mixing ratios, as determined by the sum of all hourly values ≥ 0.06 μmol mol-1 (SUM06), the sigmoidally weighted sum of all hourly values (W126), and accumulates over the threshold of 40 nmol mol-1 (AOT40), respectively. Notably, gross primary productivity stands out as the most responsive indicator to surface O3 pollution across various vegetated types in China, especially concerning the Hourly O3 Accumulates without Threshold (AOT0, R =-0.37-0.53, p < 0.001).