Ultra-high-resolution modeling of ground-level ozone for long-term exposure risk assessment driven by GTR-transformer

To address the demand for fine-grained, long-term exposure assessment of ground-level ozone pollution in China, this study overcomes the limitations of traditional kilometer-level resolution modeling by developing the geo-temporal transformer with residual enhancement (GTR-Transformer) model. This model integrates bidirectional temporal attention and spatial encoding techniques to construct a dataset of monthly average ozone concentrations at an ultra-high (100-m) resolution across representative regions of China from 2020 to 2023. The model exhibits outstanding performance in multi-dimensional validation, achieving a coefficient of determination of up to 0.94 and a root mean square error as low as 7.59 μg/m³. Employing SHapley Additive exPlanations (SHAP) analysis, we identify key drivers of ozone, including 2 m dewpoint temperature and surface solar radiation downwards (SHAP: 4.88 and 3.83, respectively), substantiating the photochemical regulation mechanism. Moreover, across scales spanning regions, counties, neighborhoods, and even blocks, our dataset reveals distinct ozone distribution patterns and concentration gradients that strongly correlated with road density. Furthermore, the 100-m resolution ozone maps showed an absolute difference of up to 8.9 percentage points in county-scale exposure assessment compared to 1-km resolution data in 2020. This disparity is further magnified when combined with the same ultra-high-resolution population data. These findings demonstrate that the proposed model and ultra-high-resolution maps provide actionable support for precise ozone control and exposure assessment.