Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler

Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O3 concentration database. In response to this demand, we innovatively construct a space–time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R2 = 0.89–0.97, RMSE = 1.97–3.42 ppbV), fair prediction accuracy (R2 = 0.69–0.77, RMSE = 5.63–7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R2 = 0.62–0.76, RMSE = 5.38–11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O3 are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990–2019, with developing countries being of the most evident differences. The globe-wide urban surface O3 are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O3 tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban–rural differentiation will lead to underestimations of population O3 exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O3 database with multiple metrics fills in the literature gap for long-term surface O3 exposure tracing.