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

Abstract

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 (R 2 = 0.89–0.97, RMSE = 1.97–3.42 ppbV), fair prediction accuracy (R 2 = 0.69–0.77, RMSE = 5.63–7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R 2 = 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.

Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis

“… 50 This estimation was much higher than the 2019 GBD reported figure of 0.31 (95% UI: 0.15–0.49) million, as highlighted in another recent study. 25 This should be attributed to the use of high HR value among all included studies. We also found other studies using a singular HR value for population risk estimations 17 , 74 , 75 , 76 , 77 but would encourage further relevant studies to consider multi-study pooled RRs.…”

“…There were two major units used to quantify the surface O 3 concentrations, nmol mol - 1 (or parts per billion by volume mixing ratio, ppbV), more frequently used by atmospheric modeling researchers, 17 , 18 , 25 and milligram per cubic meter by mass concentration (μg/m 3 ) widely used by public health studies. 12 These two units are interchangeable based on the ideal gas law PV = nRT , if the air temperatures (T) and pressures (P) are given, as presented in Equations 1 , 2 , 3 , and 4 .…”

Long-term ozone exposure associated cause-specific mortality risks with adjusted metrics by cohort studies: A systematic review and meta-analysis

“…For example, Malley et al (2017) estimated 1.23 (95% UI: 0.85-1.62) million respiratory deaths attributable to O3 exposure in 2010, 73 using the risk strength by Turner et al (2016) as HR = 1.12 (95% UI: 1.08-1.16). 50 This estimation was much higher than the 2019 GBD report: 0.31 (95% UI: 0.15-0.49) million, as had been highlighted in another recent study, 25 which should be attributed to the use of high HR value among all included studies. We had also found some other studies using one singular HR value for population risk estimations, 17,[74][75][76][77] but we would still encourage further relevant studies to consider multi-study pooled RRs, which could effectively reduce the potential biases from a single study.…”

“…The adaptability of the pooled RRs could be verified from the coverage of exposure levels, as the 25 studies identified in our review had embraced a wide range of exposure concentrations (Supplementary Text S1) to encompass the global surface O 3 variability. 25 On the other hand, the leave-one-out sensitivity analyses (Table S6) had revealed the robustness of the meta-analysis results when including sufficient numbers of studies, which was a circumstantial reflection for the representativeness of the synthesised risk association strengths. The annual GBD reports were also based on the generalisability presumption of the synthesised epidemiological evidences, but cohort-based researches in the unstudied regions are always appealed for to provide more convincing discoveries.…”

“…There were two major units used to quantify the surface O 3 concentrations, nmol mol -1 or parts per billion by volume mixing ratio (ppbV) more frequently used by atmospheric modelling researchers, 17, 18, 25 and milligram per cubic metre by mass concentration (µg/m 3 ) widely used by public health studies. 12 These two units are interchangeable to each other based on the ideal gas law PV n RT , if the air temperatures (T) and pressures (P) are given, as presented in eqs 1–4.…”