Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality

Everincreasing ambient ozone(O3)pollution in China has been exacerbating cardiopulmonary premature deaths.However,the urban-rural exposure inequity has seldom been explored.Here,we assess populationcale 03 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence.We find Chinese population have been suffering from climbing 03 exposure by 4.3±2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities.Rural residents are broadly exposed to 9.8±4.1 ppb higher ambient O3 than the adjacent urban citizens,and thus urbaniza-tion-oriented migration compromises the exposure-associated mortality on total population.Cardiopulmonary excess premature deaths attributable to longterm 03 exposure,373,500(95%uncertainty interval[U]:240,600-510,900)in 2019,is underestimated in previous studies due to ignorance of cardiovascular causes.Future 03 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.

Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models

Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget.Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority,and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required.Building on the Coupled Model Intercomparison Project Phase 6(CMIP6),we have transplanted a conventional ensemble learning approach,and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset,both of which have realised outstanding performances(R2>0.95,RMSE<2.12 ppbv).The conventional ensemble learning approach is computationally cheaper and results in higher overall performance,but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable.The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability,but induces heavier computational burdens.Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.

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

Long-term ozone(O_(3))exposure may lead to non-communicable diseases and increase mortality risk.However,cohort-based studies are relatively rare,and inconsistent exposure metrics impair the credibility of epidemiological evidence synthetization.To provide more accurate meta-estimations,this study updates existing systematic reviews by including recent studies and summarizing the quantitative associations between O_(3) exposure and cause-specific mortality risks,based on unified exposure metrics.Cross-metric conversion factors were estimated linearly by decadal observations during 1990-2019.