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.
Many studies have shown that climate change has a significant impact on crop yield in China, while results have varied due to uncertain factors. This study has drawn a highly consistent consensus from the scientific evidence based on numerous existing studies. By a highly rational systematic review methodology, we obtained 737 result samples with the theme of climate change affecting China’s crop yields. Then, we used likelihood scale and trend analysis methods to quantify the consensus level and uncertainty interval of these samples. The results showed that: (i) The crop yield decrease in the second half of the 21st century will be greater than 5% of that in the first half. (ii) The crop most affected by climate change will be maize, with the decreased value exceeding −25% at the end of this century, followed by rice and wheat exceeding −10% and −5%. (iii) The positive impact of CO2 on crop yield will change by nearly 10%. Our conclusions clarify the consensus of the impact of future climate change on China’s crop yield, and this study helps exclude the differences and examine the policies and actions that China has taken and should take in response to climate change.
The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector.
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