The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles but coincided with Beijing experiencing “pandemic haze,” raising the ...public concerns about the effectiveness of imposed traffic policies to improve the air quality. This paper explores the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown based on an integrated analysis framework, which combines a real-time on-road emission inventory, in situ air quality observations, and a localized numerical modeling system. We found that traffic emissions decreased substantially during the COVID-19 pandemic, but its imbalanced emission abatement of NO x (76%, 125.3 Mg/day) and volatile organic compounds (VOCs, 53%, 52.9 Mg/day) led to a significant rise of atmospheric oxidants in urban areas, resulting in a modest increase in secondary aerosols due to inadequate precursors, which still offset reduced primary emissions. Moreover, the enhanced oxidizing capacity in the surrounding regions greatly increased the secondary particles with relatively abundant precursors, which was transported into Beijing and mainly responsible for the aggravated haze pollution. We recommend that mitigation policies should focus on accelerating VOC emission reduction and synchronously controlling regional sources to release the benefits of local traffic emission control.
Tropospheric ozone in CMIP6 simulations Griffiths, Paul T; Murray, Lee T; Zeng, Guang ...
Atmospheric chemistry and physics,
03/2021, Letnik:
21, Številka:
5
Journal Article
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The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled ...atmosphere–ocean chemistry–climate models, focusing on the CMIP Historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric-ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde and satellite observations of the past several decades and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The multi-model mean tropospheric-ozone burden increases from 247 ± 36 Tg in 1850 to a mean value of 356 ± 31 Tg for the period 2005–2014, an increase of 44 %. Modelled present-day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP: 337 ± 23 Tg; Tropospheric Ozone Assessment Report, TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden increases to 416 ± 35 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production (PO3) and loss (LO3) diagnostics. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production (PO3-LO3) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere–troposphere transport reaches a minimum around the same time before recovering in the 21st century, while dry deposition increases steadily over the period 1850–2100. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.
A stratosphere-resolving configuration of the Met Office's Unified Model (UM) with the United Kingdom Chemistry and Aerosols (UKCA) scheme is used to investigate the atmospheric response to changes ...in (a) greenhouse gases and climate, (b) ozone-depleting substances (ODSs) and (c) non-methane ozone precursor emissions. A suite of time-slice experiments show the separate, as well as pairwise, impacts of these perturbations between the years 2000 and 2100. Sensitivity to uncertainties in future greenhouse gases and aerosols is explored through the use of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The results highlight an important role for the stratosphere in determining the annual mean tropospheric ozone response, primarily through stratosphere–troposphere exchange (STE) of ozone. Under both climate change and reductions in ODSs, increases in STE offset decreases in net chemical production and act to increase the tropospheric ozone burden. This opposes the effects of projected decreases in ozone precursors through measures to improve air quality, which act to reduce the ozone burden. The global tropospheric lifetime of ozone (τO3) does not change significantly under climate change at RCP4.5, but it decreases at RCP8.5. This opposes the increases in τO3 simulated under reductions in ODSs and ozone precursor emissions. The additivity of the changes in ozone is examined by comparing the sum of the responses in the single-forcing experiments to those from equivalent combined-forcing experiments. Whilst the ozone responses to most forcing combinations are found to be approximately additive, non-additive changes are found in both the stratosphere and troposphere when a large climate forcing (RCP8.5) is combined with the effects of ODSs.
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.
The Montreal Protocol has succeeded in limiting major ozone-depleting substance emissions, and consequently stratospheric ozone concentrations are expected to recover this century. However, there is ...a large uncertainty in the rate of regional ozone recovery in the Northern Hemisphere. Here we identify a Eurasia-North America dipole mode in the total column ozone over the Northern Hemisphere, showing negative and positive total column ozone anomaly centres over Eurasia and North America, respectively. The positive trend of this mode explains an enhanced total column ozone decline over the Eurasian continent in the past three decades, which is closely related to the polar vortex shift towards Eurasia. Multiple chemistry-climate-model simulations indicate that the positive Eurasia-North America dipole trend in late winter is likely to continue in the near future. Our findings suggest that the anticipated ozone recovery in late winter will be sensitive not only to the ozone-depleting substance decline but also to the polar vortex changes, and could be substantially delayed in some regions of the Northern Hemisphere extratropics.
Ozone (O
) isopleths describe the nonlinear responses of O
concentrations to changes in nitrogen oxides (NO
) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O
...control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O
pollution of China in 2017 and derive O
isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O
pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O
as 160 μg·m
(81.5 ppbV equally). The O
responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NO
-saturated regimes, where a small amount of NO
reduction increases O
. Ambient O
pollution in the eastern region generally is limited by VOCs, while in the west by NO
. The city-specific O
isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O
abatement in China.
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.
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•Leading-edge approach to fuse multiple state-of-the-art chemistry-climate models.•Statistical calibration for raw model simulation with long-term in-situ observation.•Ensemble machine learning method with high accuracy and computation efficiency.•Bayesian neural network ensembler with better interpretability and generalisability.•25-year historical surface ozone database construction for human impact studies.
Abstract Methane, a potent greenhouse gas, is a significant contributor to global warming, with future increases in its abundance potentially leading to an increase of more than 1 ∘ C by 2050 beyond ...other greenhouse gases if left unaddressed. To remain within the crucial target of limiting global warming to 1.5 ∘ C, it is imperative to evaluate the potential of methane removal techniques. This study presents a scoping analysis of different catalytic technologies (thermal, photochemical and electrochemical) and materials to evaluate potential limitations and energy requirements. An analysis of mass transport and reaction rates is conducted for atmospheric methane conversion system configurations. For the vast majority of catalytic technologies, the reaction rates limit the conversion which motivates future efforts for catalyst development. An analysis of energy requirements for atmospheric methane conversion shows minimum energy configurations for various catalytic technologies within classic tube or parallel plate architectures that have analogs to ventilation and industrial fins. Methane concentrations ranging from 2 ppm (ambient) to 1000 ppm (sources, such as wetlands, fossil-fuel extraction sites, landfills etc) are examined. The study finds that electrocatalysis offers the most energy efficient approach (∼0.2 GJ tonne −1 CO 2 e) for new installations in turbulent ducts, with a total energy intensity < 1 GJ tonne −1 CO 2 e. Photocatalytic methane removal catalysts are moderately more energy intensive (∼2 GJ tonne −1 CO 2 e), but could derive much of their energy input from ‘free’ solar energy sources. Thermal systems are shown to be excessively energy intensive ( > 100 GJ tonne −1 ), while combining photovoltaics with electrochemical catalysts (∼1 GJ tonne −1 CO 2 e) have comparable energy intensity to photocatalytic methane removal catalysts.
Field campaigns have been carried out with the FAGE (fluorescence assay by gas expansion) technique in remote biogenic environments in the last decade to quantify the in situ concentrations of OH, ...the main oxidant in the atmosphere. These data have revealed concentrations of OH radicals up to a factor of 10 higher than predicted by models, whereby the disagreement increases with decreasing NO concentration. This was interpreted as a major lack in our understanding of the chemistry of biogenic VOCs (volatile organic compounds), particularly isoprene, which are dominant in remote pristine conditions. But interferences in these measurements of unknown origin have also been discovered for some FAGE instruments: using a pre-injector, all ambient OH is removed by fast reaction before entering the FAGE cell, and any remaining OH signal can be attributed to an interference. This technique is now systematically used for FAGE measurements, allowing the reliable quantification of ambient OH concentrations along with the signal due to interference OH. However, the disagreement between modelled and measured high OH concentrations of earlier field campaigns as well as the origin of the now-quantifiable background OH is still not understood. We present in this paper the compelling idea that this interference, and thus the disagreement between model and measurement in earlier field campaigns, might be at least partially due to the unexpected decomposition of a new class of molecule, ROOOH, within the FAGE instruments. This idea is based on experiments, obtained with the FAGE set-up of the University of Lille, and supported by a modelling study. Even though the occurrence of this interference will be highly dependent on the design and measurement conditions of different FAGE instruments, including ROOOH in atmospheric chemistry models might reflect a missing piece of the puzzle in our understanding of OH in clean atmospheres.
We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted ...of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (~40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (~50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.
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