Contrasting chemical environments in summertime for atmospheric ozone across major Chinese industrial regions: the effectiveness of emission control strategies

Liu, Zhenze; Doherty, Ruth M.; Wild, Oliver; Hollaway, Michael; O’Connor, Fiona M.

doi:10.5194/acp-21-10689-2021

Cited by 28 publications

(10 citation statements)

References 70 publications

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“…A comprehensive analysis of the factors driving the escalation of ozone concentrations during clean air actions in China reveals that the lack of VOCs control and the decline in NO x emissions were responsible for the initial increase in ozone levels from 2013−2017, while the synergistic control of VOCs and NO x initially worked to mitigate ozone pollution during 2018−2020. 66,67 Notably, the implementation of emission reduction policies may have partially contributed to the inflection point in Shenzhen's ozone trends around 2018− 2019, which is consistent with existing observation-based studies concluding that the Pearl Delta River region met the turning point between NO x -and VOCs-limited regimes around 2019. 68 From 2015 to 2018, our analysis suggests that emission reduction contributed to a 2.6 μg•m −3 rise in MDA8h O 3 levels in Shenzhen (Figure 4).…”

Section: Environmentalsupporting

confidence: 85%

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Mai,

Shen,

Zhang

et al. 2024

Environ. Sci. Technol.

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Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13°× 13°spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing −5−6 μg• m −3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

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Section: Environmentalsupporting

confidence: 85%

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Mai,

Shen,

Zhang

et al. 2024

Environ. Sci. Technol.

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“…Other aspects of the emissions used here are the same as described in Turnock et al (2020). Anthropogenic emissions are categorised into five sectors (industry, power plants, transport, residences and agriculture) as inputs to the model, with independent diurnal and vertical emission profiles applied for each sector (Bieser et al, 2011;Mailler et al, 2013;Liu et al, 2021).…”

Section: Emissions and Experimentsmentioning

confidence: 99%

Tropospheric ozone changes and ozone sensitivity from present-day to future under shared socio-economic pathways

Liu

Doherty

Wild

et al. 2021

Preprint

Self Cite

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Abstract. Tropospheric ozone is important to future air quality and climate. We investigate ozone changes and ozone sensitivity to changing emissions in the context of climate change from the present day (2004–2014) to the future (2045–2055) under a range of shared socio-economic pathways (SSPs). We apply the United Kingdom Earth System Model, UKESM1, with an extended chemistry scheme including more reactive volatile organic compounds (VOCs) to quantify ozone burdens as well as ozone sensitivities globally and regionally based on nitrogen oxide (NOx) and VOC concentrations. We show that the tropospheric ozone burden increases by 4 % under a development pathway with higher NOx and VOC emissions (SSP3-7.0), but decreases by 7 % under the same pathway if NOx and VOC emissions are reduced (SSP3-7.0-lowNTCF) and by 5 % if atmospheric methane (CH4) concentrations are reduced (SSP3-7.0-lowCH4). Global mean surface ozone concentrations are reduced by 3–5 ppb under SSP3-7.0-lowNTCF and by 2–3 ppb under SSP3-7.0-lowCH4. However, surface ozone changes vary substantially by season in high-emission regions under future pathways, with decreased ozone concentrations in summer and increased ozone concentrations in winter when NOx emissions are reduced. VOC-limited areas are more extensive in winter (7 %) than in summer (3 %) across the globe. North America, Europe and East Asia are the dominant VOC-limited regions in the present day but North America and Europe become more NOx-limited in the future mainly due to reductions in NOx emissions. The impacts of VOC emissions on O3 sensitivity are limited in North America and Europe because reduced anthropogenic VOC emissions are offset by higher biogenic VOC emissions. O3 sensitivity is not greatly influenced by changing CH4 concentrations. South Asia becomes the dominant VOC-limited region under future pathways. We highlight that reductions in NOx emissions are required to transform O3 production from VOC- to NOx-limitation, but that these lead to increased O3 concentrations in high-emission regions, and hence emission controls on VOC and CH4 are also necessary.

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“…We note that Eq. 5 is defined empirically, as we find that the assimilated O3 matches emissions (Zheng et al, 2018;Jiang et al, 2022) and the reported NOx-limited O3 nonlinear chemical regimes in model simulations (Chen et al, 2021;Liu et al, 2021).…”

Section: Surface O3 By Assimilating Mee O3 Observationsmentioning

confidence: 55%

Rapid assimilations of O₃ observations – Part 1: methodology and tropospheric O₃ changes in China in 2015–2020

Zhu

Tang

Chen

et al. 2023

Preprint

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Abstract. The high computational cost of chemical transport models (CTMs) is a potential bottleneck for the rapid assimilation of ozone (O3) observations. Here we developed a single tracer tagged-O3 mode to build the capability of the GEOS-Chem model for rapid simulation of tropospheric O3. The tagged-O3 mode demonstrates high consistency with GEOS-Chem full-chemistry simulation and dramatic reductions in computational costs by approximately 91–94 %. The tagged-O3 simulation was combined with China Ministry of Ecology and Environment (MEE) and Ozone Monitoring Instrument (OMI) O3 observations to investigate the changes in tropospheric O3 over E. Asia in 2015–2020. The assimilated O3 concentrations demonstrate good agreement with O3 observations: surface O3 concentrations are 42.9, 41.8 and 42.1 ppb; and tropospheric O3 columns are 37.1, 37.9 and 38.0 DU in the simulations, assimilations and observations, respectively. The assimilations indicate rapid increases in surface O3 by 1.60 (spring), 1.16 (summer), 1.47 (autumn) and 0.80 (winter) ppb yr-1 over E. China in 2015–2020, and the increasing trends are underestimated by the a priori simulations. More attention is thus suggested to the rapid increases in O3 pollution in spring and autumn. Furthermore, we find stronger increases in tropospheric O3 columns over highly polluted areas, which may reflect the larger contributions of local emissions. The large discrepancy in the trends in tropospheric O3 columns by assimilating surface and satellite observations further indicates the possible uncertainties in the derived free tropospheric O3 changes. The rapid O3 assimilation capability is a useful tool for the extension and interpretation of atmospheric O3 observations.

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Contrasting chemical environments in summertime for atmospheric ozone across major Chinese industrial regions: the effectiveness of emission control strategies

Cited by 28 publications

References 70 publications

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Tropospheric ozone changes and ozone sensitivity from present-day to future under shared socio-economic pathways

Rapid assimilations of O₃ observations – Part 1: methodology and tropospheric O₃ changes in China in 2015–2020

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Contrasting chemical environments in summertime for atmospheric ozone across major Chinese industrial regions: the effectiveness of emission control strategies

Cited by 28 publications

References 70 publications

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Tropospheric ozone changes and ozone sensitivity from present-day to future under shared socio-economic pathways

Rapid assimilations of O3 observations – Part 1: methodology and tropospheric O3 changes in China in 2015–2020

Contact Info

Product

Resources

About

Rapid assimilations of O₃ observations – Part 1: methodology and tropospheric O₃ changes in China in 2015–2020