Ozone Variations Research Articles

Ozone variability, its formation potential and crops losses in the himalayan foothills

Ozone variability, its formation potential and crops losses in the himalayan foothills

Urban ozone variability using automated machine learning: inference from different feature importance schemes.

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

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.

A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends

Abstract. High-resolution modelling of surface ozone is an essential step in the quantification of the impacts on health and ecosystems from historic and future concentrations. It also provides a principled way in which to extend analysis beyond measurement locations. Often, such modelling uses relatively coarse-resolution chemistry transport models (CTMs), which exhibit biases when compared to measurements. EMEP4UK is a CTM that is used extensively to inform UK air quality policy, including the effects on ozone from mitigation of its precursors. Our evaluation of EMEP4UK for the years 2001–2018 finds a high bias in reproducing daily maximum 8 h average ozone (MDA8), due in part to the coarse spatial resolution. We present a machine learning downscaling methodology to downscale EMEP4UK ozone output from a 5×5 km to 1×1 km resolution using a gradient-boosted tree. By addressing the high bias present in EMEP4UK, the downscaled surface better represents the measured data, with a 128 % improvement in R2 and 37 % reduction in RMSE. Our analysis of the downscaled surface shows a decreasing trend in annual and March–August mean MDA8 ozone for all regions of the UK between 2001–2018, differing from increasing measurement trends in some regions. We find the proportion of the UK which fails the government objective to have at most 10 exceedances of 100 µg m−3 per annum is 27 % (2014–2018 average), compared to 99 % from the unadjusted EMEP4UK model. A statistically significant trend in this proportion of −2.19 % yr−1 is found from the downscaled surface only, highlighting the importance of bias correction in the assessment of policy metrics. Finally, we use the downscaling approach to examine the sensitivity of UK surface ozone to reductions in UK terrestrial NOx (i.e. NO + NO2) emissions on a 1×1 km surface. Moderate NOx emission reductions with respect to present day (20 % or 40 %) increase both average and high-level ozone concentrations in large portions of the UK, whereas larger NOx reductions (80 %) cause a similarly widespread decrease in high-level ozone. In all three scenarios, very urban areas (i.e. major cities) are the most affected by increasing concentrations of ozone, emphasizing the broader air quality challenges of NOx control.

Long-term variations in surface ozone at the Longfengshan Regional Atmosphere Background Station in Northeast China and related influencing factors

Surface ozone (O3) is a crucial air pollutant that affects air quality, human health, agricultural production, and climate change. Studies on long-term O3 variations and their influencing factors are essential for understanding O3 pollution and its impact. Here, we conducted an analysis of long-term variations in O3 during 2006–2022 at the Longfengshan Regional Atmosphere Background Station (LFS; 44.44°N, 127.36°E, 330.5 m a.s.l.) situated on the northeastern edge of the Northeast China Plains. The maximum daily 8-h average (MDA8) O3 fluctuated substantially, with the annual MDA8 decreasing significantly during 2006–2015 (−0.62 ppb yr−1, p < 0.05), jumping during 2015–2016 and increasing clearly during 2020–2022. Step multiple linear regression models for MDA8 were obtained using meteorological variables, to decompose anthropogenic and meteorological contributions to O3 variations. Anthropogenic activities acted as the primary drivers of the long-term trends of MDA8 O3, contributing 73% of annual MDA8 O3 variability, whereas meteorology played less important roles (27%). Elevated O3 at LFS were primarily associated with airflows originating from the North China Plain, Northeast China Plain, and coastal areas of North China, primarily occurring during the warm months (May–October). Based on satellite products of NO2 and HCHO columns, the O3 photochemical regimes over LFS revealed NOx-limited throughout the period. NO2 increased first, reaching peak in 2011, followed by substantial decrease; while HCHO exhibited significant increase, contributing to decreasing trend in MDA8 O3 during 2006–2015. The plateauing NO2 and decreasing HCHO may contribute to the increase in MDA8 O3 in 2016. Subsequently, both NO2 and HCHO exhibited notable fluctuations, leading to significant changes in O3. The study results fill the gap in the understanding of long-term O3 trends in high-latitude areas in the Northeast China Plain and offer valuable insights for assessing the impact of O3 on crop yields, forest productivity, and climate change.

Chemical ozone loss and chlorine activation in the Antarctic winters of 2013–2020

Abstract. The annual formation of an ozone hole in the austral spring has regional and global climate implications. The Antarctic ozone hole has already changed the precipitation, temperature and atmospheric circulation patterns, and thus the surface climate of many regions in the Southern Hemisphere (SH). Therefore, the study of ozone loss variability is important to assess its consequential effects on the climate and public health. Our study uses satellite observations from the Microwave Limb Sounder on Aura and the passive-tracer method to quantify the ozone loss for the past 8 years (2013–2020) in the Antarctic. We observe the highest ozone loss (about 3.5 ppmv) in 2020, owing to the high chlorine activation (about 2.2 ppbv), steady polar vortex, and huge expanses of polar stratospheric clouds (PSCs) (12.6×106 km2) in the winter. The spring of 2019 also showed a high ozone loss, although the year had a rare minor warming in mid-September. The chlorine activation in 2015 (1.9 ppbv) was the weakest, and the wave forcing from the lower latitudes was very high in 2017 (up to −60 km s−1). The analysis shows significant interannual variability in the Antarctic ozone as compared to the immediate previous decade (2000–2010). The study helps to understand the role of dynamics and chemistry in the interannual variability of ozone depletion over the years.

Validation of geostationary environment monitoring spectrometer (GEMS), TROPOspheric Monitoring Instrument (TROPOMI), and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS) using pandora measurements during GEMS Map of Air Pollution (GMAP) field campaign

This study presents the validation of total column ozone (TCO) data retrieved from the Geostationary Environment Monitoring Spectrometer (GEMS) against ground-based Pandora spectrometer observations during the GEMS Map of Air Pollution (GMAP) campaign from November 2020 to January 2021 in Seosan, South Korea. To evaluate the accuracy of the Pandora TCO measurements obtained during the campaign period, all Pandora instruments were installed at the Seosan supersite for intercomparison analysis. Subsequently, the instruments were relocated to four sites for direct sunlight measurements. The Pandora instruments exhibited a high degree of consistency with an average difference of 0.5 ± 1.0 DU. This study demonstrated that accurate comparison of ground-based ozone measurements with satellite ozone measurements depended on the threshold values set for the spatial and temporal alignment of the two datasets and the size of the satellite footprint and viewing angle. The comparison of the GEMS, TROPOspheric Monitoring Instrument (TROPOMI), and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS) satellite TCO with the Pandora TCO showed high agreement across measurements. However, a distinct downward trend was observed in the Mean Bias (MB) for GEMS from December–January, indicating an issue with the GEMS Level 1C irradiance. The validation using hourly Pandora data demonstrated GEMS capability to monitor daily ozone variations but with a bias of approximately −1% to −2.6% compared with that of Pandora.

Air quality and urban climate improvements in the world’s most populated region during the COVID-19 pandemic

In this study, we assessed air quality (AQ) and urban climate during the mobility restrictions implemented in the Greater Tokyo Area, Japan, the world’s most populated region, in response to the COVID-19 pandemic. Observations from dense surface networks were analyzed using an interpretable machine learning approach. In parallel with a ∼50% reduction in mobility and an altered lifestyle of the population, we found limited reductions in nitrogen dioxide; decreases in fine particulate matter not entirely driven by local mobility; minor variations in ozone, with a positive (negative) tendency in areas with high (low) emissions; a decrease in air temperature consistent with mobility; and pollution levels and air temperature changes with well-defined, common spatiotemporal patterns. Specifically, cooling mainly occurred in urbanized areas with an improved AQ. Overall, although reductions in mobility were moderately effective in improving the typical indicators of urban AQ, including those known to negatively impact human health, the reductions in waste heat had a stronger impact on Tokyo’s urban heat island, suggestive of a strategy to minimize exposure to heat stress. These findings can help guide urban planning strategies and policies aimed at addressing climate change.

Influences of downward transport and photochemistry on surface ozone over East Antarctica during austral summer: in situ observations and model simulations

Abstract. Studies of atmospheric trace gases in remote, pristine environments are critical for assessing the accuracy of climate models and advancing our understanding of natural processes and global changes. We investigated the surface ozone (O3) variability over East Antarctica during the austral summer of 2015–2017 by combining surface and balloon-borne measurements at the Indian station Bharati (69.4∘ S, 76.2∘ E, ∼ 35 m above mean sea level) with EMAC (ECHAM5/MESSy Atmospheric Chemistry) atmospheric chemistry–climate model simulations. The model reproduced the observed surface O3 level (18.8 ± 2.3 nmol mol−1) with negligible bias and captured much of the variability (R = 0.5). Model-simulated tropospheric O3 profiles were in reasonable agreement with balloon-borne measurements (mean bias: 2–12 nmol mol−1). Our analysis of a stratospheric tracer in the model showed that about 41 %–51 % of surface O3 over the entire Antarctic region was of stratospheric origin. Events of enhanced O3 (∼ 4–10 nmol mol−1) were investigated by combining O3 vertical profiles and air mass back trajectories, which revealed the rapid descent of O3-rich air towards the surface. The photochemical loss of O3 through its photolysis (followed by H2O + O(1D)) and reaction with hydroperoxyl radicals (O3 + HO2) dominated over production from precursor gases (NO + HO2 and NO + CH3O2) resulting in overall net O3 loss during the austral summer. Interestingly, the east coastal region, including the Bharati station, tends to act as a stronger chemical sink of O3 (∼ 190 pmol mol−1 d−1) than adjacent land and ocean regions (by ∼ 100 pmol mol−1 d−1). This is attributed to reverse latitudinal gradients between H2O and O(1D), whereby O3 loss through photolysis (H2O + O(1D)) reaches a maximum over the east coast. Further, the net photochemical loss at the surface is counterbalanced by downward O3 fluxes, maintaining the observed O3 levels. The O3 diurnal variability of ∼ 1.5 nmol mol−1 was a manifestation of combined effects of mesoscale wind changes and up- and downdrafts, in addition to the net photochemical loss. The study provides valuable insights into the intertwined dynamical and chemical processes governing the O3 levels and variability over East Antarctica.

The Variation Characteristics of Stratospheric Circulation under the Interdecadal Variability of Antarctic Total Column Ozone in Early Austral Spring

Antarctic Total Column Ozone (TCO) gradually began to recover around 2000, and a large number of studies have pointed out that the recovery of the Antarctic TCO is most significant in the austral early spring (September). Based on the Bodeker Scientific Filled Total Column Ozone and ERA5 reanalysis dataset covering 1979–2019, the variation characteristics of the Antarctic TCO and stratospheric circulation for the TCO ‘depletion’ period (1979–1999) and the ‘recovery’ period (2000–2019) are analyzed in September. Results show that: (1) Stratospheric elements significantly related to the TCO have corresponding changes during the two eras. (2) The interannual variability of the TCO and the above-mentioned stratospheric circulation elements in the recovery period are stronger than those in the depletion period. (3) Compared with the depletion period, due to the stronger amplitude of the planetary wave 1, stronger Eliassen–Palm (EP) flux corresponds to EP flux convergence, larger negative eddy heat flux, and positive eddy momentum flux in the stratosphere during the recovery period. The polar temperature rises in the lower and middle stratosphere and the polar vortex weakens in the middle and upper stratosphere, accompanied by the diminished area of PSC. This contributes to the understanding of Antarctic ozone recovery.

Understanding the spatial and seasonal variation of the ground-level ozone in Southeast China with an interpretable machine learning and multi-source remote sensing

Ground-level ozone (O3) pollution poses significant threats to both human health and air quality. This study uses ground observations and satellite retrievals to explore the spatiotemporal characteristics of ground-level O3 in Zhejiang Province, China. We created data-driven machine learning models that include meteorological, geographical and atmospheric parameters from multi-source remote sensing products, achieving good performance (Pearson's r of 0.81) in explaining regional O3 dynamics. Analyses revealed the crucial roles of temperature, relative humidity, total column O3, and the distributions and interactions of precursor (volatile organic compounds and nitrogen oxides) in driving the varied O3 patterns observed in Zhejiang. Furthermore, the interpretable modeling quantified multifactor interactions that sustain high O3 levels in spring and autumn, suppress O3 levels in summer, and inhibit O3 formation in winter. This work demonstrates the value of a combined approach using satellite and machine learning as an effective novel tool for regional air quality assessment and control.

Long-term trajectory of ozone impact on maize and soybean yields in the United States: A 40-year spatial-temporal analysis

Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone's influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981–2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of −3.5% for maize and −6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

Spatial and temporal variations of surface background ozone in China analyzed with the grid-stretching capability of GEOS-Chem High Performance

Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai–Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.

Trend analysis of measured surface ozone at the megacity of Tehran for the summertime 2007–2021

High concentrations of surface ozone in urban and industrial regions worldwide have been a major air quality issue. Tehran, as the capital and the most populous city of Iran, has experienced intense ambient air pollution over the last three decades. This study aims to assess the long-term trends of ozone over this city in recent decades. Data series of maximum 8-h daily average (MDA8) ozone were obtained for the period between 2007 and 2021. To determine the MDA8 ozone trend attributed to the long-term changes in ozone precursor emissions, we isolated the effects of meteorology and short-term ozone variability. The results show a downward trend from 2007 to 2014 (P1) and an upward trend from 2014 onward (P2). The long-term precursor emissions changes contribute to a 29.1% negative trend (=−0.7 ppb year−1) over P1 and an 8.95% upward trend (=0.1 ppb year−1) over P2. To find the causes of the trend transition, we investigated the relationship between the adjusted MDA8 and several ozone precursors, namely NOx and CO. It is found that, over P1, the dominant chemistry regime is mixed, in which the decline of CO and NOx (attributed to industrial sources) is associated with the downward trend of ozone. On the other hand, the upward trend of ozone over P2 is associated with an increase in NOx mostly attributed to road transport (passenger cars) and residential sectors.

Ozone Trend Analysis in Natal (5.4°S, 35.4°W, Brazil) Using Multi-Linear Regression and Empirical Decomposition Methods over 22 Years of Observations

Ozone plays an important role in the Earth’s atmosphere. It is mainly formed in the tropical stratosphere and is transported by the Brewer–Dobson Circulation to higher latitudes. In the stratosphere, ozone can filter the incoming solar ultraviolet radiation, thus protecting life at the surface. Although tropospheric ozone accounts for only ~10%, it is a powerful GHG and pollutant, harmful to the health of the environment and living beings. Several studies have highlighted biomass burning as a major contributor to the tropospheric ozone budget. Our study focuses on the Natal site (5.40°S, 35.40°W, Brazil), one of the oldest ozone-observing stations in Brazil, which is expected to be influenced by fire plumes in Africa and Brazil. Many studies that examined ozone trends used the total atmospheric columns of ozone, but it is important to assess ozone separately in the troposphere and the stratosphere. In this study, we have used radiosonde ozone profiles and daily TCO measurements to evaluate the variability and changes of both tropospheric and stratospheric ozone separately. The dataset in this study comprises daily total columns of colocalized ozone and weekly ozone profiles collected between 1998 and 2019. The tropospheric columns were estimated by integrating ozone profiles measured by ozone sondes up to the tropopause height. The amount of ozone in the stratosphere was then deduced by subtracting the tropospheric ozone amount from the total amount of ozone measured by the Dobson spectrometer. It was assumed that the amount of ozone in the mesosphere is negligible. This produced three distinct time series of ozone: tropospheric and stratospheric columns as well as total columns. The present study aims to apply a new decomposition method named Empirical Adaptive Wavelet Decomposition (EAWD) that is used to identify the different modes of variability present in the analyzed signal. This is achieved by summing up the most significant Intrinsic Mode Functions (IMF). The Fourier spectrum of the original signal is broken down into spectral bands that frame each IMF obtained by the Empirical Modal Decomposition (EMD). Then, the Empirical Wavelet Transform (EWT) is applied to each interval. Unlike other methods like EMD and multi-linear regression (MLR), the EAWD technique has an advantage in providing better frequency resolution and thus overcoming the phenomenon of mode-mixing, as well as detecting possible breakpoints in the trend mode. The obtained ozone datasets were analyzed using three methods: MLR, EMD, and EAWD. The EAWD algorithm exhibited the advantage of retrieving ~90% to 95% of ozone variability and detecting possible breakpoints in its trend component. Overall, the MRL and EAWD methods showed almost similar trends, a decrease in the stratosphere ozone (−1.3 ± 0.8%) and an increase in the tropospheric ozone (+4.9 ± 1.3%). This study shows the relevance of combining data to separately analyze tropospheric and stratospheric ozone variability and trends. It highlights the advantage of the EAWD algorithm in detecting modes of variability in a geophysical signal without prior knowledge of the underlying forcings.

Unveiling vertical ozone variation with UAV-Based monitoring and modeling: A new challenge for city-level ozone pollution control in the pearl river delta region

Ozone distribution and variation within the Planetary Boundary Layer (PBL) present challenges for air quality management. UAV-based ozone profile measurements offer advantages in terms of high vertical resolution throughout the PBL and flexibility. However, previous studies on vertical variation in the Pearl River Delta (PRD) region have rarely focused on measuring the vertical ozone variation during ozone pollution episodes. In this study, we integrated UAV-based monitoring, ground monitoring, and numerical modeling to investigate ozone variation, driving processes, and source regions of pollution episodic period. UAV-based observations during a 2021 ozone episode at rural areas in the PRD region revealed three ozone profiles: increasing, well-mixed, and decreasing with height. Morning profiles showed multiple layers influenced by meteorology, while peak ozone hours exhibited a well-mixed pattern. By integrating the validated numerical model, we found that chemical processes (CHEM) contributed positively to ozone episodes in both rural and urban scenarios, especially at the middle and upper PBL. Horizontal advection (HADV) significantly contributes to the episode in rural cases, while urban cases were significantly affected by vertical diffusion (VDIF). Backward trajectory and source apportionment analyses categorized PRD cities into upwind/non-upwind/local categories, showing ozone pollution originating from both upwind and non-upwind cities at different vertical levels. It is because wind shear influenced diverse source regions, and the transport channel for city-level ozone pollution exhibited daily and vertical changes. Moreover, it could also be contributed by the formed regional mixing ozone layer (ROM Layer) throughout the PBL during persistent ozone episodes, triggered by enhanced CHEM and significantly contributed by the VDIF. The ROM Layer at center cities, influencing ground-level ozone downwind through horizontal transport and vertical exchange. Considering the complex three-dimensional transport of ozone and the ROM Layer phenomenon, city-level management or regional co-control based on simple transport channels may be insufficient. Emissions control measures should cover all cities or target significant emitters to address regional ozone pollution.

Characteristics of Spatial and Temporal Variations of Ozone in Linfen City and Analysis of Influencing Factors

Characteristics of Spatial and Temporal Variations of Ozone in Linfen City and Analysis of Influencing Factors

Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe

Abstract. With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COrona VIrus Disease (COVID-19) economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted source regions, and 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August) and MAM 2021. The JJA 2021 seasonal mean was influenced by strong positive anomalies in July due to large and widespread wildfires across the western US. The polluted sites in the western US showed negative O3 anomalies during MAM 2020 and a slight recovery in 2021, resulting in a positive mean anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The eastern US sites were also characterized by below-mean O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the western US sites, which were influenced by wildfires. Concerning the rest of the world, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI (Infrared Atmospheric Sounding Interferometer) satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020.

The Responses of Ozone to the Solar Eclipse on the 21st of June 2020 in the Mesosphere and Upper Stratosphere

Microwave Limb Sounder (MLS) observations showed an obvious variation of ozone concentration during the annular solar eclipse on 21 June 2020 in the mesosphere and upper stratosphere. Ozone concentration slightly reduced near 40 km in the regions of 24°N–36°N, and increased in low latitudes at 40 km. In the heights of 45–60 km, the increase in ozone concentration in most of the regions was obvious. The ozone increases and decreases were more obvious between 60–65 km, where enhancement took the leading role. The nighttime ozone variation was weaker than the daytime in most of the heights of 30–65 km. The variation of HO2 and CO is investigated to study the photochemical and dynamical causes of ozone variation. As HO2 decreased at 1 hPa and increased at 60–65 km, ozone variation shows a mostly reversed relationship to HO2 variation. CO increased at 32–39 km and decreased at 52–60 km, which was related to the upwelling at these heights. The dynamic processes also contributed to the decrease in ozone concentration at 40 km and increase at 50–60 km.

Associations of interannual variation in summer tropospheric ozone with the Western Pacific Subtropical High in China from 1999 to 2017

Abstract. Associations between tropospheric ozone (O3) and climate variations have been extensively investigated worldwide. However, given the lack of historical O3 monitoring data, the knowledge gaps regarding the influences of climate variations on long-term O3 trends in China remain. The present study used a tropospheric O3 dataset from the summers of 1999 to 2017 simulated by an atmospheric chemistry model to explore the linkage between summer O3 and a dominant atmospheric circulation system – the Western Pacific Subtropical High (WPSH) pressure – on an interannual basis in China. During this period, both WPSH strength and O3 concentrations in eastern and central China illustrated a growing trend. An EOF analysis was conducted to examine significant summer O3 characteristics and patterns and their potential connections with the WPSH. We find that the correlation between the first principal component of summer ozone concentration in the EOF analysis and the WPSH reached 0.56 (P≤0.01) in China from 1999 to 2017. We show that the WPSH determines interannual fluctuations of summer O3, whereas O3 precursor emissions contribute primarily to the O3 long-term trend. Our results reveal that the WPSH plays a vital role in O3 perturbation in the eastern seaboard regions and inland China. Precursor emissions made more significant contributions of up to 60 % to increasing O3 trends in the inland urban agglomerations than coastal regions in eastern and southern China. The strongest contribution of meteorological conditions associated with the WPSH to summer O3 occurred in the Yangtze River Delta (YRD), accounting for over 9 % to ozone perturbations from 1999 to 2017. We find that the effect of the WPSH on regional O3 depends on the spatial proximity to the WPSH. We attributed the effects of the WPSH on O3 interannual variations to the changes in air temperature, precipitation, and winds associated with the WPSH's intensity and positions.