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It is well established that variable wintertime planetary wave forcing in the stratosphere controls the variability of Arctic stratospheric ozone through changes in the strength of the polar vortex and the residual circulation. While previous studies focused on the variations in upward wave flux entering the lower stratosphere, here the impact of downward planetary wave reflection on ozone is investigated for the first time. Utilizing the MERRA2 reanalysis and a fully coupled chemistry–climate simulation with the Community Earth System Model (CESM1(WACCM)) of the National Center for Atmospheric Research (NCAR), we find two downward wave reflection effects on ozone: (1) the direct effect in which the residual circulation is weakened during winter, reducing the typical increase of ozone due to upward planetary wave events and (2) the indirect effect in which the modification of polar temperature during winter affects the amount of ozone destruction in spring. Winter seasons dominated by downward wave reflection events (i.e., reflective winters) are characterized by lower Arctic ozone concentration, while seasons dominated by increased upward wave events (i.e., absorptive winters) are characterized by relatively higher ozone concentration. This behavior is consistent with the cumulative effects of downward and upward planetary wave events on polar stratospheric ozone via the residual circulation and the polar temperature in winter. The results establish a new perspective on dynamical processes controlling stratospheric ozone variability in the Arctic by highlighting the key role of wave reflection.

The motivation of this work is to study the relationship between gamma radiation and also ozone concentration in 60CO industrial irradiators. Because ozone is considered to be one of the most important safety issues, because it has a destructive effect on the human body and has a corrosive effect on metal parts in the irradiated area. Moreover, due to its high reactivity. So first using ozone monogram method to measure ozone concentration, and also the effect of the factor on ozone concentration was determined. MATLAB computer code was used to calculate the ozone concentration-response with completely different statistics. Among them, ozone concentration in the 60CO irradiation area is in a completely different state. These conditions include: different gamma emitters, completely different irradiators, airflow correction, and engine failure. It is suggested that ozone formation may be a direct effect of gamma radiations. the result show also that the health hazards due to the ozone produced during irradiation are significant and should not be overlooked. It was observed that in case of 1Ci motor failure, the detection time is 67.5 minutes. While, for 0.1Ci motor failure, the detection time is 32.2 minutes. To fulfill the employee safety limits. it is recommended to have a second control system with ventilation

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China’s near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China’s eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Surface ozone (O3) pollution is a serious environmental problem that endangers human health, and it is also an increasingly prominent environmental problem in the World. Existing works focus on how to directly improve the accuracy of predicting the target sequence from the input sequence while ignoring the inherent uncertainty of ozone in the atmosphere during the modeling process. Therefore, we utilize data fusion techniques to integrate ground observation data, satellite data, and reanalysis data for simulating atmospheric dynamics and enhancing prediction accuracy. We developed a sequence to sequence using a unit embedded with spatiotemporal information self attention mechanism as its encoder (OzoneNet) predict ozone concentration in the future. In the proposed method, we utilize the LSTM model with Spatiotemporal information self-attention mechanism to extract fixed Spatiotemporal data features, and the temporal dimension characteristics in long-term series are modeled by sequence-to-sequence network. Results show that the model has higher reliability and validity, outperforming benchmark models in simulating future changes in O3 concentrations. The progeress of this method can help the public take corresponding protective measures, provide scientific guidance for the government’s coordinated control of regional pollution, and can also provide important references for environmental protection and climate change research

This paper presents the results of a study of ground-level ozone concentration variability in Crimea at the background environmental monitoring station (BEMS) of the Karadag State Nature Reserve for 2012–2021 with a more detailed analysis of the last 6 years from 2016 to 2021. A significantly high level of air pollution by ground-level ozone in the observation area was revealed, despite the absence of significant sources of anthropogenic pollution in the vicinity of the station. The relationship between the ground-level ozone concentration and meteorological parameters has been studied, and characteristic wind directions leading to increased levels of ground-level ozone pollution have been established. Intra-annual variations are analyzed, and factors causing a local summer minimum of ground-level ozone concentration in individual years are identified. Using the NOAA HYSPLIT model and ERA5 reanalysis meteorological fields, a spatial analysis of the atmospheric circulation pattern in the region has been carried out. The recurrence of episodes in which the permissible 8-h average ozone concentration level of 100 μg/m 3 , as recommended by World Health Organization (WHO), was exceeded has been assessed, and the possible causes of these episodes are identified. Mechanisms of long-range transport and their contribution to the ozone regime in the station area have been established. Annual trends in ground-level ozone concentration between 2012 and 2021 are assessed as statistically insignificant.

It is of great significance to accurately grasp ground-level ozone concentration on a wide scale in order to cope with the increasingly serious ground-level ozone pollution problem. Currently, satellite technology can be used to monitor ground-level ozone concentration on a wide scale, but there is the problem of insufficient spatial resolution. The use of ground monitoring stations to detect ground-level ozone concentration can obtain more accurate ozone concentration around the station, but there are problems of limited number of stations and sparse distribution. Therefore, it is still a challenge to accurately grasp the ground-level ozone concentration at a large scale and high spatial resolution. To address this issue, this paper proposes a ground-level ozone concentration inversion algorithm based on deep learning methods, incorporating data from remote sensing satellites, ground monitoring stations, meteorological conditions, geography, and land use. Taking Yunnan Province in China as the experimental region and utilizing five-fold cross-validation, the model's Mean Square Error (MSE) reached 340.45 and Root Mean Square Error (RMSE) reached 18.45. Compared to machine learning models, the error decreased by 14.53%, outperforming Random Forest models, Convolution Neural Networks, and Vision Transformers. This study offers a referential approach for accurately capturing ground-level ozone concentrations at a large scale with high spatial resolution.

This study uses the daily product data of the concentration of ozone in the atmospheric column (ozone column concentration) collected by the Aura satellite’s Ozone Monitoring Instrument (OMI), to evaluate the ozone pollution status of the Fenwei Plain in east-central China, by employing pixel-based spatial analysis, an θ slope trend index, a Hurst index, and grey correlation. The following results were found. (1) The spatial distribution of ozone in the atmosphere of the Fenwei Plain was higher in the north and lower in the south, with high values appearing in Jinzhong, Lvliang, and other cities. (2) The changes in ozone column concentration periodically and seasonally in the Fenwei Plain. Seasonally, the ozone column concentration was highest in spring, followed by summer, winter, and autumn. (3) The pixel-based trend change of the ozone slope ( θ slope ) indicated that the ozone concentration in the region was in a downward trend, while the long-term correlation of the time series trend Hurst index found that the region should expect to see a weak rebound in the ozone column concentration in the future, so that routine monitoring should be strengthened. (4) The present study on the factors influencing the ozone concentration found that the concentration is relatively closely related to temperature, air pressure, humidity, grain sowing area, highway mileage, and secondary industry.

Ozone is a very important trace gas in the stratosphere and, thus, we need to know its time evolution over the globe. However, ground-based measurements are rare, especially in the Southern Hemisphere, and while satellite observations provide broader spatial coverage generally, they are not available everywhere. On the other hand, reanalysis data have regular spatial and temporal structure, which is beneficial for trend analysis, but temporal discontinuities might exist in the data. These discontinuities may influence the results of trend studies. The aim of this paper is to detect discontinuities in ozone data of the following reanalyses: MERRA-2, ERA-5 and JRA-55 with the help of the Pettitt, the Buishand, and the Standard Normal Homogeneity tests above the 500 hPa level. The share of discontinuities varies from 30% to 70% and they are strongly layer dependent. The share of discontinuities is the lowest for JRA-55. Differences between reanalyses were found to be larger than differences between homogeneity tests within one reanalysis. Another aim of this paper is to test the ability of homogeneity tests to detect the discontinuities in 2004 and 2015, when changes in versions of satellite data took place. We showed the discontinuities in 2004 are better detected than those in 2015.

China is experiencing increasingly serious ambient ozone pollution, including the economically developed Pearl River Delta (PRD) region. However, the underlying reasons for the ozone increase remain largely unclear, leading to perplexity regarding formulating effective ozone control strategies. In this study, we quantitatively examine the impacts of meteorology and precursor emissions from within and outside of the PRD on the evolution of ozone during the past decade by developing a statistical analysis framework combining meteorological adjustment and source apportionment. We found that meteorological conditions mitigated ozone increase, and that their variation can account for a maximum of 15 % of the annual ozone concentration in the PRD. Precursor emissions from outside the PRD (“nonlocal”) have the largest contribution to ambient ozone in the region and show a consistently increasing trend, whereas emissions from within the PRD (“local”) show a significant spatial heterogeneity and play a more important role during ozone episodes over the southwest of the region. Under general conditions, the impact on the northeastern PRD is positive but decreasing, and in the southwest it is negative but increasing. During ozone episodes, the impact on the northeastern PRD is negative and decreasing, whereas in the southwestern PRD it is positive but decreasing. The central and western PRD are the only areas with an increasing local ozone contribution. The spatial heterogeneity in both the local ozone contribution and its trend under general conditions and during ozone episodes is well interpreted by a conceptual diagram that collectively takes ozone precursor emissions and their changing trends, ozone formation regimes, and the monsoonal and microscale synoptic conditions over different subregions of the PRD into consideration. In particular, we conclude that an inappropriate NOx∕VOC control ratio within the PRD over the past few years is most likely responsible for the ozone increase over southwest of this region, both under general conditions and during ozone episodes. By investigating the ozone evolution influenced by emission changes within and outside of the PRD during the past decade, this study highlights the importance of establishing a dichotomous ozone control strategy to tackle general conditions and pollution events separately. NOx emission control should be further strengthened to alleviate the peak ozone level during episodes. Detailed investigation is needed to retrieve appropriate NOx∕VOC ratios for different emission and meteorological conditions, so as to maximize the ozone reduction efficiency in the PRD.

Particulate matter (PM2.5) concentrations in China have decreased significantly in recent years, but surface ozone (O3) concentrations showed upward trends at more than 71 % of air quality monitoring stations from 2015 to 2021. To reveal the causes of O3 increases, O3 production sensitivity is accurately diagnosed by deriving regional threshold values of the satellite tropospheric formaldehyde-to-NO2 ratio (HCHO/NO2), and O3 responses to precursor changes are evaluated by tracking volatile organic compounds (VOCs) and NOx with satellite HCHO and NO2. Results showed that the HCHO/NO2 ranges of transition from VOC-limited to NOx-limited regimes apparently vary among Chinese regions. VOC-limited regimes are found widely over megacity clusters (North China Plain, Yangtze River Delta and Pearl River Delta) and concentrated in developed cities (such as Chengdu, Chongqing, Xi'an and Wuhan). NOx-limited regimes dominate most of the remaining areas. From 2013 to 2021, satellite NO2 and HCHO columns showed an annual decrease of 3.0 % and 0.3 %, respectively, indicating an effective reduction in NOx emissions but a failure to reduce VOC emissions. This finding further shows that O3 increases in major cities occur because the Clean Air Action Plan only reduces NOx emissions without effective VOC control. Based on the O3–NOx–VOC relationship by satellite NO2 and HCHO in Beijing, Chengdu and Guangzhou, the ozone concentration can be substantially reduced if the reduction ratio of VOCs/NOx is between 2:1 and 4:1.