Ozone Variations Research Articles

Influence of precursors and meteorology on ambient ozone over Indian western Himalayas

The surface ozone, oxides of nitrogen (NOx), and carbon monoxide (CO) were measured from 2015 to 2016 at a mountain site of the Indian western Himalayan region to study the temporal variability of ozone and the influence of precursor gases and meteorological parameters. Ozone showed anti-correlation with NOx and CO, indicating its photochemical origin. Significant variabilities were recorded in the annual mean values of these species during 2015 & 2016. The NO x showed ~60% fall in the mean annual concentration in 2016 compared to 2015, while a 17% increase in CO concentration and a 27% fall in mean ozone level were recorded in 2016. The observed variabilities could be due to change in wind speed, as a ~ 50% drop was recorded in the mean wind speed in 2016. The cluster analysis showed that the site received maximum air masses from the southeast to northwest directions where point sources are also located. The influence of biomass burning in Indo-Gangetic Plains (IGP) region, along with strong valley to mountain wind flow was also observed. The high values of NO x and CO observed in 2015 may be attributed to industrial activities and meteorology in this region.

Influence of circulation types on temporal and spatial variations of ozone in Beijing

This study analyzes the impact of circulation types (CTs) on ozone (O3) pollution in Beijing. The easterly high-pressure (SWW) circulation occurred most frequently (30%; 276 day), followed by northwesterly high-pressure (AN) circulation (24.3%; 224 day). The SWW type had the highest O3 anomaly of +17.28 µg/m3, which was caused by excellent photochemical reactions, poor diffusion ability and regional transport. Due to the higher humidity and precipitation in the low-pressure type (C), the O3 increase (+8.02 µg/m3) was less than that in the SWW type. Good diffusion/wet deposition and weak formation ability contributed to O3 decrease in AN (-12.54 µg/m3) and northerly high-pressure (ESN) CTs (-12.26 µg/m3). The intra-area transport of O3 was significant in polluted circulations (SWW- and C-CTs). In addition, higher temperature, radiation and less rainfall also contributed to higher O3 in northern Beijing under the SWW type. For the clean CTs (AN and ESN CTs), precursor amount and intra-area transport played a dominant role in O3 distribution. Under the northeasterly low-pressure CT, better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O3 values in the south than in the north. The higher O3 in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O3 concentration in previous day. Annual variation in the CTs contributed up to 86.1% of the annual variation in O3. About 78%-83% of the diurnal variation in O3 resulted from local meteorological factors.

A New Chemistry-Climate Model GRIMs-CCM: Model Evaluation of Interactive Chemistry-Meteorology Simulations

We describe a new chemistry-climate model, Global/Regional Integrated Model system Chemistry Climate Model (GRIMs-CCM), developed by coupling the chemistry modules of the GEOS-Chem chemical transport model to the GRIMs general circulation model. The GRIMs-CCM is driven by meteorological variables simulated by the GRIMs and uses simulated gas and aerosol concentrations to calculate the radiative transfer equations at each time step. The model is evaluated by comparing ozone and aerosol concentrations with respective observations from the surface networks and the satellite datasets. It is found that the GRIMs-CCM successfully reproduces the observed spatial distributions of annual-mean aerosol optical depth and captures the seasonal and latitudinal variations of total column ozone. The evaluation of simulated aerosols in surface air against the observations reveals that the model reproduces the observed temporal and spatial variations but shows biases in soil dust aerosols. We also estimate the climatic impact of aerosols by conducting two sets of 10-year simulations for the preindustrial and present conditions. The GRIMs-CCM shows the aerosol radiative forcing of − 0.30 W m−2 from the preindustrial to present-day climates, comparable to the values from other climate model intercomparison projects. These results suggest that the GRIMs-CCM is suitable for studying chemistry-climate interactions and their changes over time.

A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

Abstract. Surface ozone concentrations increased in many regions of China from 2015 to 2019. While the central role of meteorology in modulating ozone pollution is widely acknowledged, its quantitative contribution remains highly uncertain. Here, we use a data-driven machine learning approach to assess the impacts of meteorology on surface ozone variations in China for the period 2015–2019, considering the months of highest ozone pollution from April to October. To quantify the importance of various meteorological driver variables, we apply nonlinear random forest regression (RFR) and linear ridge regression (RR) to learn about the relationship between meteorological variability and surface ozone in China, and contrast the results to those obtained with the widely used multiple linear regression (MLR) and stepwise MLR. We show that RFR outperforms the three linear methods when predicting ozone using local meteorological predictor variables, as evident from its higher coefficients of determination (R2) with observations (0.5–0.6 across China) when compared to the linear methods (typically R2 = 0.4–0.5). This refers to the importance of nonlinear relationships between local meteorological factors and ozone, which are not captured by linear regression algorithms. In addition, we find that including nonlocal meteorological predictors can further improve the modelling skill of RR, particularly for southern China where the averaged R2 increases from 0.47 to 0.6. Moreover, this improved RR shows a higher averaged meteorological contribution to the increased trend of ozone pollution in that region, pointing towards an elevated importance of large-scale meteorological phenomena for ozone pollution in southern China. Overall, RFR and RR are in close agreement concerning the leading meteorological drivers behind regional ozone pollution. In line with expectations, our analysis underlines that hot and dry weather conditions with high sunlight intensity are strongly related to high ozone pollution across China, thus further validating our novel approach. In contrast to previous studies, we also highlight surface solar radiation as a key meteorological variable to be considered in future analyses. By comparing our meteorology based predictions with observed ozone values between 2015 and 2019, we estimate that almost half of the 2015–2019 ozone trends across China might have been caused by meteorological variability. These insights are of particular importance given possible increases in the frequency and intensity of weather extremes such as heatwaves under climate change.

Long-term temperature and ozone response to natural drivers in the mesospheric region using 16 years (2005–2020) of TIMED/SABER observation data at 5–15°N

The long-term mesospheric (60–100 km) temperature and ozone volume mixing ratio variability during the period of January 2005 to December 2020 were analyzed to obtain trends and their response to natural influences using the SABER, an instrument onboard the TIMED satellite to obtain observation data in the latitude range of 5°N–15°N. A wavelet analysis technique has been used to identify the dominant periodic oscillations in mesospheric temperature and ozone. Using the proxy data of F10.7, Nino 3.4, and zonal wind index (QBO at 30 hPa), the mesospheric response to natural drivers was investigated using a multiple linear regression technique. Among the three natural drivers, solar radio flux (F10.7) is the dominant contributor to mesospheric variability rather than ENSO and QBO. It influences negatively the lower mesosphere (60–80 km), and above 80 km, it responds positively in temperature (2.6 K), whereas ozone responds with a constant negative value (0.12ppmv) up to 80 km, and after it influences by a maximum positive value of 0.7 ppmv. At 80 km, the temperature and ozone respond in phase to all natural influences (F10.7, ENSO, and QBO), and are out of phase below and above 80 km. Both the temperature and the ozone reveal cooling trends (-0.85 K/decade and −0.12 ppmv/decade) of the the lower mesosphere (60–80 km) and are followed by the upper mesospheric (85–100 km) warming trends (1.25 K/decade and 0.27 ppmv/ decade) over the low latitudes. In general, natural influences affected the mesospheric temperature more strongly than the ozone volume mixing ratio. Our results are expected to be an updated and reliable estimation of the mesospheric temperature and ozone variability for the equatorial mesosphere.

Мониторинг приземного озона в природном заповеднике «Карадагский» в период 2017–2021 гг.

The background environmental monitoring station in the Karadag Nature Reserve is the only point for routine measurements (since 2010) of surface ozone on the Black Sea coast of the Russian Federation. Surface ozone observations at the station over the past five years are discussed. An insignificant interannual variability of surface ozone is shown as compared to the previous years. Seasonal and diurnal variations in surface ozone averaged over the period of 2017–2021 generally coincide with the previous period and are comparable with the typical variations at foreign Mediterranean stations. The surface ozone regime at the analyzed station is characterized by an insignificant diurnal amplitude: it is about 5 μg/m3 in winter and increases by almost 3–4 times in the warm season. However, due to increased nighttime ozone, it remains significantly smaller than at lowland stations. In the dark time of the day, surface ozone is 50–55 µg/m3 in winter and autumn and about 70 µg/m3 in spring and summer. The surface ozone regime is significantly affected by the breeze circulation and, at night, by slope winds providing the influx of ozone from the free troposphere. At the background monitoring station, surface ozone values approaching the maximum single permissible concentration of ozone (MPCm.s.) are occasionally recorded. In spring, such events are associated with the movement of the upper-level frontal zone over the region. The registered cases of exceeding MPCm.s. were observed in the plumes of air polluted in remote areas and spreading to Crimea. Keywords: surface ozone, Karadag Nature Reserve, seasonal and diurnal variation, maximum permissible concentration of ozone

Factors determining the seasonal variation of ozone air quality in South Korea: Regional background versus domestic emission contributions

South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N–50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40–0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NOx titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NOx emissions throughout the year. The domestic NOx emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NOx reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R2 = 0.75, RMSE = 10.86 µg/m3). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations.

Exploring Formation of Ozone in Typical Cities in Beijing-Tianjin-Hebei Region Using Process Analysis

As the problem of O3 pollution in the Beijing-Tianjin-Hebei region becomes increasingly prominent, it is of great significance to explore and analyze the ozone variation characteristics and causes of the pollution process in the Beijing-Tianjin-Hebei region for regional air pollution prevention and control. The observations in this study showed that high O3 concentration in spring and summer of the Beijing-Tianjin-Hebei region was higher in the south and lower in the north. The high O3 concentration in Beijing, Tianjin, and Shijiazhuang was often accompanied by the influence of southern wind. Based on WRF-Chem model simulation and process analysis technology, the variation characteristics and causes of O3 in The Beijing-Tianjin-Hebei region in 2019 were deeply analyzed. The diurnal variations in chemical processes, vertical mixing, and transportation in typical cities showed distinct seasonal variations. In summer afternoons, chemical processes were the main source of O3 concentration increase in each city. Vertical mixing resulted in an increase in O3 concentration in Tianjin and Shijiazhuang but a decrease in Beijing. Tianjin and Shijiazhuang had a net output, whereas Beijing had a net inflow. In the polluted O3 process, the chemical process dominated the afternoon O3 concentration increasing in Beijing and Shijiazhuang, whereas vertical mixing dominated in Tianjin. In addition, there was a net input of O3 in Beijing and Shijiazhuang and a net output of O3 in Tianjin. In the clean O3 process, vertical mixing dominated the increase in O3 concentration in Beijing and Shijiazhuang in the afternoon, whereas in Tianjin it was chemical processes. At the same time, the net output of O3 existed in all three cities.

An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry

AbstractA major computational barrier in global modeling of atmospheric chemistry is the numerical integration of the coupled kinetic equations describing the chemical mechanism. Machine‐learned (ML) solvers can offer order of magnitude speedup relative to conventional implicit solvers but past implementations have suffered from fast error growth and only run for short simulation times (<1 month). A successful ML solver for global models must avoid error growth over yearlong simulations and allow for reinitialization of the chemical trajectory by transport at every time step. Here, we explore the capability of a neural network solver equipped with an autoencoder to achieve stable full‐year simulations of tropospheric oxidant chemistry in the global 3‐D Goddard Earth Observing System (GEOS)‐Chem model, replacing its standard mechanism (228 species) by the Super‐Fast mechanism (12 species) to avoid the curse of dimensionality. We find that online training of the ML solver within GEOS‐Chem is important for accuracy, whereas offline training from archived GEOS‐Chem inputs/outputs produces large errors. After online training, we achieve stable 1‐year simulations with five‐fold speedup compared to the standard implicit Rosenbrock solver with global tropospheric normalized mean biases of −0.3% for ozone, 1% for hydrogen oxide radicals, and −5% for nitrogen oxides. The ML solver captures the diurnal and synoptic variability of surface ozone at polluted and clean sites. There are however large regional biases for ozone and NOx under remote conditions where chemical aging leads to error accumulation. These regional biases remain a major limitation for practical application, and ML emulation would be more difficult in a more complex mechanism.

A process-oriented evaluation of CAMS reanalysis ozone during tropopause folds over Europe for the period 2003–2018

Abstract. Tropopause folds are the key process underlying stratosphere-to-troposphere transport (STT) of ozone, and thus they affect tropospheric ozone levels and variability. In the present study we perform a process-oriented evaluation of Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (CAMSRA) O3 during folding events over Europe and for the time period from 2003 to 2018. A 3-D labeling algorithm is applied to detect tropopause folds in CAMSRA, while ozonesonde data from WOUDC (World Ozone and Ultraviolet Radiation Data Centre) and aircraft measurements from IAGOS (In-service Aircraft for a Global Observing System) are used for CAMSRA O3 evaluation. The profiles of observed and CAMSRA O3 concentrations indicate that CAMSRA reproduces the observed O3 increases in the troposphere during the examined folding events. Nevertheless, at most of the examined sites, CAMSRA overestimates the observed O3 concentrations, mostly at the upper portion of the observed increases, with a median fractional gross error (FGE) among the examined sites >0.2 above 400 hPa. The use of a control run without data assimilation reveals that the aforementioned overestimation of CAMSRA O3 arises from the data assimilation implementation. Overall, although data assimilation assists CAMSRA O3 to follow the observed O3 enhancements in the troposphere during the STT events, it introduces biases in the upper troposphere resulting in no clear quantitative improvement compared to the control run without data assimilation. Less biased assimilated O3 products, with finer vertical resolution in the troposphere, in addition to higher IFS (Integrated Forecasting System) vertical resolution, are expected to provide a better representation of O3 variability during tropopause folds.

Interannual variations, sources, and health impacts of the springtime ozone in Shanghai

In spring, ozone (O3) pollution frequently occurrs in eastern China, but key drivers remain uncertain. In this study, interannual variations in springtime ozone in Shanghai, China, from 2013 to 2021, were investigated to assess the health impacts and the effectiveness of recent air pollution control measures. A combination of ground-level measurements of regulated air pollutants, lidar observations, and backward trajectories of air masses was used to identify the key drivers for enhancing springtime O3. The results show that external imports of O3 driven by atmospheric circulation are notable sources of springtime surface O3. For example, the downward transport from the free troposphere could contribute to over 50% of surface O3 in the morning. The surface O3 mixing ratios in spring exhibited an upward trend of 0.93 ppb yr−1 (p < 0.05) from 2013 to 2021. The change in meteorological variables, particularly the increase in air temperature, could explain nearly 87% of the springtime O3 upward trend. The change in anthropogenic emissions of precursors only contributed to a small fraction (<13%) of the increase in springtime O3. The cumulative exposure of urban residents to O3 in spring also exhibited a significant upward trend (111 ppb yr−1, p < 0.05). With the rapid increase in surface O3, premature respiratory mortality attributable to O3 exposure has fluctuated at approximately 2933 deaths per year since 2016, even though the total deaths from respiratory diseases have significantly declined. Long-term exposure to high O3 concentrations is a significant contributor to premature respiratory mortality.

Variation of Ozone, Carbon Monoxide, and Oxides of Nitrogen at Bengaluru, India

The present study analyses the continuous in-situ observations of surface ozone (O3), carbon monoxide (CO), and nitrogen oxides (NOX) conducted in an urban location, Bengaluru, India, during the year 2019 (January to December). The seasonal concentration of O3 fluctuated with the highest concentrations in the summer (39.6 ppbv) and winter (40.4 ppbv) and the lowest concentrations during the monsoon (16.8 ppbv). The seasonal mixing ratio of CO showed the highest value in post-monsoon (1.71 ppmv) and lowest during monsoon (0.79 ppmv). The seasonal trend of NOX showed highest in winter (56.8 ppbv) and lowest in monsoon (22.5 ppbv). The monthly mixing ratios of O3, CO, and NOX showed distinct variability, which may be attributed to changing anthropogenic activities, planetary boundary layer processes, and local meteorology. O3 was significantly related to temperature but inversely associated with relative humidity and wind speed. The association between CO, NOX with relative humidity, temperature, wind speed showed discrete results. The (dO3/dt) in the morning and evening duration were about 5.0 ppbv/h and -4.1 ppbv/h respectively.

Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021

China was seriously affected by air pollution in the past decade, especially for particulate matter (PM) and emerging ozone pollution recently. In this study, we systematically examined the spatiotemporal variations of six air pollutants and conducted ozone prediction using machine learning (ML) algorithms in the Beijing-Tianjin-Hebei (BTH) region. The annual-average concentrations of CO, PM10, PM2.5 and SO2 decreased at a rate of 141, 11.0, 6.6 and 5.6 μg/m3/year, while a pattern of initial increase and later decrease was observed for NO2 and O3_8 h. The concentration of SO2, CO and NO2 was higher in Tangshan and Xingtai, while northern BTH region has lower levels of CO, NO2 and PM. Spatial variations of ozone were relatively small in the BTH region. Monthly variations of PM10 displayed an increase in March probably due to wind-blown dusts from Northwest China. A seasonal and diurnal pattern with summer and afternoon peaks was found for ozone, which was contrast with other pollutants. Further ML algorithms such as Random Forest (RF) model and Decision tree (DT) regression showed good ozone prediction performance (daily: R2 = 0.83 and 0.73, RMSE = 30.0 and 37.3 μg/m3, respectively; monthly: R2 = 0.93 and 0.88, RMSE = 12.1 and 15.8 μg/m3, respectively) based on 10-fold cross-validation. Both RF model and DT regression relied more on the spatial trend as higher temporal prediction performance was achieved. Solar radiation- and temperature-related variables presented high importance at daily level, whereas sea level pressure dominated at monthly level. The spatiotemporal heterogeneity in variable importance was further confirmed using case studies based on RF model. In addition, variable importance was possibly influenced by the emission reductions due to COVID-19 pandemic. Despite its possible weakness to capture ozone extremes, RF model was beneficial and suggested for predicting spatiotemporal variations of ozone in future studies.

Assessing the influence of intertropical discontinuity on total column ozone variation over West Africa.

The focus of this study is to evaluate the influence of intertropical discontinuity (ITD) on the variation of total column ozone (TCO). Relevant information is supplied on the temporal and spatial variability of TCO along the ITD zone, which is an important factor influencing the earth's atmosphere. Several studies over the years have established the relationship and have influence several atmospheric processes on TCO. However, the relationship between intertropical discontinuity and TCO over West Africa has a gap. This study tends to examine the influence ITD has on TCO variation using the West African region as a case study. The study used wind, ozone, and dewpoint temperature data for the period between 1980 and 2019. To assess the variability and trend over the study region, several statistical methods were used, including Pearson correlation, Mann-Kendall, and linear regression model. The Mann-Kendall test shows an increasing trend throughout the months over the study region. Spatial analysis also revealed that regions north of the ITD have a higher concentration of TCO than the southern region of the ITD between April and September. However, ITD influence was more visible during the wet months of June to August (JJA) as the highest concentration of TCO was observed during this period across all latitude, but more deviation was observed between latitude 10 to 18° N, while the least occurrence is observed when ITD is at its minimum position in the month of December to February (DJF). The ACRV shows that 14° N exhibit the highest variation with a value of 4.84, while the deviation is also at its highest with value of 13.65. The monthly position of ITD for 40years was also analysed to observe the monthly deviation along the ITD region 40years, and the spatial distribution of TCO was analysed from January to December. It is of note that during the cause of this study, low-ozone values of 220DU are not found in the study region. The highest and the lowest value of TCO is 295 DU and 227 DU, respectively, with an average range of 68DU.

Analyzing ozone variations and uncertainties at high latitudes during sudden stratospheric warming events using MERRA-2

Abstract. Stratospheric circulation is a critical part of the Arctic ozone cycle. Sudden stratospheric warming events (SSWs) manifest the strongest alteration of stratospheric dynamics. During SSWs, changes in planetary wave propagation vigorously influence zonal mean zonal wind, temperature, and tracer concentrations in the stratosphere over the high latitudes. In this study, we examine six persistent major SSWs from 2004 to 2020 using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Using the unique density of observations around the Greenland sector at high latitudes, we perform comprehensive comparisons of high-latitude observations with the MERRA-2 ozone dataset during the six major SSWs. Our results show that MERRA-2 captures the high variability of mid-stratospheric ozone fluctuations during SSWs over high latitudes. However, larger uncertainties are observed in the lower stratosphere and troposphere. The zonally averaged stratospheric ozone shows a dramatic increase of 9 %–29 % in total column ozone (TCO) near the time of each SSW, which lasts up to 2 months. This study shows that the average shape of the Arctic polar vortex before SSWs influences the geographical extent, timing, and magnitude of ozone changes. The SSWs exhibit a more significant impact on ozone over high northern latitudes when the average polar vortex is mostly elongated as seen in 2009 and 2018 compared to the events in which the polar vortex is displaced towards Europe. Strong correlation (R2=90 %) is observed between the magnitude of change in average equivalent potential vorticity before and after SSWs and the associated averaged total column ozone changes over high latitudes. This paper investigates the different terms of the ozone continuity equation using MERRA-2 circulation, which emphasizes the key role of vertical advection in mid-stratospheric ozone during the SSWs and the magnified vertical advection in elongated vortex shape as seen in 2009 and 2018.

Impact of synoptic climate system interaction on surface ozone in China during 1950–2014

The effects of meteorological conditions controlled by specific synoptic patterns on regional surface ozone episodes have been widely discussed. However, the impacts of synoptic climate systems and their interaction on long-term variation of ozone are less known. In this study, we use the daily meteorological data and hourly surface ozone concentrations in the summer of 1950–2014 from CMIP6 to investigate the relationship between ozone variations and synoptic climate types (SCTs). Eight SCTs are identified by an objective circulation classification method of an R package called synoptReg. Our results show that the mainland of China is controlled by the low-pressure system for SCT1–5, while dominated by Siberian high for SCT6–8. During SCT1–5 days, the interactions of Asian continental low, Western Pacific Subtropical High (WPSH), warm ridge at 700 hPa, high-level shallow Asian trough, westerly belt, and topography lead to high surface temperature, low relative humidity, and poor diffusion over Northern China with frequent ozone exceedance days. However, the low-level ozone concentrations over Northern China are shown under SCT6–8, affected by the weakened Asian continental low, enhanced Siberian high, stronger WPSH, and denser westerly belt. While over Southern China, ozone concentrations are higher for SCT6–8 than SCT1–5, due to the significantly reduced cloud and precipitation affected by the delivery of cold dry air mass from high latitudes to the South. The interdecadal variabilities of ozone during all SCTs may be controlled by the large-scale climate system of the East Asian monsoon.

Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015–2020: Quantification for cities in the Yangtze River Delta

We quantify the meteorological influences on daily variations and trends of maximum daily 8-h average ozone (MDA8 O3) concentrations by using multiple linear regression (MLR) and Lindeman, Merenda, and Gold (LMG) approaches. Different from previous region-based studies, we pay special attention to meteorological influences at city scale. Over 2015–2019, daily changes in key meteorological parameters could explain 47%–74% of the observed daily variations in summertime MDA8 O3 concentrations in Yangtze River Delta (YRD) and four cities (Shanghai, Nanjing, Hangzhou, and Hefei), with RH being the top driver. Over years of 2015–2020, daily concentrations of MDA8 O3 obtained from MLR equations (MDA8O3_MLR) of the local cities always had better performance than those of YRD. Compared with the observed daily MDA8 O3 in June–July–August (JJA) over the studied period, daily MDA8O3_MLR of the local cities (of YRD) had correlation coefficients of 0.73 (0.63), 0.75 (0.74), 0.79 (0.78), and 0.76 (0.73) in Shanghai, Nanjing, Hangzhou, and Hefei, respectively, and the MDA8O3_MLR of the local cities (of YRD) captured 54% (17%), 63% (51%), 52% (27%) of the observed O3-polluted days (days with MDA8 O3 concentration exceeding 160 μg m−3) in Shanghai, Nanjing, and Hangzhou, respectively. The meteorologically driven trends (Trend_Met) in MDA8 O3 were calculated using the established MLR equations. Over 2015–2019, the observed trends (Trend_Obs) and Trend_Met in MDA8 O3 were mostly positive in YRD, Nanjing, Hangzhou, and Hefei. In Shanghai, Trend_Obs, Trend_Met, and anthropogenically driven trend (estimated as Trend_Obs minus Trend_Met) of MDA8 O3 in JJA over 2015–2019 were −1.3, +1.0, and −2.3 μg m−3 y−1, respectively, indicating that the emission control measures alleviated O3 pollution in this city. Our results suggest that it is necessary to establish MLR equations at city scale to account for the role of meteorology in the actions of O3 pollution control.

Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm

Surface ozone (O_3) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R^{2} = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R^{2} = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R^{2} ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O_3 precursors information has little effect on the ML model’s performance.

Evolution, Causes and Influence Factors of Taal Volcanic Activities

Volcanoes are a kind of geological feature which can bring both destruction and wealth to human beings. This study takes the eruption of Taal Volcano on January 12, 2020 as an example to analyze its eruption evolution, causes and influence factors via QGIS software. Taal Volcano lies at the southwestern end of a convergent boundary between the Eurasian and Philippine Sea tectonic plates where volcanic activities are frequent. Results show that the evolution of the eruption consists of increased CO2 flux, seismic swarms, phreatic explosion chronically. The origin of the volcano is the subduction of the oceanic plate and terrestrial plate. Volcanic eruptions are mostly due to pressurization by active convergent plates activities. The eruption emitted tephra and gas, which exerted impacts on the atmosphere, the nearby vegetation and the water body, and was predicted to result in an El Nino. High concentration of particles, dispersed tephra output, a sharp increase in SO2 and CO content, variation in atmospheric ozone, and rise in humidity were observed in the atmosphere following the eruption. The volcanic output wiped out the plant cover on the volcano island, and covered the vegetation outside of the volcano island, as shown in the RGB band composite and land cover change monitoring images generated using QGIS from Sentinel-2 data. The volcanic output’s influences on nearby water bodies were shown through drops in ocean salinity and Taal lake’s PH, variation in ocean temperature, and increased ocean’s surface latent heat flux.