Near-surface Ozone Research Articles

Estimation of Near-Surface Ozone Concentration Across China and Its Spatiotemporal Variations During the COVID-19 Pandemic

Estimation of Near-Surface Ozone Concentration Across China and Its Spatiotemporal Variations During the COVID-19 Pandemic

Assessment of isoprene and near-surface ozone sensitivities to water stress over the Euro-Mediterranean region

Abstract. Plants emit biogenic volatile organic compounds (BVOCs) in response to changes in environmental conditions (e.g. temperature, radiation, soil moisture). In the large family of BVOCs, isoprene is by far the strongest emitted compound and plays an important role in ozone chemistry, thus affecting both air quality and climate. In turn, climate change may alter isoprene emissions by increasing temperature as well as the occurrence and intensity of severe water stresses that alter plant functioning. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) provides different parameterizations to account for the impact of water stress on isoprene emissions, which essentially reduces emissions in response to the effect of soil moisture deficit on plant productivity. By applying the regional climate–chemistry model RegCM4chem coupled to the Community Land Model CLM4.5 and MEGAN2.1, we thus performed sensitivity simulations to assess the effects of water stress on isoprene emissions and near-surface ozone levels over the Euro-Mediterranean region and across the drier and wetter summers over the 1992–2016 period using two different parameterizations of the impact of water stress implemented in the MEGAN model. Over the Euro-Mediterranean region and across the simulated summers, water stress reduces isoprene emissions on average by nearly 6 %. However, during the warmest and driest selected summers (e.g. 2003, 2010, 2015) and over large isoprene-source areas (e.g. the Balkans), decreases in isoprene emissions range from −20 % to −60 % and co-occur with negative anomalies in precipitation, soil moisture and plant productivity. Sustained decreases in isoprene emissions also occur after prolonged or repeated dry anomalies, as observed for the summers of 2010 and 2012. Although the decrease in isoprene emissions due to water stress may be important, it only reduces near-surface ozone levels by a few percent due to a dominant VOC-limited regime over southern Europe and the Mediterranean Basin. Overall, over the selected analysis region, compared to the old MEGAN parameterization, the new one leads to localized and 25 %–50 % smaller decreases in isoprene emissions and 3 %–8 % smaller reductions in near-surface ozone levels.

Chemical kinetics of near-surface ozone at a suburban location in India

The spatial gradient in near-surface ozone (O3) is controlled by its production, sink, and net transport (advection/convection and diffusive) in the atmosphere. In this work, we used continuous long-term measurements of O3, oxides of nitrogen (NOx = NO + NO2), and meteorological data in the suburban location of Shadnagar, India. Data analyses were performed to investigate the governing processes that control O3 variability on diurnal and seasonal time scales. The role of chemistry in O3 variability, including both formation and destruction processes, was investigated using known chemical kinetics and a radiative transfer model. The residual between observations and chemical estimation was further analyzed to examine the role of transport and unresolved processes/uncertainty in the dataset. The O3 residual was duly validated using model reanalysis data of O3 and meteorological parameters to further estimate the O3 transport. Our analyses show that the average net production and net transport of near-surface O3 are 3.18 and 0.87 ppbv/h, respectively, while horizontal advection is 0.01 ppbv/h in the daytime. The production of ozone was found to be dominant, indicating the influx of ozone at the site. Overall, our results highlight that spatio-temporal variability in near-surface ozone is strongly controlled by net production in Shadnagar and may be applicable in similar environments globally.

Shifting summer holidays in Spain as an adaptation measure to climate change

This paper assesses whether moving summer holidays to the warmest period of the year in Spain could be a useful climate change adaptation strategy. While the most popular period for Spanish summer holidays has traditionally been August, we illustrate that the second half of July is the hottest period of the year and when the negative effects of high temperatures are most pronounced. If the holiday period in the second fortnight of August was moved to the second fortnight of July, some of the associated impacts would be mitigated due to the reduced anthropogenic activity during non-working days. In particular, we find a significant reduction in the annual peak of labour productivity loss (~25 %) and, to a lesser extent, of electricity demand and near-surface ozone concentrations (~3–4 %). Finally, we also show that global warming could lead to enhanced differences between both fortnights (even with no change in the seasonal cycle of temperature) because of the non-linear relationships between temperature and its impacts. Therefore, the positive effect of shifting holidays would be even larger in the coming future.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78–0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014–2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Spatiotemporal dynamics of near-surface ozone concentration and potential source areas in northern China during 2015-2020.

Near-surface ozone (O3) pollution has become one of the main factors hampering urban air quality in northern China. However, on a spatiotemporal scale, dynamic transport paths and potential source areas of O3 in northern China are ambiguous. In addition, we suspect that the contribution of transportation activities to urban O3 concentrations developed in northern China may be underestimated. In this study, the HYSPLIT, PSCF, CWT and GTWR model were used to study the transmission paths, potential source areas and driving factors of urban O3 concentration on a spatiotemporal scale. The average annual concentration of surface O3 (the 90th percentile of MDA8) was 172 ± 29μg/m3 in northern China from 2015 to 2020. In terms of inter-annual variation, the urban O3 concentration increased from 2015 to 2018, and decreased after 2018. On the spatial scale, the areas with high O3 concentration were mainly clustered in industrial cities (Tangshan, Baoding, Shijiazhuang, Xingtai and Handan). During the study period, the area with high O3 concentration in northern China shifted from northwest to southeast. From 2015 to 2020, the influence of long-distance air mass trajectories from Xinjiang and Siberi on airflow transport in Beijing city dominates (78.60%) The average percentage of short-distance transport trajectories from Shandong Peninsula region is about 21.40%. The core potential source areas of O3 pollution shifted from northwest to southeast, but the contribution to O3 pollution in Beijing gradually weakened during the same period. Temperature and relative humidity were the main meteorological driving factors affecting O3 concentration in the study area, while population density, the proportion of secondary industry in GDP, industrial smoke (dust) emissions, and passenger traffic were the main non-meteorological factors. During the period study, the influence of industrial and traffic emissions had a more significant impact on O3 concentration in northern China, which will require that more attention be paid to emission mitigation in the regional industrial and passenger transportation sector, as well as the joint prevention and control of O3 pollution in northern China in the future.

Decomposition of meteorological and anthropogenic contributions to near-surface ozone trends in Northeast China (2013–2021)

Recent years have seen an increase in regional ozone (O3) pollution in China. This study explored the interannual variability in daily maximum 8-h average O3 concentration (MDA8-O3) in Northeast China (NEC) and its three subregions (Liaoning, Jilin, and Heilongjiang) during 2013–2021, and identified the key meteorological drivers underlying the observed variability. The Kolmogorov–Zurbenko filtering technique was applied to a stepwise multiple linear regression model to decompose the meteorological and anthropogenic contributions to annual MDA8-O3 trends. The results showed that the spatiotemporal variation of MDA8-O3 in NEC is characterized by a high–south and low–north pattern with an MDA8-O3 hotspot in the Bohai Rim area. Over the 9-year study period, a significant increasing trend (∼2.5% a−1, P < 0.05) in the regional mean of the annual 90th percentile of MDA8-O3 was detected across NEC. This trend was strongly relevant to changes in relative humidity and surface solar radiation downward (SSRD). Statistical analyses revealed that SSRD dominated MDA8-O3 variability spatially over almost the entire NEC region. Additionally, the contribution decomposition suggested that the trend of increase in annual average MDA8-O3 in NEC was dominated by anthropogenic emission change, which explained 91.9% of the total variation. Regionally, although the dominant role of emissions has not changed, the meteorology-driven anomalies also explained −4.3%–3.3% of the annual average MDA8-O3 variation. This study provides insight into decoupling the complex relationships between long-term variability in regional O3 pollution and both anthropogenic emissions and meteorology, which could provide valuable information for future efforts to address the O3 pollution in NEC.

Relationship between summer time near-surface ozone concentration and planetary boundary layer height in Beijing

The planetary boundary layer height (PBLH) is critical in affecting the concentration of air pollutants at surface. The relationship between PBLH and surface ozone concentration can be complicated but not entirely clear. In this study, by using observations and WRF-Chem simulations, we explore the relationship between the surface ozone and the PBLH in the summer daytime in Beijing, a major global ozone pollution hotspot. It is found that the surface ozone-PBLH relationship exhibits a three-stage evolution with ozone: first, increasing with PBLH, then keeping steady, and finally falling. The two turning points are around 1000 and 2000 m respectively. This process is likely controlled by the different contributions of chemical production and vertical mixing. Specifically, when PBLH is below ∼1000 m, as PBLH increases, chemical production increases owing to the light enhancement whereas the ozone contribution from vertical mixing remains weak due to the decreased ozone concentration gradient between the surface and the upper layer, resulting in a net gain of surface ozone concentration. When the PBL continues to rise, chemical production contributes less mainly due to the decrease of ozone precursors at the surface, whereas vertical mixing becomes more vigorous that further diluting surface ozone, resulting in a net loss of surface ozone concentration, especially when PBLH reaches above ∼2000 m. Our results contribute to understanding the variability of surface ozone in Beijing area and provide insights for local ozone air quality control.

Source attribution of near-surface ozone trends in the United States during 1995–2019

Abstract. Emissions of ozone (O3) precursors in the United States have decreased in recent decades, and near-surface O3 concentrations showed a significant decrease in summer but an increase in winter. In this study, an O3 source tagging technique is utilized in a chemistry–climate model to investigate the source contributions to O3 mixing ratios in the US from various emitting sectors and regions of nitrogen oxides (NOx) and reactive carbon species during 1995–2019. We show that domestic emission reductions from energy and surface transportation are primarily responsible for the decrease in summertime O3 during 1995–2019. However, in winter, the emission control also weakens the NOx titration process, resulting in considerable increases in O3 levels from natural sources. Additionally, increases in aviation and shipping emissions and transpacific transport of O3 from Asia largely contribute to the winter O3 increase. We also found that changes in large-scale circulation favoring O3 transport from the upper atmosphere and foreign transport from Asia also explain 15 % of the increase in the US near-surface O3 levels in winter.

Research on the diurnal variation characteristics of ozone formation sensitivity and the impact of ozone pollution control measures in “2 + 26” cities of Henan Province in summer

Near-surface ozone pollution is becoming an increasingly serious air quality issue in China, especially in “2 + 26” cities (Beijing-Tianjin-Hebei and nearby cities). HN2 + 26 cities (“2 + 26” cities of Henan Province) are located in the south of “2 + 26” cities, with frequent and severe ozone pollution events in recent years. This study investigated the diurnal evolution characteristics of ozone formation sensitivity (OFS) of HN2 + 26 cities from May to September in 2021 by the innovative combination of Global Ozone Monitoring Experiment (GOME-2B) and Ozone Monitoring Instrument (OMI) satellite data, and assessed the impact of ozone pollution control measures (OPCMs) implemented from June 26 to July 1, 2021. The localized FNR (ratio of formaldehyde to nitrogen dioxide of satellite measurement) threshold (1.4–2.55) was established, and it was found that OFS in May–September 2021 was mainly in VOCs-limited regime in the morning (∼10:00), while transitional/NOx-limited regime in the afternoon (∼14:00). Three periods (before, during and after the OPCMs) were divided to evaluate the impact of OPCMs on OFS. It was indicated that OPCMs had no impact on the morning OFS, but had a significant impact on the afternoon OFS. Specifically, the OFS in two industrial cities Xinxiang (XX) and Zhengzhou (ZZ) shifted from transitional regime to NOx-limited regime after the OPCMs. We further investigated OFS differences between urban and suburban areas and found that OFS shift of XX only existed in urban areas, while that of ZZ existed in both urban and suburban areas. We compared their measures and found that it is effective to take hierarchical control measures on different levels of ozone pollution days to alleviate ozone pollution. This study provides an improved understanding of diurnal evolution characteristics of OFS and the impacts of OPCMs on it, which will provide a theoretical basis for formulating more scientific ozone pollution control policies.

The impacts of ambient ozone pollution on China's wheat yield and forest production from 2010 to 2021

Near-surface ozone causes damages on both crop and forest but their long-term spatiotemporal changes in China have been insufficiently explored, preventing comprehensive policy making with food security and climate targets. Moreover, limitation exists in the current metrics for long-term regional ozone risk assessment, AOT40 (the accumulated hourly ozone over a threshold of 40 ppbv) and PODY (phytotoxic ozone dose over a threshold of Y nmol ozone m−2 PLA s−1), with ignorance of meteorological influence for the former and complicated data collection and calculation procedures for the latter. Here, we developed a new metric for ozone-induced risk on winter wheat, O3MET, which can be easily derived based on ozone concentrations and meteorological variables, and is suitable for long-term assessment of ozone-induced wheat loss at the regional scale. Combining with existing metric for forest (O3RH), we comprehensively quantified the ozone damages on winter wheat yield and forest gross primary production (GPP) for mainland China during 2010–2021, the period with fast growth of ozone level across the country. The annual average losses of wheat yield and forest GPP were estimated at 26.5 Mt and 552.6 TgC, accounting for 17% and 4% of the total yield and GPP without ozone impact, respectively. Heavy dual ozone-induced damages on both wheat and forest were presented in East and South China. The ozone-induced wheat yield loss and forest GPP loss were estimated to increase at a rate of 1.8 Mt/yr and 13.9 TgC/yr for the entire country, respectively, driven mainly by the enhanced ambient ozone level within the research period. Besides ecological impact, the ozone pollution in the developed eastern China resulted in serious health burden as well, thus effective actions on ozone pollution alleviation in the region is crucial for reducing its ecological and health risks simultaneously.

Biogenic volatile organic compounds dominated the near-surface ozone generation in Sichuan Basin, China, during fall and wintertime

The complex air pollution driven by both Ozone (O3) and fine particulate matter (PM2.5) significantly influences the air quality in the Sichuan Basin (SCB). Understanding the O3 formation during autumn and winter is necessary to understand the atmospheric oxidative capacity. Therefore, continuous in-site field observations were carried out during the late summer, early autumn and winter of 2020 in a rural area of Chongqing. The total volatile organic compounds (VOCs) concentration reported by a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) were 13.66 ± 9.75 ppb, 5.50 ± 2.64 ppb, and 9.41 ± 5.11 ppb in late summer, early autumn and winter, respectively. The anthropogenic VOCs (AVOCs) and biogenic VOCs (BVOCs) were 8.48 ± 7.92 ppb and 5.18 ± 2.99 ppb in late summer, 3.31 ± 1.89 ppb and 2.19 ± 0.93 ppb in autumn, and 6.22 ± 3.99 ppb and 3.20 ± 1.27 ppb in winter. A zero-dimensional atmospheric box model was employed to investigate the sensitivity of O3-precursors by relative incremental reactivity (RIR). The RIR values of AVOCs, BVOCs, carbon monoxide (CO), and nitrogen oxides (NOx) were 0.31, 0.71, 0.09, and -0.36 for late summer, 0.24, 0.59, 0.22, and -0.38 for early autumn, and 0.30, 0.64, 0.33 and -0.70 for winter, and the results showed that the O3 formation of sampling area was in the VOC-limited region, and O3 was most sensitive to BVOCs (with highest RIR values, > 0.6). This study can be helpful in understanding O3 formation and interpreting the secondary formation of aerosols in the winter.

Summertime ozone pollution in China affected by stratospheric quasi-biennial oscillation

Abstract. In recent years, the near-surface ozone (O3) level has been rising fast in China, with increasing damage to human health and ecosystems. In this study, the impact of stratospheric quasi-biennial oscillation (QBO) on interannual variations in summertime tropospheric O3 over China is investigated based on GEOS-Chem model simulations and satellite retrievals. QBO has a significant positive correlation with near-surface O3 concentrations over central China (92.5–112.5∘ E, 26–38∘ N) when the sea surface temperature (SST) over the eastern tropical Pacific is warmer than normal, with a correlation coefficient of 0.53, but QBO has no significant effect on O3 under the cold SST anomaly. Compared to the easterly phase of QBO, the near-surface O3 concentrations have an increase of up to 3 ppb (5 % relative to the average) over central China during its westerly phase under the warm SST anomaly. O3 also increases above the surface and up to the upper troposphere, with a maximum increase of 2–3 ppb (3 %–5 %) in 850–500 hPa over central China when comparing westerly phase to easterly phase. Process-based analysis and sensitivity simulations suggest that the O3 increase over central China is mainly attributed to the anomalous downward transport of O3 during the westerly phase of QBO when a warm SST anomaly occurs in the eastern tropical Pacific, while the local chemical reactions and horizontal transport processes partly offset the O3 increase. This work suggests a potentially important role of QBO and the related vertical transport process in affecting near-surface O3 air quality, with an indication for O3 pollution prediction and prevention.

Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model

Accurate ozone concentration simulation can provide a health reference for people’s daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R2 were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 μg/m3, 114.3 μg/m3, and 228.4 μg/m3, respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang.

Contrasting Near-Surface Ozone Pollution in Wet and Dry Year over China.

The near-surface ozone concentration was evaluated in two typical years with contrasting climatic impacts over the China region induced by El Niño-Southern Oscillation, which had either dry conditions (drought) with intense solar radiation and higher temperatures or wet conditions with opposite meteorological conditions. Surface ozone was observed to aggravate notably by 30% over Northern China in summer and by 50% over Eastern China in autumn in the dry year compared to the wet year. The ozone aggravation was found to be mainly ascribed to the reduced precipitation (relative humidity), enhanced solar radiation and increased temperature rather than primary emission (indicated by carbon monoxide). The health impacts showed the mortality attributable to ozone sharply increased by ~55% in Guangdong while the number of cases dying from ozone-related respiratory diseases per 100,000 population at risk was elevated by ~41% and ~17% for Guangdong (in the Pearl River Delta) and Jiangsu (in the Yangtze River Delta) province (two regions that have been reported to be highly influenced by surface ozone in China), respectively, in the dry year relative to the wet year, indicative of the significant adverse health effects of ozone aggravation. These results highlight the essential contribution of climate anomalies to surface ozone pollution. Efforts to suppress ozone aggravation can be beneficial to public health if extreme drought is predicted, and reasonable policy is implemented.

Transmission paths and source areas of near-surface ozone pollution in the Yangtze River delta region, China from 2015 to 2021

Near-ground ozone in the Yangtze River Delta (YRD) region has become one of the main air pollutants that threaten the health of residents. However, to date, the transport behavior and source areas of ozone in the YRD region have not been systematically analyzed. In this study, by combining the ozone observational record with a HYSPLIT (hybrid single-particle Lagrangian integrated trajectory) model, we tried to reveal the spatiotemporal regularity of the airflow transport trajectory of ozone. Spatially, high ozone concentrations mainly clustered in industrial cities and resource-based cities. Temporally, the center of the ozone pollution shifted westward of Nanjing from 2015 to 2021. With the passage of time, the influence of meteorological elements on the ozone concentration in the YRD region gradually weakened. Marine atmosphere had the most significant impact on the transmission path of ozone in Shanghai, of which the trajectory frequency in 2021 accounted for 64.21% of the total frequency. The transmission trajectory of ozone in summer was different from that in other seasons, and its transmission trajectory was mainly composed of four medium-distance transmission paths: North China-Bohai Sea, East China Sea-West Pacific Ocean, Philippine Sea, and South China Sea-South China. The contribution source areas mainly shifted to the southeast, and the emission of pollutants from the Shandong Peninsula, the Korean Peninsula-Japan, and the Philippine Sea-Taiwan area increased the impact of ozone pollution in the Shanghai area from 2019 to 2021. This study identified the regional transport path of ozone in the YRD region and provided a scientific reference for the joint prevention and control of ozone pollution in this area.

基于TROPOMI的近地面臭氧浓度估算研究

基于TROPOMI的近地面臭氧浓度估算研究

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O3) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O3 concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O3 model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O3 concentration can be accurately estimated; the R2 (RMSE: μg/m3) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O3 concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O3 concentration had increased significantly. (3) The O3 concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

Impacts of deep boundary layer on near-surface ozone concentration over the Tibetan Plateau

The deep atmospheric boundary layer in winter and spring over the Tibetan Plateau (TP) facilitates the exchange of mass and chemical species between the troposphere and stratosphere. Using ERA5 reanalysis data (1979–2018), this paper analyzes the atmospheric process over the TP when the dynamical tropopause (1.5 PVU surface) approaches the top of the deep boundary layer, which would affect the concentration of near-surface ozone. The result shows that the near-surface (at the height about 500–550 hPa) ozone concentration increases when the tropopause descends and adjoins the top of the deep boundary layer. The influence mechanism of the deep boundary layer on the increase of surface ozone on February 27, 2008 is studied by the WRF-Chem model. It is found that the calculated (using Reynolds average method) near-surface ozone increases by about 8 ppb per hour due to the vertical transport when the ozone-rich air from the stratosphere penetrates into the troposphere. Furthermore, a large eddy simulation (LEM model) is used to investigate the turbulence structure and development during the formation of the deep atmospheric boundary layer in the same case. It is found that a separate weak-turbulence layer forms at the height of about 6000 m AGL at 1300 LT below the tropopause fold. The upper-level turbulence couples with the boundary-layer turbulence after 1900 LT. As a result, the strong boundary layer turbulence causes the ozone-rich air from the stratosphere to mix with air near the surface layer.

Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information.

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations.