[Simulation and Analysis of Ozone Pollution Process in Shijiazhuang Based on CMAQ-ISAM Model].

In Shijiazhuang City, ozone （O3） pollution occurs frequently in June every year. In June 2023, the average O3 8 h concentration （O3-8h） pollution exceeded 80% of the days in the month, and O3 was the primary pollutant, accounting for 100%. For an O3 heavy pollution process from June 11 to 18, the air quality model WRF-CMAQ was used for simulation, and the average error data MFB and MFE were -10.47% and 17.96%, respectively, which was within the ideal error range. The CMAQ process analysis module was used to simulate the physical and chemical processes in Shijiazhuang City, and the dry deposition （DDEP） contribution concentration was -23.88 µg·m-3, which was the main process of O3 consumption, whereas the transport process （TRAN） was the main source of O3, among which the contribution was more significant in vertical transport （VTRA）. At the same time, the source analysis module （ISAM） was used to analyze the O3 contribution of local and surrounding areas in Shijiazhuang City. The results showed that the contribution rate of local industry sources in Shijiazhuang City was as follows： traffic source （12.54%） > industrial source （6.94%） > residential source （6.56%） > power source （4.75%）. The long-distance transmission source （BCON） continued to be in the first place with a high contribution rate of 63.31%. In the heavy pollution period under stable weather, the contribution concentration of BCON in the D02 layer of the nested domain to Shijiazhuang City was lower than the sum of the marked area. Among the surrounding cities, Baoding City had the highest contribution rate under stable weather, accounting for 26.21%. In the late period, the contribution concentration of Xingtai City increased rapidly under the action of high-value southwest wind. To effectively reduce O3 pollution, it is necessary to reduce emissions in the city and to control the upwind cities in advance, and the implementation of inter-regional joint prevention and control is the key.

[Estimation of Regional Background Ozone in Henan Province from 2019 to 2021].

Clarifying the regional transmission and local generation contributions of ozone （O3） is important for controlling O3 pollution. To quantify the regional background and spatial-temporal variations of O3, a comprehensive study was conducted using multiple methods including principal component analysis （PCA） and TCEQ, with Henan Province as a case study. Based on monitoring data from 59 national sites in Henan Province during 2019-2021, four methods were employed to estimate the regional background of O3. Method 1 was the traditional method, performing O3 univariate-multisite PCA analysis. Method 2 was a multivariate-single-site PCA analysis considering nitrogen dioxide and meteorological conditions as constraints. Method 3 combined PCA and multiple linear regression （MLR） to determine regional background contributions, drawing on the idea of source apportionment. Method 4 was the TCEQ method that used the lowest measured O3-8h concentration as the regional background. The estimation results showed that Methods 1 and 2 were basically equal, and Methods 3 and 4 were approximately 37-60 µg·m-3 lower than Method 1. From 2019 to 2021, the changes in regional background ρ（O3-8h） estimated by Methods 1-4 were 1.6, -13.4, 5.9, and -3.5 µg·m-3, respectively. The average estimations derived from multiple methods showed that the regional background ρ（O3-8h） in Henan Province from 2019 to 2021 concentrations were 82.0, 79.0, and 79.7 µg·m-3, accounting for 75.9%, 76.4%, and 78.7% of the total regional O3-8h, respectively. The regional background O3-8h estimated by the four methods showed obvious seasonal changes, characterized by summer > spring > fall > winter.

[Sensitivity Analysis of Atmospheric Ozone and Source Apportionment of Volatile Organic Compounds in Yinchuan City].

Based on the observation data of O3 concentration in Yinchuan in 2022, the monthly variation characteristics of O3 concentrations were analyzed. Further, based on the observation data of meteorological elements, conventional pollutants, and volatile organic compounds （VOCs） concentrations at an urban site in Yinchuan from May to July, the difference in meteorological elements and precursor concentrations between the polluted days and the non-polluted days were compared. Then, the O3 sensitivity and the VOCs sources were discussed using the Framework for 0-D Atmospheric Modeling （F0AM） and positive matrix factorization （PMF） model, respectively. The results showed that： ① The O3 pollution occurred from May to July in 2022, and the concentrations of O3-8h-90per were 156 µg·m-3, 170 µg·m-3, and 174 µg·m-3, respectively, with exceeding standard rates of 9.7%, 26.7%, and 29.0%, respectively. ② Compared with those on the non-polluted days, the hourly mean values of temperature, total solar radiation, and concentrations of various precursors on the O3-polluted days increased, including the volume concentrations of propane, isobutane, ethane, n-butane, and dichloromethane, which increased significantly by 33.1%, 29.1%, 25.0%, 22.7%, and 21.3%, respectively. The results showed that the combined increase in pollutant emissions and adverse meteorological conditions contributed to the formation of O3. ③ From May to July 2022, the top five VOCs species in terms of ozone formation potential （OFP） value on whole, non-polluted, and polluted days were the same. They were acetaldehyde, m/p-xylene, ethylene, isoprene, and toluene, mainly from solvent use sources, natural sources, and chemical industry emissions. ④ The local O3 production was mostly controlled by VOCs, and the relative incremental reactivity （RIR） results revealed that O3 production showed strong positive sensitivity to alkene and aromatic hydrocarbon but showed negative sensitivity to NOx on both polluted and non-polluted days. The relative contributions of active species such as acetone, ethylene, and isobutane to O3 production were high, and the implementation of an emission reduction scheme with the ratio of VOCs to NOx emission reduction much greater than 1 could effectively reduce the local O3 concentration. ⑤ The main sources of atmospheric VOCs in Yinchuan were motor vehicle emission sources （32.3%）, process sources （20.7%）, combustion sources （19.2%）, solvent use sources （12.7%）, gasoline volatile sources （9.1%）, and natural sources （6%）, and the contribution rate of motor vehicle emission sources on polluted days increased by 4.6% compared with that on non-polluted days, indicating that the motor vehicle emission source was an important object of summer VOCs control in Yinchuan.

[Spatial-temporal Variations and Driving Factors of Surface Ozone over the Qinghai-Xizang Plateau from 2015 to 2021].

The spatial-temporal distribution pattern of surface O3 over the Qinghai-Xizang Plateau （QXP） was analyzed based on air quality monitoring data and meteorological data from 12 cities on the QXP from 2015 to 2021. Kolmogorov-Zurbenko （KZ） filtering was employed to separate the original O3-8h series into components at different time scales. Then, multiple linear regression of meteorological variables was used to quantitatively isolate the effects of meteorology and emissions. The results revealed that the annual mass concentrations of surface O3-8h from 2015 to 2021 in 12 cities over the QXP ranged from 78.7 to 156.7 µg·m-3, and the exceedance rates of O3 mass concentrations （National Air Quality Standard of grade II） ranged from 0.7%-1.5%. The monthly O3-8h mass concentration displayed a single-peak inverted "V"-shape and a multi-peak "M"-shape. The maximum monthly concentration of O3 occurred in April to July, and valleys occurred in July, September, and December. The short-term, seasonal, and long-term components of O3-8hdecomposed by KZ filtering contributed 29.6%, 51.4%, and 9.1% to the total variance in the original O3 sequence in 12 cities, respectively. From the whole region, the meteorological conditions were unfavorable for O3 reduction on the QXP from 2015 to 2017, which made the long-term component of O3 increase by 0.2-2.1 µg·m-3. The meteorological conditions were favorable for O3-8h reduction from 2018 to 2021, which led to the long-term component of O3-8h decrease by 0.4-1.1 µg·m-3. The meteorological conditions increased the long-term component of O3-8h in Ngari, Lhasa, Naqu, Nyingchi, Qamdo, Haixi, and Xining, with an average contribution of 30.1%. The meteorological conditions decreased the long-term component of O3-8h in Shigatse and Golog, with contributions of 359.0% and 56.5%, respectively. The increase in the long-term component of O3-8h in Ngari, Shigatse, Nagqu, Haixi, and Xining could be due to the rapid decrease in the long-term component of PM2.5 （4.04 µg·ï¼ˆm3·a）-1）.

[Variation Characteristics and Source Analysis of Atmospheric Ozone Pollution in Guanzhong Region].

Guanzhong urban agglomeration has a good development foundation and great development potential, and it has a unique strategic position in the national all-round opening up pattern. In recent years, the problem of near-surface ozone （O3） in the Guanzhong Region has become increasingly prominent, which has become a bottleneck affecting the continuous improvement of air quality. In order to effectively prevent and control O3 pollution, this study analyzed the characteristics of annual, monthly, and daily changes in O3 concentration in the Guanzhong Region based on the environmental monitoring data from 2018 to 2021. A geo-detector was used to study the driving factors of the spatial differentiation of O3 concentration, and the sources of O3 were analyzed using a backward trajectory model and emission inventory construction. The results showed that the daily and monthly variation in O3 concentration in the Guanzhong Region were unimodal. The daily maximum value appeared at 15：00, the minimum value appeared at 07：00, the peak value of the monthly average appeared in June, and the valley value appeared in December. The O3 concentration was highest in summer, followed by that in spring, and the lowest in winter. The days of O3 exceeding the standard showed mainly mild pollution, and moderate and above pollution showed a trend of decreasing first and then increasing. The O3 concentration in the Guanzhong Region was mainly closely related to precursors and meteorological factors, and the explanatory power of the interaction of each factor was significantly greater than that of any single factor. The regional transport of O3 concentration in the Guanzhong Region was mainly affected by easterly airflow, followed by the northwest direction, with the potential source areas located mainly in Henan Province and Hubei Province. The main local sources of volatile organic compounds （VOCs） were solvent use sources, process sources, and mobile sources, and the main emission sources of nitrogen oxides （NOx） were mobile sources and industrial production combustion sources. The research results have a guiding significance for O3 joint prevention and control in the Guanzhong Region.

[Meteorological Influences on Annual Variation and Trend of Autumn Surface Ozone in the Pearl River Delta].

Based on the ozone （O3） monitoring data of the Pearl River Delta （PRD） from 2015 to 2022 and the reanalysis of meteorological data, the impact of meteorological conditions on the annual variation and trends of the maximum daily 8-hour average O3 concentration （MDA8-O3） were quantified using multiple linear regression （MLR） and LMG methods. The results indicated that the MLR model constructed using meteorological parameters from individual months in autumn better simulated the variation in MDA8-O3 compared to that in the model built using meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative humidity, 2 m maximum temperature, and 850 hPa zonal wind led to a reduction of 34.1 µg·m-3 in MAD8-O3 in 2020 compared to that in 2019, with contributions of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The observed trends of MDA8-O3 in the PRD for September, October, November, and the autumn season during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 µg·ï¼ˆm3·a）-1, respectively. Among these, the trends driven by meteorological factors were 3.6, 2.4, 2.4, and 3.1 µg·ï¼ˆm3·a）-1. Overall, meteorological conditions contributed 53.4% to the variations in autumn MDA8-O3 in the PRD from 2015 to 2022.

[Characteristics and Source Analysis of Volatile Organic Compounds in Typical Ozone Pollution Processes During Summer in Jinan, China].

Based on a typical ozone （O3） pollution process in Jinan City from June 16 to 26, 2021, the variation characteristics of O3 and its precursor volatile organic compounds （VOCs） during different pollution periods （clean period （CP）, pollution rise period （PRP）, heavy pollution period （HPP）, and pollution decline period （PDP）） in the urban area were analyzed. Both positive matrix factorization （PMF） and an observation-based model （OBM） were used to identify the main sources of VOCs, O3 production mechanisms, and sensitive species. The results showed that the average value of ρ（O3-8h） during the HPP period in the urban area was （246.67±11.24） µg·m-3, and ρ（O3-1h） had a peak value of 300 µg·m-3. The volume fractions of VOCs and NO2 concentration were affected by the decrease in planetary boundary layer and wind speed, which were 76.99%-145.36% and 127.78%-141.18% higher than those in the other three periods, respectively, and were the main reasons for the aggravation of O3 pollution. Alkanes, oxygenated volatile organic compounds （OVOCs）, and halogenated hydrocarbons accounted for 43.81%, 20.98%, and 17.43% of VOCs in urban areas, respectively. All of them showed significant growth during the HPP period, with acetone, propane, and ethane being the top three species by volume in each stage and isopentane showing the highest growth during the HPP period. Alkene, alkanes, and aromatic hydrocarbons accounted for 40.19%, 28.06%, and 21.92% of the ozone generation potential （OFP）. 1-butene, toluene, isopentane, and isoprene were the species with higher OFP. Isoprene had the highest OFP during the PRP phase, and 1-butene had the highest OFP during the HPP phase. The volume fraction of isopentane significantly increased OFP. The correlation coefficient between VOCs and CO preliminarily indicated that motor vehicle exhaust and oil and gas volatilization were the main sources of VOCs during the HPP period. Further use of PMF revealed that solvent use sources, combustion sources, motor vehicle exhaust+oil and gas volatilization sources, industrial emission sources, and plant sources were important sources of VOCs in urban areas. The contribution of motor vehicle exhaust+oil and gas volatilization sources in the HPP period to VOCs was 3.09-14.72 times higher than that in other periods. The contribution of solvent use sources to VOCs was approximately 2.50 times higher than that in the CP and PRP periods. The main sources of VOCs volume fraction increase were motor vehicle exhaust, oil and gas volatilization sources, and solvent use sources. Potential sources and concentration weight analysis found that VOCs were also affected by the transmission of VOCs to Binzhou and Dongying in the northeast direction. The OBM results indicated that the main pathway of O3 formation in urban areas was the reaction of peroxide hydroxyl radicals （HO2·ï¼‰ and methyl peroxide radicals （CH3O2·ï¼‰ with NO, and the net ozone generation rate during the HPP phase [P（O3）net] was 24×10-9 h-1. Based on the sensitivity experiment results, the alkene components of 1-butene, propylene, cis-2-butene, and ethylene were the dominant species for O3 production.

[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020].

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

[Differences of Three Methods in Determining Ozone Sensitivity in Nanjing].

In recent years， ozone （O3） has become an increasingly important air pollutant in China. Identifying the sensitivity of O3 to the precursors volatile organic compounds （VOCs） and nitrogen oxides （NOx） can help make effective abatement strategies. This study compared three methods for determining O3-VOCs-NOx sensitivity： simulated photochemical indicator values and sensitivity coefficients derived from a three-dimensional air quality model and an observation-based model （OBM）， with a case study involving an O3 pollution event that occurred in Nanjing in late July 2017. The results showed that O3 sensitivity based on the photochemical indicator and sensitivity coefficients demonstrated similar spatial variations （over 50% of the grid cells of Nanjing exhibiting identical O3 sensitivity）. However， sensitivity coefficients identified a larger number of areas within a transitional O3 sensitivity regime， as opposed to the VOCs- or NOx-limited regime identified by the photochemical indicator. The determination of the latter was affected by the adopted threshold values. The OBM relied on the quality of the observational data. For example， positive biases in observed NO2 could lead to an underestimation of O3 sensitivity to NOx with the OBM. During the high pollution period， the three methods exhibited significant disparities. The photochemical indicator tended to suggest the VOCs-limited condition， whereas the OBM and sensitivity coefficients indicated the NOx-limited or transitional regimes.

[Prediction of Autumn Ozone Concentration in the Pearl River Delta Based on Machine Learning].

Based on the observation data of the daily maximum 8-hour ozone (O3) average concentration[MDA8-O3, ρ(O3-8h)] and meteorological reanalysis data in the Pearl River Delta Region from 2015 to 2022, four machine learning methods, i.e., support vector machine regression (SVR), random forest (RF), multi-layer perceptron (MLP), and lightweight gradient boosting machine (LG) were used to establish MDA8-O3 prediction models. The results showed that the SVR model had the best prediction performance on MDA8-O3 during the whole year, the coefficient of determination (R2) reached 0.86, and the root mean square error (RMSE) and mean absolute error (MAE) were 16.3 µg·m-3 and 12.3 µg·m-3, respectively. The prediction performance of the SVR model in autumn was still slightly better than that of LG and MLP, with R2,RMSE,and MAE values of 0.88, 19.8 µg·m-3,and 16.1 µg·m-3, respectively. The RF model performed the worst in the autumn prediction. In addition, the models trained by data from the whole year had better prediction ability on autumn MDA8-O3 than that of those only trained by autumn data, and the R2 differed 0.08-0.14.