Alkanes, oxygenated volatile obutene, propylene, cis-2-butene, and ethylene were the dominant types for O3 production.High spatiotemporal resolution data on near-surface ozone concentration distribution is of great importance for monitoring and managing atmospheric ozone air pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution information as estimation factors, an XGBoost-LME model had been constructed to approximate the near-surface ozone concentration when you look at the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, area 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar power radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were key elements influencing the near-surface ozone concentration when you look at the Beijing-Tianjin-Hebei area. Included in this, T2M, SSRD, and D2M had powerful correlations, with correlation coefficients of 0.82, 0.75, and 0.71, correspondingly. ② Compared with that of various other models, the XGBoost-LME design had ideal performance when it comes to different signs. The ten-foldurface ozone concentrations in this region predominantly exhibited a pattern of greater levels into the south and lower levels in the north. High-value areas were predominantly found in the ordinary areas of the southern spend the lower altitudes, dense population, and greater professional emissions； low-value areas, having said that, had been primarily located in mountainous regions of the north part with higher altitudes, simple populace, higher plant life protection, and reduced industrial emissions.Based regarding the ozone （O3） tracking data for the Pearl River Delta （PRD） from 2015 to 2022 together with reanalysis of meteorological data, the influence of meteorological conditions in the yearly difference and styles regarding the maximum daily 8-hour average O3 concentration （MDA8-O3） had been quantified using multiple linear regression （MLR） and LMG methods. The outcomes indicated that the MLR model built utilizing meteorological variables from specific months in autumn better simulated the variation in MDA8-O3 compared to that in the model built making use of meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative moisture, 2 m optimum temperature, and 850 hPa zonal wind led to a reduction of 34.1 μg·m-3 in MAD8-O3 in 2020 when compared with that in 2019, with efforts of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The noticed styles of MDA8-O3 in the PRD for September, October, November, additionally the autumn period during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 μg·（m3·a）-1, respectively. Among these, the styles driven by meteorological elements had been 3.6, 2.4, 2.4, and 3.1 μg·（m3·a）-1. Overall, meteorological conditions added 53.4% to the variants in autumn MDA8-O3 within the PRD from 2015 to 2022.The susceptibility evaluation of ozone generation in crucial ozone-polluted areas and towns and cities is an important foundation for the avoidance and control over near-surface ozone （O3） pollution. In line with the five-year data find more of ozone, VOCs, and NOx from three typical stations in Shanghai, specifically Dianshan Lake Station （suburban area）, Pudong Station （urban area）, and Xinlian Station （industrial area） from 2016 to 2020, the nonlinear relationship between ozone and precursors （VOCs and NOx） during the high-ozone period in the 5 years was quantitatively examined making use of an observation design. The outcome revealed that the top months of near-surface ozone in Shanghai had been from April to September during 2016 to 2020, because of the highest values showing up from June to August. The amount fraction of VOCs and NO2 concentration had a solid indicative relevance for the O3 concentration at Pudong Station. The O3 focus at Dianshan Lake Station had been mainly impacted by local environment, meteorological facets, and cross-regional transmission. The ozone concentration at Xinlian Station ended up being a variety of environmental back ground focus and professional area photochemical pollution. Pudong Station and Dianshan Lake Station were into the VOCs control area. Xinlian facility was gradually nearer to the NOx control zone from 2016 to 2019, transitioning to your VOCs control zone since 2020. The L·OH of Pudong facility surgeon-performed ultrasound , Dianshan Lake Station, and Xinlian facility were： NOx control area>collaborative control area>VOCs control area.Guanzhong metropolitan agglomeration has actually a good development foundation and great development potential, and possesses an original strategic position when you look at the national all-round checking pattern. In the past few years, the difficulty of near-surface ozone （O3） into the Guanzhong area has become increasingly prominent, which includes become a bottleneck impacting the constant enhancement of quality of air. So that you can effortlessly avoid and control O3 pollution, this research examined the characteristics of yearly, month-to-month, and daily alterations in O3 focus into the Guanzhong area in line with the environmental monitoring information from 2018 to 2021. A geo-detector had been utilized to study the driving elements of this spatial differentiation of O3 focus, and also the sources of O3 had been HLA-mediated immunity mutations analyzed utilizing a backward trajectory model and emission stock building. The results indicated that the daily and month-to-month variation in O3 concentration into the Guanzhong Region had been unimodal. The everyday optimum price showed up at 15：00, the minimum worth showed up at 07ndustrial manufacturing combustion sources. The study results have actually a guiding significance for O3 joint avoidance and control into the Guanzhong Region.The spatial-temporal distribution structure of surface O3 over the Qinghai-Xizang Plateau （QXP） was analyzed based on air quality tracking data and meteorological information from 12 metropolitan areas on the QXP from 2015 to 2021. Kolmogorov-Zurbenko （KZ） filtering was used to split up the original O3-8h show into components at various time scales.