[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）.

[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.

Research on land use classification of hyperspectral images based on multiscale superpixels.

With the rapid development of remote sensing technology, research on land use classification methods based on hyperspectral remote sensing images has attracted widespread attention. Existing land-use classification studies mostly use the average filtering method at a single scale for spectral image processing. These methods cannot accurately filter the window range, which leads to the neglect of image detail information, and the single kernel matrix cannot characterize multifeature information, resulting in reduced classification accuracy. Therefore, this study intended to use a superpixel segmentation method to perform multiscale superpixel segmentation on the first principal component of a hyperspectral image at multiple scales. By combining the weighted multiscale spatial-spectral kernel and the original spatial-spectral kernel to form a synthetic kernel for land use classification, the hyperspectral image of the National Mall in Washington DC was used as experimental data to test and analyze this method. The experimental results showed that the classification accuracy of this method on the experimental test set was 98.53%, which is compared with the classification results obtained by the single-scale spatial spectral synthetic nuclear method, the original spatial spectral synthetic nuclear method and the wavelength segmented synthetic nuclear method, the effective classification accuracy with this method was increased by 7.56%. The results prove that this method can effectively solve the problems of the lack of adaptability of the image spectrum and the lack of comprehensive spectral information and can significantly improve the accuracy of land use classification.