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To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization （PMF） and conditional bivariate probability function （CBPF） were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10 -9 . Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.

The spatial distribution, accumulation features, and driving factors of O 3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O 3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O 3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O 3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NO x emission, VOCs emission, and motor vehicle ownership had significant effects on O 3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14 th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NO x emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

Based on the hourly O 3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O 3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O 3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O 3 and meteorological factors. Ultimately, the long-term component of O 3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O 3 concentration showed a convergent change, that is, the volatility of O 3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O 3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.

This is a phase Ib study of anlotinib plus a programmed death-ligand 1 (PD-L1) inhibitor TQB2450 for platinum-resistant or -refractory ovarian cancer. Thirty-four patients are enrolled and receive treatment. The objective response rate (ORR) is 47.1%, and the disease control rate is 97.1%. The median duration of response (DOR) has not been reached, and 61.3% of patients have a DOR of at least 8 months. The median progression-free survival (PFS) is 7.8 months, and the median overall survival (OS) has not been reached. The PD-L1-positive group has an ORR of 25.0%, whereas the PD-L1-negative group has an ORR of 92.9%. Treatment-related grade 3 or 4 adverse events (AEs) occur in 70.6% of patients, with the most common being hypertension (29.4%) and palmar-plantar erythrodysesthesia syndrome (29.4%). Anlotinib plus TQB2450 show promising antitumor activity and manageable toxicities in patients with platinum-resistant or -refractory ovarian cancer. A phase 3 randomized controlled trial to further validate our findings is ongoing., Competing Interests: Declaration of interests Y.-F.W. is an employee of Chia Tai Tianqing Pharmaceutical Group Co., Ltd., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Fugitive dust poses an important contribution to urban air particulate matter in China. To further improve the level of dust pollution prevention and control, the emission and contribution characteristics of urban fugitive dust were summarized; the main causes of dust pollution were analyzed; and the key links, key indicators, and main measures for prevention and control were clarified, so as to further improve the concept of "accurate dust control." Among all types of fugitive dust sources, road dust and construction dust were the main emission and contribution sources, among which road dust was more prominent. Production activities, vehicle disturbances, and wind erosion were the main dust-generating links of various dust sources. Silt loading was taken as the key control index for road dust prevention and control, whereas silt loading and bare soil (or material) areas were taken as the key control index for construction and other dust sources. Around the key indicators, three main ways to control the road dust and six main measures to control the construction and other dust sources were defined. In addition, some suggestions on the necessary supporting measures for dust control were put forward, so as to provide a comprehensive and beneficial reference for the practical application of dust control in Chinese cities.