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Ground-level ozone (O 3) pollution is a major air quality issue in densely populated urban areas. Despite a significant decline in human activities in the megacity Shanghai from March 28 to May 31, 2022, ground-level measurements indicate a rise in maximum daily average 8-h (MDA8) O 3 concentrations in comparison to the corresponding period in 2021. There is a need for quantitative analysis to identify the reasons behind this increasing O 3 concentration. We analyzed ground measurements of O 3 and its precursors and meteorological parameters made in Shanghai, using random-forest (RF) model and chemical box model to elucidate the roles of meteorological and chemical factors in influencing O 3 concentrations. Across urban, suburban, semi-rural, and coastal sites, the urban center of Shanghai experienced the largest decreases in the concentrations of nitrogen oxides (NO x ; 53%) and volatile organic compounds (VOCs; 52%), with the most notable rise in MDA8 O 3 concentrations (16%). RF modeling indicates that meteorological factors reduced MDA8 O 3 concentrations by a marginal 3%, whereas a decline in anthropogenic emissions resulted in a 17% increase in MDA8 O 3 concentrations. Chemical box modeling at the Pudong urban site indicates that while the decline in VOCs reduced O 3 production by 42%, this was negated by a reduction in NO x from traffic emissions, which enhanced O 3 production by 51%, resulting in an increase in O 3 production overall. Despite a halving in precursor levels, Shanghai's urban centre remains predominately under VOC-limited conditions throughout the study period, with high NO 2 levels from the petrochemical industry and traffic emissions. Joint control of anthropogenic VOCs (AVOCs) and NO x , with a ratio greater than 1.29, could help avoid exacerbation of O 3 pollution and reduce NO 2 pollution. Our findings emphasize the necessity of reducing industrial emissions along with ongoing green transportation strategies for alleviating O 3 pollution in megacities like Shanghai. • Emission reductions drove an O 3 increase in Shanghai during the 2022 lockdown. • Shanghai's urban centre remains predominately under VOC-limited conditions. • The high NO 2 level was attributed to petrochemical industry and traffic emissions. [ABSTRACT FROM AUTHOR]

Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM 2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM 2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM 2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM 2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM 2.5 by - 19.25 to - 2.66 µgm-3 in 2019, especially in winter and spring and during high-PM 2.5 events. Secondary organic components showed the largest contribution to the PM 2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM 2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) ranks as the third most common cancer, accounting for a significant number of cancer-related deaths worldwide every year. Yet, the molecular mechanisms responsible for the progression of this malignancy are not fully understood. Numerous studies indicate that BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) plays a role in the progression of various malignant tumors. However, the specific biological functions and the detailed mechanisms of how BUB1B influences CRC are still not completely known. This study aimed to explore the expression and role of BUB1B in CRC. Materials and Methods: To achieve this, the expression levels of BUB1B in human CRC tissues and cell lines were examined using real-time polymerase chain reaction and Western blotting. The role and associated mechanisms of BUB1B in CRC cell progression were assessed both in vitro and in vivo using RNA interference. Results: The findings of this study revealed an elevated expression of BUB1B in both CRC tissues and cell lines. The silencing of BUB1B in CRC cell lines notably inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest and apoptosis. In addition, the knockdown of BUB1B inhibited the JNK/c-Jun signaling pathway, increased the expression of proapoptotic proteins, and decreased the expression of antiapoptotic proteins. The effects of BUB1B knockdown on CRC cell progression were reversed by the JNK activator PAF(C-16). Conclusions: In summary, the suppression of BUB1B hindered malignant tumor progression and heightened apoptosis and cell cycle arrest in CRC cells via the JNK/c-Jun pathway. Importantly, the removal of BUB1B expression curtailed tumor growth in human CRC xenografts in nude mice, suggesting its potential as a promising therapeutic target for CRC patients. ClinicalTrials.gov ID: No.2019 K-C086 [ABSTRACT FROM AUTHOR]

To ensure good air quality during the China International Import Expo (CIIE) event, stringent emission-reduction measures were implemented in Shanghai. To assess the efficacy of these measures, this study measured typical categories of intermediate/semi volatile organic compounds (I/SVOCs), including alkanes (C 10 –C 26 n-alkanes and pristane), EPA-priority polycyclic aromatic hydrocarbons (PAHs), alkylnaphthalenes, benzothiazole (BTH) and chlorobenzenes (CBs), at an urban site of Shanghai before and during two CIIE events (2019 and 2020; non-CIIE versus CIIE). The average concentrations of alkanes and PAHs during both 2019 and 2020 CIIE events decreased by approximately 41% and 17%, respectively, compared to non-CIIE periods. However, the decline in BTH and CBs was only observed during CIIE-2019. Secondary organic aerosol (SOA) formation from alkanes, PAHs and BTH was evaluated under atmospheric conditions, revealing considerable SOA contributions from dimethylnaphthalenes and BTH. Positive matrix factorization (PMF) analysis further revealed that life-related sources, such as cooking and residential emissions, make a noticeable contribution (21.6%) in addition to the commonly concerned gasoline-vehicle sources (31.5%), diesel-related emissions (20.8%), industrial emissions (18.6%) and ship emissions (7.5%). These findings provide valuable insights into the efficacy of the implemented measures in reducing atmospheric I/SVOCs levels. Moreover, our results highlight the significance of exploring additional individual species of I/SVOCs and life-related sources for further research and policy development. [Display omitted] • Thedrop in atmospheric I/SVOCs observed during CIIE showed the efficacy of the temporarily implemented measures. • Dimethylnaphthalenes and BTH showed considerable potentials as SOA precursors under atmospheric conditions. • Life-related sources of I/SVOCs should be considered for further research and policy design. [ABSTRACT FROM AUTHOR]

Surface ozone (O 3) pollution is an emerging concern in China. Hangzhou Bay (HZB), where the petrochemical industry is clustered, has become one of China's most O 3 polluted areas due to exposure to volatile organic compounds (VOCs) emissions and land-sea breezes. It is urgently need to investigate the multiple drivers of surface O 3 generation in HZB more specifically. The spatial distribution of O 3 trends from April to September (2015–2022) in HZB depicts a general upward trend, with an observed trend of 0.26 μg/m3 a−1, where meteorological factors contribute to 54°% based on the stepwise multiple linear regression (MLR). Ensembled machine learning is more efficient and accurate, especially the Light Gradient Boosting model (LightGBM, R2 = 0.84) outperforms other machine learning algorithms. The Shapley additive explanation (SHAP) technique allows for more in-depth quantification of the contribution of specific factors to O 3 trends. The results of the LightGBM-SHAP algorithm present that solar radiation plays a leading role in O 3 generation. More importantly, stronger solar radiation can still lead to high O 3 concentration accumulation even at lower temperature based on the interaction of SHAP values. For the precursor's emissions, the ratio of formaldehyde-to-NO 2 (HCHO/NO 2) obtained from the Tropospheric Monitoring Instrument (TROPOMI) satellite observations, shows the study area is located in the VOCs-limited and transitional regimes, highlighting that VOCs control is more cost-effective. • Meteorological drivers contribute 54°% to the O 3 increase in HZB. • Stronger solar radiation can lead to high O 3 pollution even at lower temperature. • HZB is in the VOCs-limited and transitional regimes. [ABSTRACT FROM AUTHOR]