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      Impact of cooking style and oil on semi-volatile and intermediate volatility organic compound emissions from Chinese domestic cooking

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          Abstract

          Abstract. To elucidate the molecular chemical compositions, volatility–polarity distributions, and influencing factors of Chinese cooking emissions, a comprehensive cooking emission experiment was conducted. Volatile organic compounds (VOCs), intermediate volatility, and semi-volatile organic compounds (I/SVOCs) from cooking fumes were analysed by a thermal desorption comprehensive two-dimensional gas chromatography coupled with quadrupole mass spectrometer (TD-GC × GC-qMS). Emissions from four typical Chinese dishes, i.e. fried chicken, Kung Pao chicken, pan-fried tofu, and stir-fried cabbage were investigated to illustrate the impact of cooking style and material. Fumes of chicken fried with corn, peanut, soybean, and sunflower oils were investigated to demonstrate the influence of cooking oil. A total of 201 chemicals were quantified. Kung Pao chicken emitted more pollutants than other dishes due to its rather intense cooking method. Aromatics and oxygenated compounds were extensively detected among meat-related cooking fumes, while a vegetable-related profile was observed in the emissions of stir-fried cabbage. Ozone formation potential (OFP) was dominated by chemicals in the VOC range. Of the secondary organic aerosol (SOA) estimation, 10.2 %–32.0 % could be explained by S/IVOCs. Pixel-based partial least squares discriminant analysis (PLS-DA) and multiway principal component analysis (MPCA) were utilized for sample classification and component identification. The results indicated that the oil factor explained more variance of chemical compositions than the cooking style factor. MPCA results emphasize the importance of the unsaturated fatty acid-alkadienal-volatile products mechanism (oil autoxidation) accelerated by the cooking and heating procedure.

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation

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              Elucidating severe urban haze formation in China.

              As the world's second largest economy, China has experienced severe haze pollution, with fine particulate matter (PM) recently reaching unprecedentedly high levels across many cities, and an understanding of the PM formation mechanism is critical in the development of efficient mediation policies to minimize its regional to global impacts. We demonstrate a periodic cycle of PM episodes in Beijing that is governed by meteorological conditions and characterized by two distinct aerosol formation processes of nucleation and growth, but with a small contribution from primary emissions and regional transport of particles. Nucleation consistently precedes a polluted period, producing a high number concentration of nano-sized particles under clean conditions. Accumulation of the particle mass concentration exceeding several hundred micrograms per cubic meter is accompanied by a continuous size growth from the nucleation-mode particles over multiple days to yield numerous larger particles, distinctive from the aerosol formation typically observed in other regions worldwide. The particle compositions in Beijing, on the other hand, exhibit a similarity to those commonly measured in many global areas, consistent with the chemical constituents dominated by secondary aerosol formation. Our results highlight that regulatory controls of gaseous emissions for volatile organic compounds and nitrogen oxides from local transportation and sulfur dioxide from regional industrial sources represent the key steps to reduce the urban PM level in China.
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                Author and article information

                Contributors
                Journal
                Atmospheric Chemistry and Physics
                Atmos. Chem. Phys.
                Copernicus GmbH
                1680-7324
                2022
                August 02 2022
                : 22
                : 15
                : 9827-9841
                Article
                10.5194/acp-22-9827-2022
                93681ddf-f544-4e8d-8211-087a6abdfa0d
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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