周簪荣, 张美辨, 邹华, 高向景, 全长健, 胡勇, 金玉兰. 杭州某酒店中式厨房空气中颗粒物的暴露特征[J]. 环境与职业医学, 2021, 38(2): 132-136, 151. DOI: 10.13213/j.cnki.jeom.2021.20432
引用本文: 周簪荣, 张美辨, 邹华, 高向景, 全长健, 胡勇, 金玉兰. 杭州某酒店中式厨房空气中颗粒物的暴露特征[J]. 环境与职业医学, 2021, 38(2): 132-136, 151. DOI: 10.13213/j.cnki.jeom.2021.20432
ZHOU Zanrong, ZHANG Meibian, ZOU Hua, GAO Xiangjing, QUAN Changjian, HU Yong, JIN Yulan. Exposure characteristics of airborne particulate matters in Chinese kitchen of a hotel in Hangzhou[J]. Journal of Environmental and Occupational Medicine, 2021, 38(2): 132-136, 151. DOI: 10.13213/j.cnki.jeom.2021.20432
Citation: ZHOU Zanrong, ZHANG Meibian, ZOU Hua, GAO Xiangjing, QUAN Changjian, HU Yong, JIN Yulan. Exposure characteristics of airborne particulate matters in Chinese kitchen of a hotel in Hangzhou[J]. Journal of Environmental and Occupational Medicine, 2021, 38(2): 132-136, 151. DOI: 10.13213/j.cnki.jeom.2021.20432

杭州某酒店中式厨房空气中颗粒物的暴露特征

Exposure characteristics of airborne particulate matters in Chinese kitchen of a hotel in Hangzhou

  • 摘要: 背景

    厨房颗粒物的危害已引起社会关注,但是目前其分布特征鲜有报道。

    目的

    探索厨房颗粒物的时空分布特征,分析数量浓度(NC)、质量浓度(MC)、表面积浓度(SAC)的变化特征和颗粒物粒径分布规律,为作业人群的暴露评价提供参考指标,也为超细颗粒物的健康效应研究提供实验依据。

    方法

    选取杭州某酒店中式厨房的不同烹饪岗位并对接触颗粒物进行监测,探索炒菜岗颗粒物NC、MC和SAC的时空分布特征并分析颗粒物粒径的变化规律,对炒菜岗与蒸菜岗的颗粒物个体暴露情况进行对比,同时对NC、MC和SAC三者的相关系进行分析。

    结果

    炒菜岗位的NC10~500 nm在午餐准备期和午餐高峰期的大小和波动幅度明显高于午餐收尾期;MC10~500 nm值全程波动较NC10~500 nm小,午餐准备期、高峰期和收尾期的MC10~500 nm均值分别为0.149、0.229、0.151 mg·m-3;SAC10~500 nm均值分别为225、961、466 μm2·cm-3。炒菜岗位颗粒物粒径100 nm以内的颗粒物占比为94.67%,午餐高峰期的模式直径为19 nm。炒菜岗位NC20~700 nm高于蒸菜岗位(P < 0.01),炒菜岗位暴露颗粒物的模式直径(34.98±2.33)nm高于蒸菜岗位(30.11±2.17)nm(P < 0.01)。SAC10~500 nm与NC10~500 nm间的相关性(r=0.703)强于NC10~500 nm与MC10~500 nm、SAC10~500 nm与MC10~500 nm间的相关性(r=0.412、0.351)。

    结论

    炒菜岗位颗粒物的NC、MC、SAC大小与作业情况相关。粒径在100nm以内的颗粒物数在构成中占绝对优势。SAC与NC间存在强相关性,表明SAC可能更适合用作空气污染暴露指标。

     

    Abstract: Background

    The hazards of kitchen particles have aroused social concerns, but there are few studies on their distribution characteristics.

    Objective

    This study is designed to explore the time and space distribution characteristics of kitchen particles, including the number concentration (NC), mass concentration (MC), surface area concentration (SAC), and particle size distribution, and provide a reference for assessing the exposure levels and health effects of ultrafine particles among exposed populations.

    Methods

    Particles at different cooking posts were monitored in the Chinese kitchen of a hotel in Hangzhou. The temporal and spatial distribution characteristics of NC, MC, and SAC of particles and the particle size distribution at the fried food posts were analyzed, the individual exposure levels to particles were compared between fried food posts and steaming posts, and the correlations among NC, MC and SAC were evaluated.

    Results

    At the fried food posts, the NC10~500 nm during lunch preparation period and peak lunch period were higher and more fluctuating than that during lunch closing period; the MC10~500 nm during lunch preparation, peak, and closing periods were 0.149, 0.229, and 0.151 mg·m-3, respectively, fluctuating less than NC10~500 nm throughout the whole lunch period; the SAC10~500 nm during the three periods were 225, 961, and 466 μm2·cm-3, respectively. The particles within 100 nm accounted for 94.67% of total particles, and in the peak lunch period the particles of 19 nm dominated (mode size). The NC20~700 nm of particles of the fried food posts was higher than that of the steaming posts (P < 0.01), and the mode size of particles of the cooking posts(34.98±2.33) nm was higher than that of the steaming posts(30.11±2.17) nm (P < 0.01). The correlation between SAC10~500 nm and NC10~500 nm (r=0.703) was stronger than the correlation between NC10~500 nm and MC10~500 nm (r=0.412) and between SAC10~500 nm and MC10~500 nm (r=0.351).

    Conclusion

    The NC, MC, and SAC of particles sampled from fried food posts is related to the operation activities. The number of particles with a particle size less than 100 nm occupies an absolute dominant position in the composition. The strong correlation between SAC and NC suggests that SAC might be a better indicator of air pollution exposure.

     

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