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Effects of PM 2.5 on Cardio-Pulmonary Function Injury in Open Manganese Mine Workers

Author

Listed:
  • Yuanni Huang

    (Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China)

  • Mian Bao

    (Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China)

  • Jiefeng Xiao

    (Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China)

  • Zhaolong Qiu

    (Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China)

  • Kusheng Wu

    (Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China)

Abstract

Exposure to fine particulate matter 2.5 (PM 2.5 ) is associated with adverse health effects, varying by its components. The health-related effects of PM 2.5 exposure from ore mining may be different from those of environment pollution. The aim of this study was to investigate the effects of different concentrations of PM 2.5 exposure on the cardio-pulmonary function of manganese mining workers. A total of 280 dust-exposed workers who were involved in different types of work in an open-pit manganese mine were randomly selected. According to the different concentrations of PM 2.5 in the working environment, the workers were divided into an exposed group and a control group. The electrocardiogram, blood pressure, and multiple lung function parameters of the two groups were measured and analyzed. The PM 2.5 exposed group had significantly lower values in the pulmonary function indexes of forced expiratory volume in one second (FEV1.0), maximum mid expiratory flow (MMEF), peak expiratory flow rate (PEFR), percentage of peak expiratory flow out of the overall expiratory flow volume (PEFR%), forced expiratory flow at 25% and 75% of forced vital capacity (FEF 25, FEF75), forced expiratory flow when 25%, 50%, and 75% of forced vital capacity has been exhaled (FEF25%,FEF50%, FEF75%), and FEV1.0/FVC% (the percentage of the predicted value of forced vital capacity) than the control group (all p < 0.05). Both groups had mild or moderate lung injury, most of which was restrictive ventilatory disorder, and there was significant difference in the prevalence rate of restrictive respiratory dysfunction between the two groups (41.4% vs. 23.6%, p = 0.016). Electrocardiogram (ECG) abnormalities, especially sinus bradycardia, were shown in both groups, but there was no statistical difference of the prevalence rate between the two groups ( p > 0.05). Also, no significant difference of the prevalence rate of hypertension was observed between the PM2.5 exposure and control groups ( p > 0.05). PM 2.5 exposure was associated with pulmonary function damage of the workers in the open-pit manganese mine, and the major injury was restrictive ventilatory disorder. The early effect of PM 2.5 exposure on the cardiovascular system was uncertain at current exposure levels and exposure time.

Suggested Citation

  • Yuanni Huang & Mian Bao & Jiefeng Xiao & Zhaolong Qiu & Kusheng Wu, 2019. "Effects of PM 2.5 on Cardio-Pulmonary Function Injury in Open Manganese Mine Workers," IJERPH, MDPI, vol. 16(11), pages 1-10, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:11:p:2017-:d:237674
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    References listed on IDEAS

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    1. Ching-Chang Cho & Wen-Yeh Hsieh & Chin-Hung Tsai & Cheng-Yi Chen & Hui-Fang Chang & Chih-Sheng Lin, 2018. "In Vitro and In Vivo Experimental Studies of PM 2.5 on Disease Progression," IJERPH, MDPI, vol. 15(7), pages 1-26, July.
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    Cited by:

    1. Yi Wang & Hongchu Wang & Yinru Chen & Naxin Xu & Winson Lee & Wing-Kai Lam, 2022. "Pulmonary Capacity, Blood Composition and Metabolism among Coal Mine Workers in High- and Low-Altitude Aboveground and Underground Workplaces," IJERPH, MDPI, vol. 19(14), pages 1-10, July.
    2. Ming Li & Rujia Wang & Gang Li & Xinzhu Song & Huaizhen Yang & Huinan Lai, 2022. "Comprehensive Chemical Dust Suppressant Performance Evaluation and Optimization Method," IJERPH, MDPI, vol. 19(9), pages 1-14, May.
    3. Boyu Luan & Wei Zhou & Izhar Mithal Jiskani & Zhiming Wang, 2023. "An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines," IJERPH, MDPI, vol. 20(2), pages 1-16, January.

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