WeChat Mini Program
Old Version Features

    • Global Daily Mask Use Estimation in the Pandemic and Its Post Environmental Health Risks: Analysis Based on a Validated Dynamic Mathematical Model

    Ying Zhang, Fei Jiang,Fengmin Li,Shaoyong Lu, Zihao Liu,Yuwen Wang, Yiming Chi, Chenchen Jiang,Ling Zhang,Qingfeng Chen,Zhipeng He,Xiaoli Zhao,Jianmin Qiao,Xiaoya Xu,Kenneth Mei Yee Leung,Xiaohui Liu,Fengchang Wu

    Journal of Hazardous Materials（2024）

    College of Geography and Environment

    Cited 1|Views29
    Abstract
    The outbreak of the COVID-19 pandemic led to a sharp increase in disposable surgical mask usage. Discarded masks can release microplastic and cause environmental pollution. Since masks have become a daily necessity for protection against virus infections, it is necessary to review the usage and disposal of masks during the pandemic for future management. In this study, we constructed a dynamic model by introducing related parameters to estimate daily mask usage in 214 countries from January 22, 2020 to July 31, 2022. And we validated the accuracy of our model by establishing a dataset based on published survey data. Our results show that the cumulative mask usage has reached 800 billion worldwide, and the microplastics released from discarded masks due to mismanagement account for 3.27% of global marine microplastic emissions in this period. Furthermore, we illustrated the response relationship between mask usage and the infection rates. We found a marginally significant negative correlation existing between the mean daily per capita mask usage and the rate of cumulative confirmed cases within the range of 25% to 50%. This indicates that if the rate reaches the specified threshold, the preventive effect of masks may become evident.
    More
    Translated text
    Key words
    Worldwide,Mask usage,Evaluation,Validation,Microplastic pollution,Covid-19
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined