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Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China

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Abstract

Association between fine particulate matter (PM2.5) and respiratory health has attracted great concern in China. Substantial epidemiological evidences confirm the correlational relationship between PM2.5 and respiratory disease in many Chinese cities. However, the causative impact of PM2.5 on respiratory disease remains uncertain and comparative analysis is limited. This study aims to explore and compare the correlational relationship as well as the causal connection between PM2.5 and upper respiratory tract infection (URTI) in two typical cities (Beijing, Shenzhen) with rather different ambient air environment conditions. The distributed lag nonlinear model (DLNM) was used to detect the correlational relationship between PM2.5 and URTI by revealing the lag effect pattern of PM2.5 on URTI. The convergent cross mapping (CCM) method was applied to explore the causal connection between PM2.5 and URTI. The results from DLNM indicate that an increase of 10 μg/m3 in PM2.5 concentration is associated with an increase of 1.86% (95% confidence interval: 0.74%-2.99%) in URTI at a lag of 13 days in Beijing, compared with 2.68% (95% confidence interval: 0.99–4.39%) at a lag of 1 day in Shenzhen. The causality detection with CCM quantitatively demonstrates the significant causative influence of PM2.5 on URTI in both two cities. Findings from the two methods consistently show that people living in low-concentration areas (Shenzhen) are less tolerant to PM2.5 exposure than those in high-concentration areas (Beijing). In general, our study highlights the adverse health effects of PM2.5 pollution on the general public in cities with various PM2.5 levels and emphasizes the needs for the government to provide appropriate solutions to control PM2.5 pollution, even in cities with low PM2.5 concentration.

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All data generated or analyzed during this study in this article are available from the corresponding author on reasonable request.

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Funding

This work is jointly supported by grants from the National Natural Science Foundation of China (grant number 41771380), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (grant number GML2019ZD0301), the GDAS’ Project of Science and Technology Development (grant numbers 2020GDASYL-20200103003, 2020GDASYL-20200103006), the China Postdoctoral Science Foundation (grant number 2020M682628), a grant from State Key Laboratory of Resources and Environmental Information System, the National Postdoctoral Program for Innovative Talents (grant number BX20200100), China, and the Open Research Fund of National Earth Observation Data Center (grant number NODAOP2020002).

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Authors and Affiliations

  1. Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangdong Academy of Sciences, 510070, Guangzhou, People’s Republic of China

    Xiaolin Xia, Yangxiaoyue Liu, Wenlong Jing & Yong Li

  2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Ling Yao & Jiaying Lu

  3. Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, People’s Republic of China

    Xiaolin Xia, Ling Yao, Jiaying Lu, Yangxiaoyue Liu, Wenlong Jing & Yong Li

  4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Jiaying Lu

  5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Ling Yao

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  1. Xiaolin Xia
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  2. Ling Yao
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  3. Jiaying Lu
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  4. Yangxiaoyue Liu
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  5. Wenlong Jing
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  6. Yong Li
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Contributions

XX and LY developed the concept of research work. XX, LY, and JL conceived and designed the study, collected the data, and carried out research works. XX and JL analyzed the data. LY provided oversight for the research. XX prepared the initial draft of the manuscript. YXL, WJ, and YL revised the subsequent versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ling Yao.

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Xia, X., Yao, L., Lu, J. et al. Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China. Environ Sci Pollut Res 29, 11185–11195 (2022). https://doi.org/10.1007/s11356-021-16450-5

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