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封锁前后对空气质量的影响,以及环境因素在传播 COVID-19 病例中的作用——来自印度受影响最严重的一个州的研究。

Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India.

机构信息

Department of Environmental Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India.

Instituto Tecnológico Vale, Belém, PA, 66055-090, Brazil.

出版信息

Int J Biometeorol. 2021 Feb;65(2):205-222. doi: 10.1007/s00484-020-02019-3. Epub 2020 Oct 9.

Abstract

The present study aims to examine the changes in air quality during different phases of the COVID-19 pandemic, including the lockdown (LD) and unlock period (UL) (post-lockdown) as compared to pre-lockdown (PL) and to establish the relationships of the environmental and demographic variables with COVID-19 cases in the state of Maharashtra, the worst-hit state in India. Atmospheric pollutants such as PM, PM, NOx, and CO were substantially reduced during the lockdown and unlock phases with the greatest reduction in cities having larger traffic volumes. Compared with the immediate pre-lockdown period (PL), the averaged PM and PM reduced by up to 51% and 47% respectively during the lockdown periods, which resulted in 'satisfactory' level of air quality index (AQI) as a result of reduced vehicular traffic and industrial closing. These parameters continued to reduce as much as 80% during the unlock periods due to the additive impact of weather (rainfall and temperature) combined with the lockdown conditions. Kendall's correlation matrix showed a significant negative correlation between temperature and air pollutants (r= - 0.35 to - 057). Conversely, SO and O did not improve, and in some cases, they increased during the lockdown and unlocking. COVID-19 spreading incidences were strongly and positively correlated with temperature (r < 0.62) and dew point (r < 0.73). Thus, this indicates that the increase in temperature and dew point cannot weaken the transmission of this virus. The number of COVID-19 cases relative to air pollutants was negatively correlated (r = - 0.33 to - 0.74), which may be a mere coincidence as a result of lockdown. However, based on pre-lockdown air quality data and demographic factors, it was found that particulate matter (PM and PM) and population density are closely linked with higher morbidity and mortality although a more in-depth research is required in this direction to validate this finding. The onset of COVID-19 has allowed us to determine that 'immediate' changes in air quality within densely populated/industrialized areas can improve livelihood based on pollution mitigation. These findings could be used by policymakers to set new benchmarks for air pollution that would improve the quality of life for major sectors of the World's population. COVID-19 has shown us that we can make changes when necessary, and findings may pave the way for future research to inform policy on the tough choices we will have to make between quality of life and survival. Also, our results will enrich the ongoing discussion on the role of environmental factors on the transmission of COVID-19 and will help to take necessary steps for its control.

摘要

本研究旨在探讨 COVID-19 大流行不同阶段(包括封锁期(LD)和解锁期(UL)[后封锁期])的空气质量变化,并与封锁前(PL)进行比较,确定环境和人口变量与印度受灾最严重的马哈拉施特拉邦 COVID-19 病例之间的关系。在封锁和解锁阶段,大气污染物(如 PM、PM、NOx 和 CO)大幅减少,交通量较大的城市减少幅度最大。与立即封锁前时期(PL)相比,封锁期间 PM 和 PM 的平均值分别减少了 51%和 47%,这导致空气质量指数(AQI)达到“满意”水平,原因是交通量减少和工业关闭。由于天气(降雨和温度)的附加影响以及封锁条件,这些参数在解锁期间继续减少多达 80%。肯德尔相关矩阵显示,温度与空气污染物之间存在显著负相关(r=-0.35 至-0.57)。相反,SO 和 O 并没有改善,在某些情况下,它们在封锁和解锁期间增加。COVID-19 传播发病率与温度(r<0.62)和露点(r<0.73)呈强正相关。因此,这表明温度和露点的升高并不能削弱这种病毒的传播。COVID-19 病例数与空气污染物呈负相关(r=-0.33 至-0.74),这可能只是由于封锁造成的巧合。然而,根据封锁前的空气质量数据和人口因素,发现颗粒物(PM 和 PM)和人口密度与较高的发病率和死亡率密切相关,尽管需要在这方面进行更深入的研究来验证这一发现。COVID-19 的出现使我们能够确定,在人口稠密/工业化地区空气质量的“即时”变化可以通过减轻污染来改善基于生计。这些发现可以为政策制定者提供新的空气质量基准,从而提高世界人口主要部门的生活质量。COVID-19 已经向我们表明,在必要时我们可以做出改变,并且这些发现可能为未来的研究铺平道路,为我们在生活质量和生存之间做出的艰难选择提供信息政策。此外,我们的研究结果将丰富关于环境因素对 COVID-19 传播作用的持续讨论,并有助于采取必要措施控制 COVID-19。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e9c/7544766/8c6252c784d0/484_2020_2019_Fig1_HTML.jpg

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