Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?
Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?
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DOI：
10.1007/s00477-022-02308-w
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
COVID-19第二波疫情的爆发摧毁了全球许多国家。与第一波相比，第二波在感染和死亡方面更具侵略性。在第一波COVID-19期间，就空气污染物与气象参数的关系进行了大量研究。然而，在严重的COVID-19第二波期间，人们对它们的关联知之甚少。目前的研究是基于第二波期间德里的空气质量。与封城前相比，封城期间的污染物浓度有所下降（PM2.5：67 µg m−3（封城）对81 µg m−3（封城前）; PM10：171 µg m−3对235 µg m−3; CO：0.9 mg m−3对1.1 mg m−3），但臭氧在封城期间有所增加（57 µg m−3对39 µg m−3）。污染物浓度的变化显示，PM2. 5、PM10和CO在COVID-19前期间较高，其次是第二波封城，第一波封城时最低。使用ArcGIS绘制的污染物时空变异性证实了这些变化。在封锁期间，污染物和气象变量解释了COVID-19确诊病例和死亡人数85%和52%的变异性（由一般线性模型确定）。结果表明，空气污染与气象相结合是第二波COVID-19显著增长的驱动力。除了开发新药和疫苗外，各国政府还应专注于预测模型，以更好地了解空气污染水平对COVID-19病例的影响。政策制定者和决策者可以利用这项研究的结果来实施减少空气污染的必要指导方针。此外，这里提供的信息可以帮助公众做出明智的决定，以显着改善环境和人类健康。
The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m−3 (lockdown) versus 81 µg m−3 (pre-lockdown); PM10: 171 µg m−3 versus 235 µg m−3; CO: 0.9 mg m−3 versus 1.1 mg m−3) except ozone which increased during the lockdown period (57 µg m−3 versus 39 µg m−3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.
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DOI：
10.1007/s11356-021-13813-w
发表时间：
2021-08
期刊：
Environmental science and pollution research international
影响因子：
--
作者：
Bherwani H;Kumar S;Musugu K;Nair M;Gautam S;Gupta A;Ho CH;Anshul A;Kumar R
通讯作者：
Kumar R
影响因子：
9.8
作者：
Auler, A. C.;Cassaro, F. A. M.;Pires, L. F.
通讯作者：
Pires, L. F.
影响因子：
6.9
作者：
Briz-Redon, Alvaro;Belenguer-Sapina, Carolina;Serrano-Aroca, Angel
通讯作者：
Serrano-Aroca, Angel
影响因子：
5.9
作者：
Cole, Matthew A.;Ozgen, Ceren;Strobl, Eric
通讯作者：
Strobl, Eric
影响因子：
9.8
作者：
Collivignarelli, Maria Cristina;Abba, Alessandro;Miino, Marco Carnevale
通讯作者：
Miino, Marco Carnevale