Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India

被引:10
|
作者
Mangla, Sherry [1 ]
Pathak, Ashok Kumar [2 ]
Arshad, Mohd. [3 ,4 ]
Ghosh, Doyel [2 ]
Sahoo, Prafulla Kumar [5 ]
Garg, Vinod Kumar [5 ]
Haque, Ubydul [6 ]
机构
[1] Int Inst Populat Sci, Mumbai 400088, Maharashtra, India
[2] Cent Univ Punjab, Dept Math & Stat, Bathinda 151401, Punjab, India
[3] Indian Inst Technol Indore, Dept Math, Indore 453552, Madhya Pradesh, India
[4] Aligarh Muslim Univ, Dept Stat & Operat Res, Aligarh 202002, Uttar Pradesh, India
[5] Cent Univ Punjab, Dept Environm Sci & Technol, Bathinda 151401, Punjab, India
[6] Univ North Texas, Hlth Sci Ctr, Dept Biostat & Epidemiol, Ft Worth, TX 76177 USA
来源
PATHOGENS | 2021年 / 10卷 / 08期
关键词
air pollutants; COVID-19; Delhi; humidity; rainfall; temperature; wind speed; ASSOCIATION;
D O I
10.3390/pathogens10081003
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O-3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman's rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O-3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = -0.75), explaining the variability of about 20-30% in the lockdown period and 18-19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India.
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页数:13
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