Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Aug;28(30):40474-40495.
doi: 10.1007/s11356-021-12709-z. Epub 2021 Feb 27.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Mrunmayee Manjari Sahoo  1
Affiliations

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Mrunmayee Manjari Sahoo. Environ Sci Pollut Res Int. 2021 Aug.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Keywords: Air pollution,; COVID-19,; Generalized additive model; Meteorological factors,; Spearman’s correlation,.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Locations of 32 states and union territories and cumulative COVID-19-infected cases in each state as of April 23, 2020
Fig. 2
Fig. 2
The concentration of four air pollutants (PM2.5, PM10, NO2, and SO2) based on wind speed and wind direction over the eight states and union territories
Fig. 2
Fig. 2
The concentration of four air pollutants (PM2.5, PM10, NO2, and SO2) based on wind speed and wind direction over the eight states and union territories
Fig. 3
Fig. 3
Box plots for six meteorological parameters in eight states with maximum, minimum, 1st quartile, median, and 3rd quartile values. Note: name of states and UTs: DL: Delhi, KA: Karnataka, MH: Maharashtra, MP: Madhya Pradesh, TG: Telengana, TN: Tamil Nadu, and UP: Uttar Pradesh
Fig. 4
Fig. 4
3D association of meteorological parameters, absolute humidity, relative humidity with daily mean temperature
Fig. 5
Fig. 5
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in air pollutant and meteorological concentration using single-parameter models. Note: 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2 and 1 unit increase in meteorological factors (DTR: diurnal temp range, Temp: daily mean temperature, RH: relative humidity, AH: absolute humidity, AP: air pressure, RF: rainfall)
Fig. 5
Fig. 5
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in air pollutant and meteorological concentration using single-parameter models. Note: 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2 and 1 unit increase in meteorological factors (DTR: diurnal temp range, Temp: daily mean temperature, RH: relative humidity, AH: absolute humidity, AP: air pressure, RF: rainfall)
Fig. 6
Fig. 6
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in air pollutants using single-parameter models after excluding Delhi and Maharashtra from the analysis. Note: 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2, MH: the state, Maharashtra, DL: The union territory, Delhi
Fig. 7
Fig. 7
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in meteorological concentration using single-parameter models after excluding Delhi and Maharashtra from the analysis. Note: 1 unit increase in meteorological factors (DTR: diurnal temp range, temp: daily mean temperature, RH: relative humidity, AH: absolute humidity, AP: air pressure, RF: rainfall), MH: the state, Maharashtra, DL: The union territory, Delhi
Fig. 7
Fig. 7
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in meteorological concentration using single-parameter models after excluding Delhi and Maharashtra from the analysis. Note: 1 unit increase in meteorological factors (DTR: diurnal temp range, temp: daily mean temperature, RH: relative humidity, AH: absolute humidity, AP: air pressure, RF: rainfall), MH: the state, Maharashtra, DL: The union territory, Delhi
Fig. 8
Fig. 8
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in air pollutant and meteorological concentration using single and multi-parameter models. Note: 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2 and 1 unit increase in meteorological factors (DTR: diurnal temp range, RH: relative humidity)
Fig. 8
Fig. 8
Percentage change (%) and 95% CI of daily infected COVID-19 cases correlated with a unit increase in air pollutant and meteorological concentration using single and multi-parameter models. Note: 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2 and 1 unit increase in meteorological factors (DTR: diurnal temp range, RH: relative humidity)
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ã JSP, Kumar R, Devotta S (2005) Health risks of NO 2 , SPM and SO 2 in Delhi ( India ). 39:6868–6874. 10.1016/j.atmosenv.2005.08.004
    1. As A (2010) Revised National Ambient Air Quality Standards. 826:1–18
    1. Ashikin N, Mabahwi B, Ling O, Leh H, Omar D. Human Health and Wellbeing : Human health effect of air pollution. Procedia Soc Behav Sci. 2014;153:221–229. doi: 10.1016/j.sbspro.2014.10.056. - DOI
    1. Auler AC, Cássaro FAM, Silva VO, Pires LF. Jo ur l P of. Sci Total Environ. 2020;139090:139090. doi: 10.1016/j.scitotenv.2020.139090. - DOI - PMC - PubMed
    1. Baccini M, Biggeri A, Lagazio C (2007) Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health. 51:4324–4336. 10.1016/j.csda.2006.05.026