Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

doi:10.21203/rs.3.rs-73771/v1

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Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

https://doi.org/10.21203/rs.3.rs-73771/v1

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published 27 Feb, 2021

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SARS-CoV-2 (Coronavirus) 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 infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM_2.5, 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/m³increase during (Lag0-14) in PM_2.5, PM₁₀ and NO₂ was 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 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 is significant relationship 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.

Environmental Engineering

Air pollution

Meteorological factors

COVID-19

Speraman’s correlation

Generalised additive model

Table 1: Daily Pollutant Parameters concentrations, meteorological parameters concentrations, and daily infected cases from COVID19 in India.

Parameters	Descriptive Statistics	Delhi	Karnataka	Kerala	Madhya Pradesh	Maharashtra	Tamil Nadu	Telangana	Uttar Pradesh
Air Pollutants
PM_2.5	Mean	144.21	74.36	80.09	75.31	128.58	81.66	49.04	95.95
	Std. Deviation	76.86	55.68	61.21	50.65	253.49	66.32	16.97	42.81
	Interquartile Range	103.00	22.42	58.50	52.75	41.25	45.73	18.62	51.50
PM₁₀	Mean	90.68	30.00	27.37	113.21	54.74	80.92	56.53	53.60
	Std. Deviation	41.66	6.48	7.21	19.40	11.69	29.85	21.24	18.71
	Interquartile Range	59.80	9.12	8.00	38.50	14.00	57.63	21.36	14.52
NO₂	Mean	48.78	28.80	99.09	31.62	31.32	36.33	33.27	61.30
	Std. Deviation	28.61	22.26	37.75	22.85	31.14	12.13	21.11	7.36
	Interquartile Range	48.00	37.28	33.30	44.76	34.09	12.73	32.90	13.06
SO₂	Mean	11.69	3.03	7.07	18.44	4.03	3.57	0.40	32.11
	Std. Deviation	8.30	3.17	1.65	6.26	0.39	2.36	0.00	41.46
	Interquartile Range	17.06	2.39	2.40	11.88	0.51	2.19	0.00	38.25
Meteorological Factors
Wind Speed	Mean	2.16	3.15	1.82	2.03	4.07	2.85	2.54	1.98
	Std. Deviation	1.07	1.18	1.30	0.96	1.52	0.72	1.00	0.93
	Interquartile Range	1.93	1.81	1.59	1.56	2.34	1.14	1.29	1.39
Temp	Mean	24.26	27.47	28.24	29.65	27.13	28.00	29.64	26.02
	Std. Deviation	4.24	0.85	0.52	2.39	1.22	0.96	1.55	4.17
	Interquartile Range	6.56	1.54	0.79	2.89	1.21	1.45	2.56	7.53
Diurnal Temp Range	Mean	15.87	12.43	10.64	13.61	11.76	10.80	12.48	15.91
	Std. Deviation	2.51	1.50	1.97	1.44	1.00	1.04	0.75	2.18
	Interquartile Range	3.35	1.98	2.47	2.62	1.58	1.98	0.99	3.09
Absolute Humidity	Mean	1.85	2.12	2.83	1.64	2.72	2.79	2.03	1.78
	Std. Deviation	0.17	0.18	0.12	0.30	0.18	0.11	0.23	0.22
	Interquartile Range	0.26	0.29	0.21	0.29	0.23	0.11	0.34	0.30
Relative Humidity	Mean	37.56	40.32	68.93	22.79	67.51	67.84	33.71	32.22
	Std. Deviation	11.80	8.03	5.89	12.63	6.01	3.94	10.01	13.36
	Interquartile Range	21.80	10.30	8.92	8.93	9.25	5.76	15.32	18.24
Rainfall	Mean	0.44	0.40	1.38	0.20	0.00	0.05	0.28	0.52
	Std. Deviation	1.01	0.92	2.31	0.65	0.02	0.15	0.89	1.55
	Interquartile Range	0.24	0.12	1.87	0.04	0.00	0.00	0.00	0.10
Air Pressure	Mean	983.63	915.31	1004.18	953.57	1010.84	1008.75	941.43	991.76
	Std. Deviation	7.28	3.11	5.03	1.17	1.36	1.00	17.44	2.21
	Interquartile Range	3.12	1.00	9.33	1.31	1.60	1.31	1.60	9.82
COVID 19 Cases
Infected cases	Mean	42.42	9.44	5.15	48.24	116.18	33.94	18.11	28.24
	Std. Deviation	72.37	9.62	8.00	65.11	155.18	40.52	24.17	42.66
	Interquartile Range	63.50	15.50	8.50	62.00	212.50	66.25	31.00	40.75

Table 2. Spearman’s correlation coefficients between air pollutants, meteorological factors across all districts during the study period

Parameters

Infected Cases

PM_2.5

PM₁₀

NO₂

SO₂

Wind Speed

Temperature

DTR

Relative Humidity

Air Pressure

Rainfall

Absolute Humidity

Infected Cases

PM_2.5

0.23*

PM₁₀

0.33*

0.87*

NO₂

0.21*

0.59*

0.58*

SO₂

-0.26*

0.32*

0.34*

0.47*

Wind Speed

0.02*

-0.22*

-0.16*

-0.13*

-0.03*

Temperature

0.22*

0.30*

0.33*

-0.13*

0.22*

-0.03

DTR

0.27*

0.42*

0.46*

0.24*

0.33*

0.19

-0.12*

Relative Humidity

-0.28*

-0.33*

-0.41*

-0.36*

-0.47*

0.01

-0.09*

0.19

Air Pressure

0.03*

0.08*

0.06*

0.25*

-0.19*

0.02

0.15*

0.15

0.73*

Rainfall

-0.13*

0.01*

0.03*

0.41*

0.05*

-0.33*

-0.08

0.18

0.40*

0.08

Absolute Humidity

-0.02*

0.21*

0.27*

0.22*

0.12*

0.03

-0.01

0.57*

0.96*

0.67*

0.38*

Data.xlsx
Analysed data and presented in the form of tables.

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Journal Publication

published 27 Feb, 2021

Read the published version in Environmental Science and Pollution Research →

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Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

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