Correlation Between COVID-19 Infection Rate and Atmospheric Environmental Factors in Seoul Metropolitan City, South Korea

DOI: https://doi.org/10.21203/rs.3.rs-1383594/v1

Abstract

The purpose of this study is to statistically analyze whether the change in the COVID-19 virus infection rate is affected by atmospheric environmental factors in Seoul metropolitan city of South Korea. The research period of this study is from January 1, 2020 to March 31, 2021. The correlation relationship between the COVID-19 virus infection rate and atmospheric environmental factors such as PM10, PM2.5, temperature, and humidity was analyzed by multiple regression analysis. In the correlation between PM10 and PM2.5 and the COVID-19 virus infection rate, correlation coefficient between PM10 and infection rate was r=0.114(p=0.019), showing a positive correlation. The correlation coefficient between PM2.5 and infection rate was r=0.147(p=0.002), which indicates a higher positive correlation than PM10. In the correlation between thermal factors and infection rate, the correlation coefficients between temperature and infection rate and between relative humidity and infection rate were r= -0.527(p=0.000) and r= -0.91(p=0.060), respectively. Unlike PM10 and PM2.5, temperature and relative humidity showed a negative correlation with the infection rate. Except for relative humidity, PM10, PM2.5 and temperature had statistically significant correlations with the infection rate (p<0.05). Based on the results obtained from this study, it is recommended to avoid outdoor activities as much as possible in weather with high PM10 and PM2.5 and low temperature and humidity.

1. Introduction

Currently, the world is in a pandemic situation due to the COVID-19 virus pandemic. The COVID-19 virus has suspended worldwide activities in months and has been the leading cause of death for thousands of people in cities around the world (Rodriguez et al., 2020). The COVID-19 virus has been reported as one of the deadliest viruses in the past 100 years (Lu et al., 2020).

The event of COVID-19 first occurred in Wuhan, Hubei Province, China in December 2019 (Fanelli et al., 2020). Since then, it has spread beyond China to the world. COVID-19 is a new type of coronavirus and is a respiratory infection disease (Li et al., 2020). At the beginning of the outbreak, the cause of the epidemic pneumonia was unknown, but the pathogen was identified when the World Health Organization (WHO) announced on January 9, 2020 that it was the coronavirus (Yuki et al., 2020). The COVID-19 virus spreads through droplets between infected people and penetrates and is transmitted through the mucous membranes of the eyes, nose, mouth, and respiratory tract (Chaudhuri et al., 2020; Oldfield et al., 2020). As of July 2021, the cumulative number of infected people worldwide exceeded 180 million and the death toll exceeded 3.9 million.

South Korea has received global attention as an advanced quarantine case for actively responding to the COVID-19 virus (Team et al., 2020). Seoul is the capital of South Korea and is a metropolitan city with a population of about 10 million. The Seoul carried out the quarantine system with the highest intensity and the longest period. Therefore, we tried to reduce the transmission caused by human-to-human contact as much as possible. However, despite the high-intensity quarantine, the number of infected people is increasing the most in Seoul. As of June 29, 2021, South Korea maintains 150,000 confirmed patients, 2,000 deaths, and 600 daily confirmed patients. Considering that the total population of Korea is 51 million, this figure has an infection rate of 0.3%. And the mortality rate is 1.3% compared to the number of infected people.

Viruses are generally known to have both biological and inanimate properties (Doerbecker et al., 2011). Viruses cannot proliferate on their own without hosts and simply exist in an inanimate state of protein and nucleic acid masses (Buttner et al., 1997). Particulate matter in the air can be a carrier of viruses as well as bacteria (Carugno et al., 2018; Groulx et al., 2018; Su et al., 2019; Tao et al., 2020; Otmani et al.,2020). It is reported that PM10 and PM2.5 among air pollution plays a role in facilitating the spread of the virus (Mishra et al., 2020; Adhikari et al., 2020; Hashim et al., 2021). In a study conducted in California in the United States, studies have been conducted that air pollutants are correlated with COVID-19 (Bashir et al., 2020).

The Seoul of South Korea, is a metropolitan city with a population of 10 million and serious air pollution worldwide (Kumbhakar et al., 2021). Air pollution in Seoul is known to have a serious proportion and risk of PM10 and PM2.5 among various air pollutants (Choe et al., 2018). Therefore, it is estimated that PM10 and PM2.5 in Seoul will also affect the spread of the COVID-19 virus, and that the high concentration of PM10 and PM2.5 will be very advantageous for the spread of the COVID-19 virus However, no studies have been conducted so far on the correlation between PM10 and PM2.5 concentration in Seoul and the infection rate of COVID-19. Therefore, the purpose of this study is to examine the daily changes in Seoul's COVID-19 infection rate and atmospheric environmental factors through statistical correlation analysis, and use it as basic data for setting up COVID-19 prevention methods according to air quality conditions in the future.

2. Method

2.1. Subject

The Seoul metropolitan city, where about 10 million people live in the study target area, was selected as the study area. The average annual population of Seoul was about 9,668,000 in 2020 and 9,589,000 in 2021, which was substituted for the calculation of the COVID-19 infection rate. Data on the number of confirmed cases of COVID-19 virus infection in Seoul were obtained through the state-run Public Data Portal (http://www.data.go.kr). The number of confirmed cases on the same day was the number of reported cases from 0:00 to 24:00. For the concentration data of PM10 and PM2.5, the daily average data provided by "Air Korea (www.airkorea.or.kr)" operated by the Ministry of Environment was used. The daily average data of fine dust concentration used as an mean concentration in Seoul is the average value calculated from 0:00 to 24:00 monitored at the Jung-gu Metropolitan Office, which is designated as an urban atmospheric measurement network by the National Institute of Environmental Research. Daily average data of Seoul provided by the Meteorological Administration were utilized for temperature and relative humidity data. The application period of this study data is from January 1, 2020 to March 31, 2021.

2.2. Data analysis

The daily COVID-19 virus infection rate was calculated by dividing the number of confirmed daily COVID-19 virus infections by the number of people in Seoul. Daily data of average PM10 and PM2.5 concentration and COVID-19 virus infection rate were reviewed by delaying the infection date by 2 days in consideration of the COVID-19 virus incubation period and test time.

The data obtained from this study were statistically analyzed with the SPSS program (IBM, version 25). Pearson correlation analysis was performed to determine the relative influence between COVID-19 infection rate, PM10, PM2.5, temperature, and relative humidity, and the correlation between COVI-19 infection rate and respective variables was visualized through a simple scatter plot graph. In addition, multiple regression analysis was conducted to understand the causal relationship between variables. The tolerance (TOL) and variance inflation Factor (VIF) values which represent multicollinearity were indicated to show that there is no multicollinearity. The statistical significance test was set at the significance level of 0.05. The model equation of multiple regression analysis applied to this study is shown in < Figure 1>.

3. Results

3.1. Correlation relationship analysis between COVID-19 virus infection rate and atmospheric environmental factors

As shown in the Table 1, in the correlation between PM10 and PM2.5 and COVID-19 infection rate, the correlation coefficient between PM10 and infection rate was r = 0.114 (p = 0.019), showing a positive correlation. The correlation coefficient between PM2.5 and infection rate was r = 0.147 (p = 0.002), which showed a higher positive correlation than PM10. In the correlation between temperature and relative humidity and infection rate, temperature and infection rate showed a high negative correlation with r= -0.527 (p = 0.000), and the infection rate with relative humidity was r= -0.91 (p = 0.060), which was lower than temperature. As a result of statistical analysis, it was found that the correlation of the COVID-19 virus infection rate with all other factors except relative humidity was statistically significant (p < 0.05).

Table 1

Correlation analysis of COVID-19 infection rate, PM10 and PM2.5, temperature, and relative humidity in Seoul.
 

Infection rate

PM10

PM2.5

Temperature

Relative humidity

Infection rate

1

        

PM10

0.114*

(0.019)

1

      

PM2.5

0.147**

(0.002)

0.780**

(0.000)

1

    

Temperature

-0.527**

(0.000)

− .186**

(0.000)

− .179**

(0.000)

1

  

Relative humidity

-0.091

(0.060)

− .206**

(0.000)

− .052

(0.285)

.500**

(0.000)

1
*p < 0.05, **p < 0.01

 

3.2. Simple scatter plot between COVID-19 virus infection rate and atmospheric environmental factors

As indicated in the Fig. 2, atmospheric environmental factors such as PM10 and PM2.5, temperature and relative humidity, which are expected to affect the change in the COVID-19 infection rate, was presented on the X-axis as independent variables and the COVID-19 infection rate affected by them was presented on the Y-axis as a dependent variable. Based on the graph showing upwards to the right, it was confirmed that the COVID-19 infection rate increased as the concentrations of PM10 and PM2.5 increased. On the other hand, in the temperature and relative humidity, a graph descending to the right appeared, confirming that the COVID-19 infection rate decreased as the temperature and relative humidity increased. This finding was contrary to the results of multiple regression analysis. As a result of the distribution of scatter plot, when the concentration of PM10 and PM2.5 increases the COVID-19 infection rate also increases at the same time, while when the temperature and relative humidity increase the COVID-19 infection rate tends to decrease.

3.3. Multiple regression analysis between COVID-19 infection rate and atmospheric environmental factors

As shown in the Table 2, it was analyzed that when the concentrations of PM10 and PM2.5 increased by 1㎍/㎥, the COVID-19 infection rate increased by 8.83X10− 7 and 2.32X10− 6, respectively. When the relative humidity rises by 1%, the COVID-19 infection rate increases by 1.53X10− 5. On the other hand, in the case of temperature, it was analyzed that the COVID-19 infection rate decreased by 6.47 X10− 5 when the temperature increased by 1℃.

Table 2

Multiple regression analysis of COVID-19 infection rate, PM10 and PM2.5, temperature, and relative humidity in Seoul.

Variable

Non-standardized

coefficient

Standardized

coefficient

t(p)

TOL

VIF

B

SE

β

(Constant)

0.001

0.000

  

2.492*

(0.013)

    

PM10

8.825E-7

0.000

0.020

0.303

(0.762)

0.362

2.760

PM2.5

2.316E-6

0.000

0.030

0.453

(0.650)

0.369

2.709

Temperature

-6.466E-5

0.000

-0.633

-13.370**

(0.000)

0.723

1.383

Relative humidity

1.530E-5

0.000

0.231

4.792**

(0.000)

0.697

1.435

F(p)

49.186(0.000b)

adj. R2

0.319
*p < 0.05; **p < 0.01
t(p)

 

Therefore, assuming that the population of Seoul is 10 million, the increase in PM10 and PM2.5 concentrations by 1 μg/m3 and the increase in relative humidity by 1% mean that the number of COVID-19 confirmed patients in Seoul increases by about 8, 20, and 153, respectively. On the other hand, it can be seen that when the temperature rises by 1℃, the number of COVID-19 confirmed patients in Seoul decreases by about 600. In terms of the standardization coefficient, it was analyzed that PM10, PM2.5 and relative humidity had a positive effect of 0.02, 0.03 and 0.231, respectively, on the COVID-19 infection rate, while the temperature had a negative effect of -0.633 on it. However, the concentrations of PM10 and PM2.5 were not a variable that had a significant effect on the infection rate (p > 0.05), whereas temperature and relative humidity were variables that had a significant effect (p < 0.05).

4. Discussion

Based on the results obtained from this study, the correlation analysis showed that PM10, PM2.5, and temperature were statistically significant with the COVID-19 infection rate. However, statistical significance of COVID-19 infection rate was found in temperature and humidity in the regression analysis. Considering the specificity of the epidemic data, there are special situations such as mass infection, in which the number of infected people suddenly increases (Kang, 2020). Therefore, it can be explained that the analysis results between correlation and regression showed different significance since the raw data were inconsistent and their concentration was low. In special circumstances, data need to be corrected. However, in this study, the original data were not corrected and used as it is in order to reflect real situation at the time in Seoul, South Korea.

The concentration of PM10 and PM2.5 in Seoul is based on a nationally operated monitoring station. The PM10 and PM2.5 concentration monitoring station and the location of COVID-19 infection in Seoul were not considered in this study. Thus, it is predicted that more accurate research results can be obtained if the PM10 and PM2.5 monitoring station and the location of the infected person are matched and analyzed.

Mean concentrations of PM10 and PM2.5 in Seoul used in this study are the official data of air concentration in Seoul. However, considering that the spread of the virus is mainly caused by droplet transmission indoors, it is difficult to grasp the cross-sectional relationship between the concentration of PM10 and PM2.5 in the atmosphere and virus infection (Li et al., 2020).

In addition, in this study, environmental variables were analyzed for correlation with COVID-19 infection rate only with PM10 and PM2.5 concentration, temperature, and humidity, but the factors affecting viral infection are more diverse. For example, the route of movement of infected people, the use of multi-use facilities, gender, age, and wearing a mask are variables to be considered (Plohl et al., 2021; Lim et al., 2021).

During the pandemic period caused by COVID-19, it was found that the concentration of PM10 and PM2.5 in China decreased due to reduced air pollutants emissions from transportation and industries (Wang et al., 2020 ; Chen et al., 2020). South Korea, which is influenced geographically by the west wind, is highly related to the atmospheric situation in China (Park et al., 2018). Therefore, it is estimated that the decrease in industrial air pollutants emissions in China during the Pandemic period had a decrease in the concentration of PM10 and PM2.5 in South Korea (Ma et al., 2020).

Fine dust itself is harmful to health. PM10 and PM2.5 has even become a more harmful air pollutant as it acts as a carrier of the COVID-19 virus (Rohrer et al., 2020; Zoran et al., 2020). The previous study reporting the association between air pollutants and daily deaths by COVID-19 in Iran from March to August 2020 also confirmed that air pollution was significantly associated with an increase in the mortality rate of COVID-19 and observed that improved air pollution could reduce the risk of mortality (Norouzi et al., 2021). Therefore, more beneficial results will be obtained if the research on the correlation between air pollution and virus infection rate is conducted in depth and more variables and social phenomena are considered than this study.

5. Conclusion

As a result of statistical analysis of accredited data in Seoul, South Korea for 15 months from January 2020 to March 2021, the COVID-19 infection rate showed a statistically significant positive correlation with the concentrations of PM10 and PM2.5 (p < 0.05). On the other hand, the COVID-19 infection rate showed a negative correlation with temperature (p < 0.05) and relative humidity (p > 0.05). Therefore, based on the results of this study, it can be recommended that one way to prevent COVID-19 infection in Seoul is to avoid outside activities as much as possible when the concentration of PM10 and PM2.5 is high and the temperature is low. Although it is not possible to directly present infection prevention measures, the finding that the COVID-19 infection rate has a significant correlation with PM10 and PM2.5 and air temperature would be applied as basic data to suggest lifestyle patterns to the public during the pandemic.

Declarations

-Ethics approval and consent to participate (Human Ethics, Animal Ethics or Plant Ethics)

Not Applicable

-Consent for publication

All writers agreed to the posting.

-Availability of data and materials

The data and materials used in this document are available because they used public data.

-Competing interests

We don't have a competitive interest.

-Funding

Not Applicable

-Authors' contributions

Won Choi wrote most of the manuscript and Ki-Youn Kim reviewed it.

-Acknowledgements

All authors approve the submission of this manuscript.

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