Is air pollution a risk factor for COVID-19 infection?

The COVID-19 pandemic began at the very end of 2019 in Wuhan city, Hubei province, China. However, its spread soon began to threaten the health of the whole country and then of the world, leading to nearly 5.7 million cases and over 355,000 dead as of today. A new study, published on the preprint server medRxiv* in May 2020, explores the effects of air pollution on COVID-19 infections in China.

Association Between Cardiovascular Disease and Long-term Exposure to Air Pollution With the Risk of Dementia. Image Credit:  VanderWolf Images / Shutterstock
Study: Outdoor Air Pollutant Concentration and COVID-19 Infection in Wuhan, China. Image Credit: VanderWolf Images / Shutterstock

How Does Weather and Air Pollution Affect Influenza Risk?

Earlier studies on influenza, which has caused similar outbreaks of a seasonal nature, have reported that air pollution exposures may independently, or in conjunction with changing weather conditions, increase the risk of infection with the flu virus. The same patterns were observed with the earlier SARS and MERS epidemics.

Recent research indicates that this may hold good with COVID-19 as well. Changing meteorological conditions may underlie the rapid spread of this illness. Chronic exposure to air pollution has been observed to predict COVID-19 mortality in Europe and the USA.

Air Pollutants and COVID-19 Deaths

Some studies have proposed that air pollution is a significant contributor to the horrific COVID-19 mortality in Italy and China. Though a few scientists have begun to explore this field, the effect of public health interventions, changes in testing capacity, and case definitions, as well as the interactions between weather and air pollutant concentration, are yet to be defined.

The current study aims to use rigorous mathematical modeling to achieve a more reliable prediction. The objective is to test the relationship between outdoor air pollutant concentration in terms of very fine particulate matter (PM2.5) and the rate at which the number of new cases of COVID-19 appeared in Wuhan per day. This rate of change in the number of daily cases is a valuable criterion that accounts for faulty case definition, the testing capacity, the effect of the lockdown, and other indicators like people moving between and within the city of Wuhan.

The Current Study

The study period covered January 1 to March 20, 2020. In this period, the lockdown was taking shape, the presence of COVID-11 was being published in China’s official news organizations, and Wuhan was locked down completely. As control measures were rapidly put in place, the case count first peaked and then began to drop.

The investigators collected daily data from a commonly used online platform. At the same time, the Chinese National Environmental Monitoring Center offers the concentration of air pollutants for each day over the same period.

The study found that air pollution concentrations were a median of 1-12 days in advance of the corresponding change in COVID-19 infections and cases. This is explained by the incubation period of the virus as well as the delay before a reported case is confirmed.

PM2.5 and Rate of Change in Case Counts

The researchers found that the cumulative number of cases tested and positive for the infection, as well as air pollution (PM2.5) levels, began to show a decline from January 23. 2020 onwards. This was the date when the city was completely locked down.

Pre-lockdown PM2.5 concentrations were 63 μg/m3 and the post-lockdown value is 39 μg/m3. The rate of change in the daily case count is significantly associated with the PM 2.5 concentration. The underreporting of cases because of limited testing ability, the changes in the official case diagnostic protocol, and the asymptomatic cases is thus controlled for.

Using this, not only can the trend of infection be accurately observed, but the effects of government interventions such as lockdowns and restriction of mobility within the city, as well as from Wuhan to other cities, can be adjusted for as well.

Other Meteorological Variations and Change in Case Counts

However, the other meteorological observations, such as the relationship of PM2.5 to the dew point, and the ultraviolet index, are also significant. The higher the dew point and the higher the PM2.5 concentration, the greater is the rate of change in the daily case count.

A higher UV index and a higher PM2.5 concentration are inversely correlated to the rate of change in the number of daily infections. The researchers found that rather than using mean concentrations, the PM2.5 level on a certain day was a stronger predictive for a rise in the number of infections.

The model also arrived at a lag time of about eight days before clinical symptoms set in. It showed that the lockdown was not a significant factor in changing the rate of change of daily infection numbers; neither was the restriction on intercity and intracity movement.

The researchers summarize: “Our study bears significance to the understanding of the effect of air pollutant (PM2.5) on COVID-19 infection, the interaction effects of both the air pollutant concentration (PM2.5) and the meteorological conditions on the rate of change in infection, as well as the insights into whether lockdown should affect COVID-19 infection.”

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Dr. Liji Thomas

Written by

Dr. Liji Thomas

Dr. Liji Thomas is an OB-GYN, who graduated from the Government Medical College, University of Calicut, Kerala, in 2001. Liji practiced as a full-time consultant in obstetrics/gynecology in a private hospital for a few years following her graduation. She has counseled hundreds of patients facing issues from pregnancy-related problems and infertility, and has been in charge of over 2,000 deliveries, striving always to achieve a normal delivery rather than operative.


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