Journal of Marine Medical Society (Aug 2024)

Distribution of COVID-19 Cases According to Environmental Pollutants and Meteorological Factors in Districts of Maharashtra

  • B. Madhu,
  • Brototi Biswas,
  • Dhivya Karegam,
  • Arun Kumar Yadav

DOI
https://doi.org/10.4103/jmms.jmms_130_23
Journal volume & issue
Vol. 26, no. 2
pp. 212 – 215

Abstract

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Introduction: Environmental factors such as temperature, precipitation, and various other pollutants in the air like nitrogen dioxide (NO2), CO2, etc., have been studied for sustenance and transmission of Coronavirus disease-19 (COVID-19). Studies from India are lacking about the distribution of COVID-19 cases according to air pollutants and meteorological factors. Hence, the present study was conducted to study the relationship between COVID-19 and environmental factors with the objectives of Cluster mapping of the COVID-19 cases district-wise for the state of Maharashtra. Methodology: The air pollution variables NO2 column density (mol/m2), sulfur dioxide column density (mol/m2), Aerosol absorbing index (Ai), Carbon monoxide (CO) column density (mol/m2), and Ozone column density (mol/m2) were extracted from Sentinel-5P datasets available in Google Earth Engine (GEE) (https://code.earthengine.google.com/). The meteorological variables precipitation (m), temperature (t), and humidity (k) were extracted from the European Environmental Agency 5-Land reanalysis dataset in GEE, which provides data at a resolution of 1113.2 m. The preprocessing and retrieval of data were carried out in GEE. These values were correlated with daily cases, density, and other demographic factors. Results: The study identified a cluster of cases in few districts of Maharashtra. These districts were Pune, Ahmednagar, Mumbai, Nashik, and Nagpur. Population density and NO2 were important factors in most of the monthly data. Conclusion: The present study used data available on public platforms to assess the association between COVID-19 and environmental factors. The study found that various environmental factors such as NO2, CO, and Ai may be associated with increase cases of COVID-19 in the district. However, ecological bias may be kept in mind.

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