Prediction of COVID-19 Cases from the Nexus of Air Quality and Meteorological Phenomena: Bangladesh Perspective

Abstract

An integrated approach was used to estimate the number of COVID-19 patients related to air quality and meteorological phenomena. Additionally, the air quality during pre-lockdown, lockdown, and post-lockdown stages of the COVID-19 pandemic was assessed to determine the effect of the infection containment measures taken in Bangladesh during the pandemic. The air quality was assessed based on measurements of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), black carbon, particulate matter (PM_2.5 and PM₁₀), and aerosol optical depth. Time-averaged maps of these parameters have been generated from NASA's (National Aeronautics and Space Administration) website. Values of these parameters have also been collected from a continuous air monitoring station (CAMS) located in Bangladesh's north-western city Rajshahi. The comparison shows that lockdown during the pandemic has brought significant improvements in air quality. However, the improvement was not sustained, since rapid increases in the air pollutant concentrations were observed in the post-lockdown period. Furthermore, Pearson correlation coefficients between each air quality variable and the daily new COVID-19 case rates were calculated. Different meteorological variables during the same time periods were determined to observe the variation in Rajshahi city. Relationships of these variables with the case rates were also established. Additionally, statistical analyses of the obtained data have been conducted for the measured variables using the Kruskal-Wallis test to assess the differences in the observed data among the pre-lockdown, lockdown, and post-lockdown periods. Dunn's "Q" test was employed to test if the variables showed significance statistical difference during the Kruskal-Wallis test for pairwise comparisons. From the study, it has been observed that both meteorological variables and air quality parameters have significant relationship with daily new COVID-19 case rates. Both positive and negative associations of these parameters with the COVID-19 case rates have been observed. Very high air pollution has been observed in the post-lockdown period. Thus, it is recommended that appropriate authorities undertake corrective measures to protect the environment in cities with large populations. This study provides guidance for decision makers and health officials for future research and potentially reducing the spread of COVID-19.

Keywords: CAMS; COVID-19; Lockdown; Meteorological variable; Statistical analysis; Time-averaged map.

Fig. 1

Daily lab tests, confirmed cases, and case rates in recent 30 days (06 April 2021–06 May 2021) in Bangladesh

Fig. 2

Time-averaged maps of pollutants (a) NO₂ total column (cm⁻²), b O₃ total column (DU), c CO mass concentration (kg.m⁻².s⁻¹), d SO₂ column mass density (kg m⁻²), e PM_2.5 column mass density (kg m⁻³), f AOD, and g black carbon column mass density (kg m⁻²) over Rajshahi, Bangladesh during pre-lockdown, lockdown, and post-lockdown periods (GIOVANNI 2021)

Fig. 2

Time-averaged maps of pollutants (a) NO₂ total column (cm⁻²), b O₃ total column (DU), c CO mass concentration (kg.m⁻².s⁻¹), d SO₂ column mass density (kg m⁻²), e PM_2.5 column mass density (kg m⁻³), f AOD, and g black carbon column mass density (kg m⁻²) over Rajshahi, Bangladesh during pre-lockdown, lockdown, and post-lockdown periods (GIOVANNI 2021)

Fig. 3

Variation of each pollutant (a) 24 h average NO₂ emission (ppb), b 8 h average ozone concentration (ppb), c 8 h average CO emissions (ppm), d 24 h average SO₂ concentration (ppb), e 24 h average PM_2.5 concentration (µg/m³), and f 24 h average PM₁₀ concentration (µg/m³) (CASE 2021)

Fig. 4

Variation of (a) average temperature (°F), b dew-point temperature (°F), c average wind speed (m/s), and d precipitation (cm) during pre-lockdown, lockdown, and post-lockdown period (NOAA 2021)

Fig. 5

Correlation between COVID-19 case rates and each pollutant: (a) 24 h average NO₂ emission (ppb), b 8 h average ozone concentration (ppb), c 8 h average CO emissions (ppm), d 24 h average SO₂ concentration (ppb), e 24 h average PM2.5 concentration (µg/m³), and f 24 h average PM10 concentration (µg/m³), obtained from CAMS over Rajshahi city, Bangladesh

Fig. 6

Correlation between COVID-19 case rates and meteorological phenomena: (a) average temperature (°F), b dew-point temperature (°F), c average wind speed (m/s), and d precipitation (cm) over Rajshahi city, Bangladesh