COVID-19 has caused a global health crisis. In December 2019, Wuhan, China, reported the outbreak, which the WHO declared a pandemic in March 2020. As of November 21, 2022, this illness had 688,320,211 cases globally, including 40,720,516 in India [1] . According to the WHO website, most nations had COVID-19 [1] . However, many areas of the world have been hit significantly worse than others, both in terms of the number of infections and the fatality rates. According to the data provided by the WHO, India placed second in the world in terms of the number of cases of COVID-19 and third in terms of mortality. So far, 2,006,883 cases have been reported in Delhi alone, with 26,516 people losing their lives as a result. The majority of cases and fatalities related to the COVID-19 epidemic were reported in Delhi during all three waves [2] [3] . COVID-19 infection symptoms observed globally included high-grade fever, cold, pneumonia, shortness of breath, and body ache, all of which caused kidney failure and even death in the short course, and its incubation time period ranged from 6 to 14 days [3] . Some of the best ways to stop a pandemic are to keep your distance from other people, wash your hands often, and try not to touch your face, especially your mouth, nose, and eyes [5] . Surprisingly, the vast majority of the world’s population has been rendered helpless and is in danger as a direct result of the exponential rise in the number of deaths and infections [6] . The epidemic has had a considerable impact not only on the availability of goods and services but also on the methods in which people live their lives, the health and education systems, the environment, their daily routines, as well as commercial, sporting, religious, political, and cultural activities. Globally, the annualized rate of economic expansion is expected to fall by 3% in 2020 [7] .

On January 30, 2020, India confirmed the first case of COVID-19. To raise public awareness, India announced a curfew for the general public on March 22, 2020, and on March 24, 2020, the country went into a 21-day lockdown. After that, the lockdown was gradually extended due to rising infection rates. Despite the fact that India has a far larger population than nations like the United States, China, France, and Italy, its death rate is thought to be lower because of the lockdown [8] .

Infectious disease and mortality rates vary widely not only between nations but also within them [9] . Researchers from a variety of disciplines are urged to conduct exploratory experiments to better understand the role that COVID-19 may play in the spread of the virus and the resulting illnesses and deaths. Many studies from all over the world point to the possibility that air pollution and weather conditions play a role in how diseases affect different places [10] . Italy, like other pandemic-stricken nations, needed to know instantly how air pollution and meteorological aspects influenced disease transmission [11] . In Italy, COVID-19 transmission was linked to particulate matter pollution, temperature, and humidity [12] [13] [14] [15] . In a French city research study, PM_2.5 and PM₁₀ were associated with COVID-19 mortality [16] . Researchers from China also concluded that air pollutants, meteorological parameters are responsible for COVID-19 spread [17] . They found that temperature differences within a day were responsible for deaths due to COVID-19 in Wuhan. In a study of three countries i.e., China, the United States, and Italy done by [18] , PM_2.5, CO, and NO₂ were found to be linked to COVID-19 deaths [19] [20] . In Wuhan, China, where the COVID-19 pandemic’s outbreak, researchers looked at how weather affects the number of new COVID-19-related deaths each day. They found a strong link between the air quality index, humidity, and deaths [21] . In a similar way, [22] looked at how COVID-19 spread in China when temperature and air quality were taken into account as a whole. They found that when the temperature went up, the air quality didn’t get worse, which made it take more time for diseases to spread. [23] expressed similar things and highlighted the link between temperature and Germany’s significant COVID-19 spread. They also said that the country’s “controlled” pollution was to thank for the slow spread of the pandemic in the country. As the pandemic got worse, the Chinese government put restrictions on how vehicles could move in the worst-affected cities. This was done to reduce air pollution, and the policy to limit private transport could cut PM_2.5 pollution by 32% [24] .

The impact of COVID-19 has been found more in countries where temperatures range from 0˚C to 15˚C as compared to warm-weather and very low-temperature countries [25] . According to Shaobo Shi [26] , there is a direct correlation between daily temperature and the number of confirmed COVID-19 pandemic cases in some regions of China, and Shi also stated that if daily temperatures rose above 10˚C, the number of confirmed cases would decrease. In the same way, if the temperature went up by 1˚C, Brazil’s daily cases of COVID-19 infection would decrease [27] . Whereas, there haven’t been any consistent relationships between the number of COVID-19 pandemic cases [26] and higher average, minimum, and maximum temperatures [28] [29] . Many studies have also found that the number of COVID-19 cases and deaths goes down when the temperature and humidity around the world go up [17] [30] [31] . Overall, scientists and people believe that the number of COVID-19 infection transmissions depends upon the environmental conditions.

Based on geography and environment, we can figure out how likely a COVID-19 pandemic is in a certain climate zone. Numerous studies have demonstrated that environmental elements like temperature, rainfall, humidity, wind speed, pressure, evaporation, and sunlight affect the start and transmission of infectious diseases like pneumonia and influenza [32] . Similarly, a number of research studies have looked at the link between different environmental conditions and the spread of the COVID-19 pandemic and found strong positive correlations, just like they did with the spread of other communicable diseases [17] [28] [33] [34] [35] [36] [37] .

In India, no thorough academic study has been published to inform researchers and policymakers on how climate affects the spread of COVID-19 in megacities. Apart from this, the past studies in India only considered a short span of time and studied only two or three meteorological parameters; however, in Delhi, India, there were three waves of COVID-19, and most of the cases and deaths occurred in this time period; during these waves, there were seasonal differences, which meant the levels of meteorological parameters were different. This study was done to fill a gap in the research. The main purpose of this study is to investigate, compare, and discuss the eight meteorological parameters of Delhi: maximum temperature, relative temperature, minimum temperature, dew point, humidity, wind speed, rainfall, and air pressure. For three study periods, i.e., the 1st wave of COVID-19 (September 1, 2020-December 11, 2020), the 2nd wave of COVID-19 (April 1, 2021-May 22, 2021), and the 3rd wave of COVID-19 (January 1, 2022-January 30, 2022). This is the first study to look at the COVID-19 pandemic as well as various meteorological parameters in India’s most densely populated and rapidly growing metropolis in all of its waves. Researchers, academics, and health policymakers will get a comprehensive understanding of COVID-19 and numerous climatic factors from the study. These data will also suggest new studies on climate and COVID-19 infection spreadibility in tropical and subtropical Indian megacities.

The linear regression analysis yields the values shown in (Table 2), which describe the positive correlation of temperature (maximum, minimum, and average) with all three waves of the pandemic for cases at a 95% confidence level. We also observed cases increasing with increases in temperature during all the waves. These results agree with what [40] found, which is that the number of cases of COVID-19 is likely to increase as the air temperature in India rises. However, it is not clear what role humidity plays. [41] looked at the climate records of temperature, sun light, humidity, population density, rainfall, and wind speed. They found that COVID-19 is more likely to spread in areas of India that are hot and dry and at a lower altitude. In Japan, though, the correlation was found to be positive (r = 0.416, P = 0.001). Many cities, including Shanghai (China), Hong Kong (China), Seoul (South Korea), Kuala Lumpur (Malaysia) and Tokyo (Japan), had a positive association with the number of new daily confirmed cases, deceased cases and the average temperature and relative humidity. This was especially true when using lagged 3D modelling, which showed that temperature was responsible for confirmed cases of disease in the locations (exceptions: Beijing, Malaysia, and Korea) [42] . COVID-19 pandemic cases are statistically associated with minimum, maximum temperatures (˚C), and humidity (%) (r values = 0.93, 0.94, 0.83, and 0.30, respectively, at the 99% confidence significance level). Cold temperatures increase COVID-19 cases (r = 0.447, p = 0.01, and r = 0.362, p = 0.05, respectively) [43] .

The r value in Table 3 reflects the strong association of dew point with the spreadibility of COVID-19 cases in all three waves, which are significant at a 95% confidence level. The fact that the dew point is going up suggests that there will be more cases. Temperatures, dew point, wind, and humidity were all found to be related to the emergence of new cases of COVID-19 in studies of correlation and regression (P 0.05). In high population densities, humidity does not show any association with COVID-19 cases; in low population densities, the opposite was observed [44] . [45] demonstrated that high temperatures and high relative humidity had a detrimental impact on the survival and transmission of COVID-19, whereas low temperatures, rainfall, dew point, wind speed, and surface pressure all helped to prolong the life and spreadibility of the virus for a longer length of time.

The regression analysis witnessed the link of the humidity with the COVID-19 disease cases; it witnessed a strong association with the rise in new cases. All these were significant at a 95% confidence level. These results match up with what [34] found, to the fact that states with moist and very wet weather have taken on the majority of the COVID-19 pandemic’s confirmed cases. But [46] found that higher temperatures contributed to the rapid spread of COVID-19. On the other hand, in China, researchers found no relationship between COVID-19 infection spreadibility and humidity. The spreadibility of this disease is affected by a number of things, such as the number of people in a location, the number of people moving in and out of that area, the host’s immunity, the quality of medical care, and, most likely, the weather (temperature and humidity) [32] [47] . Also, [37] has proven that humidity does not usually cause COVID-19 pandemics in Türkiye. [36] has demonstrated that there is a slightly negative significant association between the level of humidity and the number of confirmed cases of the COVID-19 pandemic in New York City.

Wind speed shows the correlation values are very low as compared to other parameters at the 95% confidence level. The Karl-Pearson test was applied to get to know by which level COVID-19 cases are related to factors such as the speed of the wind, the temperature, the amount of daylight hours, the amount of rainfall, and the level of humidity. Low wind speed increased COVID-19 instances (r = 0.314; p = 0.05) [43] . PM_2.5 pollution increased COVID-19 instances. In conclusion, the maximum wind speed, rain, PM_2.5 pollution, and temperature may increase COVID-19 spreadability [48] . Transmission was higher when wind speeds were less than 5.5 mph. Scientists believe higher wind speeds disperse SARS-CoV-2 particles and minimize infection risk. Since cooler outside temperatures discourage socializing, they may have indirectly boosted infection rates by encouraging people to stay indoors. Airflow and quality will also reduce infection risk because transmission rates are higher inside [49] . After accounting for a variety of potential confounding factors, the results demonstrate that high temperatures and high winds have a strong inhibitory influence on virus propagation [50] . What has been seen with SARS-CoV and MERS-CoV-2 is also true for COVID-19. They also found an inverse association with wind speed, suggesting COVID-19 stays in the air for a shorter duration, and a higher UV index (but just for the 14 days before the pandemic), which makes sense because viruses die at higher temperatures [49] .

Air pressure also showed a correlation with COVID-19 spreadibility in the NCT of Delhi, but the strength of the correlation was lower at a 95% significant level. We tried those meteorological variables (air temperature, average humidity, rainfall, wind speed, and air pressure) are not linked to COVID-19’s ability to spread when the basic reproduction rate is used to measure it (R0). We used data that was available to the public about the number of COVID-19 cases each day (n = 108,308), the weather every three hours, and how people moved around their communities over the course of three months [51] . Weather conditions and air pollution factors spread COVID-19 to 3739 locations of the world. The predicted reproduction number was considerably negative with temperatures over 25˚C and weakly positive with air pressure, wind speed, rainfall, SO₂, and O₃. The findings indicated that, whereas higher air pressure was associated with a lower incidence rate, higher wind speeds had the opposite impact. Crucial meteorological parameters had a comparable effect on the incidence rate and the total number of reported cases [52] .

There was no rainfall during the chosen time period of the COVID-19 pandemic waves, so there was no correlation between the three waves of the COVID-19 pandemic [53] . Wind speed and rainfall appear unrelated, but they may influence behaviour to limit exposure and infection risk. One study examined how weather affects COVID-19 outcomes in all Chinese regions. Rain was associated with Fujian COVID-19 fatalities and cases in Shanghai [54] . The average rainfall in the chosen cities is between 20 and 40 mm, which makes COVID cases drop by 18% and 26% over the next three days [55] . COVID-19 is often effective at 27˚C - 32˚C and 250 - 350 mm of monthly average rainfall. This study reveals that both temperature and rainfall aren’t the main factors that cause COVID-19; however, both variables appear to play substantial roles in the spread of it in India, particularly during the summer time of 2020 and 2021, when the rainy season was heavier in some states. This was especially true during the summer time of 2020 and 2021 [56] . In one study RH and rainfall were found to be negatively related to new daily cases and mortality [57] .

In this study, we selected the following meteorological parameters: maximum temperature (˚F), relative temperature (˚F), minimum temperature (˚F), dew point (˚F), humidity (%), air pressure (in), wind speed (mph), and rainfall (in). The Mann-Kendall test and Sens slope were used to obtain the trend in the COVID-19 data, and the Karl-Pearson analysis was used to determine the role of meteorological parameters in the COVID-19 pandemic waves. From the overall analysis, it can be concluded that the meteorological parameters such as maximum temperature (˚F), relative temperature (˚F), minimum temperature (˚F), wind speed (mph), dew point (˚F), humidity (%), and air pressure (in) were positively associated with the spreadibility of COVID-19 pandemic waves. However, some of the parameters of this study show a very weak association. The whole analysis was done at a 95% significant level. The findings of this study have the potential to provide information on how changes in the environment impact urban areas with high population densities that are located in hotter, more tropical regions. This study will also serve as a new base for more research on climatic factors and the potential for the COVID-19 pandemic to spread to other megacities located in the subtropical region of India. Researchers and those who are responsible for making choices on health policy might make use of these results.

The author thanks the Indian government, MOHFW, IARI, CPCB, and WHO for providing and maintaining the datasets.

The authors declare no conflicts of interest regarding the publication of this paper.

Gupta, D. and Duan, X.L. (2023) Role of Meteorological Parameters in COVID-19 Pandemic Waves in Delhi, India. Open Journal of Air Pollution, 12, 31-49. https://doi.org/10.4236/ojap.2023.122002