Testing Link of Climatic Factors and Air Pollution with COVID-19 amid the Second Wave in India

Several recent works indicated the association of climatic factors and air pollution with the spread of coronavirus disease-2019 (COVID-19, total daily confirmed deaths and new cases). Nonetheless, the impact of climatic and environmental conditions concerning virus spread and demise, yet not been solved. Present research work focuses on the spread of COVID-19 over five distinct states/territories of India; those have pretty different climatic conditions and the air quality index. The Kendall rank correlation and Spearman rank correlation tests at 95% significance were used from 3 Jan-15 May 15, 2021 (132 days), including April 2021, a month of sharp rise COVID-19 second wave. For all the regions, a significant positive correlation of temperature (>0.6) and a significant negative correlation of humidity (<−0.5) is found with COVID-19 except for Kerala (insignificant and weak relation-ship). However, in the case of wind speed, a lower correlation is found eve-rywhere except over Andhra Pradesh (>0.5). For all the states, a significant and consistent inverse relationship (<−0.3) is found between COVID-19 and the daily Air Quality Index (AQI) from 1 Mar to 13 May 2021 (74 days). Thus, increase in temperature and winds, decrease in relative humidity were the climatic reasons, those chipped in COVID-19 cases surge and consequen-tial enhancement of Air quality. These results are quite promising to com-prehend the mutual tie-up between climate and

based on data of 122 cities of China. [21] suggested that local weather conditions with low temperature, mild diurnal temperature range, and low humidity likely favor transmission. [22] found no evidence of reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures in a study over Spain. [23] suggested a strong association between higher average temperature and lower COVID-19 incidences.
Air pollution has also been found to be associated with the spread of COVID-19 by several researchers [24]. [24] found the significant role of particulate matter (PM) in the spread of COVID-19 in Italy. [25] reported positive relationships between air pollutant concentrations, specifically NO 2 with COVID-19 mortality and infectivity in England and found PM2.5 a significant contributor to COVID-19 cases. In China also, a positive correlation of the Air Quality Index (AQI) is reported with COVID-19 cases [1]. A negative correlation of the lower environmental quality with the human immune system was suggested in China [26]. Though, in Germany, a significant relationship of the environmental quality index is found only with the active cases from COVID-19 pandemic [27].
Till now, relatively few studies have been reported analyzing the effect of weather conditions and air pollution on COVID-19 in different Indian states, although there is a significant difference between the weather conditions and air pollution over the other states of India. The difference in the correlation trends of meteorological parameters with COVID-19 over different regions prompted us to undertake this study over five states of India. This study provides a comparative analysis of the link between climatic variables and air pollution with COVID-19 and further enhances our understanding of climatic variables' impact on the spread of pandemics.

Research Methodology
The present study is done from 3 Jan to 15 May 2021 to explore the effect of the climate parameters on the COVID-19 cases. This period includes the second wave of COVID-19 in India and the Indian subcontinent, in which the double mutant B.1.617 of SARS-CoV-2 is supposed to be dominant in several states of India. For this period, the correlation of the climatic variables with the COVID-19 total confirmed cases, daily cases and fatalities is tested using non-parametric Kendall and Spearman rank correlation tests because the data was not found to be normally distributed. Spearman's rank correlation coefficient and Kendall's rank correlation coefficient are non-linear rank-based correlation coefficients that are frequently used to test the association between two variables. All the correlations are tested at 95% significance, otherwise stated at the bottom of each table. Results on Chandigarh were analyzed (not shown) on 99% significance and interestingly there was hardly a change in the correlation coefficient values. The correlation test between AQI and COVID cases is done for the period of around two and half months, i.e., 1 Mar-May 13, 2021 (44 days). We tried Leg-lead correlation between COVID-19 and climatic variables, and the 7-days running mean of COVID-19 case data with climatic variables did not enhance the Journal of Environmental Protection correlation coefficient than we showed in the present research work.

Study Area
Five Indian states/union territories are selected for the present study, i.e., Andhra Pradesh, Chandigarh, Delhi, Kerala and Puducherry. The reasons for the selection of these areas for the study are as follows: 1) There is a difference in temperature and humidity of the above states, so it provides the different climatic conditions (Figure 1) for exploring the link with COVID-19.
2) These states differ in respect to the air quality index (see Table 1). Out of the above, Puducherry remains least polluted while Delhi remains among India's most polluted regions. Therefore, to relatively analyze the effect of pollution on the fatality rate of Covid-19, the above states with different AQI records are chosen.

Data Collection
For the above five regions, daily data for climate parameters like mean temperature (˚C), relative humidity (%) and wind speed (m/s) at 2 m height are taken from NASA Power Data Access Viewer [28]. For analyzing the effect of air pol-

Contribution of Mean Temperature
During COVID-19, a general belief was quite prevalent in the community that people living in hotter regions are less prone to virus infection. The same was believed that if the temperature rises, COVID-19 may go away [31]. Figure 1 shows the average temperature 3 Jan 2021 to 15 May 2021 for the five states/territories along with daywise COVID-19 total confirmed cases, total deaths and new cases. The estimated Kendall rank correlation coefficient values for average temperature and COVID-19 (total confirmed cases, total deaths and daily new cases) are given in Table 2. The correlation between temperature and COVID-19 remains relatively high > 0.6 except over Kerala. Temperature over Kerala does not vary except 24˚C to 30˚C as compared to other states. For all the states/territories in the period of study, a significant positive correlation between mean temperature and the COVID-19 confirmed cases are found ( Table 2), which is in agreement with the recent studies done in the Indian context [16] [17] [18]. A positive correlation of temperature with COVID-19 total cases has been reported by several other researchers also, for instance, for New York [14] and Jakarta, Indonesia [15]. [20] found that the mean temperature of the last two weeks was positively associated with newly confirmed COVID-19 cases on the basis of a study over 122 cities from China, while a negative correlation of COVID-19 transmission and temperature was reported for China [11]. A negative correlation between temperature and COVID-19 cases has been reported for several other regions, for Brazil [12] and Turkey [13].
For all 5-India regions, the Kendall correlation coefficient for mean temperature and total deaths due to COVID-19 is positive (Table 2), which agrees with previous studies (Singh et al., 2020; Bashir et al., 2020). The degree of correlation is found to be similar to that found for confirmed cases. [17] found a strong positive relationship of mean, maximum and minimum temperature with the count of COVID-19 pandemic confirmed cases and deaths. [14] also reported a positive correlation of temperature with COVID-19 deaths for New York. A positive correlation of temperature with mortality rate was obtained [6] in China. [32] found an inverse correlation between COVID-19 deaths per million people and average monthly high temperatures considering the data of twenty-five areas located in Europe and the United States for the month of April 2020 ( Figure 2).   to be negative with a 2-sided p-value of 0.018 (Table 2).

Role of Moisture
Humidity is another relevant factor that helps in fighting against viruses and infection. In general, the virus dies faster in the higher humidity; close to 60%, relative humidity is optimum for that. Figure 2 Table 3. In most of the cases, the correlation remains negative (<−0.4) except over Kerala.
It is found that there is an inverse relation of humidity with the total confirmed cases, deaths and daily new cases for all the regions taken for study except for Kerala, where no significant correlation is found ( Table 3

Wind Factor
A safe distance of 6-feet was recommended from the WHO so that the spread of the virus was restricted further due to the limited nature of the airborne COVID-19 virus. A positive correlation is found of wind speed with COVID-19 cases for Andhra Pradesh and Chandigarh, while for Kerala and Puducherry, the correlation with COVID-19 is found to be negative (Table 4). In Andhra Pradesh, the degree of wind speed correlation is higher than in other regions because of the increase in the wind in the latter period. In Delhi, no significant correlation of confirmed cases and deaths with wind speed is found, while a weak positive correlation is found with daily new cases of COVID-19. Before June, the wind pattern over Delhi remains pretty chaotic; a regular wind pattern forms from mid of June. Singh et al. (2020) have found a weak correlation of wind speed with COVID-19 confirmed and deceased cases for Delhi, while Sahin (2020) showed a negative correlation with the wind speed on the day in Turkey (Figure 4).  The droplet pruced during cough, sneeze and talk by a COVID-19 patient may acrried away by wind for a particular distance. On drying the dropmet the COVID-19 virus may hand in the air for minutes to hours.
Supplementary tables (S1-S3) provide the value of Spearman's rank correlation coefficient for three Climate variables (average temperature, humidity and wind speed) and COVID-19 cases (total confirmed cases, total deaths and daily new cases). It is observed that the Spearman rank correlation test also comes out with a similar correlation in magnitude and sign but slightly higher as compared to the Kendall rank correlation test.

Consequence of COVID-19 on Air Quality
Before the monsoon season (June to September), the quality of the air remains quite fluctuating over the Indian region. Figure 5 shows the daywise variation of AQI for the states along with COVID-19 cases from 1 Mar to 15 May 2021. Some of the low and high peaks in AQI are quite visible over five regions.      Table 5 lists the Kendall rank correlation coefficient for air quality index (AQI) and COVID-19 for all the regions from 1 Mar to 13 May 2021. Overall a negative association is observed between the AQI and COVID-19 total confirmed cases, total deaths and daily new cases for all the states/territories. Similar results are obtained with the Spearman rank correlation test (Table 5). These results are not consistent with [24] [25], where positive relationships were reported between air pollutant concentrations and COVID-19 infectivity for England and Italy, respectively. A positive correlation of AQI is reported based on the data of 120 cities of China for the duration 23 Jan to 29 Feb 2020 [1]. Chinese provincial data suggested a negative correlation of the lower environmental quality with the human immune system (Han et al., 2020). Though in Germany, it was found that the environmental quality index has a significant relationship only with the active cases from COVID-19 pandemic (Bilal et al., 2020). The disparity in the trends of correlation of COVID cases with AQI obtained in the present work possibly indicates that other factors may too dominate the association with AQI. Wherever the covid cases were rising, there were a few top reasons for the increase in AQI, e.g., deployment of lockdown, work from home, shutting down of many small and medium industries. Many of the leading newspapers covered the fact of clearly visible Himalayan peaks from Chandigarh. Improve in AQI will undoubtedly lessen the risk from COVID-19 illness, especially in preexisting respiratory conditions public.
Overall, as per the present study, a positive correlation with temperature and a negative correlation with humidity are found (except Kerala). However, the different trends of association of meteorological parameters with COVID-19 cases reported from different parts of the world indicate that the effect of other factors like proper prevention policies and strict implementation of these policies is far more on the spread of the disease as compared to the meteorological factors. The present study's limitation is with the use of the short duration (data from the single peak of COVID case) of datasets, limited statistical methods, and other related factors that might have aided in the surge of covid-19 cases. There was a reduction of aerosol loading (aerosol optical depth) over the Indo-Gangetic plain by 35% during the COVID time [33]. April 2021 lockdown reduced industrial, business, and energy consumption were few significant sectors responsible for cair quality enhancement.

Conclusions
An association between COVID-19 and climate variables is investigated during 3 Jan-15 May 15, 2021 (132 days), which includes April 2021, a month of a sharp rise in COVID-19, second wave. Five regions (Andhra Pradesh, Chandigarh, Delhi, Kerala and Puducherry) of distinguished climate and weather are selected for this study, where the Kendall rank correlation test and Spearman correlation are being calculated at 95% significance. We summarize the results as follows: 1) COVID-19 cases and the mean temperature have a decent association over five selected regions, Kendall rank correlation (up to 0.81) and Spearman correlation (0.95). COVID-19 cases rise with the rising temperature over Indian regions.
2) A negative correlation is obtained between COVID-19 and relative humidity cases. It shows that humidity also controls the spread of COVID-19 cases.
3) Winds can carry the air-borne particle over a specific distance. However, in the case of COVID-19, a weak link is found with wind speed except over Andhra Pradesh.
4) The presence of aerosol (PM2.5) in the air may aid in spreading COVID-19. However, over Indian regions, a negative correlation of Air Quality Index (AQI, 1 Mar to 13 May 2021) with COVID-19 cases shows a consequence of lockdown, an improvement in air quality.
The results presented here tried to find a monotonic link between COVID-19 cases and climatic conditions over selected regions. Further, we regard the inadequacy of the correlation test in relating the effect of temperature, humidity, wind speed, AQI on the transmission of the COVID-19 spread. The association between climate over a region is always being related to regional and local diseases. Thus, the notion of the impact of weather on disease and illness will always remain one of the research topics. How much one impacts others is a bit complex to separate their influences. The apparent interruption caused by COVID-19 brought several impressions on the environment, business, transportation, education and society. The positive impact was seen on greenhouse gases, and noise pollution. On ecology point of view water quality was improved, and few cases where forest animals were came out on roads and in residential areas. Air quality had been improved in many cities due to travel and social gatherings restrictions.
For better understanding, the authors suggest using suitable statistical methods (e.g., multiple regression analysis), including influences from many other variables. Such inclusion may help find a reliable and robust outcome. Long term impact of COVID-19 on air quality is still unknown. However, the way COVID-19 has changed the lifestyle towards online learning and working from home may have a greater impact on societal and environmental impacts. Further scope to this work may be testing this correlation further for longer and different periods incorporating effects of other factors like population density and prevention policies/measures.