|Year : 2021 | Volume
| Issue : 4 | Page : 116-126
Micro-environmental conditions and high population density affects the transmission of severe acute respiratory syndrome corona virus-2 in metropolitan cities of India
Sanjay Dwivedi1, Seema Mishra2, Ruchi Agnihotri1, Vishnu Kumar1, Pragya Sharma1, Geetgovind Sinam1, Vivek Pandey1
1 Plant Ecology and Climate Changes Science Division, CSIR-National Botanical Research Institute, Lucknow, India
2 Plant Ecology and Climate Changes Science Division, CSIR-National Botanical Research Institute, Lucknow; Department of Chemistry, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India
|Date of Submission||04-Aug-2021|
|Date of Decision||16-Nov-2021|
|Date of Acceptance||18-Nov-2021|
|Date of Web Publication||29-Dec-2021|
Department of Chemistry, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur - 273 009
Source of Support: None, Conflict of Interest: None
Aim: The present study explores the effects of high population density (PD), climatic and environmental factors on transmission of coronavirus disease 2019 (COVID-19) in selected metropolitan cities of India.
Materials and Methods: A data extraction sheet has been prepared to summarize the data of confirmed severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) cases and number of deaths in ten metropolitan cities, which was taken from Government of India website. The data on environmental factors of each selected metropolitan city were compiled from the official website and climatic conditions from Meteorological Department Government of India.
Results: In India, maximum positive COVID-19 cases (>32%) has been found in tropical wet and dry climate zone. While the incidence of COVID-19 cases has been found less in the arid zone of India. Poor correlation has been found between level of Vitamin D, total COVID-19 cases, and mortalities in the studied metropolitan cities. No significant correlation was found between the health care index and COVID-19 cases and mortality.
Conclusions: Correspondence and principal component analysis statistics showed high PD, poverty, climatic and environmental factors influenced the SARS-CoV-2 transmission in metropolitan cities of India.
Keywords: Corona virus disease 2019, environmental factor, PM2.5, transmission rate, Vitamin D
|How to cite this article:|
Dwivedi S, Mishra S, Agnihotri R, Kumar V, Sharma P, Sinam G, Pandey V. Micro-environmental conditions and high population density affects the transmission of severe acute respiratory syndrome corona virus-2 in metropolitan cities of India. Environ Dis 2021;6:116-26
|How to cite this URL:|
Dwivedi S, Mishra S, Agnihotri R, Kumar V, Sharma P, Sinam G, Pandey V. Micro-environmental conditions and high population density affects the transmission of severe acute respiratory syndrome corona virus-2 in metropolitan cities of India. Environ Dis [serial online] 2021 [cited 2022 Jan 24];6:116-26. Available from: http://www.environmentmed.org/text.asp?2021/6/4/116/334313
| Introduction|| |
The current outbreak of novel coronavirus disease 2019 (COVID-19) is in >200 Countries and Territories and has become a serious threat to the health of peoples around the world with >920,000 infected cases and >46,000 casualties (as of June 20, 2020). The worst part of the disease is that the transmission of the causative virus, the severe acute respiratory syndrome corona virus-2 (SARS-CoV-2), occurs human-to-human and most often when people are in the incubation stage of the disease, which can be several days, and have no symptoms. In the absence of specific medicine or vaccine, governments worldwide relied on social distancing and personal hygiene for preventing further spread of the disease. Strict guidelines were issued for potentially vulnerable populations such as children, elderly and people with comorbidities. Until the development of a specific therapy or vaccine against COVID-19, enhancing the understanding of factors influencing the rate of infection and severity of the disease is crucial. In this regard, the factors which affect the stability and transmission of virus such as climatic, environmental and meteorological and, nutritional health of people which may exacerbate the infection and severity of disease should be considered. Pollutants which cause chronic lung diseases may also be a significant factor to enhance COVID-19 related mortality. Recent clinical and epidemiologic studies on COVID-19 revealed that there is a potential association between mean levels of vitamin D in various countries with cases and mortality caused by COVID-19., In addition to already reported factors influencing the transmission of viruses, environmental factors can significantly affect the virus's survival and persistence of virus-carrying droplets or aerosol. Some studies have demonstrated that humidity and temperature are crucial factors for COVID-19 infection and related mortality.,,,,, However, the results are contrasting such as Xie and Zhu found no supporting evidence for association of temperature with epidemic growth of COVID-19 and case counts. Similar findings were reported by Jüni et al. Further, the microclimatic conditions and socioeconomic status of individuals, which are associated with co-morbidities, particularly in urban areas, also significantly contribute to infectious diseases and mortality., Thus, environmental factors and micro-climatic conditions also seem important for the COVID-19 transmission and mortality.
India has four seasons: Winter (January–February), summer (March–May), a monsoon (rainy) season (June–September), and a postmonsoon period (October–December). However, India comprises a wide range of weather conditions. Based on the Köppen system, India has six major climatic subtypes, from arid desert in west, alpine tundra and glaciers in the north to humid tropical in the southwest and the island territories. These climate subtypes have different socioeconomic conditions and population density (PD) depending on livelihood opportunities. However, urbanization particularly due to rural-urban migration has resulted in rapid population growth in metropolitan cities. The high urban population has caused serious environmental problems in cities such as crowding, environmental degradation, water scarcity and contamination, and insufficient sanitation. The consequence is spreading of slum numbers, disparities in living conditions, and poor access to clean environment and health care services. Thus, increasing the vulnerability to disease and health of urban population, particularly the urban poor., In India, Maharashtra had the most immigration with 2.3 million, followed by the National capital Delhi, Gujarat, and Haryana, thus urbanization varies widely among the states and cities. The process of urbanization and industrialization during the last two decades has resulted in increased level of air pollution. In most of the metropolitan cities of India, the public health is gravely threatened by severe particulate matter2.5 (PM2.5) exposure. PM2.5 is a mixture of organic and inorganic matter, nitrogen compounds, Sulphur compounds, Polycyclic Aromatic Hydrocarbons (PAHs), several heavy metals, and radionuclides. A study by Chen et al. found that the population-weighted annual mean PM2.5 across the Indian metropolitan cities is 72 μgm−3, ~3.5 times higher than the global level of 20 μgm−3 and 1.8 times the annual criterion defined in the Indian national ambient air quality standards. The health effects associated with exposure to PM2.5 are lung and cardiovascular disease and increase in mortalities in adults and elderly, reduced lung function in children, and increased chances of lung cancers., During the COVID-19 pandemic these compromised health conditions may influence the rate of infection and severity of symptoms of disease. Further, in India, >80% population is deficient in the level of Vitamin D. Although at national level, the mortality caused by COVID-19 was comparatively less (2.82%, up to June 2020) in India than other countries such as the USA, Spain, Brazil, Italy, and Switzerland, but >56% COVID-19 cases and incidence of deaths has been reported from top ten metropolitan cities only. These metropolitan cities falling in different climatic zones differ considerably in weather and micro-climatic conditions. Thus, in the current study, the PD, poverty, pollution index, climatic conditions, health care index and the mean level of vitamin D were analyzed in the top ten COVID-19 affected metropolitan cities of India against monthly cases of COVID-19 and related mortality to understand the factors influencing the transmission of SARS-CoV-2 and death caused by COVID-19.
| Materials and Methods|| |
We collected the monthly data of COVID-19 cases and number of mortalities from the ten most affected metropolitan cities of India as of 20 June 2020. The data were collected from February 20, 2020, to June 20, 2020, i.e., from no cases to around a month later after the onset of community transfer. At this stage at least 2000 confirmed cases from each of the selected cities were reported [Figure 1], and contact tracing became impossible, though officially community transfer was not declared. The numbers of COVID-19 cases started to increase exponentially, particularly in urban areas. To study the effect of climatic conditions on COVID-19 spread, we classified these cities according to their climatic zones as per Köppen climate classification. The selected cities lie in four different climatic zones of India: Mumbai, Chennai, Thane, and Kolkata fall in tropical wet and dry; Delhi, Ahmadabad, Pune, and Surat in semi-arid; Indore in humid sub-tropical, and Jaipur fall in arid zone.
|Figure 1: Status of COVID-19 in ten most affected metropolitan cities of India|
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A data extraction sheet has been prepared to summarize the data of confirmed SARS-CoV-2 cases and number of deaths taken from Government of India website (www.covid19india.org). The data on environmental factors, such as temperature (⁰F), rainfall (inches), humidity (%) and ultraviolet index (UVI) of the past 5 months of each selected metropolitan city were compiled from the official website; Meteorological Department Government of India and weather-ind.com, while pollution index, climate index, and health care index were taken from the Central Pollution Control Board (CPCB) and other reputed websites related to environmental pollution., The 5 years' data of the level of PM2.5 of each selected city has been compiled from the CPCB and from the published data in Tayade and Maji et al. The level of Vitamin D in the population living in these cities was compiled from various studies,,,,,,,,,, and mean level of Vitamin D calculated for the population inhabiting in particular climatic zone was used for the correlation and other statistical analysis. The data on poverty rate (PR) (%) and PD (people per mi²) were taken from Census of India, District human development report, Government of Tamil Nadu and NITI Aayog.
The abbreviations for different parameters are denoted as: Temperature = Temp, UV Index = UVI, Pollution Index = PI, Particulate matter2.5 = PM2.5, Poverty rate = PR, Population Density = PD, Health Care Index = HCI, throughout the manuscript.
Statistical analyses of data
The data of various parameters have been compiled by using an excel spreadsheet for different statistical analyses through SPSS 16.0 (IBM, Chicago, USA). Correlation matrices between the various climatic factors such as temperature (°C), UVI, rainfall (inches) and humidity (%); environmental factors, i.e., PM2.5 and pollution index; socioeconomic conditions i.e., PR, level of Vitamin D and health care index, PD and SARS-CoV-2 transmission, and COVID-19 induced mortalities were performed to know the influence of environmental factors and socioeconomic conditions on the spread of SARS-CoV-2 and mortalities. Principle component analysis (PCA) was performed to deduce how each of the variables i.e., temperature, UVI, rainfall and humidity; PM2.5, pollution index; PR, PD; health care index, and level of Vitamin D influence the total confirmed case and mortality. PCA estimates the correlation structure of the variables by finding hypothetical new variables (principal components [PC]) that account for as much as possible of the variance (or correlation) in a multidimensional data set. The new variables are linear combinations of the original variables. PCA helps to identify groups of variables (environmental factors) based on the loadings and groups of samples (total COVID-19 confirmed case, mortality) based on the scores. The correspondence analysis (CA) was also performed on different factors influencing the spread of COVID-19 and related mortalities. In this study, the association between the COVID-19 confirmed case and environmental variables was analyzed by CA using the SPSS 16.0. In CA, the axes of the ordination are directly derived under the condition that they are linear function of the environmental variables. CA is a correlative study where the environmental variables data are incorporated from the statistical model. Duncan's multiple range test and analysis of variance were performed to know the significance of the results for spread of SARS-CoV-2 and mortalities due to COVID-19 pandemic.
| Results|| |
Status of COVID-19 pandemic in India
The first case of COVID-19 in India was reported on January 30, 2020, from the state of Kerala with a travel history from Wuhan, China. There was no further report of infection until March 2, 2020, however by April 14, 2020, SARS-CoV-2 spread to each state and union territory of India, except Lakshadweep. At this stage the cases in most of the states have a travel history from the virus-infected countries, in a few states virus was carried through the secondary transmission, i.e., by the person having no travel history but in the direct contact with the infected person having a travel history. Despite strict measures taken by State Governments., until June 20, 2020, the number of cases in India shot up to over 0.4 million with 13,277 deaths with a global ranking of fourth after the United States, Brazil, and Russia in the list of worst-affected countries by the COVID-19 disease. At this stage, travel history and contact tracing were not possible. However, cases were still in clusters, mostly in metropolitan cities. More than 56% COVID-19 burden has been reported in ten metropolitan cities of India [Figure 1] and [Supplementary Table 1]. At this stage, several local factors may have played a role in rapid spread of disease and higher mortality in some cities/zone in comparison to others [Figure 2]a, [Figure 2]b, [Figure 2]c, [Figure 2]d. The metropolitan cities, i.e., Mumbai, Chennai, Thane, and Kolkata lying in tropical wet and dry climate zone had >32% cases of COVID-19 followed by Delhi, Ahmadabad, Pune, and Surat from semi-arid zone (>22%) then Indore from humid subtropical zone (>1%), and least cases (0.68%) were recorded from Jaipur, lying in arid zone [Supplementary Table 1]. Mumbai is the worst-hit city with more than 55% of the total cases of Maharashtra state, and >15% total cases of India, followed by Delhi (>13%) and Chennai (~10%) [Figure 2]a and [Supplementary Table 1]. The significant variation was also observed in the spread rate of COVID-19 in different metropolitan cities and their respective zones. The mean of spread rate was also high in tropical wet and dry zone in May (>92%) and June (>72%) followed by semi-arid zone >76% and >65% respectively, while it was >66% (May) and 36% (June) in humid subtropical zone and >64% (May) and 40% (June) in arid zone. A significant difference was found in the death rate caused by SARS-CoV-2, which was varied from 2.99 to 4.68 in four climatic zones [Figure 3]a and 0.62–6.51 among the cities [Supplementary Figure 1]. Kolkata, from wet and dry climate zone, has the highest mortality rate (7.13%), followed by Ahmadabad (7.06%) from semi-arid zone in June.
|Figure 2: Doughnut Pie Chart showing total cases of COVID-19 and total deaths in ten most affected metropolitan cities of India (a and b); and their climatic zone representation (c and d). The data till June 20, 2020 were compiled from Govt. of India website (www.mygov.in)|
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|Figure 3: COVID-19 induced mortality rate in four climatic zones (a); and mean level of Vitamin D (b) in the population inhabiting in these climatic zones of India. Data for number of severe acute respiratory syndrome corona virus-2 positive cases and number of deaths is taken from Government of India website (www.mygov.in), till June 20, 2020|
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Effect of poverty and population density on COVID-19 cases and deaths
The PR and PD are high in these ten metropolitan cities [Figure 4]a and [Figure 4]b in comparison to other cities of India. A strong correlation has been found between the incidence of COVID-19 (r = 0.915), related deaths (r = 0.901), and rate of poverty in each climatic zone. Similarly, PD of these four zones also has relatively strong positive correlation with COVID-19 cases (r = 0.842) and deaths (r = 0.811) [Supplementary Table 2]. The PR (10.31%) and PD (49,055.75 people/mi2) are maximum in the tropical wet and dry zone, where maximum COVID-19 cases have been reported. Furthermore, the semi-arid zone which has the second-highest PD (26,314.78 people/mi2) and PR (8.36%) also recorded high number of COVID-19 cases after tropical wet and dry zone. Although the PR of arid zone is higher than the humid sub-tropical zone, it has the lowest PD [Figure 4]a and [Figure 4]b and thus, the lowest COVID-19 cases and mortalities.
|Figure 4: Poverty rate (a) and population density (b) of four climatic zones of India|
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Effects of level of Vitamin D on COVID-19 cases and deaths
A significant variation has been found in the mean level of Vitamin D in the population inhabiting in these four climatic zones of India [Figure 3]b. A negative correlation was observed between mean levels of Vitamin D and the number of COVID-19 cases (−0.762), and mortality (−0.787). The mean level of Vitamin D in the Indian population is at deficient level [<20 ngml−1, [Supplementary Figure 2] and [Table 4]] in all zones, except HST zone, where the mean level of Vitamin D is ~24 ngml−1. The level of Vitamin D is far underneath the insufficient level in the populace of tropical wet and dry and semi-arid zone and thus, might be the reason for the higher number of COVID-19 cases accounted in these two climatic zones. While, in the case of health care index, there is no significant correlation exist with COVID-19 cases (0.098) and mortality (0.090) in different metropolitan cities falling in these four zones of India [Figure 5]b and [Supplementary Table 2].
|Figure 5: Level of particulate matter2.5 and pollution index (a), and health care index (b) of four climatic zones of India|
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Effects of climatic and environmental factors on COVID-19 cases and deaths
India is one of the most climatically diverse countries in the world, it has six major climatic subtypes viz. alpine, humid subtropical zone, tropical wet and dry, tropical wet, semi-arid, and arid as per Köppen system. Zone wise correlation analysis indicated that the total positive cases of COVID-19 and mortalities have strong positive correlation with humidity (r = 0.956 and r = 0.932, respectively), and pollution index [r = 0.992 and r = 0.994, respectively; [Supplementary Table 3]]. Albeit, the rainfall has significant positive correlation with humidity (0.922), but the relation with COVID-19 cases (0.801) and death (0.764) was nonsignificant [Figure 6]b. The tropical wet and dry climate zone having maximum, >32% and >38%, COVID-19 cases, and death respectively, where average humidity in last 5 months (February–June) was recorded >65% [Figure 6]c. While least cases and deaths were observed in HST and arid zone, ~1% COVID-19 cases and mortalities in India [Supplementary Table 1] and [Supplementary Table 4]. Metropolitan cities wise data analysis showed that ~27% COVID-19 cases and ~40% mortalities were found in the cities where average humidity was >70% in June [Supplementary Figure 3] and [Supplementary Figure 4]. In most of the metropolitan cities, UVI showed nonsignificant positive correlation with the number of COVID-19 cases and mortality [Supplementary Table 5], [Supplementary Table 6], [Supplementary Table 7], [Supplementary Table 8], except Chennai, where UVI showed a strong negative correlation between total cases (−0.978) and mortalities (−0.994) [Supplementary Table 5]. In Chennai city UVI was high since February 2020. Although the effect of temperature on COVID-19 cases was variable, but in general, cities having high monthly temperature have higher spread rate such as Delhi, where humidity is relatively low [Figure 6]a. A constant high temperature seems to inhibit COVID-19 spread rate. It is interesting to mention that the arid zone (Jaipur city) had maximum average temperature [~32°C, [Supplementary Figure 5]] in the month of June have lowest positive COVID-19 cases (2,797 only) in top ten affected cities of India. Further, the mean of UVI of all four zones in the last 5 months was ~10, but it has been increased from April 11 to June 12 in the arid zone [Figure 6]d.
|Figure 6: Changes in climatic factors: (a) Temperature, (b) Rainfall, (c) Humidity, and (d) ultraviolet index of four climatic zones of India. The monthly mean data of climatic factors from February to June 2020 were obtained from India Meteorological Department, Government of India (2020) except for ultraviolet index which was taken from weather-ind.com website|
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Principal component analysis for factors responsible for COVID-19 spread and related deaths
To understand the correlation between the COVID-19 cases and related deaths versus various factors, i.e., population index (PI), PR, PD, climatic and environmental conditions were performed and data transformed into a smaller set of composite indicators, uncorrelated variables called PC by PCA. From screen plot graph of Eigenvalues [Supplementary Figure 6], it can be seen that the first four PCs are enough to explain 85.3% of the pattern variation. The factor of PI, a confirmed case of COVID-19, mortalities, and PM2.5 were the major contributors to PC1 (34.8%), while the factor humidity, rainfall, and PD was the major contributor to PC2 (20.5%). Thus from PCA, it was observed that the confirmed case and mortalities were grouped together in PC1 along with PI and PM2.5. The PC2 which had a weak correlation with PC1 was dominated by rainfall, humidity, and PD [Figure 7]. In PC1, the highest score contributor was PM2.5 (0.311) followed by PI (0.295), while in PC2, the highest score contributor was humidity (0.322) followed by PR (0.270) and PD (0.195) [Supplementary Table 9].
|Figure 7: Principal component analysis depicting the overall impact of each factor responsible for spread of COVID-19 in four climatic zones of India. Abbreviations: Temp: Temperature, UVI: UV Index, PI: Pollution Index, PM2.5: Particulate matter2.5, PR: Poverty rate, PD: Population Density, Vit-D: Vitamin-D, HCI: Health Care Index|
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CA and PCA are both exploratory methods that attempt to explain the variance in a model into a low-dimensional representation. The PCA extracts which variables explain the largest amount of variance in the data set, whereas, the purpose of CA is to examine the associations amongst the variables. The CA could explain 7.2% of the relationship between COVID-19 case, mortalities, and other indicators to different climatic zone [Figure 8]. The association between the two is weak, but still highly significant as shown by the Chi-square test [Supplementary Table 10] and [Supplementary Table 11]. In the zone-wise comparison, the humid subtropical zone was the major contributor (0.405) to the first dimension; whereas, the A (arid) zone was the major contributor (0.335) to the second dimension and also equally contributing to the first dimension. In the environment factor-wise comparison, the major contributor to dimension-1 was the total confirmed case (0.616) followed by PD (0.343).
|Figure 8: Correspondence analysis showing the effect of climatic, environmental, and socio-economic factors versus total COVID-19 cases and mortalities in four climatic zones of India. Abbreviations: Temp: Temperature, UVI: UV Index, PI: Pollution Index, PM2.5: Particulate matter2.5, PR: Poverty rate, PD: Population Density, Vit-D: Vitamin-D, HCI: Health Care Index, TC: Total cases, TWD: Tropical Wet & Dry, SA: Semi Arid, HST: Humid & Subtropical, A: Arid zone|
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| Discussion|| |
SARS-CoV-2 has been spread in 203 Countries and Territories around the world. During the study period, the number of positive COVID-19 cases and mortalities has increased exponentially across the world, and India has the fourth-highest number of cases reported, despite the country being in a nationwide lockdown since March 25, 2020. Although nearly 66% of the Indian population lives in rural areas, large number of cases (>56%) reported so far were from the ten metropolitan cities falling in different climatic zones. The maximum COVID-19 cases have been reported from Mumbai, around >15% of total cases of India, followed by Delhi (>13%) and ~10% in Chennai. There could be several factors promoting the stability and spread of the virus. High PD and poverty in these metropolitan cities seem one of the important factors for disease spread due to limitations in following the social distancing and sanitation norms. A large proportion of population in these metropolitan cities lives in slums and faces a daily struggle to meet their basic needs. For instance, Dharavi in Mumbai is believed to be the largest slum in Asia, thus one of the most vulnerable places for COVID-19 spread due to the density of its population and poor sanitation. Delhi, Chennai, and Kolkata are also among the top 20 most populated cities in the world have also high number of COVID-19 cases.
Among the climatic conditions which may influence the survival and transmission of SARS-CoV-2, humidity and temperature are found to be most important in the current study. Other studies have also reported that humidity and temperature affect the infectivity and transmissibility of the SARS-CoV-2.,,,, Seasonal variations have been common in many acute infectious diseases such as influenza commonly occurring in winter season. However, in case of SARS-CoV-2, the findings of various epidemiological studies, carried out in different parts of world, are contrasting. Some studies found a negative relation between temperature and COVID-19 infection, conversely Xie and Zhu reported a 4.861% increase in the daily confirmed COVID-19 with each 1°C rise of temperature at mean temperature <3°C. Ma et al. found a positive association between daily deaths of COVID-19 and diurnal temperature range. Jüni et al. found no supporting evidence for the association of temperature with epidemic growth of COVID-19. Albeit most of the studies reported a negative correlation between COVID-19 cases/mortality and relative humidity.,,,, In the current study a positive correlation was observed between relative humidity and COVID-19 confirmed cases as well as mortality. Metropolitan cities; Mumbai, Thane, and Kolkata lying in tropical wet and dry zone, received >4 inches rainfall in 5 months and average relative humidity 67% with gradual increased from February to June (58%–78%) in comparison to other climatic zones, having a maximum number of COVID-19 cases. This was also confirmed by COVID-19 cases reported from Chennai where the rainfall was low (only 1 inch) and humidity decreased gradually till June (74%–59%) having less number of COVID-19 cases. Increase in temperature was also found to be weakly positively correlated with COVID-19 cases, a high monthly temperature range facilitated initial spread of COVID-19 cases. However, temperature >30°C had a negative effect. Temperature and sunlight can facilitate the destruction of SARS-COV-2 and the stability of it on surfaces as observed in Jaipur city, which falls in the arid zone, where maximum temperature (>32°C) and UVI (12) were reported in June 2020 and thus, minimum COVID-19 cases. Wang et al. reported that when the minimum ambient air temperature increases by 1°C, the cumulative number of cases decreases by 0.86%. Recent studies reported that temperature and relative humidity could have a significant impact on the incidence rate and transmission of SARS-CoV-2. The findings of the current study and earlier reports indicate that there may be an optimum temperature for maximum survival of virus. This could be a reason that studies carried out in colder climates found an increase in cases with increasing temperature while those carried out in warmer climates found contrasting results. While considering the transmission of SARS-CoV-2 virus and severity of disease, the effect of humidity should be considered together with temperature. Air humidity can influence in two ways; first, it may influence the persistence of droplets in the environment, thereby contributing to the spread of disease, and second, low-humidity levels are reported to damage the epithelial tissue and/or reduction of mucociliary clearance, which may result in the increase of mortality rates by COVID-19.
Respiratory diseases including SARS are more common in late winter and early spring,, the occurrence of COVID-19 might be subjected to environmental factors such as pollution index, level of PM2.5, nitrogen dioxide and have a positive correlation with COVID-19 confirmed cases in the cities and/or countries.,,, In this study, we have studied the correlation between level of PM2.5 and pollution index and COVID-19 confirmed cases in ten metropolitan cities of India and found that pollution index has a positive correlation with COVID-19 cases and mortality. Thus, the poor air quality and high level of PM2.5 [Supplementary Figure 7] further increases the susceptibility to respiratory infections. The air quality of these cities is fairly to severely poor. Delhi is having the worst air quality among all the metropolitan cities, with citizens exposed to “severely polluted” air for 10% of the time and to unhealthy conditions for 70% of the time. However, the percentage share of fine particulate toxic particles was highest in Mumbai as compared to Delhi, Pune, and Ahmadabad. Overall the absolute level of PM2.5 was much higher for Delhi followed by Kolkata and Mumbai [Supplementary Figure 7]. The annual average PM2.5 concentrations in Mumbai from 2000 to 2010 were 78.8 ± 42 μgm−3, and in this period PM2.5 concentration increased up to 24.6 μgm−3. Thus, high level of PM2.5 and pollution index has been found in tropical wet and dry and semi-arid zone [Figure 5]a. The high prevalence of co-morbidities in people living in urban areas is linked to high blood pressure, diabetes, and respiratory diseases. Such co-morbidities may significantly enhance the severity of COVID-19, and this might be a significant factor along with meteorological and socioeconomic factors for high occurrence of COVID-19 in these metropolitan cities and related mortalities.
This is the first statistically proven study on COVID-19 that high poverty, PD, climatic and environmental factors, such as humidity, rainfall, and pollution index facilitate SARS-CoV-2 transmission and influence the COVID-19 induced mortalities. The spread of COVID-19 has been maximum in Mumbai, having the highest PD, PR and humid climate, followed by Delhi and Kolkata. However, increasing temperature restrict the spread of SARS-CoV-2 in arid zone of India, as observed in Jaipur city. Therefore, poverty and high PD, when it is supported by humid climate and poor environmental conditions, provide a conducive environment for the spread of SARS-CoV-2 and mortalities in urban areas. Thus, SARS-CoV-2 transmits more at below 30°C temperatures and in humid conditions (>65%), although the cause and underlying mechanism are not well-known yet. This study would be helpful to the policymakers and stakeholders to device an improved action plan for the management of second and third waves of COVID-19 among the countries having diverse climatic conditions, high poverty, and PD.
Declaration of ethical approval and patient consent
Ethics committee approval and patient consent were not applicable for the retrospective study.
The authors are thankful to Director, CSIR-National Botanical Research Institute, Lucknow, for providing the infrastructure and laboratory facilities. SEED-DST, New Delhi, is acknowledged for the project grant (SEED/TIASN/2 018/74). UGC-Start up grant (F.30-505/2020(BSR)) to SM is also acknowledged.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Eslami H, Jalili M. The role of environmental factors to transmission of SARS-CoV-2 (COVID-19). AMB Express 2020;10:92.
Ilie PC, Stefanescu S, Smith L. The role of vitamin D in the prevention of corona virus disease 2019 infection and mortality. Aging Clin Exp Res 2020;6:1-4.
Sasikala T, Mukherjee B, Sahoo S, Sahoo AK. Vitamin-D deficiency as a predisposing cause for COVID19 morbidities. J Crit Rev 2020;7:2522-6.
Guo XJ, Zhang H, Zeng YP. Transmissibility of COVID-19 and its association with temperature and humidity. Preprint from Research Square [doi: 10.21203/rs. 3.rs-17715/v1]. PPR: PPR121851.
Huang Z, Huang J, Gu Q, Du P, Liang H, Dong Q. Optimal temperature zone for the dispersal of COVID-19. Sci Total Environ 2020;736:139487.
Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, et al.
Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Sci Total Environ 2020;724:138226.
Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang Y, et al.
Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ 2020;729:139051.
Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ 2020;724:138201.
Jüni P, Rothenbühler M, Bobos P, Thorpe KE, da Costa BR, Fisman DN, et al.
Impact of climate and public health interventions on the COVID-19 pandemic: A prospective cohort study. CMAJ 2020;192:E566-73.
Chakrabarti SS, Kaur U, Banerjee A, Ganguly U, Banerjee T, Saha S, et al.
COVID-19 in India: Are biological and environmental factors helping to stem the incidence and severity? Aging Dis 2020;11:480-8.
Finch WH, Hernández Finch ME. Poverty and COVID-19: Rates of incidence and deaths in the United States during the first 10 weeks of the pandemic. Front Sociol 2020;5:47.
Banerjee R. Wrath of nature on India's future: A critical analysis. Int J Soc Sci Interdiscip Res 2014;3:154-64.
Mohammad P, Goswami A. Temperature and precipitation trend over 139 major Indian cities: An assessment over a century. Model Earth Syst Environ 2019;5:1481-93.
Garg A, Chutke N, Ambulkar M, Aggarwal A. An environmental pollution study of Indian metropolitan cities and industrial surroundings by INAA. J. Radioanalyt Nucl Chem 1995;192:307-20.
Fund, U.N.P., 2007. UNFPA state of world population: unleashing the potential of urban growth. United Nations Population Fund, New York City, United States.2007;12-019.
Buhaug H, Urdal H. An urbanization bomb? Population growth and social disorder in cities. Glob Environ Change 2013;23:1-10.
Maji KJ, Dikshit AK, Chaudhary R. Human health risk assessment due to air pollution in the megacity Mumbai in India. Asian J Atmos Environ 2017;11:61-70.
Chen Y, Wild O, Conibear L, Ran L, He J, Wang L, et al.
Local characteristics of and exposure to fine particulate matter (PM2.5) in four Indian megacities. Atmos Environ 2020;5:100052.
Selvarajan S, Gunaseelan V, Anandabaskar N, Xavier AS, Srinivasamurthy S, Kamalanathan SK, et al.
Systematic review on vitamin D level in apparently healthy Indian population and analysis of its associated factors. Indian J Endocrinol Metabol 2017;21:765.
Khafaie MA, Rahim F. Cross-country comparison of case fatality rates of COVID-19/SARS-COV-2. Osong Public Health Res Perspect 2020;11:74-80.
India Meteorological Department: Ministry of Earth Sciences, Government of India; 2020. Available from: https://mausam.imd. gov.in
, [Last accessed on 2021 Aug 02].
CPCB (Central Pollution Control Board) (2020). National Ambient Air Quality Monitoring Programme (NAMP) Data Year wise. https:// cpcb. nic. in/ namp- data/
[Last accessed on 2021 Mar 09].
Tayade AY. Using seasonal variations and air quality index for ambient air quality at govt polytechnique premises, Kherwadi in Mumbai. Int J Stat Mathemtika 2015;13:32-7.
Marwaha RK, Tandon N, Reddy DR, Aggarwal R, Singh R, Sawhney RC, et al.
Vitamin D and bone mineral density status of healthy school children in northern India. Am J Clin Nutr 2005;82:477-82.
Marwaha RK, Tandon N, Shivaprasad C, Kanwar R, Mani K, Aggarwal R, et al.
Peak bone mineral density of physically active healthy Indian men with adequate nutrition and no known current constraints to bone mineralization. J Clin Densitom 2009;12:314-21.
Vupputuri MR, Goswami R, Gupta N, Ray D, Tandon N, Kumar N. Prevalence and functional significance of 25-hydroxyvitamin D deficiency and vitamin D receptor gene polymorphisms in Asian Indians. Am J Clin Nutr 2006;83:1411-9.
Ramakrishnan S, Bhansali A, Bhadada SK, Sharma R, Walia R, Ravikiran M, et al.
Vitamin D status and its seasonal variability in healthy young adults in an Asian Indian urban population. Endocr Pract 2011;17:185-91.
Agarwal N, Mithal A, Dhingra V, Kaur P, Godbole MM, Shukla M. Effect of two different doses of oral cholecalciferol supplementation on serum 25-hydroxy-vitamin D levels in healthy Indian postmenopausal women: A randomized controlled trial. Indian J Endocrinol Metab 2013;17:883-9.
Bhatt SP, Misra A, Sharma M, Guleria R, Pandey RM, Luthra K, et al.
Vitamin D insufficiency is associated with abdominal obesity in urban Asian Indians without diabetes in North India. Diabetes Technol Ther 2014;16:392-7.
Maisnam I, Dutta D, Mukhopadhyay S, Chowdhury S. Lean mass is the strongest predictor of bone mineral content in type-2 diabetes and normal individuals: An eastern India perspective. J Diabetes Metab Disord 2014;13:90.
Basu S, Gupta R, Mitra M, Ghosh A. Prevalence of vitamin d deficiency in a pediatric hospital of eastern India. Indian J Clin Biochem 2015;30:167-73.
Qureshi P, Meena RC, Husain J, Deshwar G, Maheshwari V, Yadav L. Study of prevalence of osteoporosis in males of above 40 years age group attending SMS Hospital, Jaipur. Int J Res Orthop 2017;3:837-40.
Census of India. Provisional Population Totals. New Delhi: Office of the Registrar General and Census Commissioner; 2011.
Ter Braak CJ. CANOCO – An extension of DECORANA to analyze species-environment relationships. Hydrobiologia 1989;184:169-70.
Subbaraman R, Shitole S, Shitole T, Sawant K, O'Brien J, Bloom DE, et al.
The social ecology of water in a Mumbai slum: Failures in water quality, quantity, and reliability. BMC Public Health 2013;13:173.
Berumen J, Schmulson M, Guerrero G, Barrera E, Larriva-Sahd J, OlaizG, Garcia-Leyva R, Wong-Chew RM, Betancourt-Cravioto M,Gallardo H, Fajardo-Dolci G (2020) Trends of SARS-Cov-2 infec-tion in 67 countries: role of climate zone, temperature, humidity andcurve behavior of cumulative frequency on duplication time.medRxiv. https://doi.org/10.1101/2020.04.18.20070920
Qi H, Xiao S, Shi R, Ward MP, Chen Y, Tu W, et al.
COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Sci Total Environ 2020;728:138778.
Wang J, Tang K, Feng K, Li X, Lv W, Chen K, Wang F. High temperature and high humidity reduce the transmission of COVID-19. arXiv preprint arXiv:2003.05003. 2020.
Barreca AI, Shimshack JP. Absolute humidity, temperature, and influenza mortality: 30 years of country-level evidence from the United States. Am J Epidemiol 2012;176 Suppl 7:S114-22.
Islam N, Shabnam S, Erzurumluoglu AM. Temperature, humidity, and wind speed are associated with lower COVID-19 incidence. MedRxiv 2020. [doi: 10.1101/2020.03.27.20045658].
Cui Y, Zhang ZF, Froines J, Zhao J, Wang H, Yu SZ, et al.
Air pollution and case fatality of SARS in the people's republic of China: An ecologic study. Environ Health 2003;2:15.
Lin K, Yee-Tak Fong D, Zhu B, Karlberg J. Environmental factors on the SARS epidemic: Air temperature, passage of time and multiplicative effect of hospital infection. Epidemiol Infect 2006;134:223-30.
Ogen Y. Assessing nitrogen dioxide (NO2) levels as a contributing factor to the coronavirus (COVID-19) fatality rate. Sci Total Environ 2020;726:138605.
Xu H, Yan C, Fu Q, Xiao K, Yu Y, Han D, et al.
Possible environmental effects on the spread of COVID-19 in China. Sci Total Environ 2020;731:139211.
Xu, K., Cui, K., Young, L.H., Wang, Y.F., Hsieh, Y.K., Wan, S. and Zhang, J. Air quality index, indicatory air pollutants and impact of COVID-19 event on the air quality near central China. Aerosol Air Qual. Res 2020;20:1204-21.
Zoran MA, Savastru RS, Savastru DM, Tautan MN. Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Sci Total Environ 2020;738:139825.
World Health Organization. Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group, Bonn, Germany 13-15 January 2003 (No. EUR/03/5042688). Copenhagen: WHO Regional Office for Europe; 2003.
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