Introduction

In late December 2019, an unprecedented global observation was repositioned in Wuhan city, Hubei province, China. An outbreak of a respiratory illness caused by an unidentified etiological agent with suspicious epidemiological associations to the local seafood market elicited unrest within the health authorities (Lu et al., 2020; “WHO|Pneumonia of Unknown Cause-China,” 2020a). Manifestations of variable severity, ranging from mild cough and fever to acute interstitial pneumonia and acute respiratory distress syndrome (ARDS), were identified as symptoms in the patients reported to the WHO China Country Office (Chen et al., 2020a, b; Ramanathan et al., 2020). January 7, 2020, marked the conclusion of the speculations, when the Chinese Center for Disease Control and Prevention (CDC) characterized novel Coronavirus (nCoV) as the causative virus of the pneumonia outbreak and tentatively named it nCoV2019. (“WHO|Novel Coronavirus-China,” 2020b; Johnson, 2020). The genomic and phylogenetic investigations unveiled striking sequence homology relating SARS-CoV-2 and bat CoV RaTG13 (96%) more than SARS-CoV (approximately 80%) and MERS-CoV (51%) (Chan & Kok, 2020; Xiantian et al., 2020; Zhou et al., 2020). To date, the bat is a potential natural reservoir of this virus (Zhou et al., 2020). Ensuing, the virus was renamed to SARS-CoV-2 by the International Committee of Taxonomy of Viruses. On 30th January, WHO reported a Public Health Emergency of National Concern (PHEIC) including 19 countries affected in the outbreak (Sohrabi et al., 2020). With the incessant evaluation of the outbreak, WHO declared COVID-19, the first-ever coronavirus pandemic, given the exponential trajectory exacerbated by the lack of a potential treatment (Shen et al., 2020). Geographically, the spread circumvents 213 countries with 9,295,365 cases reported with 478,289 fatalities as of 24th June 2020.

The SARS-CoV-2 is a non-segmented positive-sense RNA virus ranked under β-coronavirus, known to infect humans. Coronaviruses (CoVs) are single-stranded RNA-viruses of vertebrates infecting respiratory, gastrointestinal, hepatic, and central nervous systems of humans, livestock, birds, bat, mouse, and many other wild animals (Su et al., 2016; Weiss & Navas-Martin, 2005). Coronaviruses are not unheard of. Hitherto, human-susceptibility of 6 CoVs has been recognized, four with mild pathogenicity and minor symptom comparable to common cold while the other two who are gravely fatal (Kin et al., 2015). Studies assessed the basic reproduction number (R0) of SARS-CoV-2 to be around 2.2, or even more (ranging from 1.4 to 6.5) (Riou & Althaus, 2020; WHO, 2020).

Historically, the twenty-first century saw the sporadic emergence of two coronavirus outbreaks: the severe acute respiratory syndrome (SARS-CoV) epidemic to China in December 2002 with a fatality rate 9.3% and the Middle East respiratory syndrome (MERS-CoV) endemic to UAE in June 2012, with a mortality of 23.1% (Zulma et al., 2015; Song et al., 2019; Graham et al., 2013; Minhas, 2015; Su et al., 2015; Hui et al., 2018). These rates vary with the COVID-19 CFR (case fatality rate) of 2.6% (WHO, 2020; World Health Organization, 2020c). Despite the lower mortality rate, the community spread (human-to-human transmission) is a significant contributor to the frenzy of fear and worldwide panic (Chan et al., 2020; Li et al., 2020). In the succeeding Tatem and Lai’s model (Chen et al., 2020a, b; Lai et al., 2020) assessing non-pharmaceutical interventions amidst the Wuhan outbreak, governments across the globe prioritized principally early detection and isolation, ensued by limitations on population movement. The promising results of mass quarantine in China to dam the tidal corona wave led to nationwide lockdown or geographic quarantines across the globe. The world’s biggest economies like China, India, USA, Russia, France, Italy, UK, and Spain suspended public gatherings (leisure, religious, political, business, sports, educational), international and domestic travel, public transport and non-essential industries at different time dots of the pandemic with varying strictness (Lau et al., 2020; Pepe et al., 2020; Pulla, 2020). The duration varied from months to a few weeks. Albeit, this dark cloud definitely has silver lining of its own. Social lockdown not only reduced inter-personal interaction but a significant human–environment interaction de-escalated including the relentless anthropogenic exploitation of the entire biosphere. This resulted in some unexpected prosperity of the Earth. A relative drop as drastic as global carbon dioxide emissions in 2020 has not been reported since World War II (Rutz et al., 2020). The NO2 emission drop during the lockdown has been unparalleled. The decline in the noise of human activities is seen as a boon for geoscientists, studying the earthquakes and other seismic parameters. This work aims to measure the declines in the environmental pollutants (air and water) as catalyzed by the lockdown in the urban centers of India like Delhi, Karnataka, West Bengal, and Tamil Nadu.

Albeit global efforts, the exact predisposing factors contributing to higher incidences and death in certain geographical regions remain obscure. Individuals with comorbidities, including cardiovascular, chronic respiratory infirmities, diabetes, and cancer are at greater risk of exacerbated clinical severity and death (Verity et al., 2020). Several studies have established augmented respiratory and cardiovascular toxicity upon chronic exposure to unhealthy levels of air pollutants (WHO, 2018; Faustini et al., 2014). Hence, some countries have hypothesized and demonstrated that air pollutants correlate with COVID-19 transmission trajectory and mortality rate (Brandt et al., 2020; Shakoor et al., 2020; Conticini et al., 2020).

The expeditious augmentation of anthropogenic activities, including automobiles, industrial processes, and mining, has led to burgeoning the pollution mortality burden (Sharma et al., 2020). Continued exposure to transportation pollutants like nitrogen oxides can provoke inflammation and oxidative tension, likely to incite and exacerbate infirmities such as childhood asthma, lung cancers, chronic obstructive pulmonary disease, and diabetes (Guarnieri & Balmes, 2014; WHO, 2018; Achakulwisut et al., 2019; Strak et al., 2017). This has bolstered the hypothesis examination of a relationship between air pollution and COVID-19 (Conticini et al., 2020; Wu et al., 2020). Therefore, we further aimed to explore the relationship between air pollution exposure and COVID-19 mortality and infectivity in the major urban states of India.

Methodology

A literature survey was conducted using different databases including PubMed, Science Direct, Google Scholar, N-LIST (National Library and Information Services), and World Health Organization.

The level of air pollutants was analyzed using the real time monitoring data of Central Pollution Control Board (CPCB), India, for the states of Delhi, Tamil Nadu, Karnataka, and West Bengal. Delhi, Karnataka, Tamil Nadu, and West Bengal were chosen as they house the major metropolitan cities of India and were worst hit by the pandemic. The name and number of stations were chosen according to the full availability of data for the required period (Table 1). The filtering of air pollutants was done to NO2, PM 2.5, and PM10 and SO2. In this study, daily 24 h concentrations of pollutants, nitrogen-dioxide (NO2), sulfur dioxide (SO2), PM2.5, and PM10 were measured. The 24 h average concentration of AQI was also collected and compared to the 2019 mean levels. The time-period was divided into pre-lockdown (1st March 2020–22nd March 2020), lockdown (22nd March–1st June 2020) and unlock 1.0 (1st June–14th June 2020).

Table 1 Names of stations selected for the study in four states of India
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The mortality rate per million population and infectivity rate per million populations were retrieved through Institute for Health Metrics and Evaluation (IHME), Washington. To find the association between COVID-19 and environmental pollutants and COVID-19 mortality and infectivity rate, the data was analyzed for 1st April 2020 to 14th June 2020. Correlation analysis between AQI, NO2, and PM2.5 and SO2 levels was done using Pearson’s co-efficient. Spearman and Kendell correlation co-efficient were also calculated. Two-tailed ANOVA test was performed in calculating the p-value.

For the analysis of the water pollutants, the data from Uttarakhand Pollution Control Board (UPCB) was analyzed for 11 stations. The mean 2019 and 2020 levels were analyzed for the month of March and April 2020 in contrast to 2019 mean levels.

The relevant studies were explored using keywords: COVID-19, environment, positive impact, seismic noise, GHG, air pollution, telemedicine, Ganga, underwater noise, water pollution, and India.

Results and discussion

Air pollution and COVID -19

Stringent lockdown measures declared to stem the escalating virus spread resulted in a drastic decrease in the levels of air pollutants. The combined effects of a substantial reduction in surface transport, aviation, and electricity consumption lead to dramatic drops in daily global CO2 emissions. The following de-escalated by −17% (−11 to −25% for ± 1σ) by early April 2020 in contrast with the calculated average of 2019. This drop shows a direct dependence on the duration of the lockdown, with a minimum approximation of 4% if mid-June marks the return of pre-pandemic carbon-intensive cycles and a skyrocketing 7% if 2020 witnesses some restrictions throughout the year (Le Quéré et al., 2020). The decline rates could translate into limiting climate change to a 1.5 °C warming if the rates of decline remain maintained (United Nations Environment Program, 2019; Wigley et al., 1981). At this paradigm, government policies and economic incentives post crisis might determine the global CO2 emission trajectories for decades to come.

Air pollution levels over Northern India plummeted to a marveling 20-year-low amidst the lockdown period (Gautam, 2020). Of the 103 cities in India, 21 are amongst the world’s 30 most polluted cities, which recorded improved air quality throughout the lockdown (Bedi et al., 2020; Ginzburg et al., 2020; IQAir, 2019). Our results from the real time monitoring data from CPCB of the air pollutants in the states’ housing the 4 major metropolitan cities in India, namely, Delhi, Karnataka, Tamil Nadu, and West Bengal demonstrated noteworthy drops of −31.3513%, −34.3513%, −32.6367%, and −29.2515%, respectively, in contrast to mean 2019 AQI averages (Fig. 1a−d). Amongst the four states, Delhi observed the highest AQI levels (Fig. 1e).

Fig. 1
figure 1

Effect of lockdown on air pollution. a The weekly average AQI levels of Delhi during the period of 7th March 2020–14th June 2020 vs 7th March 2019–14th June 2020. b The weekly average AQI levels of Chennai during the period of 7th March 2020–14th June 2020 vs 7th March 2019–14th June 2020. c The weekly average AQI levels of Bengaluru during the period of 7th March 2020–14th June 2020 vs 7th March 2019–14th June 2020. d The weekly average AQI levels of Kolkata during the period of 7th March 2020–14th June 2020 vs 7th March 2019–14th June 2020. e The intercity comparsion of AQI levels of the 4 megacities: Delhi, Bengaluru, Chennai, and Kolkata

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Our results from the interstate analysis of the real time monitoring air pollutant data from all the available stations in the CPCB database reveal an average concentration de-escalation of PM2.5 level by − 52.83%, −46.82%, −44.68%, and −37.69% in Delhi, Karnataka, Tamil Nadu, and West Bengal (Fig. 2). PM2.5 and PM10 can potentially damage the lungs, heart, and brain and have exacerbated the mortalities attributed to air pollution. The percentage decrease of PM10 in all the four states was drastic, with West Bengal showing the greatest plummets (Fig. 2c). This can be attributed to the fact that the monitored stations are located within the industrial hubs of West Bengal. Preliminary investigations also indicate increased COVID-19 mortality rates with greater exposure to air pollutants, mainly PM2.5 (Wu et al., 2020). Further investigation burgeoning further insights of this relationship is required in the Indian subcontinent. Furthermore, we observed tremendous NO2 declines in daily averages by −40.5, −46.69 −32.69, and −58.01 in Delhi, Karnataka, Tamil Nadu, and West Bengal (Fig. 2d). Our results are coherent with other studies (Vadrevu et al., 2020). The abated air pollution levels may translate into reducing the burden of air pollution mortalities, weighing down a load of one quarter of all adult deaths from cardiac disorders, one quarter from stroke, 43% from chronic obstructive pulmonary disease, and 29% from lung cancer (WHO, 2018). Air pollution triggers the premature deaths of 7 million people globally (WHO, 2019).

Fig. 2
figure 2

Effect of lockdown on air pollutants. a Pre-lockdown vs lockdown daily averages of PM2.5, PM10, NO2, and SO2 from the real-time monitoring data from CPCB in Delhi. b Pre-lockdown vs lockdown daily averages of PM2.5, PM10, NO2, and SO2 from the real-time monitoring data from CPCB in Karnataka. c Pre-lockdown vs lockdown daily averages of PM2.5, PM10, NO2, and SO2 from the real-time monitoring data from CPCB in West Bengal. d Pre-lockdown vs lockdown daily averages of PM2.5, PM10, NO2, and SO2 from the real-time monitoring data from CPCB in Tamil Nadu

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The TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) calculations quantified an unprecedented −40% drop in NO2 levels over Chinese cities during the lockdown. Wuhan, the epicenter of the outbreak, saw a −60% drop, hitting the rock-bottom in the last 15 years (Bauwens et al., 2020). The European cities witnessed parallel NO2 decline of −27% and −25% between the two instruments. Milan (Italy) showed a maximum de-escalation of −38%, outranking the consolidated reduction of −20% shown by emission regulations and the European economic recession spanning the 2004–2010 period (Bauwens et al., 2020; Castellanos & Boersma, 2012). Studies have attributed accelerated diffusion of COVID-19 to local air pollution levels in parts of Europe, especially NO2 and ozone (Coccia, 2020). Ogen revealed NO2 exposure as the leading contributor to COVID-19 fatalities (Ogen, 2020). Moreover, NO2 is the single leading source of childhood asthma in urban areas across the globe (Achakulwisut et al., 2019).

The USA also saw a decrease of −24%– −28% in NO2 level. Fuel combustion, being the chief anthropogenic cause of NOx, attributes to approximately 65% of total NOx emission globally. Therefore, the key driver of the plummet reduced traffic levels. In China and the USA, road traffic drops of −81% and −45% were observed (Schuman, 2020). With surface transport reckoning a significant portion of the slide in emissions, the surface transportation sector could be an invaluable subject of policy changes. Compliant to social distancing, mandating active travel policies could help to condense CO2 emissions (Kissler et al., 2020). The de-escalated pollution levels is a reprieve for 90% population of the world that breathes polluted air, as according to the WHO (World Health Organization, 2018). Being able to measure the effect of every anthropogenic activity profoundly on the air pollution levels on one hand, the significant results of the applied lockdown will form the basis to reform the world pollution policies to minimize the concentration of air pollutants on a long-term basis.

The differential air pollutant concentrations might play a pivotal role in not only the transmission of COVID-19 but might also be one of the reasons responsible for the variations in the viral trajectory of different states. Table 2 displays the empirical results of the association between COVID-19 (infectivity and mortality) and AQI across the four major states of India. Delhi and Tamil Nadu showed strong positive correlation (p-value < 0.005), whereas West Bengal and Karnataka showed weak negative correlation. This might be attributable to higher mean levels of AQI in Tamil Nadu and Delhi than in other study states. These two areas recorded the highest weekly average concentration (μg/m3) of nitrogen dioxides.

Table 2 Pearson co-efficent b/w AQI and infectivity and mortality rates
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Furthermore, Table 3 represents the different correlation co-efficient for COVID-19 infectivity and mortality for all the air pollutants (NO2, PM10, SO2, PM2.5) and for all the Indian study states combined. Our findings show that COVID-19 mortality and infectivity show significantly positive correlations with all the air pollutants in all the three correlation co-efficients. AQI showed the highest correlation with mortality and infectivity rate in all the three correlation co-efficient. PM2.5, PM10, and NO2 showed positive association with the COVID-19 transmission in Pearson’s co-efficient. While in Spearman’s and Kendal’s co-efficient, we found that NO2 showed strong positive association and PM 2.5 and PM10 showed weak positive correlations.

Table 3 Correlation co-efficents b/w daily average concentrations of AQI, PM10,PM2.5, NO2, SO2 and COVID-19 infectivty and mortality rates
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A combined correlation plot for the four states indicated significant (p < 0.001) positive correlations with all the five air quality indicators. The highest Pearson’s co-efficient was observed for AQI (0.48) while the least was for SO2 (Fig. 3).

Fig. 3
figure 3

The correlation plot between COVID-19 infectivity, mortality rates, and major air pollutants

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Fig. 4
figure 4

Effect of lockdown on water pollution. a 2019 vs 2020 monthly averages comparison of DO for 11 stations in Uttrakhand. b 2019 vs 2020 monthly averages comparison of total coliform levels for 11 stations in Uttrakhand. c 2019 vs 2020 monthly averages comparison of BOD levels for 11 stations in Uttrakhand. d 2019 vs 2020 monthly averages comparison of COD levels for 11 stations in Uttrakhand

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Bilal et al. (2020) analyzed and reported that O3, PM2.5, and NO2 were positively associated with COVID-19 cases and deaths. An investigation from Thailand purported that CO, PM10, NO2, O3, and SO2 significantly increased the risk of respiratory diseases (Phosri et al., 2019). Travaglio et al. (2021) showed significant spatial correlation between NO2 and the COVID-19 deaths and infectivity in England. The study further corroborated PM2.5 as a major contributor to the COVID-19 trajectory in England by establishing that a 1-m3 increase in long-term PM2.5 was responsible for 12% increase in COVID-19 cases. An investigation also purported that the severity of COVID-19 outbreaks in Northern Italy could be due to the PM concentrations, one of the highest in Europe (Setti et al., 2020). In Italy, Bernardini et al. (2020) also demonstrated the putative links between air pollutants (CO, NO2, O3, and PM) and COVID-19 and reported significant positive correlation. These evidences bolster our investigation and suggest that COVID-19 transmission and severity might spread through environmental pollutants.

Our study may have certain limitations. Primarily, we investigated the association between COVID-19 mortality/infection and air pollutants, but our findings are not a complete representation due to the inclusion of only four Indian states. Therefore, studies which could include all the Indian states might be beneficial. Future studies should address these limitations.

Noise pollution and COVID-19

Seismic noise: the golden silence

Seismic noise is the vibration in the Earth’s crust caused by a multitude of natural and anthropogenic activities (Brune & Oliver, 1959). With the pandemic in force, the anthropic noise, usually detected as a near-continuous signal or background noise at the seismometers, came to a halt (Boese et al., 2015; McNamara & Buland, 2004). Seismometers recorded sharp declines in the seismic noise. Royal Observatory in Belgium observed 30–50% drops in ambient seismic noise from the levels at the beginning of the year (Mallapaty, 2020). In Northern Italy, an almost 50% energy minimization in the frequency spectrum of 1–10 Hz was reported (Poli et al., 2020). China saw a ~4–12 dB attenuation in the 1–8 Hz frequency band (Xiao et al., 2020). Countries like the USA, Spain, and Mexico witnessed similar slopes. This unprecedented phenomenon on a global scale was an extraordinary opportunity to analyze the anthropogenic seismic network elaborately, which in history was far-fetched. During the interlude of March to May 2020, Lecocq et al. reported a 50% plunge in the global median hiFSN (Lecocq et al., 2020). The data indicates the most extensive and coherent declines in seismic noise documented in the chronicles. A significant correlation between alterations in seismic noise and population mobility emphasizes seismology offering real-time monitoring for human activity patterns (Lecocq et al., 2020; Lindsey et al., 2020), moreover, offering fewer privacy concerns over mobility data tracking.

A stunning 80% plummet in the seismic plots of some cities in Nepal was published (https://www.nationalgeographic.com/science/2020/04/coronavirus-is-quieting-the-world-seismic-data-shows). These declines turned out unequivocally significant to geoscientist. In urban centers, the anthropic signal degrades the capacity of seismometers to identify transient signals from earthquakes and volcanoes, hindering their ability to predict geological hazards (Ashenden et al., 2011). This detection predicament is graver in populous areas, where the spectrum of anthropogenic noise interferes with earthquake signal spectra. The heightened stillness that elicited quarantine measures is crucial for seismologists to study fainter earthquakes that slipped past their instruments previously (Ross et al., 2019). Accurate detection of fainter earthquakes in the high-risk earthquake zones like Los Angeles will be a boon. Detection of tremors from potential volcanic unrest that was concealed by anthropogenic noise could be detected (Boese et al., 2015). The lately identified signals could form the potential distinct templates for locating comparable waveforms in noisier data amidst the pre- and post lockdown period. Better detection and understanding of ocean waves and volcanic eruptions could unlock research doors.

The reduced seismic graph gives unparalleled clarity on the coherent contribution of human behavior on seismic noise and Earth’s crust. With the exponential increase in population, exposure to potential, natural, and induced geohazards will develop (Mccall et al., 1998). The swift urban wave will undulate high tides of anthropogenic noise, complicating seismic monitoring efforts. Therefore, the opportunity that the pandemic presents to characterize anthropogenic noise will facilitate its minimization in the future, enabling seismic signatures of harmful subsurface jeopardizes with greater accuracy.

Noise pollution and health consequences

“One day man will have to fight noise as fiercely as cholera and pest” are the famous words of Robert Koch. Long-term and short-term exposure to elevated levels of environmental noise is concerned with diabetes, poor sleep, anxiety, noise-induced hearing loss (NIHL), and heart diseases (Clark and Paunovic, 2018; van Kempen et al., 2018). According to a recent study in the European Heart Journal, a 34% augmentation in risk for heart attack, heightened aerial inflammation, and stroke was associated with every 5 decibels increase in environmental noise (Osborne et al., 2020). Clutching 58 million people worldwide, one of the most predominant irreversible occupational hazards is NIHL (Heiberger & Holland, 2015). About 290 million people in Europe alone are exposed to unhealthy levels of noise. While the world stayed indoors, drastic reductions in noise pollution were recorded. According to CPCB, Delhi witnessed drastic drops as low as 50 db (A) in contrast to unprecedented high records of 100 db (A) in the pre-lockdown period (Ibrar & Gandhiok, 2020). Indian metropolitan cities observed an overall drop of 15–25%. The reduced trends were noted in parts of Europe and USA as a period of seismic silence (Asensio et al., 2020; Basu et al., 2020; El País, 2020; Smith et al., 2020). The reduced burden of vehicular road was the fundamental contributor of the same, indicating the need for sustainable stringent transportation strategies in order to meet the WHO permissible levels. The amount of reduction can be extrapolated to determine the amount of behavioral shifts necessary to regulate permissible levels.

Under water noise pollution: the paradigm of stress to marine life by anthropogenic activities

Although imperceptible, underwater noise (UWN) pollution is an emerging environmental concern that will undulate through oceans with an expected 87–102% escalation by 2030. The mounting tide of human maritime activities, with shipping as the ambassador, is accountable for this acoustic pollution (Kaplan & Solomon, 2016).

Following the global lockdown, oceans witnessed a breach from the clatter of anthropogenic activities. A new study using near real-time ocean noise sensing observatories (Venus and Neptune) declared large negative trends in the first quarter of 2020. A 1.5-dB drop in year-over-year weekly noise, both in the Inland waters and deep ocean of Canada’s Pacific West coast at low frequencies (100 Hz) was reported (Thomson & Barclay, 2020). This is roughly a 25% decrease in power. Reduced ship traffic due to a 14% decline in import–export at the coast is the key contributor to the result.

Marine fauna relying on acoustic signaling as one of the most efficient ways of communication for their vital biological processes benefited the most. The significant overlap between the low-frequency spectrum of shipping traffic noise and acoustic signals (2-200 Hz) interferes with this communication leading to habitat displacement, behavioral changes, and altered foraging patterns in the organisms (Clark et al., 2009; Nowacek et al., 2007; Tyack, 2008). A study stated the reduction of stress-related fecal hormone metabolites in whales following the decline in the underwater noise after the 9/11 episode (Rolland et al., 2012). This decline could be a reprieve to whales, crustaceans, and marine mammals communicating in the same frequency spectra. The data is indicative of an oceanographic noise environment closer to that of 150 years ago. The scientists across the globe foresee the hushed oceans as a once in a lifetime opportunity to listen to the marine fauna and study the effects of anthropogenic induced noise on the same and in a profound way.

Coronavirus: a vaccine for the Indian waters

While 1.3 billion people adhered to community quarantine to curb the spread of the COVID-19 outbreak, at Haridwar, Ganga’s water quality was ranked as fit for drinking (class A) by UPCB (Dutta et al., 2020). The analysis of the water characteristics of 11 station in Uttrakhand from the UPCB database revealed a tremendous −71.94570136, −61.32596685, and −77.94676806 decrease in BOD and COD, Total coliform levels and an 11.75213675 raise in the average DO levels in contrast to 2019 levels (Fig. 4). The industrial shutdown and improved air quality could be the main contributors to these noteworthy improvements.

Hitherto, since the foundation of Uttrakhand, this extraordinary phenomenon was unheard-of. CPCB’s real-time water quality monitoring system (RTWQMS) recorded suitability for bathing (class B) for an enormous 75% stations, installed at the chief channels of the Ganga River (Singhal, 2020). Former reports enunciated that most of the monitoring stations recorded water quality inappropriate for bathing (Kamboj & Kamboj, 2019). The research also affirms unprecedented declines in fecal coliform and nitrate concentration, increase in DO (3–20%) (Dutta et al., 2020). With 4 million annual deaths attributed to water and sanitation in India (Francis, 2011), the health expenses credited to water pollution in India are INR 470–610 billion (US $6.7–8.7 billion) each annum (World Bank, 2013). The improved hallmarks might translate into a meaningful reduction of overall risk of water-related mortalities and infirmities, which are a direct consequence of unhealthy water characteristics and globally might qualify the burden of 1.5 million children under the age of five, who succumb to water-related diseases, and 485,000 annual diarrheal deaths (UNICEF, 2006). The steep decline in the industrial discharge of untreated wastewater coupled with increased rains was the ambassador of this significant improvement, as the discharge of domestic sewerage was unaltered. This emphasizes the influential impact of untreated or semi-treated commercial–industrial wastewater on water quality, which is about 650 million liters per day (Trivedi, 2010). The optical depth analysis of the Indo-Gangetic Plains by NASA highlighted a 20-year-plumet in aerosol. The assessment gives the government regulators unequivocal proof to stringent up the policies for untreated wastewater. The media conglomerate delineated it as an unparalleled, swift redemption of the holy river that was unheard off in the preceding three to four decades Shukla, 2020; Ani, 2020). Achieving this markedly improved water quality, a few weeks of lockdown outshone what governmental regulation and polices undertook in decades. The Nizamuddin Bridge, a major effluent hotspot of the Yamuna River, observed enormous abatements in the BOD (90%) and COD (82%) levels (Arif et al., 2020). Nine sites recorded an improvement of 37% in water quality index during the lockdown period. In contrast to the pre-lockdown phase, the biological oxygen demand, fecal coliform and chemical oxygen demand de-escalated by 42.83%, 40%, and 39.25%, respectively (Patel et al., 2020). The Yamuna, alias Ganges is the most polluted river worldwide and the largest tributary of river Ganga. Additionally, Yamuna’s Secchi disk depth, marked a 42-year high (Katariya, 2020). The increasing depth could be a ventilator for marine flora. The TDS concentration in the Dwarka river significantly reduced from 2457 to 987 mg/l amidst the lockdown (Mandal & Pal, 2020). The marked improvements in the Beas waters resuscitated the Baes Conservation Reserve with one of the rarest mammals worldwide, the Indus river dolphins (Kumar, 2020). The pandemic has impelled a necessary rethink on the rejuvenation of Ganga waters and flagged some fundamental responses:

  1. 1.

    Redefining stringent regulations and enforcements for treatment and discharge of industrial wastewaters

  2. 2.

    Escalating the pace of the project implementation and exhaustive monitoring and assessment

  3. 3.

    Designing specific strategies for urban centers and hotspots

The swift improvements in water qualities in the urban centers enunciate the unequivocal attribution of anthropogenic activities and an opportunity to reflect.

Conclusion

The study includes the variations in the environmental pollutants (AQI, NO2, SO2, PM2.5, and PM10) in the lockdown period of the four major urban states of India. The findings emphasize that the anthropogenic halt during the lockdown period significantly reduced the concentrations of environmental pollutants in all the study states. Among selected pollutants, AQI, NO2, and PM2.5  demonstrated significant, postive corelations with COVID-19 mortality and infectivity. Furthermore, this investigation contributes towards a better comprehension in the contribution and role that restricted anthropogenic activities illustrate in the reduction of environmental pollutants, thereby, further addressing the speculations on the implementation of partial lockdowns to ensure healthy improved environmental characteristics. Consequently, aiding the establishment of a sustainable environment, inarguably, the most indispensable and paramount challenge of the twenty-first era.