Impact of lockdown during the COVID-19 outbreak on multi-scale air quality.

One of the multi-facet impacts of lockdowns during the unprecedented COVID-19 pandemic was restricted economic and transport activities. This has resulted in the reduction of air pollution concentrations observed globally. This study is aimed at examining the concentration changes in air pollutants (i.e., carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matters (PM2.5 and PM10) during the period March-April 2020. Data from both satellite observations (for NO2) and ground-based measurements (for all other pollutants) were utilized to analyze the changes when compared against the same months between 2015 and 2019. Globally, space borne NO2 column observations observed by satellite (OMI on Aura) were reduced by approximately 9.19% and 9.57%, in March and April 2020, respectively because of public health measures enforced to contain the coronavirus disease outbreak (COVID-19). On a regional scale and after accounting for the effects of meteorological variability, most monitoring sites in Europe, USA, China, and India showed declines in CO, NO2, SO2, PM2.5, and PM10 concentrations during the period of analysis. An increase in O3 concentrations occurred during the same period. Meanwhile, four major cities case studies i.e. in New York City (USA), Milan (Italy), Wuhan (China), and New Delhi (India) have also shown a similar reduction trends as observed on the regional scale, and an increase in ozone concentration. This study highlights that the reductions in air pollutant concentrations have overall improved global air quality likely driven in part by economic slowdowns resulting from the global pandemic.

Introduction

The rapid spread of Coronavirus Disease (2019) (COVID-19) is having profound human health, environmental, social, and economic impacts worldwide. On March 11, 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic, and by December 31, 2020 there were over 112 million cases globally, and over 2.49million people had lost their lives (Johns Hopkins University and Medicine, 2020). The novel coronavirus that causes COVID-19 has been linked to animals and was reportedly transmitted to humans in Wuhan, China, in December 2019 (Chen et al., 2020).

The US Centers for Disease Control and Prevention (2017) estimates that three-quarters of new or emerging diseases that infect humans originate in animals. Globally, the outbreaks of infectious diseases are on the rise (Smith et al., 2014) and are likely to become more common as human populations destroy habitats, forcing wildlife into closer proximity to humans. The World Health Organization (WHO) team recently completed its fact-finding investigation in China regarding the origins of the COVID-19 pandemic. Their report concluded that the virus probably originated in bats and passed to people through an intermediate animal (Mallapaty, 2021). Human well-being and planetary health are inextricably linked. Some air quality improvements have also been noted as countries have locked down to prevent the spread of Coronavirus (Venter et al., 2020); but ozone mean concentrations increased at urban stations compared to the same period (Sicard et al., 2020; Allu et al., 2021). However, COVID-19's impact on the global atmospheric environment needs examining. This will allow linking air quality changes to human health. The impact of reduced air pollution and greenhouse gas emissions and energy consumption will be temporary unless governments adopt new approaches to development that protect both the health of the planet and those that inhabit it. This will require more stringent regulations and, ultimately, the transition to clean energy (e.g. supporting renewables and energy efficiency).

Air pollution is known to weaken the immune system, compromising people's ability to fight off infection, thus severely adversely effecting COVID-19 impact (The European Public Health Alliance, 2020). The SARS outbreak in China (2003), infected patients in areas with higher air pollution were 84% more likely to die than in less polluted areas (Cui et al., 2003). The research in the US suggests that air pollution has significantly worsened the COVID-19 outbreak and led to more deaths than if pollution-free skies were the norm. Moreover, recent research suggests that atmospheric transport of air pollution (i.e. fine particulate matter (PM2.5) particles) may be acting as vehicles for viral transmission. For example, an increase of just 1 μg per cubic meter of PM2.5 is associated with an 8% increase in the COVID-19 death rate (Wu et al., 2020) as well as 9.4 more COVID-19 cases, 3.0 more hospital admissions, and 2.3 more deaths (Cole et al., 2020). WHO data shows that 9 out of 10 people are exposed to elevated concentrations of pollutants, therefore, the WHO is working with countries to monitor air pollution and improve air quality.

As countries scale up responses to COVID-19, an opportunity exists to align with the proposed redefined values of development, which embrace a safer planet and a promise of improved health and environment for all. As we become more aware of our dependence on the environment, governments must focus on effective science-policy interface or changes in policy, which is informed by science. Once we emerge from lockdown it will be into a new and uncertain world with a serious need for air pollution and climate change curve still to flatten. We thus need policies not for the short term, but for the long term. Possible solutions include raising fuel efficiency standards for the transport sector and supporting zero-emission vehicles; promote mass transit in urban areas as well as cycling and pedestrian activities; reducing air pollution and greenhouse gas emissions in the electricity and agricultural sectors by supporting renewables and energy and nutrient use efficiencies and adopt a suite of other policies in the manufacturing, industrial, and agricultural sectors that also reduce both air pollution and greenhouse gases.

Surface measurements made at more than 800 monitoring stations show that the mean levels of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in northern China have decreased by approximately 29 ± 22% and 53 ± 10%, respectively, after the lockdown following the COVID-19 outbreak of early 2020; while simultaneously, the ozone (O3) concentration, a secondary pollutant responsible for severe health problems, has increased by a factor 1.5–2 (Shi and Brasseur, 2020). Satellite NO2 data show substantial decreases by 40% on average over Chinese cities due to lockdown measures against the coronavirus outbreak; while Western Europe and U.S. display robust NO2 decreases in 2020, 20–38% relative to the same period in 2019 (Bauwens et al., 2020). Venter et al. (2020) provide similar results (after accounting for the effects of meteorological variability) for reductions in tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations (in 34 countries). Elsewhere, Dantas et al. (2020) and Siciliano et al. (2020) indicated a reduction of carbon monoxide (CO) and NO2 in Rio de Jainero and São Paulo, Brazil during the partial lockdown. Their study also found that in the same period O3 concentrations increased. The reduction between 9% and 69% in CO, NO2, and PM were also observed in Northern South America (Mendez-Espinosa et al., 2020). In Saudi Arabia, Anil and Alagha (2020) conducted their study on air pollutant concentrations before and after the lockdown. They found that, excluding O3, air pollutant concentrations reduced between 6% and 86%. Tobias et al. (2020) looked at the changes of NO2, black carbon (BC), PM10, and O3 concentrations during the lockdown in Barcelona, Spain, and found a similar pattern observed in other locations, i.e., reduction in all but O3 concentrations. Moreover, daily global CO2 emissions decreased by −17% (−11 to −25% for ±1σ) by early April 2020 compared with the mean 2019 levels (Le Quéré et al., 2020). Kumari and Toshniwal (2020) examined the global impact of COVID-19 on the air quality based on ground-based data from 162 monitoring stations from 12 cities across the globe. The concentration of PM2.5, PM10 and NO2 were reduced by 20–34%, 24–47% and 32–64%, respectively, owing to restriction on anthropogenic emission sources during lockdown. However, SO2 concentration level showed a mixed trend during the lockdown phase. For few cities like Lima, Madrid, Moscow, Rome, Sao Paulo and Wuhan, SO2 concentration remained unchanged in lockdown phase as the main emission source of SO2 is power plants, which remained operational at most of the location.

The main objective of this study is to determine the impact on air quality (after accounting for meteorological variations) due to the measures taken globally related to the coronavirus- COVID-19 -outbreak using both satellite and ground-based measurements in the United States, India, and Europe. Furthermore, city-wide case studies for New York City (USA), Milan (Italy), Wuhan (China) and New Delhi (India) were also conducted. Satellite and ground-based measurements were analyzed for March and April 2020, with data from China also being analyzed for February 2020, then compared against ground-based measurements and satellite retrieval data for the same months from 2015 to 2019. The compared five-year period was used as the baseline for air quality not influenced by the lockdowns. The purpose of the inclusion of this period is two-fold: (1) to normalize the impacts of meteorological factors to the atmospheric concentrations of air pollutants; (2) to narrow down the case studies to locations that have long-term measurements and data-series. By means of normalizing the meteorological influences and narrowing down the case studies, this study attempts to corroborate a closer examination on the lockdown effect rather than to generalize the findings by incorporating multiple locations with a shorter data coverage.

Satellite measurements revealed how air pollution has fallen dramatically in cities across the world due to COVID-19 lockdown measures. Ground level measurements confirm these observations.

Data & methodology

Changes in CO, O3, PM2.5, and PM10 were analyzed using ground-based measurements. Changes in NO2 were evaluated using a combination of ground-based measurements and satellite-based measurements. For the purposes of the current study, NO2 concentrations were particularly amenable to analysis using satellite data, since motor vehicle emissions of NO2 were affected by the shutdowns and also amenable to detection on a regional scale. SO2 is also measured by satellite, but emissions of this gas are primarily from point sources, industrial factories and electric power generation stations. Elevated concentrations of SO2 are localized around these sources, and the sources were not as strongly affected by the shutdowns as motor vehicles. Satellite measurements of O3 measure a combination of stratospheric and tropospheric O3. Variations in stratospheric O3 make it difficult to evaluate changes in tropospheric O3. Satellites are also used to measure aerosol optical depth (AOD), which is often used as a surrogate for particulate matter. However, AOD represents a combination of natural and anthropogenic sources, and transport. Therefore, only NO2 was evaluated using satellite data.

Satellite data for NO2 were obtained from the OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column Level 3 (https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary). The daily data have a 0.25 ° × 0.25 ° resolution with a global spatial coverage, while the temporal coverage for this data is from January 2015 to April 2020. Based on the temporal average, this dataset is divided into two timeframes. The 2015–2019 timeframe is the reference period and the 2020 timeframe are designated as the analyzed period. All daily data in the 2015–2019 timeframe were averaged to generate a single baseline as the reference period. For the analyzed period, the 2020 timeframe was averaged by each month in the timeframe (i.e., January, February, March, and April 2020) and was compared to the reference period (for the same months) to determine the change of NO2.

Ground-based measurements data from Europe were obtained from the European Environment Agency (https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm) for the same coverage period as the NO2 satellite dataset. The pollutants of interest are CO, NO2, O3, PM2.5, PM10, and sulfur dioxide (SO2). For uniformity, European countries that are contributing to the dataset must follow a reporting template. However, the data availability for each country is subject to the type of instruments or measurements used in collecting the data. The data for ground-based measurements from India were downloaded from the Central Control Room for Air Quality Management, the Indian Ministry of Environment, Forest, and Climate Change (https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing). The collected pollutants data and timeframe analysis are similar to the European dataset. However, due to limitation of data availability there were only two stations, both located in the New Delhi region that have a complete temporal coverage. These stations are Mandir Marg and Shadipur. Finally, the ground-based measurements for the United States were obtained from the United States Environmental Protection Agency's AirData website (https://www.epa.gov/outdoor-air-quality-data), which primarily contains data from the Air Quality System (AQS) database. While data for CO, NO2, SO2, O3, PM2.5, and PM10 are all available from this data source, only O3 and PM2.5 measurements were available for the study period and are therefore the only pollutants utilized in this work. The analysis for these ground-based datasets follow a similar approach as the satellite dataset.

Meteorological influences on the air pollutant datasets used in this study were filtered out by two means. Firstly, the utilization of a five-year period baseline rather than 1–3 years provided a more robust normalized meteorological effects on air pollutant concentrations. That is a minimized bias toward prevalent meteorological conditions occurred in a shorter timeframe. Secondly, a statistical approach following a method developed by Rao and Zurbenko (1994) was performed to filter out the concentration variations influenced by meteorology. This method resulted in slight changes in air pollutant concentrations between −1% and 3%. These changes have been applied in the datasets used in the analysis.

Results

Global NO2 emissions in March and April 2020

On a global scale (Table 1 ), the impact of lockdowns on air quality can be examined from NO2 tropospheric column loadings during March and April 2020. Nitrogen oxides (NOX = NO2+NO), which is emitted by fossil fuel combustion (i.e. automotive exhaust) amongst other sources, is responsible for air quality degradation in urban/industrialized centers. It leads to the photochemical formation of tropospheric ozone (NRC 1991; Uttamang et al., 2018; Uttamang and Hanna., 2020), and is a precursor of secondary inorganic aerosols, which have consequences for climate and human health (Myhre et al., 2013; Lelieveld et al., 2015; Monks et al., 2015; Battye et al., 2017, 2020; Atkinson et al., 2018). As shown in Fig. 1 A and B, the major global contributors for NO2 are China, India, USA, and Europe, where most highly urbanized areas are located (Bechle et al., 2011). When these concentrations are compared against the 5-year average loadings for the respective month (Fig. 1C and D), these regions indicate a reduction of at least 10%. Depending on when the lockdowns were implemented; and the magnitudes of reduction vary from one region to another. In China, massive reduction of NO2 concentrations in March 2020 was observed primarily in the eastern part of the country. In April 2020, however, much of this region saw an increase in NO2 after the lockdown measures were relaxed. Elsewhere, western, southern, and central Europe featured with NO2 reduction in both March and April 2020. The reduction occurred more prominently in April 2020 when most European countries-imposed lockdowns and travel restriction. When comparing NO2 tropospheric column loadings in March and April in 2020 with loadings observed in March/April 2015–2019, reductions of NO2 in landmass were observed at 9.19% and 9.57% for March and April 2020, respectively (Table 1). North America saw the highest reduction in March 2020 with 16.20% lower than the average; while the highest reduction in Europe occurred during April 2020 (17.83% NO2 loadings reduced). Even though, there is overall average reduction in NO2 during lockdown, there is variation between countries in the magnitude of change after accounting for meteorological variations.

Table 1

Global changes in tropospheric column NO2 between corresponding months in the base period (2015–2019) and 2020.

	Average tropospheric column loading in 2015–2019 (μmol m−2)	Average tropospheric column loading in 2020 (μmol m−2)	Change (%)
March
Total Globe	6.83 ± 0.25	6.31	−7.6
Total Land (exc. Antarctica)	14.97 ± 0.67	13.60	−9.2
Oceans	4.04 ± 0.11	3.83	−5.3
April
Total Globe	6.55 ± 0.18	6.09	−7.1
Total Land (exc. Antarctica)	14.30 ± 0.45	12.93	−9.6
Oceans	3.88 ± 0.09	3.74	−3.6

Fig. 1

Global tropospheric NO2 column loadings with 30% cloud screened, presented in μmol m−2 during March and April 2020. Top panel figures show NO2 observed in (A) March 2020, and (B) April 2020. Figures on the bottom panel illustrate the difference of NO2 loadings in (C) March 2020, and (D) April 2020 when compared against the average loadings for the respective month in the period 2015–2019.

Regional air pollutants concentrations during lockdowns (Europe, USA, China, and India)

When comparing ambient concentrations of pollutants across Europe for March and April 2020 against the average concentrations of each respective month for 2015–2019 (Fig. 2, Fig. 3 ), ambient concentrations of NO2, PM2.5, particulate matter (PM10) and carbon monoxide (CO) were all below average in both March and April (Table 2 ). Ambient concentrations and satellite retrievals of NO2 were, on average, 32% and 22% lower, respectively, then average in March 2020; and 34% and 26% lower than average in April 2020. PM2.5 concentrations in March 2020 were 15% lower than average, when compared against ambient concentrations in March 2015–2019, but then by April, PM2.5 concentrations in Europe were only 6% lower than normal, suggesting an increase in emission sources. Similarly, PM10 and CO concentrations went from 10% below average in March to 2% and 4%, respectively, below average in April 2020. Ambient concentrations of SO2 were 10% lower in March 2020 than in March 2015–2019 but then ambient concentrations in April 2020 were 0.4% higher than average when compared against previous years. The drastic reductions, particularly in March 2020, can likely be attributed to the mandatory stay at home orders that began in March. However, O3 concentrations were 5% and 7% above average in March and April, respectively, when compared against concentrations from 2015 to 2019. Sicard et al. (2020) observed the daily O3 mean concentrations increased at urban stations in Europe by 24% in Nice, 14% in Rome, 27% in Turin, 2.4% in Valencia. The reduction in NO2, PM and CO can likely be primarily attributed to the reductions in automotive traffic and industrial activity associated with lockdown measures taken to contain COVID-19. Because SO2 is a product of the combustion of coal and diesel combustion, it is possible these activities were considered essential, and therefore changes in these activities were slight.

Fig. 2

Percent changes of gaseous pollutants in March 2020 (left figures) and April 2020 (right figures) measured over ground-based monitoring stations in Europe. The pollutants are (A, B) carbon monoxide (CO); (C, D) nitrogen dioxide (NO2); (E, F) ozone (O3); and (G, H) sulfur dioxide (SO2). The changes are based on the concentration difference between the period 2015–2019 and 2020 for respective month.

Fig. 3

Percent changes of particulate pollutants in March 2020 (left figures) and April 2020 (right figures) measured over ground-based monitoring stations in Europe. The particulate pollutants are (A, B) PM10, and (C, D) PM2.5. The changes are based on the concentration difference between the period 2015–2019 and 2020 for respective month.

Table 2

Continental changes in tropospheric column NO2 between corresponding months in the base period (2015–2019) and 2020.

	Average tropospheric column loading in 2015–2019 (μmol m−2)	Average tropospheric column loading in 2020 (μmol m−2)	Change (%)
February
Asia	20.53 ± 0.81	16.22	−21.0
March
Africa	8.43 ± 0.22	8.10	−4.0
Asia	22.41 ± 1.43	19.99	−10.8
Australia	8.42 ± 0.21	7.67	−8.9
Europe	25.62 ± 3.44	24.74	−3.5
North America	13.11 ± 0.61	10.99	−16.2
Oceania	7.90 ± 0.19	7.11	−10.0
South America	6.87 ± 0.26	6.81	−0.8
April
Africa	8.76 ± 0.34	8.25	−5.8
Asia	20.53 ± 0.81	18.90	−7.9
Australia	8.00 ± 0.16	7.68	−4.1
Europe	25.31 ± 1.78	20.80	−17.8
North America	12.75 ± 0.73	10.86	−14.8
Oceania	6.66 ± 0.13	5.71	−14.3
South America	6.81 ± 0.25	6.61	−2.9

In Europe (Habibi et al., 2020) showed that the NO2 response was site-specific; Lyon-France (−46% in April), followed by Milan-Italy (−18% in April); however, Berlin-Germany (−1% in March and April) had the lowest reduction levels during these three months. Milan-Italy, one of the worst COVID-19-hit European cities, had a moderate reduction in NO2 (−18%) compared to Lyon-France. The increase in O3 is believed to be attributable to the complex relationships among O3 precursors and meteorological conditions. O3 is a secondary photochemical pollutant, produced by reactions of nitrogen oxides (NOX), including NO2 and nitric oxide (NO), volatile organic compounds (VOCs) and other precursors. We have shown that NO2 emissions were decreased during the study period. Any change in NO2 emissions is accompanied with a similar change in NO, as the two are emitted together. In fact, NO emissions comprise the bulk, 90–95%, of NOX emissions (EPA inventory documentation). In the short term, NO can destroy ozone, while in the longer term, NO is converted to NO2 and catalyzes O3 production. This rate of production of O3 process is dependent on the magnitudes of NO and NO2 in comparison with VOCs and other O3 precursors. Therefore, this increase in O3 concentrations is mainly explained by an unprecedented reduction in NOx emissions leading to a lower O3 titration by NO. Ozone concentration is also strongly dependent on meteorological conditions, with O3 formation enhanced by higher temperatures. Thus, O3 concentrations are generally higher in summer than in spring. It is not known whether NOX emission reductions would have increased O3 concentrations in Europe if these reductions had occurred in summer rather than in Spring.

In the US, the average concentrations of NO2, PM2.5, CO, SO2 and O3 for March and April 2020 were compared against that average concentrations for the respective months in 2015–2019. NO2, both from measurements and from satellite observations were 22% and 15% lower, respectively, than average in 2020 (Goldberg et al., 2020). Similarly, concentrations of SO2 and PM2.5 were 35% and 6% lower, respectively, when compared against previous years. In contrast to this, concentrations of CO and O3 were, on average, 7% and 11% higher than the average concentrations from 2015 to 2019 (Fig. 4 ). By April 2020, satellite observations of NO2 were 19% lower than average while concentrations of PM2.5 and O3 were 3% and 15% above average. Like Europe, these changes in ambient concentrations are likely due to lockdown measures taken during mid-March across the US. The observed increase in ozone, shown in Fig. 4, is slightly more prominent in the US than in Europe due to the southern areas of the US observing much warmer temperatures in March and April, which is favorable for ozone formation.

Fig. 4

Percent change of O3 in March and April 2020, observed in ground-based monitoring stations across the contiguous USA as a departure from the average concentrations in the base period.

Satellite observations of NO2 were also observed at a continental scale. Europe saw the largest reduction in NO2 column loading in April (18%) when compared against the base years, while South America saw the smallest reduction (−0.79%) in March. The average reduction of NO2 over all continents except Antarctica was about 14–15% in March and April. This is consistent with the reduction in CO2 emissions from combustion estimated by Le Quéré et al. (2020) during this period. While there was an overall global reduction, the magnitude of the reductions can likely be attributed to restrictions placed upon each country during this period. Because COVID-19 did not reach South America until late February (compared with December 2019 in the US and January 2020 in Europe), restrictions were not as strict during this study period when compared against other continents and countries. For example, São Paulo, Brazil, is the largest city in Latin America, with a population of 12,252,023 people. Nakada and Urban (2020) study during similar time period, indicated the pollution of the megacity Sao Paula observed averaged concentrations of PM10 increased and PM2.5 decreased by about 9% and −0.3% respectively; while gaseous pollutants between pre and during lockdown reduced by about NO2 (−22%), CO (−30%). The maximum concentration of O3 increased about (+11% overall variation).

A study by Shi and Brasseur (2020) using data collected from ground-based monitoring stations during lockdowns in China found that PM2.5 and NO2 concentrations have decreased by about 35% and 60%, respectively. Similarly, a study by Venter et al. (2020) using a network of >10,000 air quality stations in 34 countries found that PM2.5 and NO2 concentrations decreased by about 31% and 60%, respectively. These reductions were attributed to the reduced economic and transport activities in major cities. Like the US and Europe, their study also observed an increase by a factor 1.5–2 in O3 concentrations during the same period.

In India, Sharma et al. (2020) investigated the changes in air pollutant concentrations in 22 cities during the period March 16 to April 14, 2020. Their finding indicated reductions in PM2.5, PM10, CO, and NO2 by 43%, 31%, 10%, and 18%, respectively. An increase in O3 by 17% was observed in their analysis, suggesting these cities underwent the same pathway of O3 production and its precursors (explained earlier). Moreover, Mahato et al. (2020) study during similar time period, indicated the pollution of the megacity Delhi observed substantial reduction of the particulate matter pollutants averaged concentrations for PM10 and PM2.5 by about −52% and −53% respectively; while gaseous pollutants between pre and during lockdown reduced by about NO2 (−53%), CO (−30%), SO2 (−18%), and NH3 (−12%). However, the 8 h average daily maximum concentration of O3 increased about (+1% overall variation).

Case studies on changes of pollutant concentrations at city level

The change in pollutants, both satellite measurements of NO2 and ground measurements of PM2.5, PM10, CO, NO2, SO2 and O3, at the city level were also examined. Four major cities were chosen in this analysis: New York City (USA), Milan (Italy), Wuhan (China), and New Delhi (India). Fig. 5 A shows the concentrations of pollutants in New York as a fraction of the average concentration of the base period (2015–2019) on a monthly temporal scale. The monthly analysis shows concentrations of PM2.5, NO2, SO2 and CO declining (90 ± 5%, 36 ± 8%, 40 ± 35%, and 10 ± 3%, respectively) in the study period. In New York, NO2 satellite observations were 51% and 28% lower, on average, in March and April 2020, respectively, when compared with the baseline average column loadings for the given month from 2015 to 2020. In contrast, ambient measurements of SO2 show concentrations increasing by 10 ± 10% during the period. In Milan, concentrations of PM, NO2, CO and SO2 all drastically declined (22 ± 19%, 40 ± 10%, 30 ± 30%, and 30 ± 30%, respectively) between January and February before increasing through the end of the study period (Fig. 5B). In contrast, O3 concentrations were above average (18 ± 8%) compared with previous years. In Milan, NO2 satellite observations were 55% and 49% lower, on average, in March and April 2020, respectively, when compared with the baseline average column loadings for the given month from 2015 to 2020. The concentrations of pollutants from January to April of 2020 as a fraction of the average concentration in the base period for Wuhan, China, were also observed on a monthly scale (Fig. 5C). On a monthly scale, the concentrations of CO declined through the period, while concentrations of SO2 and O3 steadily increased. In contrast to this, PM and NO2 concentrations initially decreased before starting to increase in March/April. In Wuhan, NO2 satellite observations were 62%, 50% and 18% lower, on average, in February, March and April 2020, respectively, when compared with the baseline average column loadings for the given month from 2015 to 2020. The concentrations of pollutants from January to April of 2020 as a fraction of the average concentration in the base period for New Delhi, India, were also observed on a monthly scale (Fig. 5D). The average monthly concentrations of CO increased through March before dropping in April, while concentrations of NO2, PM and O3 all peaked in February before declining, with all pollutants, except SO2, at a minimum in April. In New Delhi, India, NO2 satellite observations were 24% and 54% lower, on average, in March and April 2020, respectively, when compared with the baseline average column loadings for the given month from 2015 to 2020. The reduction in CO, PM and NO2 concentrations can likely be primarily attributed to the mandatory shut down of non-essential personnel. Because the timings of the mandatory quarantine periods varied city to city, the timelines for changes in the concentrations also vary. The increase in O3 can be attributed to the reduction of NO emissions from automobiles and industrial activities. Similarly, the increase in NO2 and decrease in O3 concentrations toward the end of the study period can be attributed to a likely increase in NO emissions which then react with the O3 to create NO2. SO2 concentrations sharply increase through the period toward the end of the study period. A potential cause for this sharp increase in emissions could potentially be from an increase in truck and air traffic associated with shipping. As stores remained closed through the lockdown process, online sales skyrocketed.

Fig. 5

2020 monthly average concentrations of PM2.5, PM10, NO2, O3, CO and SO2 in New York City, USA (A), Milan, Italy (B), Wuhan, China (C) and New Delhi, India (D) as a fraction of the average concentration in the base period (the vertical bars present ± 1 standard deviation).

Fig. 6 provides the relationship for New Delhi, India, and Milan, Italy, between the NO2 observations during maximum photochemical activity (11.00 a.m.–6.00 p.m.) versus maximum 8 h O3 concentrations during the same time window (also reflecting maximum photochemical activity) for March and April for 2015 to 2019 (before lockdown i.e. Fig. 6 A and C), and 2020 (during and after lockdown i.e. Fig. 6 B and D). Before the lockdown in New Delhi (Fig. 6 A), the ozone concentration increases with increasing NO2 (the information on the emissions of VOCs is not available) (NRC 1991). However, in Milan (Fig. 6 C) the concentration of surface ozone decreases with increased concentration of NO2. When the nitric oxide (NO) concentrations are large, NO released in the atmosphere from fossil fuel combustion reacts with ozone to form NO2. After lockdown, Fig. 6 B and D we observe a reduction in NO2 leads to an increase of the ozone concentration. When the lockdown kicked in New Delhi and Milan, nitrogen dioxide levels plummeted as the automotive traffic reduced, but ozone levels rose. Nitric oxide, which is also in traffic exhaust reacts with ozone to produce NO2 (NO + O3 → NO2 + O2). This happens almost instantaneously. In effect ozone is being “converted” into nitrogen dioxide in equal measure, so that the total of both gases (“OX”) remains about the same (Sillman et al., 1990; Monks et al., 2015; Shi and Brasseur, 2020). However, it is important to note that there is a lot of variability and uncertainty in the data because of the influence of other variables (e.g. measurement uncertainty, meteorology, transport of background ozone and its precursors, etc.).

Fig. 6

Scatterplot depicting relationships between NO2 and O3 measured in April at (A, B) New Delhi, India, and (C, D), Milan, Italy. Figures on the left panel show the relationships during the period 2015–2019 while figures on the right panel are for 2020. (NO2 concentrations are averaged values from 11.00 a.m. to 6.00 p.m. i.e. during the time of photochemical activity; and O3 concentrations are maximum 8-h value during the same time window – reflecting maximum photochemical activity).

Conclusions

We incorporate 5-years of data into our calculation of the baseline prior to the COVID-19 lockdowns. The compared five-year period was used as the baseline for air quality not influenced by the lockdowns provides a more robust analysis. The purpose of the inclusion of this period is two-fold: (1) to normalize the impacts of meteorological factors to the atmospheric concentrations of air pollutants; (2) to narrow down the case studies to locations that have long-term measurements and data-series. By means of normalizing the meteorological influences and narrowing down the case studies, this study attempts to corroborate a closer examination on the lockdown effect rather than to generalize the findings by incorporating more locations with a shorter data coverage. Moreover, incorporating a short baseline period may result in a bias inherited from prevalent meteorological conditions during that period. By introducing a longer baseline period, this bias may be minimized. This is evident from the suggested examination of meteorological effects on air pollutant concentrations that indicates meteorology has a small influence on the changes of air quality during the lockdowns (−1%–3% across all species). Ground based measurements of SO2, CO, PM2.5, PM10, O3 and NO2 were examined across Europe and for four major cities: Milan (Italy), Wuhan (China), New York City (USA) and New Delhi (India). Ground based measurements of PM2.5 and O3 concentrations in the continental United States were also analyzed for study period; and satellite measurements of NO2 column density were examined at a global and regional scale. The results of this study (based on satellite observations) showed that NO2 column loadings were generally lower than normal on both a city-wide, regional, and national scale. Ambient concentrations of CO, NO2, PM2.5 and PM10 were generally lower than normal to normal, with certain cities observing higher than normal conditions in Europe over the study period. In contrast to this, O3 concentrations were generally higher than normal, which can be attributed to lower than normal emissions of NO due to restrictions placed on industrial activities and travel. Therefore, this increase in O3 concentrations is mainly explained by an unprecedented reduction in NOX emissions leading to a lower O3 titration by NO. Similarly, PM2.5 concentrations in the United States were primarily below average or around average for much of the US while O3 concentrations were slightly above average.

Ambient concentrations of each pollutant were also examined on a city-wide scale. Ambient concentrations of PM2.5, PM10, NO2 and CO in each city generally declined in association with quarantine orders placed upon the states, while SO2 and O3 concentrations generally increased. The only city that did not see an increase in O3 concentrations through the study period, which can potentially be attributed to meteorological conditions in New Delhi. These changes in the concentrations of these pollutants can likely be, in part, attributed to the reductions in traffic and industrial activity associated with lockdown measures taken to contain COVID-19. The increase in O3 concentrations can likely be attributed to a reduction in NO emissions, which thus reduces the reaction of NO with O3 to form NO2. In some regions (e.g. India and China), SO2 concentrations increased through the period which can likely, in part, be attributed to an increase in the combustion of coal to heat houses and electricity production.

Based on the results of this study as well as the results of Shi and Brasseur (2020), Venter et al. (2020), and Bauwens et al. (2020), it is evident the lockdown orders associated with COVID-19 have had a profound impact on our atmospheric environment. With the reduction in industrial activities and residential automobile activity, the ambient concentrations of PM, CO, NO2 and, in some cases, SO2 were much lower than they normally are likely due to a combination of lower emissions and meteorological conditions. In contrast to this, O3 concentrations were higher than normal due to the reduction in emissions from traffic. It is important to note that these changes in ambient concentrations are not permanent and it is not possible at this time to determine any long-term impacts of this reduction in emissions on air quality at a global, regional and urban scales. However, lack of preparedness for the coronavirus only highlights the need for a long-term air pollution and climate change strategy requiring transitioning to clean-energy (i.e. supporting renewables and energy efficiency). Additionally, this does not mean poor air quality should return. It too, should be made history.

CRediT authorship contribution statement

Casey D. Bray: initial draft preparation, reviewing and editing. Alberth Nahas: Data curation, Formal analysis. William H. Battye: Data curation, Formal analysis, reviewing and editing. Viney P. Aneja: response to reviewers, reviewing and editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.