Gantuya Ganbat This email address is being protected from spambots. You need JavaScript enabled to view it.1, Halim Lee  2, Hyun-Woo Jo3, Batbayar Jadamba4, Daniel Karthe  This email address is being protected from spambots. You need JavaScript enabled to view it.2,1,5

1 Engineering Faculty, German-Mongolian Institute for Resources and Technology (GMIT), Nalaikh, Ulaanbaatar, Mongolia
2 United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), Dresden, Germany
3 Department of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
4 National Agency for Meteorology and Environmental Monitoring, Ministry of Environment and Tourism, Ulaanbaatar, Mongolia
5 Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, Germany


Received: May 14, 2022
Revised: July 10, 2022
Accepted: August 18, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220196  

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Cite this article:

Ganbat, G., Lee, H., Jo, H.W., Jadamba, B., Karthe, D. (2022). Assessment of COVID-19 Impacts on Air Quality in Ulaanbaatar, Mongolia, Based on Terrestrial and Sentinel-5P TROPOMI Data. Aerosol Air Qual. Res. 22, 220196. https://doi.org/10.4209/aaqr.220196


HIGHLIGHTS

  • Impacts of COVID-19 on air pollution in Ulaanbaatar are studied.
  • Terrestrial and satellite data were used.
  • NO2 concentrations decreased by up to 45% during the strict-lockdowns.
  • Ground measurements agree with satellite measurements on NO2.
 

ABSTRACT


The study aims to reveal the impact of three sequential strict-lockdowns of COVID-19 measures on the air pollutants including NO2, SO2, PM10, and PM2.5 in Ulaanbaatar, Mongolia during November 2020–February 2021 based on air quality network and satellite data. Based on measurements of automatic air quality sites in Ulaanbaatar, we found a substantial decrease in NO2 (up to 45%), PM10 (72%), and PM2.5 (59%) compared to the same periods in the previous five years. On the other hand, up to a threefold increase in SO2 concentration was seen. Compared to 2015–2020, the number of days exceeding the national air quality standard level of NO2 decreased by 55% during November 2020–February 2021. A similar trend was observed for PM10 and PM2.5 (30% and 14%, respectively). Conversely, days exceeding the national air quality standard level of SO2 increased by 58%. The third strict-lockdown exhibited significant reductions in pollutant concentrations. The percentage exceeding the national standard level for NO2, PM10, and PM2.5 constituted 23%, 50%, and 67% during the lockdown periods while it was 89%, 84%, and 91%, respectively, for the same periods in the previous five years. Even though Sentinel 5P-TROPOMI data do not fully reflect the above findings, they add valuable insights into the spatial pollution pattern during strict-lockdown and non-lockdown periods. The study demonstrates that measures taken during the strict-lockdown periods clearly influenced the values of daily patterns of NO2, PM10, and PM2.5 concentrations. On the contrary, it is important to note that SO2 concentration increased during the last two winter months after 2019.


Keywords: Air pollution, Strict lockdown, COVID-19, Ulaanbaatar


1 INTRODUCTION


COVID-19 and air pollution are linked in at least two ways. On the one hand, lockdowns often led to reductions in traffic and industrial production, and thus a reduction in air pollution. On the other hand, exposure to air pollution has been identified as a factor that aggravates the course of COVID-19 infections.

 
1.1 Impacts of COVID-19 on Global Air Pollution

The lockdowns introduced in about 140 countries to slow down the spread of COVID-19 constitute the largest quarantine policy in the history of public health (Liu et al., 2021a), and led to unprecedented declines in land and air transportation and economic activities (Venter et al., 2021). The following lockdown measures potentially contributed to changes in air quality: (1) internal travel restrictions (domestic and within certain cities); (2) international travel restrictions; (3) closure of public transport systems; (4) (partial) shutdown of industry; (5) stay-at-home requirements and (6) contact limitations, e.g., through closure of shops, kindergartens, schools and universities, cancellation of events, restrictions on meetings and gatherings (Liu et al., 2021a). At the global level, daily PM2.5, PM10, SO2, NO2, and CO concentrations decreased during lockdown periods. The greatest reductions (23 to 60%) were observed for NO2, mostly due to restrictions on intracity travel, and for PM (by 7% to 45%). Reductions were almost negligible for SO2, which is typically emitted by industry and energy production, and O3 concentrations were slightly higher during lockdowns as compared to reference periods (Liu et al., 2021a; Venter et al., 2021). In Italy, the worst-affected country during the first wave of the pandemic, strict lockdowns were imposed from March 2020 onwards. While NO2 concentrations dropped remarkably in cities throughout Italy (by 25% to 59%), PM2.5 concentrations mostly decreased but partially also increased, presumably due to increased domestic heating (Gualtieri et al., 2020). In the UK, the implementation of a lockdown at the end of March 2020 induced an abrupt reduction in NO2, NO, and NOx at urban roadside monitoring stations, but a gradual return of traffic offset more than half of the reductions by summer 2020 (Ropkins and Tate, 2021). Menut et al. (2020) cautioned that different meteorological conditions and a general decrease in air pollution in western Europe make direct comparisons difficult. Nevertheless, through a modelling approach that considered meteorology and general air pollution trends, the authors confirmed that NO2 strongly and PM moderately decreased due to the lockdowns (Menut et al., 2020). In the US, Son et al. (2020) showed that in 10 states, regions with high baseline levels of air pollution experienced the largest air quality improvements following travel and other restrictions.

Improvement in air quality due to lockdowns could also be noticed in Asian countries. Many countries in the region, particularly India and China, experienced a strong rise in air pollution as a consequence of their economic development that went along with increasing fossil fuel consumption in industry and transportation (Smith et al., 2001; Streets et al., 2003; Zhao et al., 2008; Smith et al., 2011; Chin et al., 2014). The core findings of case studies from East and Central Asia are summarized in Table 1. Changes in pollutant concentrations in the table have been rounded to whole numbers. It is important to note that the term “lockdown” refers to a wide range of restrictions, including those in mobility (up to curfews) and partial to full shutdowns of industrial production. Governments have also introduced different terminologies to refer to such periods, including COVID19 control policies, COVID-19 emergency responses, movement control orders, large-scale social restrictions and shutdowns. As the exact character of lockdowns and their implementation has often not been documented in detail, and papers are based on different numbers and durations of lockdown events and reference periods, the information provided should be considered as indicative values for the range of changes observed across Asia. In addition, it should be noted that a direct comparison between the different data is not meaningful since the studies did not only differ in methodologies, but also regarding the lockdown periods (timing, duration, and strength of the lockdown) and the reference periods against which the reductions have been calculated. Nevertheless, data from all countries, regions, and cities show lockdown-related reductions in air pollution.

Reductions in air pollution were observed not only over land but also in close proximity to pollution sources. Griffith et al. (2020) showed that long-range transport (LRT) of air pollutants from East Asia was approximately 50% less than during normal periods, especially from China. For South East Asia, Kanniah et al. (2020) reported a 27% to 30% reduction in NO2 outflow over oceanic regions.

 
1.2 Impacts of Air Pollution on COVID-19 Pathogenesis

Even though the combined health impacts of air pollution and COVID-19 are not yet fully understood, several recent studies showed that air pollution appears to aggravate the risks related to COVID-19 infections (Srivastava, 2021). This is plausible since air pollution is known to be the cause of several respiratory illnesses such as chronic obstructive pulmonary disorder (COPD), cancer, and infections of the lung, but also to compromised immune systems (Gupta et al., 2021). A study in the Netherlands showed that PM2.5, NO2, and SO2 concentrations in 355 municipalities correlated positively with registered COVID-19 cases, hospital admissions and deaths due to COVID-19 (Cole et al., 2020). Exposure to air pollution has in general been found to increase viral infections of the respiratory tract (Travaglio et al., 2021; Conticini et al., 2020). According to Setti et al. (2020), particulate matter can actually be a carrier of the COVID-19 virus. Research on urban air pollution in cities of China, India, Indonesia, and Pakistan revealed that long-term exposures to high air pollution, particularly to PM2.5, negatively impacted the outcomes of COVID-19 infections. The mortality rate associated with COVID-19 is significantly correlated with PM2.5 (p < 0.05), which is responsible for most of the air pollution-related deaths in the world (Gupta et al., 2021). Copat et al. (2020) found that PM2.5 and NO2 concentrations were more closely correlated to the spread and lethality of COVID-19 than PM10. Filippini et al. (2021) showed for two regions of Italy that NO2 concentrations correlated positively with COVID-19 severity. In a study on COVID-19 mortality in 66 administrative regions in Italy, Spain, France, and Germany, Ogen (2020) found 78% of the deaths to have occurred in those five regions that had the highest NO2 concentrations and concluded that long-term exposure to this pollutant may be one of the most important contributors to COVID-19 fatality. According to Karuppasamy et al. (2020), improvements in air quality can therefore have the co-benefit to reduce mortality directly and indirectly (via reducing the risks associated with COVID-19 infections).

Table 1. Findings of case studies on the impacts of lockdowns on air pollution.


  Table 1. (continued).

Table 1. (continued).

 
1.3 COVID-19 Measures Taken in Mongolia

According to the Law on Disaster Protection (Mongolian Parliament, 2003), the disaster preparedness regime in Mongolia is divided into three levels—daily preparedness, enhanced readiness, and public emergency readiness. Mongolia went into the state of “enhanced readiness” level on February 11, 2020. The precautionary measures of the “enhanced readiness” level include travel restrictions, partial remote work from home, school closings, and restrictions in public activities. The first positive case of coronavirus, who arrived in Mongolia via an international flight was reported on March 10, 2020 (Erkhembayar et al., 2020). On November 10, 2020, the first case of community transmission from an individual arriving from Russia was registered. After the patient was released from isolation for 21 days, Mongolia’s State Emergency Committee (SEC) announced the “public emergency readiness” level (or strict-lockdown) measures: all services and businesses except essential sectors were closed to work from home, the stay-at-home regime was activated, all international and domestic traffic beyond city boundaries was temporarily suspended, public gatherings suspended, and all levels of educational institutions are closed. In order to prevent the spread of coronavirus, three periods (a total of 63 days) of strict-lockdown periods were set from November 2020 to February 2021 (see Table 5).

According to Shrestha et al. (2020), declines in air pollutant concentrations up to 43% related to COVID-19 lockdowns in Ulaanbaatar were reported. However, there was no mention of the combined reason for the declines which is indeed related to change in fuel type since winter of 2019-2020 and seasonal variations of the pollutants. The winter of 2019–2020 was characterized by substantial declines in PM2.5 and PM10 concentrations due to the transition from raw coal to briquette fuel in Ulaanbaatar (Ganbat et al., 2020). The current study investigates the effect of COVID-19 strict lockdowns on air pollutants in Ulaanbaatar, Mongolia, using terrestrial and satellite observations.

 
2 MATERIALS AND METHODS


 
2.1 Study Area

Ulaanbaatar, the capital of Mongolia, is located in a valley between the Bogd Khan mountain in the south and the extensions of the Khentii Mountains in the north (Fig. 1) at an approximate altitude of 1300 m above sea level. Ulaanbaatar has a population of nearly 1.5 million inhabitants, accounting for 47% of the total population of Mongolia. Ulaanbaatar is known as the ‘coldest capital’ in the world with winter temperatures often dropping below –20°C and the cold weather in winter is attributed to the Siberian high-pressure system, which causes the formation of a temperature inversion (Ganbat and Baik, 2016). In poor vertical mixing under the weather condition with temperature inversions, the majority of pollutant sources is crustal matter and coal combustion (Davy et al., 2011) from ger areas, where around half of the Ulaanbaatar’s population lives (Karthe et al., 2022). Heating in ger areas during the heating season is supplied by fuel-stoves. After a decade of the severe air pollution problem, a notable reduction in air pollution in Ulaanbaatar is seen after introducing upgraded briquette fuel since winter 2019–2020 (Ganbat et al., 2020; Soyol-Erdene et al., 2021). However, the PM concentrations in winter 2019–2020 still exceeded the national air quality standard levels.

 Fig. 1. Air quality monitoring sites in Ulaanbaatar.
Fig. 1. Air quality monitoring sites in Ulaanbaatar.

 
2.2 Terrestrial Data

Concentrations of air pollutants—NO2, SO2, PM10, and PM2.5, which are commonly used to assess air quality, are used in this study to investigate the effects of COVID-19 measures on air quality in Ulaanbaatar. Data from 1 January 2015 to 1 March 2021 were obtained from 12 air quality monitoring sites located at various points in Ulaanbaatar (Fig. 1 and Table 2). The sites are operated by the National Agency for Meteorology and Environmental Monitoring (NAMEM) and the Air Pollution Reduction Department (APRD) of the Municipality.

 Table 2. Air quality monitoring sites in Ulaanbaatar and measuring pollutants.

According to the current national air quality standard, MNS 4585:2016, which is amended in 2016, the national standard levels of 24-h NO2, SO2, PM10, and PM2.5 are set 50 µg m–3, 50 µg m–3, 100 µg m–3, and 50 µg m–3, respectively. The annual standard levels of NO2, SO2, PM10, and PM2.5 are 40 µg m–3, 20 µg m–3, 50 µg m–3, and 25 µg m–3, respectively.

 
2.3 Air Pollution Monitoring by Remote Sensing

Meteorological satellites have been able to monitor atmospheric water vapor content since the early 1970s, and the Advanced Very-High-Resolution Radiometer (AvHRR) sensors have provided global information on the occurrence and distribution of aerosols since 1981 (Stowe et al., 2002; Zhang et al., 2020). The introduction of the Global Ozone Measurement Experiment (GOME) spectrometer aboard the ERS-2 satellite in 1995, and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) aboard the ENVISAT satellites since 2002 significantly widened the potential of satellite remote sensing for air pollution monitoring as it provided information on aerosols and a wide range of trace gases including NO2, SO2 and several others (Bovensmann et al., 1999; Noël et al., 2003). Further improvements in spectral, radiometric, spatial, and temporal resolution have been realized with the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel 5 Precursor satellite. It allows the monitoring of temporal changes and assessment of long-term trends in atmospheric chemistry. It can quantify aerosols and several trace gases including NO2, O3, SO2, CO, CH4, and CH2O. TROPOMI is based on a UVNS (UV–VIS–NIR–SWIR) spectrometer that scans an approximately 2600 km wide swath, along which it moves at a speed of 7 km s–1. It offers a daily coverage at a spatial resolution of 7 km × 7 km in most bands, and 21 km × 28 km in the UV band (Veefkind et al., 2012). The accuracy and precision of the TROPOMI instrument for the parameters considered in this study are shown in Table 3.

 Table 3. Accuracy and precision of the TROPOMI measurements.

China has recently developed into another major operator of satellites capable of air pollution monitoring, including aerosols, NO2, SO2, and several other pollutants (Zhang et al., 2020). Remote sensing-based approaches have contributed to air quality assessments at various scales ranging from local studies—often in affected cities— (e.g., Huang et al., 2021; Prunet et al., 2020) to regional and national-level investigations (e.g., Shikwambana et al., 2020; Stratoulias and Nuthammachot, 2020; Zheng et al., 2019) and global studies (e.g., Lim et al., 2020). Particularly, studies focusing on NO2 to thoroughly understand the effects of the COVID-19 lockdown on the atmosphere are gaining popularity (e.g., Scheibenreif et al., 2021; Vîrghileanu et al., 2020; Wang et al., 2021). This study uses TROPOMI NO2 spatial variations as an extension to illustrate changes in several pollutants during the COVID-19 strict and non-strict lockdown periods.

 
3 RESULTS AND DISCUSSION


The time series of daily mean NO2, SO2, PM10, and PM2.5 concentrations from January 2015 to March 2021 averaged over the air quality monitoring sites in Ulaanbaatar are shown in Fig. 2. In general, decreasing trends in NO2, PM10, and PM2.5 after winter 2019–2020 can be clearly seen. The pollutant concentrations show that the main air pollutants in Ulaanbaatar were PM10 and PM2.5 before the winter of 2019–2020. The concentrations of all pollutants have strong seasonal patterns. The winter of 2020–2021 was the period with the lowest PM10 (108.5 ± 36.2 µg m–3) and PM2.5 (76.0 ± 38.9 µg m–3) since 2016. On the contrary, it is clearly seen that SO2 concentrations significantly increased in the last two winters.

Fig. 2. Time series of daily mean NO2, SO2, PM10, and PM2.5 and concentrations from 1 January 2015 to 1 March 2021 averaged over the air quality monitoring sites in Ulaanbaatar.Fig. 2. Time series of daily mean NO2, SO2, PM10, and PM2.5 and concentrations from 1 January 2015 to 1 March 2021 averaged over the air quality monitoring sites in Ulaanbaatar.

A decrease in yearly-mean NO2 concentration of 17% was observed in 2020 compared to the mean of 2015–2019. The decreasing trend is mild for the yearly values but more pronounced in winter. An increasing trend in SO2 concentrations is observed in winters after 2019–2020. An increase in SO2 concentration of 79% was observed in 2020 compared to 2015–2019 mean and 2.7 times higher than the national standard value of 20 µg m–3.

A decrease in PM10 concentration of 24% was observed in 2020 compared to the 2015–2019 mean. However, the annual PM10 concentration still exceeds the national standard value of 50 µg m–3. Decrease in PM2.5 concentration of 51% was observed in 2020 compared to 2015–2019 mean. Comparisons of PM10 and PM2.5 concentrations between before and after the 2019–2021 winters show decreased trends while SO2 concentration showed a notable increased trend. Specifically, SO2 concentrations in winters 2019–2020 and 2020–2021 are steadily increased. The reason for the increased SO2 concentration could be attributed to contents of the briquette fuel consumed in households in ger areas since 2019 and the increased demand in consumption of briquette fuel in households due to stay-at-home activity. In line with a previous study by Ganbat et al. (2020), the immediate reductions of PM10 and PM2.5 in winter 2019–2020 are related to the change in fuel type. Thus, the declines in PM10 and PM2.5 in winter 2020–2021 are likely attributed to the combined effects of measures of the transition from raw coal to upgraded briquette fuel and the effects of the COVID-19 strict-lockdowns.

The combined effects of this source change and weather condition may affect the changes in pollutant concentrations. To mention, according to official reports released by the NAMEM, the weather condition during the winter 2020–2021 was not peculiar (www.tsag-agaar.mn). The weather condition in the winter 2020–2021 showed the similar characteristics compared to the previous winters—low wind speed and temperature similar to previous years. To clearly see the impacts of strict-lockdowns to air pollution, the periods of strict-lockdowns and non-lockdowns are compared (please see Table 5 and Fig. 5 later).

It should be noted that even the concentrations are significantly reduced, the yearly-mean values of the pollutant concentrations remained persistently above the national standard values.

Table 4 supplements Fig. 2 providing a number of days with average pollutant concentration from November 1 to February 28, which covers three sequential strict-lockdowns, exceeding one, two, and three times the national air quality standard levels.

 Table 4. Days with average concentrations exceeding the national air quality standard levels of NO2, SO2, PM10, and PM2.5.

From November-February in 2014–2020 to 2020–2021, the days with an average NO2 concentration above the national air quality standard level reduced from 101 to 45. The strict-lockdowns during November 2020–February 2021 have resulted in a 55% reduction in the number of days exceeding the national air quality standard level of NO2 compared to 2014-2020. During November 2020–February 2021, there were no days exceeding two and three times the national air quality standard level of NO2. A similar feature is seen in many cities in the world (Acharya et al., 2021). It is important to note that one of the substantial reasons for the decrease in NO2 concentration was restrictions on city traffic during the strict-lockdown periods.

For winters 2019–2020 and 2020–2021, the number of days exceeding the national air quality standard level for PM10 decreased by 30%. For winters before 2019, the number of days with average PM10 concentrations exceeding three times the national air quality standard level ranged from 9 to 65, but for the past two winters, there were no such days.

For November 2017–February 2018, in 65 days the PM2.5 concentration exceeded three times the national air quality standard level. For winters 2019–2020 and 2020–2021, the number of days exceeding the national air quality standard level for PM2.5 decreased by 14% compared to 2014–2019. These sudden reductions of PM10 and PM2.5 concentrations during the last two winters could be linked to the transition from raw coal to briquette fuel. However, a greater declining trend is exhibited in winter 2020–2021, when the sequential strict-lockdowns have been imposed due to the COVID-19 pandemic.

Before winter 2019–2020, there were no days with average SO2 concentrations exceeding three times the national air quality standard level, but for the last two winters, the days exceeding three times the national air quality standard level significantly increased—no days versus 11 and 56 days in winter 2019–2020 and 2020–2021, respectively.

Descriptive statistics of each strict-lockdown period with respect to the same periods in the same periods in the previous five years are done. The mean and standard deviation of pollutant concentrations during the strict-lockdown periods and their changes from the same periods in the previous five years are shown in Table 5. All pollutants except SO2 present declines for the period from November 2020 to February 2021. Notable declines are apparent during the strict-lockdown periods (L1, L2, and L3). During the strict-lockdown periods, lower concentrations (from 39% to 72%) were observed. The concentration levels are likely to go up once the situation back to normal (N1 and N2) but remain still lower compared to the same periods in the previous five years.

Table 5. Information of COVID-19 strict-lockdown periods in Ulaanbaatar and mean and standard deviation of NO2, SO2, PM10, and PM2.5 concentrations during and between the strict-lockdown periods.

On average, a decrease of 41%, 53%, and 55% in NO2, PM10, and PM2.5 concentrations, respectively, is found, whereas a 229% increase in SO2 concentrations during the strict-lockdown periods compared to the same periods in the previous five years. Such effect of lockdown on the reduction of NO2 was observed in other cities of the world (Fu et al., 2020; Acharya et al., 2021) as a result of decreases in various anthropogenic activities.

Visible reductions in PM10 and PM2.5 remain to be consistently seen for the period from November 2020 to February 2021. The substantial reductions in PM10 and PM2.5 concentrations from November 2020 to February 2021 compared to the previous five years could be due to the combination of fuel change and COVID-19 mitigation measures. The maximum decrease in NO2, PM10, and PM2.5 concentrations was observed in L3. An opposite trend is observed for SO2 concentration. SO2 concentration increased from twofold (212%) to threefold (331%) during the periods. Time series of daily mean NO2, SO2, PM10, and PM2.5 concentrations from November to March are shown in Fig. 3. The figure reveals significant decreases in NO2, PM10, and PM2.5 concentrations during the whole period covering three sequential strict-lockdowns.

 Fig. 3. Time series of NO2, SO2, PM10, and PM2.5 concentrations from 1 November to 28 February.Fig. 3. Time series of NO2, SO2, PM10, and PM2.5 concentrations from 1 November to 28 February.

The daily-mean (highest) NO2 concentration reduction ranged from 27 (25) % to 41 (39) % between November 2020–March 2021 and the same periods in the previous five years. NO2 concentration for the period between November 2020 and March 2021 decreased by 35% on average compared to the previous five years. During the three sequential strict-lockdowns, the daily NO2 concentration drops to below 20 µg m–3 first ever since 2015.

To take a further look at the variations of pollutant concentrations between strict-lockdowns, we compare the pollutants concentrations averaged over seven days before and after the start of the strict-lockdown. The daily mean concentrations of NO2 were 41.8, 63.6, and 53.6 µg m–3 before the first, second, and third strict-lockdown periods, which reduced to 33.5, 47.5, and 35.3 µg m–3, respectively. The NO2 concentrations were reduced by 20%, 25%, and 34%, respectively.

The daily mean concentrations of SO2 were 46.8, 233.6, and 167.5 µg m–3 before the first, second, and third strict-lockdown periods, which reduced to 49.9, 156.9, and 139.5 µg m–3, respectively. The SO2 concentrations changed by –7%, 33%, and 17%, respectively.

The daily mean concentrations of PM10 were 118.6, 142.0, and 108.9 µg m–3 before the first, second, and third strict-lockdown periods, which reduced to 87.6, 103.5, and 78.4 µg m–3, respectively. The PM10 concentrations were reduced by 26–28%.

The daily mean concentrations of PM2.5 were 45.9, 100.4, and 85.9 µg m–3 before the first, second, and third strict-lockdown periods, which reduced to 34.9, 77.2, and 57.4 µg m–3, respectively. The PM2.5 concentrations are reduced by 24%, 23%, and 33%, respectively.

Among NO2, PM10, and PM2.5, the NO2 concentrations significantly reduced by 34% after the third strict-lockdown. The third strict-lockdown exhibited the greatest reductions in pollutant concentrations.

Fig. 4 shows that the probability distribution functions (PDFs) of concentrations of NO2, PM10, and PM2.5 were consistently lower during the three sequential strict-lockdown periods.

 Fig. 4. Probability distribution functions of (a) NO2, (b) SO2, (c) PM10, and (d) PM2.5 concentrations for averaged over strict-lockdown periods (red) and the same periods in previous five years (blue).Fig. 4. Probability distribution functions of (a) NO2, (b) SO2, (c) PM10, and (d) PM2.5 concentrations for averaged over strict-lockdown periods (red) and the same periods in previous five years (blue).

The peak occurrence of NO2, PM10, and PM2.5 concentrations during strict-lockdown periods are 45.3, 76.7, and 52.5 µg m–3, while they were 70.3, 128.6, and 113.1 µg m–3, respectively, during the same periods in the previous five years. The percentage exceeding the national standard (MNS 4585:2016) level for NO2, PM10, and PM2.5 constituted 23%, 50%, and 67% during the lockdown periods while it was 89%, 84%, and 91%, respectively, during the same periods in previous five years. NO2 experienced the greatest benefit, with a –66% reduction in percentage exceeding the national standard value.

For SO2, high concentrations of SO2 became more frequent during the strict-lockdown periods compared to the same periods in the previous five years. The percentage exceeding the national standard level for SO2 increased from 54% to 89%. The peak occurrence of SO2 concentration during strict-lockdown periods was 46.8 µg m–3, while it is increased to 123.5 µg m–3 during the same periods in the previous five years.

The hourly patterns of the pollutant concentrations are illustrated in Fig. 5. The pollutant concentrations showed two peaks during the day.

 Fig. 5. Hourly variations in (a) NO2, (b) SO2, (c) PM10, and (d) PM2.5 concentrations averaged over strict-lockdown periods (blue) and the same periods in the previous five years (red).Fig. 5. Hourly variations in (a) NO2, (b) SO2, (c) PM10, and (d) PM2.5 concentrations averaged over strict-lockdown periods (blue) and the same periods in the previous five years (red).

The concentrations of NO2, PM10, and PM2.5 are recorded as being substantially less during the strict-lockdown periods. For example, daily mean NO2, PM10, and PM2.5 concentrations during strict-lockdowns were 63.3, 158.7, and 120.2 µg m–3, which were reduced by 33, 39, and 46 %, respectively, when compared with those of the same periods in the previous five years. SO2 concentration exhibited an increasing trend against previous years. The changes in concentrations were observed, however, the time of maximum and minimum concentrations are not changed much. These results clearly indicate that measures taken during the strict-lockdowns have substantially affected the air pollution in Ulaanbaatar.

As an extension of the measurement data to illustrate the spatial variations of NO2 for the study period, tropospheric NO2 columns from TROPOMI during COVID-19 strict-lockdown and no strict-lockdown periods (L1, N1, L2, N2, and L3) in winter 2020-2021 are shown in Fig. 6. The tropospheric NO2 columns over the Ulaanbaatar are shown in Fig. 6(a) for the first COVID-19 strict-lockdown period from November 11, 2020 to December 14, 2020 (L1). Fig. 6(b) illustrates that the tropospheric NO2 concentration has remained high (> 0.00021 mol m–2) from December 14, 2020 to December 22, 2020 during no strict-lockdown (N1).

 Fig. 6. Tropospheric NO2 column densities over Ulaanbaatar observed by Sentinel-5P TROPOMI during the strict-lockdowns (left panel) and periods between them (right panel). Details of the abbreviations (L1, N1, L2, N2, and L3) are given in Table 5.Fig. 6. Tropospheric NO2 column densities over Ulaanbaatar observed by Sentinel-5P TROPOMI during the strict-lockdowns (left panel) and periods between them (right panel). Details of the abbreviations (L1, N1, L2, N2, and L3) are given in Table 5.

TROPOMI data for NO2 at the city scale showed a reduction for lockdown periods as compared to non-lockdown periods. The observed reduction between N1 and L2 based on TROPOMI data was 48.6% for the whole city, as compared to an average reduction of 28.5% for the 11 monitoring stations equipped with NO2 sensors. Between N2 and L3, a reduction of 35.2% was observed for the city area based on TROPOMI data, as compared to an average reduction of 34.6% for the 11 monitoring stations. In this context, it should be mentioned that the monitoring stations were set up at locations that represent different pollution pattern (including sites with a high density of heating stoves and sites with significant traffic), but that averages may not be fully representative for the entire city. Moreover, one of the monitoring stations (Bayankhoshuu) was not equipped with an NO2 sensor, thus leading to an information gap for ground-based data in one of the city’s largest and most highly polluted ger areas. As standard deviations for NO2 concentrations across the city were high for TROPOMI data (between ±30% and ±59% depending on the time period), averages were also calculated for 1 km buffer zones around the monitoring stations. In this case, standard deviations were far smaller (between ±8% and ±11%) for the time periods considered, and the observed reductions were 53.4% (between N1 and L2) and 35.7% (between N2 and L3). All in all, TROPOMI-based observations allowed a realistic assessment of general trends of NO2 pollution (see Fig. 7) while also providing coverage of areas without monitoring stations.

Fig. 7. The correlation analysis between terrestrial data and TROPOMI NO2 tropospheric concentration.Fig. 7. The correlation analysis between terrestrial data and TROPOMI NO2 tropospheric concentration.

Studies have proved these tropospheric NO2 hotspots in downtown and midtown areas are associated with human activities including ground traffic and industrial activities (Hashim et al., 2021; Huang et al., 2021; Rissman et al., 2013). Apparently, just after lockdown measurements were imposed from December 23, 2020 to January 10, 2021 (L2), the NO2 concentrations dropped sharply as shown in Fig. 6(c). However, during no strict-lockdown period from January 11, 2021 to February 10, 2021 (N2), NO2 concentrations once again increased as exhibited in Fig. 6(d). Another significant reduction in NO2 concentrations was observed during the third lockdown from February 11, 2021 to February 23, 2021 (L3) as documented in Fig. 6(e).

Previous studies have also shown a reduction of tropospheric NO2 columns in 2020 lockdown period over various regions in the world (Goldberg et al., 2020; Hashim et al., 2021; Huang et al., 2021; Wang et al., 2021). Likewise, similar results have been reported in Asia regions, particularly Wang and Su (2020) and Bauwens et al. (2020) investigated a sharp decline in NO2 concentrations over China and South Korea respectively based on Sentinel-5P data. Our results are comparable with these studies, indicating that the COVID-19 lockdown played an important role in the NO2 reduction.

The results of correlation analysis between terrestrial data over the ground station and TROPOMI NO2 tropospheric concentration from Sentinel-5P are shown in Fig. 7. The results show that the NO2 concentration retrieved by TROPOMI is highly correlated with the surface monitoring concentration of NO2 in Ulaanbaatar (correlation coefficient = 0.91, R2 = 0.85).

 
4 SUMMARY


Following the novel COVID-19 (Coronavirus) case that was spread worldwide, prevention measures were implemented by the Government of Mongolia. The spread of COVID-19 disease to the community was first reported on 10 November 2020 in Mongolia. Starting 11 November 2020, the Government of Mongolia and the State Emergency Commission announced a series of intermittent strict-lockdowns to prevent the COVID-19 spread. This study focuses on three sequential strict-lockdowns from November 2020 to February 2021 which were announced to discontinue the novel coronavirus spread. A significant effect on air quality has been seen in Ulaanbaatar, Mongolia.

The impact of strict-lockdown on air pollution in Ulaanbaatar was assessed by comparing air pollutant concentrations before and during strict-lockdowns and during non-strict lockdown periods. Changes in Ulaanbaatar’s air quality showed significant declines in NO2 (up to 45%), PM10 (up to 72%), and PM2.5 (up to 59%) concentrations between November 2020 and March 2021 compared to the previous five years. These reductions were among the greatest observed across Asia, which is plausible considering the strict character of the lockdowns and the transition to cleaner coal briquettes. However, the concentrations were still above the national standard values, and SO2 concentrations showed an increasing trend in the last two years, especially in winter. The reason could be attributed to the contents of the briquette fuel consumed in households in ger areas after the city-wide transfer from the raw coal to briquette fuel and the increased demand in consumption of briquette fuel in households due to long hours spent at home. The measures taken during the strict-lockdown periods clearly influenced the values of daily patterns of NO2, PM10, and PM2.5 concentrations. The maximum concentration peaks appreciably decreased. In contrast, it is important to note that SO2 concentration increased during the last two winter months after 2019. Furthermore, Sentinel-5P retrieved NO2 tropospheric concentrations which were employed as an extension to illustrate changes of several pollutants during the COVID-19 strict and non-strict lockdown periods, are in agreement with the reductions observed at ground stations. Our current study underlines the findings of studies from other parts of the world, revealing both positive and negative impacts of lockdowns on air quality for Ulaanbaatar, Mongolia. However, as Ulaanbaatar has been developing very dynamically in recent years, and a major campaign to substitute the fuel for heating for about half of the city’s households was implemented just prior to the pandemic, it is difficult to fully disentangle the impacts of COVID-19 from other developments. As only preliminary findings on the fuel substitution exist so far, the related uncertainties need to be addressed once the pandemic situation has improved and consolidated findings on the impacts of fuel substitution become possible.

 
ACKNOWLEDGEMENTS


Daniel Karthe would like to express his gratitude to German Academic Exchange Service (DAAD) for funding a long-term lectureship at the German-Mongolian Institute for Resources and Technology (GMIT).


REFERENCES


  1. Acharya, P., Barik, G., Gayen, B.K., Bar, S., Maiti, A., Sarkar, A., Ghosh, S., De, S.K., Sreekesh, S. (2021). Revisiting the levels of Aerosol Optical Depth in south-southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations. Environ. Res. 193, 110514. https://doi.org/10.1016/j.envres.2020.110514

  2. Assanov, D., Kerimray, A., Batkeyev, B., Kapsalyamova, Z. (2021). The effects of COVID-19-related driving restrictions on air quality in an industrial city. Aerosol Air Qual. Res. 21, 200663. https://doi.org/10.4209/aaqr.200663

  3. Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.F., Gent, J. van, Eskes, H., Levelt, P.F., van der A., R., Veefkind, J.P., Vlietinck, J., Yu, H., Zehner, C. (2020). Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. Geophys. Res. Lett. 47, e2020GL087978. https://doi.org/10.1029/2020GL087978

  4. Bedi, J.S., Dhaka, P., Vijay, D., Aulakh, R.S., Gill, J.P.S. (2020). Assessment of air quality changes in the four metropolitan cities of India during COVID-19 pandemic lockdown. Aerosol Air Qual. Res. 20, 2062–2070. https://doi.org/10.4209/aaqr.2020.05.0209

  5. Bovensmann, H., Burrows, J.P., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V.V., Chance, K.V., Goede, A.P.H. (1999). SCIAMACHY: Mission Objectives and Measurement Modes. J. Atmos. Sci. 56, 127–150. https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2

  6. Broomandi, P., Karaca, F., Nikfal, A., Jahanbakhshi, A., Tamjidi, M., Kim, J.R. (2020). Impact of COVID-19 event on the air quality in Iran. Aerosol Air Qual. Res. 20, 1793–1804. https://doi.org/​10.4209/aaqr.2020.05.0205

  7. Chen, Q.X., Huang, C.L., Yuan, Y., Tan, H.P. (2020). Influence of COVID-19 event on air quality and their association in mainland China. Aerosol Air Qual. Res. 20, 1541–1551. https://doi.org/​10.4209/aaqr.2020.05.0224

  8. Chin, M., Diehl, T., Tan, Q., Prospero, J.M., Kahn, R.A., Remer, L.A., Yu, H., Sayer, A.M., Bian, H., Geogdzhayev, I.V., Holben, B.N., Howell, S.G., Huebert, B.J., Hsu, N.C., Kim, D., Kucsera, T.L., Levy, R.C., Mishchenko, M.I., Pan, X., Quinn, P.K., et al. (2014). Multi-decadal aerosol variations from 1980 to 2009: A perspective from observations and a global model. Atmos. Chem. Phys. 14, 3657–3690. https://doi.org/10.5194/acp-14-3657-2014

  9. Chowdhuri, I., Pal, S.C., Saha, A., Chakrabortty, R., Ghosh, M., Roy, P. (2020). Significant decrease of lightning activities during COVID-19 lockdown period over Kolkata megacity in India. Sci. Total Environ. 747, 141321. https://doi.org/10.1016/j.scitotenv.2020.141321

  10. Cole, M.A., Ozgen, C., Strobl, E. (2020). Air pollution exposure and Covid-19 in Dutch municipalities. Environ. Resour. Econ. 76, 581–610. https://doi.org/10.1007/s10640-020-00491-4

  11. Conticini, E., Frediani, B., Caro, D. (2020). Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy? Environ. Pollut. 261, 114465. https://doi.org/10.1016/j.envpol.2020.114465

  12. Copat, C., Cristaldi, A., Fiore, M., Grasso, A., Zuccarello, P., Santo Signorelli, S., Conti, G.O., Ferrante, M. (2020). The role of air pollution (PM and NO2) in COVID-19 spread and lethality: A systematic review. Environ. Res. 191, 110129. https://doi.org/10.1016/j.envres.2020.110129

  13. Davy, P.K., Gunchin, G., Markwitz, A., Trompetter, W.J., Barry, B.J., Shagjjamba, D., Lodoysamba, S. (2011). Air particulate matter pollution in Ulaanbaatar, Mongolia: Determination of composition, source contributions and source locations. Atmos. Pollut. Res. 2, 126–137. https://doi.org/10.5094/APR.2011.017

  14. Dejchanchaiwong, R., Tekasakul, P. (2021). Effects of coronavirus induced city lockdown on PM2.5 and gaseous pollutant concentrations in Bangkok. Aerosol Air Qual. Res. 21, 200418. https://doi.org/10.4209/aaqr.200418

  15. Erkhembayar, R., Dickinson, E., Badarch, D., Narula, I., Warburton, D., Thomas, G.N., Ochir, C., Manaseki-Holland, S. (2020). Early policy actions and emergency response to the COVID-19 pandemic in Mongolia: Experiences and challenges. Lancet Glob. Health 8, e1234–e1241. https://doi.org/10.1016/S2214-109X(20)30295-3

  16. Filippini, T., Rothman, K.J., Cocchio, S., Narne, E., Mantoan, D., Saia, M., Goffi, A., Ferrari, F., Maffeis, G., Orsini, N., Baldo, V., Vinceti, M. (2021). Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide. Sci. Total Environ. 760, 143355. https://doi.org/10.1016/j.scitotenv.2020.143355

  17. Fu, F., Purvis-Roberts, K.L., Williams, B. (2020). Impact of the covid-19 pandemic lockdown on air pollution in 20 major cities around the world. Atmosphere 11, 1189. https://doi.org/10.3390/​atmos11111189

  18. Ganbat, G., Baik, J.J. (2016). Wintertime winds in and around the Ulaanbaatar metropolitan area in the presence of a temperature inversion. Asia-Pac. J. Atmos. Sci. 52, 309–325. https://doi.org/​10.1007/s13143-016-0007-y

  19. Ganbat, G., Soyol-Erdene, T.O., Jadamba, B. (2020). Recent improvement in particulate matter (PM) pollution in Ulaanbaatar, Mongolia. Aerosol Air Qual. Res. 20, 2280–2288. https://doi.org/​10.4209/aaqr.2020.04.0170

  20. Ghahremanloo, M., Lops, Y., Choi, Y., Mousavinezhad, S. (2021). Impact of the COVID-19 outbreak on air pollution levels in East Asia. Sci. Total Environ. 754, 142226. https://doi.org/10.1016/j.​scitotenv.2020.142226

  21. Goldberg, D.L., Anenberg, S.C., Griffin, D., McLinden, C.A., Lu, Z., Streets, D.G. (2020). Disentangling the impact of the COVID-19 lockdowns on urban NO2 from natural variability. Geophys. Res. Lett. 47, e2020GL089269. https://doi.org/10.1029/2020GL089269

  22. Griffith, S.M., Huang, W.S., Lin, C.C., Chen, Y.C., Chang, K.E., Lin, T.H., Wang, S.H., Lin, N.H. (2020). Long-range air pollution transport in East Asia during the first week of the COVID-19 lockdown in China. Sci. Total Environ. 741, 140214. https://doi.org/10.1016/j.scitotenv.2020.140214

  23. Gualtieri, G., Brilli, L., Carotenuto, F., Vagnoli, C., Zaldei, A., Gioli, B. (2020). Quantifying road traffic impact on air quality in urban areas: A COVID-19-induced lockdown analysis in Italy. Environ. Pollut. 267, 115682. https://doi.org/10.1016/j.envpol.2020.115682

  24. Gupta, A., Bherwani, H., Gautam, S., Anjum, S., Musugu, K., Kumar, N., Anshul, A., Kumar, R. (2021). Air pollution aggravating COVID-19 lethality? Exploration in Asian cities using statistical models. Environ. Dev. Sust. 23, 6408–6417. https://doi.org/10.1007/s10668-020-00878-9

  25. Hashim, B.M., Al-Naseri, S.K., Al-Maliki, A., Al-Ansari, N. (2021). Impact of COVID-19 lockdown on NO2, O3, PM2.5 and PM10 concentrations and assessing air quality changes in Baghdad, Iraq. Sci. Total Environ. 754, 141978. https://doi.org/10.1016/j.scitotenv.2020.141978

  26. Huang, G., Ponder, R., Bond, A., Brim, H., Temeng, A., Naeger, A.R., Zhu, L. (2021). Unexpected impact of COVID-19 lockdown on the air quality in the Metro Atlanta, USA using ground-based and satellite observations. Aerosol Air Qual. Res. 21, 210153. https://doi.org/10.4209/aaqr.210153

  27. Islam, M.S., Rahman, M., Tusher, T.R., Roy, S., Razi, M.A. (2021). Assessing the relationship between COVID-19, air quality, and meteorological variables: A case study of Dhaka city in Bangladesh. Aerosol Air Qual. Res. 21, 200609. https://doi.org/10.4209/aaqr.200609

  28. Kanniah, K.D., Zaman, N.A.F.K., Kaskaoutis, D.G., Latif, M.T. (2020). COVID-19's impact on the atmospheric environment in the Southeast Asia region. Sci. Total Environ. 736, 139658. https://doi.org/10.1016/j.scitotenv.2020.139658

  29. Karthe, D., Lee, H., Ganbat, G. (2022). Fragmented Infrastructure Systems in Ulaanbaatar, Mongolia: Assessment from an Environmental Resource Nexus and Public Health Perspective, in: Iossifova, D., Gasparatos, A., Zavos, S., Gamal, Y., Long, Y. (Eds.), Urban Infrastructuring, Sustainable Development Goals Series, Springer, Singapore, pp. 15–34.

  30. Karuppasamy, M.B., Seshachalam, S., Natesan, U., Ayyamperumal, R., Karuppannan, S., Gopalakrishnan, G., Nazir, N. (2020). Air pollution improvement and mortality rate during COVID-19 pandemic in India: Global intersectional study. Air Qual Atmos Health. 13, 1375–1384. https://doi.org/10.1007/s11869-020-00892-w

  31. Kerimray, A., Baimatova, N., Ibragimova, O.P., Bukenov, B., Kenessov, B., Plotitsyn, P., Karaca, F. (2020). Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci. Total Environ. 730, 139179. https://doi.org/10.1016/j.scitotenv.2020.139179

  32. Khan, R., Kumar, K.R., Zhao, T. (2021). The impact of lockdown on air quality in Pakistan during the COVID-19 pandemic inferred from the multi-sensor remote sensed data. Aerosol Air Qual. Res. 21, 200597. https://doi.org/10.4209/aaqr.200597

  33. Kumari, S., Lakhani, A., Kumari, K.M. (2020). COVID-19 and air pollution in Indian cities: World’s most polluted cities. Aerosol Air Qual. Res. 20, 2592–2603. https://doi.org/10.4209/aaqr.2020.​05.0262

  34. Le, N.H., Ly, B.T., Thai, P.K., Pham, G.H., Ngo, I.H., Do, V.N., Le, T.T., Nhu, L.V., Son, H.D., Nguyen, Y.L.T., Pham, D.H., Vu, T.V. (2021). Assessing the impact of traffic emissions on fine particulate matter and carbon monoxide levels in Hanoi through COVID-19 social distancing periods. Aerosol Air Qual. Res. 21, 210081. https://doi.org/10.4209/aaqr.210081

  35. Li, Y., Xu, H. (2021). Assessment of reductions in emission-driven air pollution during the Beijing Olympic Games, Shanghai World Expo, Guangzhou Asian Games and Wuhan COVID-19 lockdown. Aerosol Air Qual. Res. 21, 200644. https://doi.org/10.4209/aaqr.200644

  36. Lim, C.H., Ryu, J., Choi, Y., Jeon, S.W., Lee, W.K. (2020). Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016). Environ. Int. 144, 106011. https://doi.org/​10.1016/j.envint.2020.106011

  37. Liu, F., Wang, M., Zheng, M. (2021a). Effects of COVID-19 lockdown on global air quality and health. Sci. Total Environ. 755, 142533. https://doi.org/10.1016/j.scitotenv.2020.142533

  38. Liu, L., Zhang, J., Du, R., Teng, X., Hu, R., Yuan, Q., et al. (2021b). Chemistry of atmospheric fine particles during the COVID-19 pandemic in a megacity of Eastern China. Geophys. Res. Lett. 48, e2020GL091611. https://doi.org/10.1029/2020GL091611

  39. Menut, L., Bessagnet, B., Siour, G., Mailler, S., Pennel, R., Cholakian, A. (2020). Impact of lockdown measures to combat Covid-19 on air quality over western Europe. Sci. Total Environ. 741, 140426. https://doi.org/10.1016/j.scitotenv.2020.140426

  40. Mongolian Parliament (2003). Law on Disaster Protection. Parliament law of Mongolia on Disaster Protection 20 June 2003, Ulaanbaatar.

  41. Noël, S., Bovensmann, H., Skupin, J., Wuttke, M.W., Burrows, J.P., Gottwald, M., Krieg, E. (2003). The SCIAMACHY calibration/monitoring concept and first results. Adv. Space. Res. 32, 2123–2128. https://doi.org/10.1016/S0273-1177(03)90532-1

  42. Ogen, Y. (2020). Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Sci. Total Environ. 726, 138605. https://doi.org/10.1016/j.scitotenv.2020.​138605

  43. Oo, T.K., Arunrat, N., Kongsurakan, P., Sereenonchai, S., Wang, C. (2021). Nitrogen dioxide (NO2) Level changes during the control of COVID-19 pandemic in Thailand. Aerosol Air Qual. Res. 21, 200440. https://doi.org/10.4209/aaqr.200440

  44. Park, I.S., Park, M.S., Kim, S.H., Jang, Y.W., Lee, J., Owen, J.S., Cho, C.R., Jee, J.B., Chae, J.H., Kang, M.S. (2021). Meteorological characteristics during periods of greatly reduced PM2.5 concentrations in March 2020 in Seoul. Aerosol Air Qual. Res. 21, 200512. https://doi.org/10.4209/aaqr.200512

  45. Prunet, P., Lezeaux, O., Camy-Peyret, C., Thevenon, H. (2020). Analysis of the NO2 tropospheric product from S5P TROPOMI for monitoring pollution at city scale. City Environ. Interact. 8, 100051. https://doi.org/10.1016/j.cacint.2020.100051

  46. Rissman, J., Arunachalam, S., BenDor, T., West, J.J. (2013). Equity and health impacts of aircraft emissions at the Hartsfield-Jackson Atlanta International Airport. Landscape Urban Plann. 120, 234–247. https://doi.org/10.1016/j.landurbplan.2013.07.010

  47. Ropkins, K., Tate, J.E. (2021). Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.​2020.142374

  48. Santoso, M., Hopke, P.K., Permadi, D.A., Damastuti, E., Lestiani, D.D., Kurniawati, S., Khoerotunnisya, D., Sukir, S.K. (2021). Multiple air quality monitoring evidence of the impacts of large-scale social restrictions during the COVID-19 pandemic in Jakarta, Indonesia. Aerosol Air Qual. Res. 21, 200645. https://doi.org/10.4209/aaqr.200645

  49. Scheibenreif, L., Mommert, M., Borth, D. (2021). A Novel Dataset and Benchmark for Surface NO2 Prediction from Remote Sensing Data Including COVID Lockdown Measures. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.

  50. Selvam, S., Muthukumar, P., Venkatramanan, S., Roy, P.D., Bharath, K.M., Jesuraja, K. (2020). SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India. Sci. Total Environ. 737, 140391. https://doi.org/10.1016/j.scitotenv.2020.140391

  51. Setti, L., Passarini, F., Gennaro, G.D., Barbieri, P., Licen, S., Perrone, M.G., Piazzalunga, A., Borelli, M., Palmisani, J., Gilio, A.D., Rizzo, E., Colao, A., Piscitelli, P., Miani, A. (2020). Potential role of particulate matter in the spreading of COVID-19 in Northern Italy: First observational study based on initial epidemic diffusion. BMJ Open 10, e039338. https://doi.org/10.1136/bmjopen-2020-039338

  52. Shafeeque, M., Arshad, A., Elbeltagi, A., Sarwar, A., Pham, Q.B., Khan, S.N., Dilawar, A., Al-Ansari, N. (2021). Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: A case study of South Asia. Geomatics Nat. Hazards Risk 12, 560–580. https://doi.org/10.1080/19475705.2021.1885503

  53. Shikwambana, L., Mhangara, P., Mbatha, N. (2020). Trend analysis and first time observations of sulphur dioxide and nitrogen dioxide in South Africa using TROPOMI/Sentinel-5 P data. Int. J. Appl. Earth Obs. Geoinf. 91, 102130. https://doi.org/10.1016/j.jag.2020.102130

  54. Shrestha, A., Shrestha, U., Sharma, R., Bhattarai, S., Tran, H., Rupakheti, M. (2020). Lockdown caused by COVID-19 pandemic reduces air pollution in cities worldwide (preprint). Life Sciences. https://doi.org/10.31223/osf.io/edt4j

  55. Singh, J., Tyagi, B. (2021). Transformation of air quality over a coastal tropical station Chennai during COVID-19 lockdown in India. Aerosol Air Qual. Res. 21, 200490. https://doi.org/10.4209/​aaqr.200490

  56. Smith, S.J., Pitchera, H., Wigley, T.M.L. (2001). Global and regional anthropogenic sulfur dioxide emissions. Global Planet. Change 29, 99–119. https://doi.org/10.1016/S0921-8181(00)00057-6

  57. Smith, S.J., van Aardenne, J., Klimont, Z., Andres, R.J., Volke, A., Delgado Arias, S. (2011). Anthropogenic sulfur dioxide emissions: 1850-2005. Atmos. Chem. Phys. 11, 1101–1116. https://doi.org/10.5194/acp-11-1101-2011

  58. Son, J.Y., Fong, K.C., Heo, S., Kim, H., Lim, C.C., Bell, M.L. (2020). Reductions in mortality resulting from reduced air pollution levels due to COVID-19 mitigation measures. Sci. Total Environ. 744, 141012. https://doi.org/10.1016/j.scitotenv.2020.141012

  59. Soyol-Erdene, T.O., Ganbat, G., Baldorj, B. (2021). Urban air quality studies in Mongolia: Pollution Characteristics and future research needs. Aerosol Air Qual. Res. 21, 210163. https://doi.org/​10.4209/aaqr.210163

  60. Srivastava, A. (2021). COVID-19 and air pollution and meteorology-an intricate relationship: A review. Chemosphere 263, 128297. https://doi.org/10.1016/j.chemosphere.2020.128297

  61. Stowe, L.L., Jacobowitz, H., Ohring, G., Knapp, K.R., Nalli, N.R. (2002). The advanced very high resolution radiometer (AVHRR) pathfinder atmosphere (PATMOS) climate dataset: initial analyses and evaluations. J. Clim. 15, 1243–1260. https://doi.org/10.1175/1520-0442(2002)​015<1243:TAVHRR>2.0.CO;2

  62. Stratoulias, D., Nuthammachot, N. (2020). Air quality development during the COVID-19 pandemic over a medium-sized urban area in Thailand. Sci. Total Environ. 746, 141320. https://doi.org/​10.1016/j.scitotenv.2020.141320

  63. Streets, D.G., Bond, T.C., Carmichael, G.R., Fernandes, S.D., Fu, Q., He, D., Klimont, Z., Nelson, S.M., Tsai, N.Y., Wang, M.Q., Woo, J.H., Yarber, K.F. (2003). An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 108, 8809. https://doi.org/10.1029/​2002jd003093

  64. Suhaimi, N.F., Jalaludin, J., Latif, M.T. (2020). Demystifying a possible relationship between COVID-19, air quality and meteorological Factors: Evidence from Kuala Lumpur, Malaysia. Aerosol Air Qual. Res. 20, 1520–1529. https://doi.org/10.4209/aaqr.2020.05.0218

  65. Travaglio, M., Yu, Y., Popovic, R., Selley, L., Leal, N.S., Martins, L.M. (2021). Links between air pollution and COVID-19 in England. Environ. Pollut. 268, 115859. https://doi.org/10.1016/​j.envpol.2020.115859

  66. Veefkind, J.P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H.J., de Haan, J.F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., et al. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 120, 70–83. https://doi.org/​10.1016/j.rse.2011.09.027

  67. Venter, Z.S., Aunan, K., Chowdhury, S., Lelieveld, J. (2021). Air pollution declines during COVID-19 lockdowns mitigate the global health burden. Environ. Res. 192, 110403. https://doi.org/​10.1016/j.envres.2020.110403

  68. Verma, R.L., Kamyotra, J.S. (2021). Impacts of COVID-19 on air quality in India. Aerosol Air Qual. Res. 21, 200482. https://doi.org/10.4209/aaqr.200482

  69. Vîrghileanu, M., Săvulescu, I., Mihai, B. A., Nistor, C., Dobre, R. (2020). Nitrogen dioxide (NO2) pollution monitoring with sentinel-5P satellite imagery over Europe during the coronavirus pandemic outbreak. Remote Sens. 12, 3575. https://doi.org/10.3390/rs12213575

  70. Wang, M., Liu, F., Zheng, M. (2020a). Air quality improvement from COVID-19 lockdown: Evidence from China. Air Qual. Atmos. Health. 1–14. https://doi.org/10.1007/s11869-020-00963-y

  71. Wang, P., Chen, K., Zhu, S., Wang, P., Zhang, H. (2020b). Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour. Conserv. Recycl. 158, 104814. https://doi.org/10.1016/j.resconrec.2020.104814

  72. Wang, Q., Su, M. (2020). A preliminary assessment of the impact of COVID-19 on environment–A case study of China. Sci. Total Environ. 728, 138915. https://doi.org/10.1016/j.scitotenv.​2020.138915

  73. Wang, Z., Uno, I., Yumimoto, K., Itahashi, S., Chen, X., Yang, W., Wang, Z. (2021). Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in-situ observations, satellite retrievals and model simulations. Atmos. Environ. 244, 117972. https://doi.org/10.1016/j.atmosenv.2020.117972

  74. Wetchayont, P., Hayasaka, T., Khatri, P. (2021). Air quality improvement during COVID-19 lockdown in Bangkok Metropolitan, Thailand: Effect of the long-range transport of air pollutants. Aerosol Air Qual. Res. 21, 200662. https://doi.org/10.4209/aaqr.200662

  75. Zhang, X., Wang, F., Wang, W., Huang, F., Chen, B., Gao, L., Wang, S., Yan, H., Ye, H., Si, F., Hong, J., Li, X., Cao, Q., Che, H., Li, Z. (2020). The development and application of satellite remote sensing for atmospheric compositions in China. Atmos. Res. 245, 105056. https://doi.org/​10.1016/j.atmosres.2020.105056

  76. Zhao, T.X.P., Laszlo, I., Guo, W., Heidinger, A., Cao, C., Jelenak, A., Tarpley, D., Sullivan, J. (2008). Study of long-term trend in aerosol optical thickness observed from operational AVHRR satellite instrument. J. Geophys. Res. 113, 1–14. https://doi.org/10.1029/2007JD009061

  77. Zheng, Z., Yang, Z., Wu, Z., Marinello, F. (2019). Spatial variation of NO2 and its impact factors in China: An application of sentinel-5P products. Remote Sens. 11, 1939. https://doi.org/10.3390/​rs11161939


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