Comparing the Change in Air Quality during the COVID-19 Lockdown between Dry and Wet Seasons in Nepal

Received: May 4, 2022
Revised: June 30, 2022
Accepted: July 5, 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.

Cite this article:

Dhital, N.B., Bhattarai, D.R., Sapkota, R.P., Rijal, K., Byanju, R.M., Yang, H.H. (2022). Comparing the Change in Air Quality during the COVID-19 Lockdown between Dry and Wet Seasons in Nepal. Aerosol Air Qual. Res. 22, 220201. https://doi.org/10.4209/aaqr.220201

HIGHLIGHTS

• Remarkable improvement in air quality was observed during the dry season lockdown.
• The change in air quality was less prominent during the wet season lockdown.
• The change in air quality showed marked spatial heterogeneity.

Keywords: Aerosol optical depth, Carbon monoxide, Google Earth Engine, Kathmandu Valley, Nitrogen dioxide

1 INTRODUCTION

The recent novel coronavirus disease (COVID-19) pandemic significantly impacted society, the economy, and the environment irrespective of geographical and political boundaries (Elsaid et al., 2021; Osterrieder et al., 2021; Poudel and Subedi, 2020; Pradhan et al., 2021; Shakil et al., 2020). Restrictions on outdoor activities and travel, including strict health and safety protocols and lockdowns imposed by governments of the countries to contain the pandemic, significantly reduced the vehicular traffic and industrial and commercial activities globally since the beginning of 2020 (Jephcote et al., 2021; Madineni et al., 2021; Yang et al., 2021; Zhang et al., 2020). Those restrictions affected air quality in urban and commercial areas around the world (Dasgupta and Srikanth, 2020; Rudke et al., 2022; Sannigrahi et al., 2021). Several recent studies have shown that COVID-19-related restrictions and lockdowns resulted in reduced levels of air pollutants, such as particulate matter (PM), nitrogen dioxide (NO₂), and carbon monoxide (CO), whereas increased levels of ozone (O₃) in the atmosphere (Adam et al., 2021; Elsaid et al., 2021; Ghahremanloo et al., 2021).

There are a number of assessments conducted on a national, regional, or global scale investigating the association between the COVID-19 lockdown and air quality (Dasgupta and Srikanth, 2020; Madineni et al., 2021; Moore and Semple, 2021; Nigam et al., 2021; Pozzer et al., 2020; Prakash et al., 2021; Sannigrahi et al., 2021; Tang et al., 2021; Upadhyay et al., 2022). Those studies have shown a mix of findings with marked spatial heterogeneity in the change in air quality parameters during the lockdown period compared to the baselines. Furthermore, lockdowns had varying degrees of impact on atmospheric concentrations of different air pollutants. For example, in Brazil, substantial reductions in CO (–53% in the State of Rio Grande do Sul), NO₂ (–34% in Rio de Janerio), and PM₁₀ (–23% in Espírito Santo) concentrations, but an increase in O₃ concentration (40% in Paraná), were reported during the first month of the implementation of COVID-19-related restrictions (Rudke et al., 2022). Similarly, in the UK, mean NO₂ and PM_2.5 levels decreased by 38.3% and 16.5%, respectively, whereas O₃ increased by 7.6% (Jephcote et al., 2021). In addition, a recent study shows an increase in the atmospheric concentrations of both CO (5.1%) and NO₂ (12.6%) in the South Asia region during lockdowns (Upadhyay et al., 2022). However, the same study also reports a wide spatial heterogeneity in the change in air quality during the lockdown period compared to the baseline, with some sub-regions experiencing improved air quality while the other sub-regions experiencing worsened air quality. Over the Indian sub-continent, such spatial heterogeneities have been explained as the combined effect of change in anthropogenic and natural emissions and meteorological dynamics during the lockdown (Madineni et al., 2021). Another study conducted in China showed substantial reductions in concentrations of many air pollutants, such as NO₂ (–49.96%), CO (–24.99%), PM₁₀ (particulate matter with a diameter of 10 µm or less, –39.30%), and PM_2.5 (particulate matter with a diameter of 2.5 µm or less, –32.34%), in the atmosphere during the implementation of the most restrictive control measures compared to the respective air pollutant concentrations during the normal years (Ai et al., 2022).

Nepal, a South Asian country located in the central Himalayan region, is full of high mountains and deep valleys intersected by rivers. The Kathmandu Valley, which consists of the country’s three administrative districts, namely Kathmandu—the capital city, Lalitpur, and Bhaktapur, is the one having unique topography. Its bowl-shaped topography leading to restricted ventilation has been frequently highlighted by researchers as one of the major causes of poor air quality in the valley (Becker et al., 2021; Cho et al., 2017; Mahapatra et al., 2019; Panday et al., 2009; Sarkar et al., 2016; Shrestha et al., 2013; Mahata et al., 2017). The topographical uniqueness coupled with rapid urbanization and the tremendous population of the Kathmandu Valley has put the valley’s residents in prominent danger of air pollution (Saud and Paudel, 2018). Moreover, Nepal’s air quality was ranked the last among 180 countries by Environmental Performance Index in 2020 (Wendling et al., 2020). This shows that independent of the land setting, other major urban zones of Nepal too have noteworthy air quality issues. Therefore, air pollution has been a pertinent public health issue with increasing incidences of acute and chronic diseases attributable to air pollution across the country (Gurung and Bell, 2013).

In Nepal, a countrywide lockdown was imposed from 24 March to 20 July to contain the spread of the SARS-CoV-2 virus during the first wave of the COVID-19 pandemic. The air quality change during lockdowns are not geographically uniform and are determined by a mix of factors, such as major air pollutant sources, and the meteorology and topography of a region (Gao et al., 2021; Moore and Semple, 2021; Tang et al., 2021; Upadhyay et al., 2022). A recent study analyzed the change in air quality over the South Asia region, where Nepal is located, during a narrow window (25 March–30 April 2020; dry season) of the lockdown period (Upadhyay et al., 2022). However, no prior studies investigated the effect of lockdown on air quality in Nepal and its major urban center, the Kathmandu Valley, during the entire lockdown period, which included both dry and wet seasons.

This study leveraged the unprecedented incidence of the countrywide lockdown to analyze air quality in Nepal and its topographically unique urban center, the Kathmandu Valley. We investigated the change in air quality during the entire lockdown period, segmented into dry and wet seasons, based on satellite remote sensing and ground-based air quality monitoring data. Since the lockdown mostly curtailed people’s mobility, consequently affecting transport sector emissions, our results provide insights into the potential benefits, in terms of improved air quality, of controlling transport sector emissions in the urban areas of Nepal. The findings will also provide valuable insights into the country’s air pollution issues and help devise air quality management strategies.

 
2 METHODS

 
2.1 Study Domain

The geographical domain of this study is Nepal. It is a south Asian country bordered by China in the north and India in the east, west, and south. It extends from the Himalayas in the north to the Indo-Gangetic Plain in the south. The country covers more than 14.7 thousand square kilometers of the area, with a population of approximately 29.2 million in 2021 (CBS, 2022). Analyses were also conducted for the Kathmandu Valley, which is one of the largest urban centers in the country with a total area of more than 930 square kilometers and approximately three million people living in the valley as of 2021. It has a unique bowl-shaped topography with prominent air quality issues in recent decades. Both Nepal and the Kathmandu Valley have been experiencing worsening air quality in the recent past.

 
2.2 Study Design

Nepal enforced a countrywide lockdown for 119 days, from 24 March to 20 July 2020 (Pradhan et al., 2021), during the first wave of the COVID-19 pandemic. The lockdown was observed during two seasons, namely the pre-monsoon or dry season (March–May) and the monsoon or wet season (June–September). Seasonal variation in air quality is high in the Kathmandu Valley, as well as Nepal (Becker et al., 2021). Therefore, in this study, the lockdown period was segmented by seasons into two segmentslockdown during the dry season (LDD; 24 March–31 May; 69 days) and lockdown during the wet season (LDW; 1 June–20 July; 50 days)and the analysis of air quality change was done separately in the two segments. Hereafter, LDD refers to the period from 24 March to 31 May and LDW refers to the period from 1 June to 20 July in any year.

Moreover, 2020 was taken as the pandemic year, whereas years before 2020 were taken as the pre-pandemic or normal years. The air quality data for LDD and LDW in the pandemic year were compared with those in the pre-pandemic (baseline) years. This approach has been applied by researchers to analyze the effect of the COVID-19 forced lockdown on air quality (Ali et al., 2021).

There was a marked drop in road traffic and people’s mobility during the lockdown period. This can be evidenced by Google’s community mobility report for Nepal (Google LLC, 2022) (Fig. S1). For example, the mean change in community mobility at transit stations (mobility at public transport hubs, such as bus stations) was –66.6 ± 5.69% during the first segment of the lockdown (LDD in 2020) and –41.2 ± 7.42% during the second segment of the lockdown (LDW in 2020), compared to the pre-pandemic baseline.

 
2.3 Data Source and Analysis

This study used ground-based and satellite remote sensing air quality data (Table 1) to evaluate air quality change during the lockdown in 2020 in Nepal. Ground-based monitoring data of PM_2.5 from two ambient air quality monitoring stations (one at the U.S. Embassy, Kathmandu, and the other at Phora Durbar Square, Kathmandu) established by the U.S. Embassy, Kathmandu, were available from 2017 onwards. The monitoring data were obtained from the data archive of the AirNow Department of State website. Ground-based monitoring data were not openly available for other monitoring locations in the country. The available data were filtered and post-processed, and different statistics were calculated to compare the valley’s air quality between the pre-pandemic and pandemic years. The mean PM_2.5 concentrations during the LDD and LDW periods in 2017–2019 and 2020 were considered as the pre-pandemic and during-pandemic concentrations, respectively.

The data for aerosol optical depth (AOD), NO₂, and CO were obtained from the satellite remote sensing products. Google Earth Engine (GEE) is a cloud-based platform with geospatial analytical capabilities to process large geospatial datasets (Gorelick et al., 2017). We obtained all the satellite-based data products from the GEE repository of geospatial datasets and analyzed them on GEE and ArcGIS 10.7 (ESRI, 2019).

AOD (550 nm) data were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration (NASA) Terra and Aqua satellite platforms. The MCD19A2.006 data product (land AOD Level 2), which is a Terra and Aqua combined with Multi-angle Implementation of Atmospheric Correction (MAIAC) AOD product with 1 km spatial resolution, was used in the present study. The product has been shown to exhibit a good agreement with the ground-based observations and is found to be a suitable alternative for the ground-based monitoring data (Chen et al., 2021). AOD data were obtained for the 21-year period from 2000 to 2020. For the AOD analysis, 2000 to 2019 were considered the pre-pandemic years, while 2020 was considered the pandemic year.

Atmospheric NO₂ and CO column number densities were obtained from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the European Space Agency (ESA) Sentinel-5 Precursor (S-5P) satellite. The TROPOMI Offline NO₂ (OFFL L3) and CO (OFFL L3) products were available on the GEE repository at the spatial resolution of 0.01° and used in the present study. The datasets were available for the LDD and LDW periods from 2019 onwards. Therefore, the mean NO₂ and CO column number densities during the LDD and LDW periods in 2019 and 2020 were considered as the pre-pandemic levels and during-pandemic levels, respectively, as assumed by a previous study of a similar kind (Ali et al., 2021).

 
3 RESULTS AND DISCUSSION

 
3.1 Change in AOD and PM_2.5

Fig. 1 shows the long-term trend (2000–2020) of the annual spatial mean AOD during the LDD period over Nepal and the Kathmandu Valley. The long-term spatial mean or the baseline AOD during LDD for the pre-pandemic years (2000–2019) was found to be 0.381 ± 0.0803 for Nepal and 0.450 ± 0.129 for the Kathmandu Valley. As the figure depicts, the mean AODs were above the baseline in 2008–2010, 2012, 2016, 2018, and 2019 in Nepal, whereas in the remaining years, the annual mean AODs were less than the baseline. Likewise, for the Kathmandu Valley, the mean AODs were higher than the baseline in 2006, 2008–2010, 2013, 2016, 2018, and 2019, whereas in the remaining years, the annual mean AODs were less than the long-term mean. It is worth noting that, during the period of 21 years (2000–2020), the highest decrease in mean AOD compared to the baseline was observed in 2020 for both Nepal and the Kathmandu Valley. In 2020, the mean AOD decreased by 27.7% and 38.0%, respectively, in Nepal and Kathmandu, compared to the long-term means. A prior study showed an 8.6% to 24.3% decrease in average AOD over India, Bangladesh, and Pakistan, while a 2.6% increase in average AOD over the south Asia region during the lockdown period (dry season) compared to the long-term average (Upadhyay et al., 2022). Compared to those results, both Nepal and the Kathmandu Valley experienced a higher drop in the mean AOD during the lockdown.

Fig. 1. Long-term trend of mean AOD during the LDD period: (a) mean AOD over Nepal and the Kathmandu Valley; reference lines represent 20-year mean AOD for Nepal (0.381) and the Kathmandu Valley (0.450); (b) deviation of mean AOD in different years from the 20-year mean.

Table 2 shows the change in mean ambient PM_2.5 concentrations obtained from the ground-based air quality monitoring at two sites in the Kathmandu Valley during the LDD period in the pre-pandemic years (2017–2019) and the pandemic year (2020). The pre-pandemic average, which was used as the baseline for calculating deviations, was found to be 57.3 ± 5.66 µg m^–3. Compared to the baseline, mean PM_2.5 concentrations were less by 10.3 % in 2017 and 38.1% in 2020 and more by 0.975% in 2018 and 9.36% in 2019. In Jiangsu Province, China, the PM_2.5 concentration decreased by 28.9% during the implementation of the most restrictive control measures related to the COVID-19 pandemic (Ai et al., 2022), which was similar to the change in PM_2.5 concentration in the Kathmandu Valley obtained during the LDD period in 2020. These ground-based monitoring data indicated that there was a remarkable drop in ambient PM_2.5 concentrations in the Kathmandu Valley during the LDD period in 2020.

As discussed above, the analyses based on the satellite and ground-based monitoring showed a 38.0% and 38.1% drop in AOD and PM_2.5, respectively, in the Kathmandu Valley during the LDD period in 2020, compared to the pre-pandemic means. Therefore, the air quality change calculated using the satellite remote sensing and ground-based monitoring data agree well, and both suggest a notable drop in particulate matter pollution over the valley during the lockdown.

Fig. 2 shows the long-term trend (2000–2020) of the annual spatial mean of AOD during LDW over Nepal and the Kathmandu Valley. The mean AOD for the pre-pandemic years (2000–2019) was found to be 0.338 ± 0.0693 and 0.333 ± 0.615 for Nepal and the Kathmandu Valley, respectively.

Fig. 2. Long-term trend of mean AOD during the LDW period: (a) mean AOD over Nepal and the Kathmandu Valley; reference lines represent 20-year mean AOD for Nepal (0.381) and the Kathmandu Valley (0.450); (b) deviation of mean AOD in different years from the 20-year mean.

The annual mean AODs were less than the baseline mean AOD in most of the years between 2000 and 2020 (2000, 2001, 2004, 2005, 2008–2011, 2013, 2017, and 2020) in Nepal. During the LDW period in 2020, the mean AOD decreased by 7.41% from the long-term mean AOD for Nepal. The result was similar for the Kathmandu Valley too, where the mean AOD in 2020 decreased by 2.44% from the long-term mean. However, the decrease in mean AOD during the monsoon lockdown period was not the highest in 2020 over the period of 21 years (2000–2020). Despite the countrywide lockdown in 2020, the decrease in mean AOD in the LDW period was not as striking as in the case of the LDD period.

In contrast, the ground-based air quality monitoring data of the Kathmandu Valley showed a remarkable decrease in the mean PM_2.5 concentration during the LDW period in 2020. Table 2 shows the change in mean ambient PM_2.5 concentration obtained from the ground-based air quality monitoring at two sites in the Kathmandu Valley during the LDW period. The baseline mean was found to be 26.0 ± 1.38 µg m^–3. Compared to the baseline, mean PM_2.5 concentrations were less by 1.60 % in 2018, 4.32 % in 2019, and 56.4% in 2020. These data indicated the highest drop in PM_2.5 concentration in 2020 over the period of four years (2017–2020), which could be the effect of the lockdown.

Although the analysis of satellite-based AOD and the ground-based PM_2.5 data suggested improved air quality in the Kathmandu Valley during the LDW period of 2020, the amount of drop in the AOD and PM_2.5 differed greatly. One of the reasons for this might be the limitation of the satellite AOD data. The MODIS AOD data used in the present study could suffer from the bias caused by the cloud correction during the monsoon season (Becker et al., 2021). Another plausible reason is that the ground-based monitoring data used in the present study represented only a tiny portion (city core area) of the Kathmandu Valley, where the ground-level particulate pollution might have reduced significantly. However, AOD was calculated for the entire valley incorporating many land-use types with different emission sources. This means a smaller change in AOD over certain areas where the predominant emission sources (such as household cooking and refuse burning) might have remained unaffected by the lockdown, thus leading to a smaller change in the valley-average AOD.

 
3.2 Change in NO₂ Column Number Density

Figs. 3(a) and 3(b) compare average NO₂ column number densities over Nepal during the LDD period between the pandemic year (2020) and pre-pandemic year (2019). It is worth noting that the NO₂ column number densities were higher in 2019 than in 2020 over most parts of the country. The decrease in NO₂ column number densities in 2020 compared to those in 2019, expressed as percentages, were calculated for Nepal (Fig. 3(c)) and the Kathmandu Valley (Fig. 3(d)). As the figures show, the decrease was relatively higher in the southern belt of the country and the Kathmandu Valley. The mean NO₂ column number density in 2020 over Nepal was 70.5 ± 10.6 µmol m^–3, which was 12.7% less than that in 2019 (Table 3). Likewise, over the Kathmandu Valley, there was a 16.5% drop in the mean NO₂ column number density in 2020 compared to 2019. The decrease in NO₂ concentration was more striking in the Kathmandu Valley and the southern plains of the country, where the country’s major urban centers are located. One of the major NO₂ emission sources in the south Asian region, including Nepal, is road transportation (Sadavarte et al., 2019; Upadhyay et al., 2022). During the lockdown period, people’s mobility and road transportation were restricted resulting into a massive drop in transport sector NO₂ emissions, which might have caused a higher drop in NO₂ concentration over the heavily urbanized areas, such as Kathmandu, compared to rural areas in the country. It suggests that the NO₂ would be more sensitive among the pollutants to the lockdown in urban areas, as a prior study conducted in Jiangsu Province, China, suggested (Ai et al., 2022). This result also agrees with a prior investigation conducted in the UK, which concluded that NO₂ reduction during lockdown was higher at urban traffic sites and lower at background sites (Jephcote et al., 2021). Therefore, the spatial pattern of the decrease in NO₂ concentrations suggests that the lockdown had a major role to play in the drop in NO₂ column number density during the LDD period in 2020 compared to 2019. A study showed a 4.1% to 10.0% decrease in average NO₂ over Bangladesh, Pakistan, and India, while a 12.6% increase over the south Asia region during the lockdown period compared to the long-term averages (Upadhyay et al., 2022). The decrease in NO₂ column number density over Nepal obtained in the present study was comparable to that over India reported by Upadhyay et al. Moreover, a study conducted in Jiangsu Province, China, showed a 12.0% decrease in NO₂ during the implementation of the most restrictive control measure to contain the COVID-19 pandemic (Ai et al., 2022), which was comparable to the findings of the present study. However, a 12.6% increase in the region-average NO₂ column number density during the lockdown has been reported over south Asia (Upadhyay et al., 2022), which suggested that the regional analysis may not represent the sub-regional scenario of Nepal.

Fig. 3. Comparison of NO₂ column number density (µmol m^–2) during the LDD period between 2019 and 2020: (a) mean during 2019; (b) mean during 2020; (c) deviation (%) of mean NO₂ in 2020 from that in 2019 over Nepal; (d) deviation (%) of mean NO₂ in 2020 from that in 2019 over the Kathmandu Valley.

A comparison of spatial mean NO₂ column number densities over Nepal during the LDW periods was made between the pandemic year (2020) and the pre-pandemic year (2019), and presented in Figs. 4(a) and 4(b). Moreover, the decrease in NO₂ column number densities in 2020 compared to those in 2019, expressed as the percentage, was also calculated for Nepal (Fig. 3(c)) and the Kathmandu Valley (Fig. 3(d)). The NO₂ concentration difference was less prominent during the LDW period than during the LDD period. During the LDW period, the mean NO₂ column number density in 2020 over Nepal was 69.9 ± 8.85 µmol m^-2, which was only 6.87% less than that in 2019 (Table 3). Similarly, over the Kathmandu Valley, there was a nominal drop (1.50%) in the mean NO₂ column number density in 2020 compared to 2019. Although the differences in NO₂ column number densities were small, the spatial pattern of the decrease during the LDW period in 2020 was similar to that observed during the LDD period in 2020. Furthermore, a prior study reported that the decrease in NO₂ levels during lockdowns was highly correlated with mobility change (Wijnands et al., 2022). In Nepal, despite the lockdown being in effect, there was a steady increase in mobility to transit stations during the LDW period (Fig. S1). This could be one of the reasons for a smaller drop in atmospheric NO₂ concentration during the LDW period than during the LDD period.

Fig. 4. Comparison of NO₂ column number density (µmol m^–2) during the LDW period between 2019 and 2020: (a) mean during 2019; (b) mean during 2020; (c) deviation (%) of mean NO₂ in 2020 from that in 2019 over Nepal; (d) deviation (%) of mean NO₂ in 2020 from that in 2019 over the Kathmandu Valley.

 
3.3 Change in CO Column Number Density

Figs. 5(a) and 5(b) compare the average CO column number densities over Nepal during the LDD period between the pandemic year (2020) and the pre-pandemic year (2019). As the figure depicts, the CO column number densities were higher in 2019 than in 2020 over most parts of the country. Likewise, the decrease in CO column number densities in 2020 compared to those in 2019 was calculated for Nepal (Fig. 5(c)) and the Kathmandu Valley (Fig. 5(d)). The decrease in CO concentration in 2020 was higher in the southwest region of the country than in the remaining areas. However, in the Kathmandu Valley, where a notable drop in NO₂ was observed, the decrease in CO concentration was less prominent. This result reveals a different spatial pattern of the decrease in CO from that of NO₂. This observation was similar to that reported for Wuhan, where NO₂ decreased by 83%, whereas CO decreased by only 4% during the COVID-19 lockdown (Ghahremanloo et al., 2021). Furthermore, the mean CO column number density in 2020 over Nepal was 35.0 ± 11.1 mmol m^–2, which was 5.12% less than that in 2019 (Table 4). Likewise, the mean CO column number density in 2020 over the Kathmandu Valley was 39.1 ± 2.26 mmol m^–2, which was 6.03% less than that in 2019 (Table 4). The result of the present analysis done on the change in CO column number density over Nepal contrasts with that done over the South Asia region, where the region-average CO level has been shown to increase by 5.1% during the lockdown (Upadhyay et al., 2022).

Fig. 5. Comparison of CO column number density (mmol m^–2) during the LDD period between 2019 and 2020: (a) mean in 2019; (b) mean in 2020; (c) deviation (%) of mean CO in 2020 from that in 2019 over Nepal; (d) deviation (%) of mean CO in 2020 from that in 2019 over the Kathmandu Valley.

Figs. 6(a) and 6(b) compare the average CO column number densities over Nepal during the LDW period between the pandemic year (2020) and the pre-pandemic year (2019). The CO column number densities were relatively higher in 2019 than in 2020 over the southern belt of the country. Furthermore, the decrease in CO column number densities in the pandemic year compared to those in the pre-pandemic year were also calculated for Nepal and the Kathmandu Valley and presented in Figs. 6(c) and 6(d). The mean CO column number density in 2020 over Nepal was 32.2 ± 7.89 mmol m^–2, which was 2.80% less than that in 2019 (Table 4). Likewise, the mean CO column number density in 2020 over the Kathmandu Valley was 35.0 ± 1.55 mmol m^–2, which was 2.29% less than that in 2019. The decrease in CO concentration during the LDW period was less than that during the LDD period in the pandemic year. As depicted by Fig. S1, mobility to transit stations during the LDW period was steadily increasing despite the lockdown being in effect in Nepal. This might be a reason for a smaller drop in CO concentration during the LDW period than during the LDD period.

Fig. 6. Comparison of CO column number density (mmol m^–2) during the LDW period between 2019 and 2020: (a) mean in 2019; (b) mean in 2020; (c) deviation (%) of mean CO in 2020 from that in 2019 over Nepal; (d) deviation (%) of mean CO in 2020 from that in 2019 over the Kathmandu Valley.

In summary, the decrease in column number density for CO was less than that for NO₂ during both LDD and LDW periods. This observation might be explained by the characteristics of the major emission sectors of air pollutants in this region. In Nepal, the residential sector is the predominant CO emission contributor (Sadavarte et al., 2019). The residential sector emission activities can be expected to be less affected during the lockdown, thus making the change in the CO concentration less conspicuous than that in the NO₂ concentration during the lockdown.

 
4 CONCLUSIONS

This study analyzed the air quality change in Nepal and the Kathmandu Valley during the COVID-19 lockdown in 2020 based on satellite remote sensing and ground-based air quality monitoring data. There was a remarkable improvement in air quality during the pre-monsoon (dry season) period of the lockdown (24 March–31 June), as suggested by a clear decrease in PM_2.5, AOD, NO₂, and CO. However, during the monsoon (wet season) period of the lockdown (1 June–20 July), the change in AOD, NO₂, and CO concentrations was less noticeable, which could be partly due to a steady increase in mobility to transit stations during the LDW period in Nepal. Since the lockdown mainly restricted people's mobility and operation of industries, it might have caused reduced emissions from the transport and industrial sectors. Therefore, the results of the present study provide insights into the potential improvement in air quality that could be achieved by controlling emissions from those sectors, which is useful in formulating urban air quality management strategies.