Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

Received: May 29, 2022
Revised: December 7, 2022
Accepted: January 9, 2023

 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:

Pushpawela, B., Shelton, S., Liyanage, G., Jayasekara, S., Rajapaksha, D., Jayasundara, A., Jayasuriya, L.D. (2023). Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka. Aerosol Air Qual. Res. 23, 220223. https://doi.org/10.4209/aaqr.220223

HIGHLIGHTS

• Analyses the impact of COVID-19 lockdown on CO, O₃, NO₂, SO₂, and PM concentrations.
• The response of NO₂ to the lockdown was the most sensitive in the two cities.
• PM₁₀ and PM_2.5 concentrations declined by 40–55% during the lockdown.
• Daytime O₃ concentration has increased by 7–28%, compensated for drops in NO₂.
• Findings  highlight air quality burden on the industrial and transportation sectors.

Keywords: COVID-19, Air quality, Air pollutants, Polar plots

1 INTRODUCTION

COVID-19 is caused by newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first identified in Wuhan, China, in December 2019. Compared with other coronaviruses such as SARS-CoV and MERS-CoV, SARS-CoV-2 has higher transmissibility and infectivity (Wang et al., 2020a; Zhang et al., 2021). Consequently, the virus has spread uncontrollably worldwide within a short period. The outbreak was declared a public health emergency of international concern in January 2020 and a pandemic in March 2020 (WHO, 2020). To prevent the countrywide epidemic, many countries implemented safety protocols that should effectively prevent symptomatic individuals from exposing others and entering full or partial lockdown (Cohen and Kupferschmidt, 2020; Pepe et al., 2020; Rahaman et al., 2021). Controlling the mobility associated with transport and industrial sectors also affects the socioeconomic situation and environment, yet to be quantified (Venter et al., 2020; Hernández-Paniagua et al., 2021; Rahaman et al., 2021).Air pollution has become one of the most critical problems worldwide. In urban areas, it has been observed that vehicular emissions, industrial emissions, fossil fuel combustion, and biomass burning are some of the major sources of air pollution (Wu et al., 2016; Gong et al., 2017). Several studies have shown that limiting anthropogenic emissions and activities during the lockdown period significantly changes the concentration of air pollutants and has contributed to cleaner air quality worldwide. For example, during the lockdown period, the burning of fossil fuels to generate energy is minimized due to limited human and industrial activities, reducing atmospheric pollution intensely (Paital, 2020; Venter et al., 2020; Albayati et al., 2021; Hernández-Paniagua et al., 2021; Kandari and Kumar, 2021). Paital (2020) found that air quality and pollution-free water have increased in many countries due to reducing NO₂, CO₂, and PM in the air during the lockdown period. In Lucknow and New Delhi, India, PM_2.5, NO₂, and CO decreased during the lockdown period indicating a sharp decline in overall air quality indices (Srivastava et al., 2020). Moreover, it was observed that the average concentration of SO₂ was reduced remarkably in Islamabad, Pakistan (Kandari and Kumar, 2021). Biswal et al. (2020) found that the reduction of NO₂ concentration in India was attributed to restricted anthropogenic activities during the lockdown period. Sharma et al. (2020) analyzed the concentrations of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ from March to April 2017 to 2020 in 22 cities in India and found that PM₁₀, PM_2.5, CO, and NO₂ concentrations decreased by 43%, 31%, 10%, and 18% during the lockdown period compared to previous years. They also found that O₃ concentration increased by 17% and negligible changes in SO₂.

The pandemic has also reduced air pollution in the USA, one of the world’s largest economies, whose production and consumption activities cause a high rate of air pollution. Several studies empirically investigated the association between COVID-19 cases and deaths, and PM emissions. For example, Pata (2020) studied the effects of the COVID-19 pandemic on PM_2.5 emissions in eight selected U.S. cities with populations of more than 1 million using the asymmetric Fourier causality test. Based on the Fourier test, they concluded that COVID-19 reduced PM_2.5 emissions in cities, and the increase in the number of cases of COVID-19 affected the pressure on the environment more than the increase in the number of deaths. In addition, Sarfraz et al. (2020) analyzed the relationship between COVID-19 and NO₂ in New York City during the pandemic using the Non-Linear Autoregressive Distributed Lag (NARDL) model. They showed that lockdown had played a significant role in the environmental quality of the USA.

In summary, these facts suggest that lockdown measures have positively and negatively affected air pollution concentrations. For example, the concentration of PM, CO, NO₂ and SO₂ has decreased in some countries such as China, India, and the USA (Le et al., 2020; Sharma et al., 2020; Berman and Ebisu et al., 2020), due to the reductions in the emissions of air pollutants from primary sources; therefore, outdoor air quality was improved. In contrast, other pollutants, such as O₃, have increased in some European, Asian, and Latin American countries (Sicard et al., 2020; Kerimray et al., 2020; Nakada and Urban, 2020). In addition, lockdown measures provide an opportunity to estimate the short-term effects of economic activity on air pollution and identify associated human health outcomes. For example, Dutheil et al. (2020) revealed that attempts to control the transmission of the virus might have indirect health benefits by lowering the impacts of air pollution. Nie et al. (2021) found that the largest health and economic benefits were during the lockdown period mainly due to reduction of NO₂ in main cities in China.

With the outbreak of COVID-19, Sri Lanka also implemented control strategies, including wearing masks, social distancing, quarantine, lockdowns, and travel restrictions (Erandi et al., 2020). As strict measures to minimize human mobility, the government of Sri Lanka enforced a national lockdown from March 20 to May 10, 2020 (Erandi et al., 2020). Hence, we labeled these 52 days, from March 20, 2020, to May 10, 2020, as the lockdown period in this study. The government eased the restrictions in late May 2020; therefore, 2021 was not impacted by the lockdown. During the enforced lockdown period, complete closures of schools and nonessential businesses and, in some, restricted citizen mobility were observed; however, activities related to health and essential services were not suspended. As a result of less fossil fuel combustion in transport and industrial sectors, we hypothesize that the COVID-19 lockdown period was associated with declines in ambient air pollutants in two urban cities in Sri Lanka. To the best of the author's knowledge, a detailed study of the changes in air pollutants such as CO, O₃, NO₂, SO₂, PM₁₀, and PM_2.5 and the effect of meteorological factors on air pollutant concentrations during the COVID-19 lockdown has not been previously reported for urban cities in Sri Lanka. This work thus evaluates the impact of COVID-19 lockdown on air pollutants (CO, O₃, NO₂, SO₂, PM₁₀, PM_2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka by comparison of data from the lockdown period (March 20–May 10, 2020) with its analogous period of 2019 and 2021.

 
2 METHODS

2.1 Study Area

The particle mass concentration (PM₁₀, PM_2.5) and the range of gaseous concentration (CO, NO₂, SO₂, O₃) were obtained from two Ambient Air Quality Monitoring Stations (AQMS) at Battaramulla (6.90103N:79.9265E) and Kandy (7.29262N:80.63564E). These two AQMS are operated and monitored by the air resource management and monitoring unit of the central environmental authority, Sri Lanka. The AQMS at Battaramulla (from now on, AQMS_Bat) is situated in the Colombo district and is characterized by a high degree of industrialization. In Colombo, ambient air pollution is contributed by traffic, industrial activities, power plants, and natural dust and sea salt. The AQMS at Kandy (from now on, AQMS_Kan) is located in a populated city in the middle of the country on a plateau surrounded by Knuckles and Hunnasgiriya mountain ranges (Fig. 1). The primary sources of air pollution in Kandy are road transport and household gas. This study will restrict the observations based on two stations due to limited AQMS in Sri Lanka.

Fig. 1. The location of the ambient air quality monitoring stations (red dots) on the elevation map of Sri Lanka. The green dots show the location of six major oil-fired power stations (Kelanitissa, Yugadanavi, Sapugaskanda, Lakdhanavi, Colombo Port, and Heladhanavi) in Sri Lanka.

 
2.2 Air Sampling and Analysis

To assess the air quality status of two cities during the lockdown period, data from AQMS_Bat and AQMS_Kan has been considered. The hourly concentration of six air pollutants (CO, NO₂ SO₂, O₃, PM_2.5, and PM₁₀) have been obtained. These two AQMS have been fixed in places where unrestricted airflow is ensured, with minimal influence from nearby buildings. NO₂, SO₂, CO, O₃, and air samples were collected 5 m above the ground level, and PM₁₀ and PM_2.5 samples were extracted 6 m above the ground level to minimize the ground turbulence effect. To eliminate other aerosols that cause interferences, all the gas samples pass through several filters before the measurements.

To measure the O₃ concentration, the Serinus 10 ozone analyzer with a sensitivity of 0.5 ppb (U.S. EPA designated range is 0–0.5 ppm) was used, which adopts non-dispersive ultraviolet absorption technology. The CO in ambient air was measured using the Ecotech Serinus 30 carbon monoxide analyzer, which operates in the auto-ranging range of 0–200 ppm (U.S. EPA designated range is 0–50 ppm). The Serinus 40 oxides of nitrogen analyzer measured NO₂ concentration. The range of the analyzer is 0–20 ppm (U.S. EPA designated range is 0–0.5 ppm). Sulfur dioxide (SO₂) concentration was measured using a Serinus 50 analyzer ranging from 0.3 ppb to 20 ppm (U.S. EPA designated range is 0–0.5 ppm). Ecotech SPIRANT BAM is defined as a Federal Equivalent Method of the U.S. EPA for particulate matter monitoring, which is used to measure PM₁₀ and PM_2.5. At the final stage, hourly timescale data are collected from the analyzers into the data loggers (WinAQMS) and then sent to the Client and Report Manager (Airodis). Each instrument is maintained and calibrated as directed by the operational manual and the general guidance provided by U.S. EPA.

Wind speed and direction are used to identify different source types and the concentration of a species (Harrison et al., 2001; Kassomenos et al., 2012). Polar plots visualize the mean pollutant concentrations for single species based on wind speed and direction (Grange et al., 2016). The statistical software 'R' (https://www.r-project.org/) and the open-air package (http://www.open​airproject.org/) was used to perform the analysis. In addition, in this study, we used Wilcoxon signed-rank test, which tests the hypothesis that two dependent groups have identical distributions (Wilcox, 2003).

 
3 RESULTS AND DISCUSSION

 
3.1 Diurnal Variation of Pollutants in Normal and Lockdown Periods

The diurnal variation of air pollutants arises from changes in meteorology and pollutant emissions, mainly from fossil fuel combustion and industrial sources (Hossain and Easa, 2012) and also in the rural region where people depend upon biomass burning. The restrictions on population mobility, industrial production, office work, and leisure activities may have reduced the air pollutant emissions in the two cities. Hence, the effect of restrictions on normal anthropogenic activities is analyzed by comparing air pollutant diurnal cycles constructed from hourly measurements during the lockdown period with the corresponding measure in the normal period.

Fig. 2 illustrates the diurnal variation of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ concentrations at the AQMS_Bat and AQMS_Kan during the normal and lockdown periods. Regarding PM₁₀, two prominent peaks could be identified: one in the morning between 6.00 and 8:00 LT and one in the night between 18:00 and 24:00 LT (Figs. 2(a) and 2(b)). It is observed that peak values at Kandy are slightly higher than that of Colombo. During the lockdown period, the peak daytime concentration of PM₁₀ in Colombo was reduced by 50% and in Kandy reduced by 33%. Similarly, the peak nighttime concentration of PM₁₀ in Colombo was reduced by 33% and in Kandy reduced by 28%. During the normal and lockdown periods, PM_2.5 variation closely followed the PM₁₀ pattern (Figs. 2(c) and 2(d)).

Fig. 2. Diurnal variation of average hourly concentrations of (a) PM₁₀, (b) PM_2.5, (c) CO, (d) NO₂, and (e) O₃, and (f) SO₂ during the normal (black line) and lockdown (red line) period at AQMS_Bat (left panel) and AQMS_Kan (right panel). The daytime (06:00–18:00 LT) and nighttime (18:00–06:00 LT) are demarcated by ash-shaded strips.

Figs. 2(e) and 2(f) show CO concentration at the AQMS_Bat and AQMS_Kan, respectively, consisting of two peaks during both periods. For instance, morning and nighttime peaks of AQMS_Bat have reached around 900 ppb and 750 ppb, respectively, during the normal period, and the corresponding values for AQMS_Kan were 825 ppb and 750 ppb. During the lockdown, the average CO concentration was lower than the normal period in both stations, with the highest reduction observed at the AQMS_Bat. At the AQMS_Bat, for NO₂ under the normal period (Fig. 2(g)), the morning and nighttime peaks appear between 06:00 and 08:00 LT and between 21:00 and 22:00 LT with approximately 20 ppb peak value. Interestingly, during the normal period in Kandy, the average NO₂ concentration decreased to 5 ppb after 18:00 LT (Fig. 2(h)). Compared to NO₂ concentration in the normal period, both cities depicted relatively low NO₂ concentration during the lockdown period. However, the diurnal variation of NO₂ in both cities does not show a significant variability in average concentration during the lockdown period (Figs. 2(g–h)). Both cities showed an average NO₂ concentration less than 4 ppb. The observed morning and evening peaks of PM₁₀, PM_2.5, CO, and NO₂ at both cities coincided with the morning and evening rush hour traffic.

Figs. 2(i) and 2(j) show that the diurnal variation of O₃ concentration at the AQMS_Bat and AQMS_Kan deviates from the above patterns and has only a single peak around midday during both periods. The O₃ concentration during the lockdown period is marginally higher than during the normal period. Moreover, we found that the concentration gradually decreases from evening to morning. We also found that the daytime concentration of O₃ at the AQMS_Kan is higher than that of AQMS_Bat. For instance, Colombo experiences a peak value of 32 ppb, and Kandy experiences a peak value of 36 ppb during the lockdown period, as shown in Figs. 2(i) and 2(j). Surprisingly, the average SO₂ concentration is higher in Colombo during the lockdown period, with a one-morning peak (7 ppb) around 08:00–10:00 LT. However, the nocturnal variation of average SO₂ concentration at the AQMS_Kan does not show up to a detectable level to illustrate in a graph.

Table 1 summarises the average daytime and nighttime concentrations of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ in Colombo and Kandy during normal and lockdown periods. During the lockdown period, the average PM₁₀, PM_2.5, CO, and NO₂ daytime concentrations in Colombo declined by –42.3%, –46.0%, –14.7%, and –82.2%, respectively. In contrast, O₃ and SO₂ daytime concentrations increased by 6.7% and 22.9% in Colombo. Like daytime concentrations, the nighttime pollutant concentrations, except for SO₂ and O₃ in Colombo, have reduced during the lockdown period. For instance, PM₁₀, PM_2.5, CO, and NO₂ nighttime concentrations during the lockdown were reduced by –40.2%, –41.5%, –23.8%, and –82.1% compared to the normal period. In Kandy, the average PM₁₀, PM_2.5, CO, NO₂, and SO₂ daytime concentration decreased by –39.5%, –54.2%, –8.8%, –80.9%, and –40.0%, respectively. In contrast, the O₃ concentration increased by 26.2% during the lockdown period compared to the normal period. Similarly, the average nighttime concentrations of PM₁₀, PM_2.5, NO₂, and SO₂ were reduced by –30.5%, –50.8%, –77.3%, and –3.3%, respectively. In contrast, nighttime CO and O₃ concentrations increased by 5% and 43.4% during the lockdown period.

Overall, PM₁₀, PM_2.5, CO, and NO₂ average concentrations have decreased in both cities, whereas average O₃ and SO₂ concentrations have increased during the lockdown period compared to normal conditions. The observed reduction is almost certainly due to the substantial reduction in the movement of vehicles and the shutting down of industries, restaurants, shops, administrative centers, and many others during the lockdown period.

 
3.2 Variation of Pollutants Concentration during Normal and Lockdown Periods

Hourly and daily air pollutant concentrations vary due to precursor emissions and weather conditions. Therefore, those pollutants exhibit “an anomaly” from the mean values. In this section, we calculated the concentration of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ anomalies in Colombo and Kandy from March 1, 2020, to May 31, 2020. First, we selected the normal period from March 1 to May 31, 2019, and from March 1 to May 31, 2021, and calculated the long-term mean for each air pollutant (x̅). To get the anomalous concentration, we subtracted the concentration of air pollutants on each day (x) between March 1, 2020 to May 31, 2020, from the long-term mean (x). That is, anomalous concentrations are defined as x – x̅. The positive y values in the figure indicate a positive anomaly (concentration of air pollutant is above the average concentration) of the pollutant concentrations, and when it is negative, it means a negative anomaly (concentration of air pollutant is below the average concentration).

Figs. 3(a) and 3(b) show that the anomalous PM₁₀ is averagely negative in both cities during the lockdown period. In Colombo, the anomalous PM₁₀ is positive for four days at the beginning of the lockdown period. Moreover, this graph shows a predominately positive anomalous pre-lockdown period and a negative anomalous post-lockdown period. In Kandy, the anomalous PM₁₀ has shown positive in only two days in the last week of the lockdown period. Also, the anomalous are negative just before and just after the lockdown period. Similarly, Figs. 3(c) and 3(d) show the anomalous PM_2.5 at the AQMS_Bat, and AQMS_Kan, respectively. During the lockdown period, the anomalous PM_2.5 shows negative in both cities. The anomalous graph for AQMS_Kan shows negative both pre-lockdown and post-lockdown periods. Also, the anomalous graph for AQMS_Bat shows a positive anomalous pre-lockdown period while a negative anomalous post-lockdown period.

Fig. 3. Temporal evolution of anomalous daily (a) PM₁₀, (b) PM_2.5, (c) CO, (d) NO₂, (e) O₃, and (f) SO₂ concentrations relative to the normal period (March 20–May 10 in 2019 and 2021) at AQMS_Bat (left panel) and AQMS_Kan (right panel). Note that data are shown in the pre-, during, and the post-lockdown period from March 1 to May 31, 2020. The ash-shaded strips demarcate the lockdown period (March 20–May 10, 2020).

Figs. 3(e) and 3(f) illustrate that the anomalous CO at AQMS_Bat and AQMS_Kan have similar patterns. For both cities, positive anomalous CO dominates the pre-lockdown period, while a contrast pattern is observed during the lockdown period. In addition, the graph drawn for AQMS_Bat shows a higher level of a negative anomaly during the post-lockdown period. Negative anomalous NO₂ is observed in both cities during the lockdown, whereas high negative anomalous NO₂ is recorded at the AQMS_Bat relative to the concentration recorded at AQMS_Kan (Figs. 3(g) and 3(h)). Interestingly, we found negative anomalies just before and after the lockdown period; however, the recorded magnitude of negative anomalous NO₂ is lower than that of the values obtained for the lockdown period. Meanwhile, a few days with positive anomalies are also observed in both cities during the pre-lockdown period.

Compared to the concentration of PM₁₀, PM_2.5, NO₂, and CO anomalies, a contrasting pattern with mostly positive anomaly values is observed in the graphs for O₃ concentrations of both cities (Figs. 3(i) and 3(j)). Nevertheless, different from the NO₂ graph, the values in the O₃ graph have fluctuated highly. A few days with negative anomalies are also present in both graphs during the lockdown and the post-lockdown periods. The anomalous SO₂ for the two cities (Figs. 3(k) and 3(l)) largely differ. The graph obtained for Colombo only shows positive anomalies throughout the study period, which may be attributed to the continuous operation of power plants and port activities in Colombo. However, the graph for Kandy mainly consists of negative anomalous SO₂, especially during the lockdown period, which may be attributed to transport restrictions.

 
3.3 Contributions of Meteorological Factors and Emission Patterns

The air pollutants concentrations and dispersion are mainly influenced by meteorological parameters such as temperature, solar radiation, relative humidity, rainfall, wind speed, and wind direction (Pushpawela et al., 2018, 2019; Shelton et al., 2022). Therefore, analysis of meteorological parameters would be helpful to assess the role of meteorology on the observed concentration changes and the impact of the emission reductions.

In Fig. 4, we look closely at the O₃ concentration at AQMS_Bat. Here, we can see a strong positive relationship between O₃ concentration and temperature, solar radiation, and wind speed during both periods and a strong negative relationship between O₃ concentration and relative humidity. The CO concentration showed the opposite pattern. That is, CO concentration negatively correlates with temperature, solar radiation, and wind speed during both periods and positively correlates with relative humidity. Wind speed has negatively influenced PM_2.5 and PM₁₀ concentrations during both periods. Furthermore, we observed that PM_2.5 concentrations vary with air temperature and solar radiation. The NO₂ concentration negatively correlated with wind speed, while the other metrological variables have no statistically significant relationship with NO₂ concentration.

Fig. 4. Corrplot for the daily air pollutant concentration (CO, NO₂, SO₂, O₃, PM_2.5, and PM₁₀) and meteorological variables for (a) normal and (b) lockdown periods at AQMS_Bat (c) normal and (d) lockdown periods at AQMS_Kan. The insignificant correlation at 95% is marked out from the corrplot. AT, RH, SR, RF, and WS represent air temperature, relative humidity, solar radiation, rainfall, and wind speed, respectively.

It is suggested from this study that the influence of temperature, wind speed, relative humidity, and solar radiation is effective in modulating air pollutant concentrations in both cities. Therefore, we further investigate the mean difference of selected parameters during two periods.

Fig. 5 shows the mean difference in temperature, relative humidity, solar radiation, and wind speed at AQMS_Bat and AQMS_Kan for the normal (black) and lockdown (red) periods. During the daytime, the mean differences for temperature and wind speed in both stations between normal and lockdown periods show a statistically significant difference (Figs. 5(a) and 5(g)). In contrast, the mean differences for relative humidity and solar radiation were not significant (Figs. 5(c) and 5(e)). During the nighttime, the mean of all meteorological variables except wind speed for the normal and lockdown periods are not statistically significant (Figs. 5(b), 5(d), and 5(f)); however, wind speed showed a statistically significant mean difference between the two periods (Fig. 5(h)). These findings suggest that the mean difference in the concentrations of the air pollutants during the normal and lockdown periods was not a product of the varying solar radiation and relative humidity as well as nighttime temperature. Thus, it can be assured that changes in the emission patterns of pollutants during the two periods are due to the lockdown and changes in wind speed and daytime temperature and will be further studied under Section 3.4.

 Fig. 5. Mean comparison of the daytime (a) temperature, (b) relative humidity, (c) solar radiation, and (d) wind speed at AQMS_Bat and AQMS_Kan (shaded) for the normal and lockdown periods. The Whisker lines indicate the 95% confidence level. The rights panel is the same as the left panel but for the nighttime. The mean with the star sign is significantly different (Wilcoxon Signed Rank Test- p < 0.05).

3.4 Relationship between Wind Speed, Direction, and Pollutant Concentrations

Wind speeds and directions strongly control the air pollutant concentration and distribution in an urban environment (Pushpawela et al., 2019). Therefore, we have investigated the dependence of pollutant concentration on wind speed and direction using bivariate polar plots. These plots visualize the mean pollutant concentration for single species based on wind speed and direction and provide information on source locations and characteristics to identify the pollution sources (Grange et al., 2016).

Fig. 6 shows the variation of mean concentrations of air pollutants with wind speed and direction during the normal and lockdown period in Colombo. During the normal period, the PM₁₀ concentration of > 60 µg m^–3 was measured at AQMS_Bat when the wind direction was predominantly from the east and the southeast (2 m s^–1). Meanwhile, a PM₁₀ concentration of 40–60 µgm^–3 was measured when the wind arrived from the west, the southwest (3.5 m s^–1), and the south (2 m s^–1). Furthermore, we found PM₁₀ concentration of > 60 µg m^–3 when the winds from the northeast (1.5 m s^–1) (Fig. 6(a)). Similar patterns were observed for PM_2.5 concentrations (Fig. 6(b)). During the lockdown period, the distribution pattern of PM₁₀ and PM_2.5 was quite similar to the normal period, with a notable reduction in concentration levels. However, Figs. 6(d) and 6(e) suggest that during the lockdown, a strong pollution source was present, as indicated by the elevated PM concentrations (> 40 µg m^–3) at low wind speeds (0–1.5 m s^–1) when the wind was from the north and the northeast. Figs. 6(c–f) showed the CO concentration with wind speed and direction during normal and lockdown periods. As shown, CO mainly arises (> 700 ppb) when the winds from northeast to south, while a comparatively lower concentration (< 400 ppb) was observed when the winds from the southwest during the normal period. However, Fig. 6(f) suggests that the potential CO source was present from the northeast to the southeast near the AQMS_Bat during the lockdown period.

Fig. 6. Polar plots of mean concentrations of (a) PM₁₀, (b) PM_2.5, (c) CO, (g) NO₂, (h) O₃, and (i) SO₂ at AQMS_Bat for the normal period (March 20–May 10 in 2019 and 2021). d, e, f, j, k, and l represent the Polar plots of mean concentrations of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ for the lockdown period. The PM₁₀ and PM_2.5 concentrations are measured in µg m^–3, while the rest of the pollutants are in ppb. The radial scale represents the wind speed in m s^–1.

According to Fig. 6(d), higher NO₂ concentration was observed during the normal period when the wind direction was from east to south (15–20 ppb) with the wind speed 0–2 m s^–1 and from the west (15–20 ppb) with the wind speed 0–3 m s^–1. Compared to the normal period, a considerable drop in the NO₂ concentration is observed in the lockdown period, with the highest recorded value of 10 ppb when the wind comes from the west to the north (0–1 m s^–1). As shown in Fig. 6(h), the O₃ concentration for the normal period at AQMS_Bat shows its maximum (> 50 ppb) when the wind arrives from the southwest at 1–3.5 m s^–1. We can see that this concentration was reduced during the lockdown period. Moreover, it could be seen that the maximum O₃ concentration recorded during the lockdown period has dropped below 30 ppb. Fig. 6(i) shows that, during the normal period, relatively high SO₂ concentrations over 10 ppb are measured when the wind comes from the west and the south with speeds 1–3 m s^–1. Similarly, the concentration obtained for the lockdown period follows a close pattern but comparatively lowered concentration levels (maximum 8 ppb).

Fig. 7. Polar plots of mean concentrations of (a) PM₁₀, (b) PM_2.5, (c) CO, (g) NO₂, (h) O₃, and (i) SO₂ at AQMS_Kan for the normal period. d, e, f, j, k, and l represent the Polar plots of mean concentrations of PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ for the lockdown period. The PM₁₀ and PM_2.5 concentrations are measured in µg m^–3, while the rest of the pollutants are in ppb. The radial scale represents the wind speed in m s^–1.

Fig. 7 shows the variation of mean concentrations of air pollutants with wind speed and direction during the normal and lockdown period in Kandy. The high PM₁₀ concentration of 40–60 µg m^–3 was measured at the AQMS_Kan when the wind arrived from the west (2–3 m s^–1) to the southeast during the normal period (Fig. 7(a)). Meanwhile, we observed a similar distribution pattern for PM₁₀ during the lockdown, with concentrations less than 40 µg m^–3. Wind speed and direction had similar influences on PM_2.5 (Fig. 7(b)).

According to Fig. 7(c), the highest CO concentrations observed during the normal period at the AQMS_Kan were > 800 ppb when the wind from the west to the northwest (1–2 m s^–1). In addition, a CO concentration of 600–700 ppb was displayed when the wind was from the southwest and the southeast. We observed a similar pattern for the lockdown period with less concentration; however, the primary source regions were not changed, as shown in Fig. 7(f). According to Fig. 7(g), it can be seen that NO₂ concentration of 15–25 ppb was measured at the AQMS_Kan when the wind was from the southeast to the west (1–3 m s^–1); meanwhile, less than 5 ppb concentrations were measured when the wind arrived from the east (0–1.5 m s^–1). During the lockdown period, the NO₂ showed a similar pattern; however, the concentration reduced dramatically (Fig. 7(j)).

The maximum O₃ concentration measured at the AQMS_Kan was > 30 ppb, especially when the wind was from the southeast during the normal period. In contrast to other pollutants, we observed higher O₃ concentrations during the lockdown period than during the normal period. During the lockdown period, the wind direction was predominantly from the southeast to the west (Fig. 7(k)). Furthermore, we observed high O₃ concentration at high wind speed and low O₃ concentration at low wind speed. The maximum SO₂ concentration measured at the AQMS_Kan was > 4 ppb when the wind was from the southeast with a speed of 1–2 m s^–1 during the normal period, which is further reduced during the lockdown period.

 
3.5 Discussion

Air pollution is mainly impacted by local emissions, meteorological factors, and chemical reactions. Several studies have found that air pollution dropped in many cities, mainly in Europe, China, India, and the USA during the lockdown, compared to pre-lockdown periods or previous years (Mahato et al., 2020; Srivastava et al., 2020; Venter et al., 2020; Gao et al., 2021; Rahaman et al., 2021).

Hoang et al. (2021) reported a decline in NO₂ during the lockdown period compared to the same time in 2019, estimating a 20% reduction of NO₂ emissions in 2020 globally. Our study also found a significant decline in the daytime and nighttime NO₂ concentration in Colombo and Kandy during the lockdown, mainly due to a reduction of NO_x emissions from vehicular traffic and industrial activities and due to a concurrent increment in O₃. Compared to other pollutants, NO₂ experienced the most significant decrease in both cities. Similarly, Gao et al. (2021) also revealed that the response of NO₂ was the most sensitive during the lockdown period in megacities in China. Rahaman et al. (2021) observed decreasing NO₂ by more than 35% in 15 major cities in India during the lockdown period. Further, Sicard et al. (2020) reported that they observed substantial reductions in NO₂ concentrations (63%) in 4 European cities (Nice, Rome, Valencia, Turin).

During the lockdown, we observed an increase in O₃ concentration in both stations, primarily due to the photolysis of NO₂ to form O₃. This indicates that the decrease in PM and NO₂ concentration may cause an increase in O₃ concentration. Similar to our findings, the mean O₃ concentration increased in 33 European countries and the UK (Lee et al., 2020; Grange et al., 2021; Sicard et al., 2020), Delhi, India (Mahato et al., 2020), Wuhan, China (Gao et al., 2021; Wang et al., 2021) and Vienna (Brancher, 2021) during the lockdown in 2020 and can be attributed to photochemical repartitioning due to the reduction in NO_x.

This study found that daytime PM was reduced in two cities by more than 40% during the lockdown period due to the substantially reduced movement of vehicles and the closing of industries, restaurants, shops, administrative centers, and many others. Similar findings were reported in India (Sharma et al., 2020; Kumari and Toshniwal, 2022). Mahato et al. (2020) found that the average concentrations of PM₁₀ and PM_2.5 in Delhi have reduced by 52% and 53%, respectively, suggesting the positive effect of the lockdown on air quality. In China, the suspension of industry and transportation is the critical reason for decreasing PM_2.5 (Wang et al., 2020b). In contrast to our findings, Gao et al. (2021) found an abnormal increase in the average atmospheric PM_2.5 concentration in Beijing during the lockdown period due to the increased formation of secondary aerosols under high humidity, wind conditions, decreasing boundary layer heights and increasing O₃ concentrations. Sicard et al. (2020) reported that the reduction in PM is much higher in Wuhan, China (42%) than in Europe (8%). Rahaman et al. (2021) found more than 40% and 47% decreases in PM_2.5 and PM₁₀ in Indian cities. We also observed a similar pattern for PM₁₀ and PM_2.5 in both cities in our study.

SO₂ is one of the critical indicators of air pollutants that are strongly related to the combustion of coal, petroleum, and chemical fuel emissions (Otmani et al., 2020). In our study, compared to Kandy, high SO₂ concentrations were found in Colombo, a highly industrialized region and a region close to the coast. Similarly, Filonchyk et al. (2020) found high concentrations of SO₂ in highly industrialized areas of China, such as the North China Plain and Yangtze River Delta.

Zambrano-Monserrate and Ruano (2020) analyzed the effect of quarantine policies on air quality in Quito, Ecuador, using a parametric approach. They found that NO₂ and PM_2.5 concentrations decreased significantly during the lockdown, but O₃ concentrations increased considerably in 2020 compared to 2018 and 2019. A similar observation was reported by Kumari and Toshniwal (2020) for, Delhi, Mumbai, and Singrauli in India. In addition, the study by Kumari and Toshniwal (2020) evaluated the global impact of COVID-19 on air quality, using ground-based data from 162 monitoring stations from 12 cities worldwide. They observed that PM_2.5, PM₁₀, and NO₂ concentrations reduced significantly during the lockdown. SO₂ concentration showed a mixed trend, and the O₃ concentration level demonstrated an increment during the lockdown at many locations, which might be due to the declined emission of NO_x.

Motor vehicles, power plants, and industrial emissions are the primary anthropogenic sources of CO in the atmosphere. Due to the limited vehicle movements and shutting-down industries, the atmospheric CO concentration in both cities in our study has decreased during the lockdown period. These results reflect those of Filonchyk et al. (2020), who also found a reduction in CO emissions in East China of about 20% due to the reduced use of coal and oil during the lockdown period. In India, the CO level decreased by 10% during the lockdown period compared to the previous years (Sharma et al., 2020).

Transportation and industry were considered the two most significant contributors to ambient VOCs (Wang et al., 2021), which play a vital role in atmospheric chemistry by contributing to the formation of ground-level ozone, influencing hydroxyl radical (OH*) and nitrogen oxides (NO_x) concentrations (Pakkattil et al., 2021; Wang et al., 2021). Rathod et al. (2021) found a decline of VOC by 38 % during the COVID-19 lockdown in Delhi compared to the normal period in 2019. Wang et al. (2021) also observed a decline of reactive aromatics and alkenes (49%–92%) during the lockdown in Nanjing, China. In our study, the understanding of VOC in the two cities was not known due to the lack of measurements.

Meteorological conditions can influence the formation, transportation, dispersion, and removal of air pollutants in the atmosphere. In our study, the analysis of Corrplot highlighted that temperature, wind speed, relative humidity, and solar radiation play an essential role in modulating air pollutant concentrations in Colombo and Kandy during the normal and lockdown periods. By comparing the mean differences of the above meteorological parameters during both periods, we noticed that wind speed and daytime temperature were the most important meteorological factors influencing the concentration of air pollutants in Colombo and Kandy. The study by Bedi et al. (2020) analyzed the effect of meteorological factors on air quality in four major cities in India. They found an increase in daily temperature and a change in relative humidity during the lockdown period. However, they concluded that the significant difference in air quality in those cities was mainly because of lockdown measures. Similarly, Ding et al. (2021) reported an increase in nitrate generation in Tianjin, China, due to higher temperature and relative humidity during the lockdown period than in the same period in 2019.

Based on the geographic location in Sri Lanka, Colombo experiences land and sea breezes, and the transport and dispersion of air pollutants in Colombo may be significantly influenced by local circulation (Shelton and Pushpawela, 2022). Previous studies also highlighted that the transport and dispersion of air pollutants in coastal areas might be substantially affected by these circulations (Shang et al., 2019; Geddes et al., 2021). As shown in Fig. 1, five major oil-fired power plants are located near AQMS in Colombo. These power plants are located north, east, and west of the AQMS. During the wind circulation in the nighttime (land breezes-breezes blowing from the land to the sea), air pollutants mainly come from the north and east and are transported toward the AQMS. During the daytime (sea breezes-breezes blowing toward the land from the sea), air pollutants from the port and power plants on the west are transported toward the AQMS. Therefore, as shown in Figs. 6(a) and 6(b), the potential pollution sources originated locally and would be located in the north, east, and west at the AQMA in Colombo. During the lockdown, the high PM concentration observed at the AQMS in Colombo suggests that the significant pollution sources would be located at the north-northeast side of the AQMS. In Kandy, fewer industries are located near the AQMS, and most air pollutants come from vehicular emissions. In addition, we assume that the wind at 850 hPa levels brings pollutants from the west and south toward Kandy. With the relatively cold temperatures in Kandy, air pollutants at higher levels move to the ground. In the AQMS, wind speeds are measured above 6 m from the ground (surface wind), which does not capture wind speed at 850 hPa. Therefore, significant PM sources in Kandy would be related to vehicular emissions and the pollutants transported from the 850 hPa levels. Yang et al. (2021) reported a similar observation. They analyzed the wind variations at the surface and their vertical profiles and found that low-level jets (850 hPa) transport air pollutants from the North China Plain.

We can use polar plots to visualize the mean pollutant concentration for single species based on wind speed and direction. These plots provide information on source locations and characteristics and can be used to identify the pollution sources (Grange et al., 2016). For example, Grange et al. (2016) used the bivariate polar plot of the mean concentration of PM₁₀, PM_2.5, and NO_x to identify sources of air pollution at monitoring sites in London. He calculated the mean concentration for wind speed and directions and showed that local and long-range sources contributed to air pollution. Similarly, Sooktawee et al. (2020) used a bivariate polar plot to characterize PM source contributions in the pollution control zone of mining and related industries in Na Phra Lan Subdistrict, Thailand. In our study, the polar plots for Colombo indicated that pollution sources were mainly localized, as depicted by high concentrations from the north, east, and west at low wind speeds (< 4 m s^–1), contributed by the emissions from power plants, industries, port, and vehicles. The results also confirmed that emissions from these sources were limited during the lockdown (Fig. 6(d) and 6(e)). In Kandy, the variation in pollutant concentrations at lower wind speeds (< 3 m s^–1) suggests that local traffic emissions influence the monitoring site during the lockdown.

 
4 CONCLUSIONS

Reducing air pollution due to restricted human activities during the lockdown period provides a unique opportunity to understand emission reductions and their effects on air quality. Therefore, we examined the spatiotemporal characteristics of particulate matter (PM₁₀ and PM_2.5) and trace gases (NO₂, O₃, SO₂, and CO) in two industrialized cities, Colombo and Kandy, Sri Lanka, during normal and lockdown periods. The findings of the study can be used to scale the emissions inventory to evaluate other policy strategies for mitigating future air pollution and calibrating the photochemical models.

Our results suggest that the NO₂, PM₁₀, and PM_2.5 concentrations during the lockdown were significantly lower than those during the normal period. In contrast, both stations showed comparatively higher O₃ concentrations during the lockdown period. From the findings of the study, it is apparent that there was a considerable improvement in air quality despite the increases in O₃ levels due to less fossil fuel combustion in transport and industrial sectors during the lockdown. This study also showed the challenges of reducing the secondary air pollutants even with strict measures to control the primary pollutant emissions.

The present work has limitations that can be worked upon in future studies. Firstly, in Sri Lanka, continuous air quality measuring stations are limited and located only in two major cities, Colombo and Kandy. Secondly, the long-term continuous air pollutant data set is limited. Therefore, our study was limited to the analysis of data available in the two urban cities, and it should be extended to a regional scale to assess the influence of air pollutant emission sources. For this purpose, data from an adequate number of AQMS or Air Quality Zonal Modeling must be adopted to assess the spatial and temporal variation of air pollution. The variation of other pollutants, such as VOCs, black carbon, and air quality index, can also be studied in future research. The results suggest the need for national-level policies for controlling traffic and industrial emissions and further studies on the formation and mitigation of secondary air pollutants. The observed positive impact of the lockdown on air pollution can provide confidence to the government and authorities that implementing strict air quality policies and emission control strategies can significantly improve the air quality, and environment of Sri Lanka, and also health of Sri Lankans.

 
ACKNOWLEDGMENTS

The authors thank the Central Environmental Authority-Sri Lanka for providing air pollutant emission data.

 
ADDITIONAL INFORMATION AND DECLARATIONS

Disclaimer

Reference to any companies or specific commercial products does not constitute.

 
Data Availability Statement

The datasets analyzed in this manuscript are not publicly available. However, request to access the datasets should be directed to This email address is being protected from spambots. You need JavaScript enabled to view it..

 
Author Contributions

SS, BP, and GL designed the research, analyzed the data, and generated the figures. BP, SS, GL, SJ, DR, and AJ wrote the original manuscript. BP reviewed the manuscript. LDJ and AJ measured air pollutant emissions. BP and SS revised the manuscript based on the reviewers’ and editor’s comments.