Aung et al. Environmental Health and Preventive Medicine
https://doi.org/10.1186/s12199-021-01014-3(2021) 26:92

RESEARCH ARTICLE

Environmental Health andPreventive Medicine
Open Access

Effect of COVID-19-restrictive measures onambient particulate matter pollution in
Yangon, Myanmar
Win-Yu Aung1, Paw-Min-Thein-Oo1, Zaw-Lin Thein1, Sadao Matsuzawa2, Takehiro Suzuki2, Yo Ishigaki3,
Akihiro Fushimi2, Ohn Mar1, Daisuke Nakajima2 and Tin-Tin Win-Shwe2*

Abstract
Background: Particulate matter (PM) is recognized as the most harmful air pollutant to the human health. The
Yangon city indeed suffers much from PM-related air pollution. Recent research has interestingly been focused on
the novel subject of changes in the air quality associated with the restrictive measures in place during the current
coronavirus disease-2019 (COVID-19) pandemic. The first case of COVID-19 in Myanmar was diagnosed on March 23,
2020. In this article, we report on our attempt to evaluate any effects of the COVID-19-restrictive measures on theambient PM pollution in Yangon.
Methods: We measured the PM concentrations every second for 1 week on four occasions at three study sites withdifferent characteristics; the first occasion was before the start of the COVID-19 pandemic and the remaining threeoccasions were while the COVID-19-restrictive measures were in place, including Stay-At-Home and Work-FromHome orders. The Pocket PM2.5 Sensor [PRO] designed by the National Institute for Environmental Studies (NIES),
Japan, in cooperation with Yaguchi Electric Co., Ltd., (Miyagi, Japan) was used for the measurement of the ambient
PM2.5 and PM10 concentrations.
Results: The results showed that there was a significant reduction (P < 0.001) in both the PM2.5 and PM10
concentrations while the COVID-19-restrictive measures were in place as compared to the measured values prior to
the pandemic. The city experienced a profound improvement in the PM-related air quality from the “unhealthy”
category prior to the onset of the COVID-19 pandemic to the “good” category during the pandemic, when the
restrictive measures were in place. The percent changes in the PM concentrations varied among the three study
sites, with the highest percent reduction noted in a semi-commercial crowded area (84.8% for PM2.5; 88.6% forPM10) and the lowest percent reduction noted in a residential quiet area (15.6% for PM2.5; 12.0% for PM10); thepercent reductions also varied among the different occasions during the COVID-19 pandemic that themeasurements were made.

* Correspondence: tin.tin.win.shwe@nies.go.jp
2
Health and Environmental Risk Division, National Institute for Environmental
Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
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Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

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Conclusions: We concluded that the restrictive measures which were in effect to combat the COVID-19 pandemic
had a positive impact on the ambient PM concentrations. The changes in the PM concentrations are considered tobe largely attributable to reduction in anthropogenic emissions as a result of the restrictive measures, althoughseasonal influences could also have contributed in part. Thus, frequent, once- or twice-weekly Stay-At-Home or
Telework campaigns, may be feasible measures to reduce PM-related air pollution. When devising such an action
plan, it would be essential to raise the awareness of public about the health risks associated with air pollution andcreate a social environment in which Telework can be carried out, in order to ensure active compliance by the
citizens.
Keywords: Air quality, COVID-19, Mobile PM Sensor, Stay-At-Home, Telework, Yangon

Background
Air pollution is the greatest environmental risk to human health and represents the world’s fourth leading
cause of premature deaths [1]. It has become a global
public health emergency that affects people of all ages inevery part of the world [2]. An increasing range of adverse health effects has been linked to air pollution, even
low levels of pollution, and this is especially true of airborne particulate matter (PM). The World HealthOrganization (WHO) estimates that about seven million
people die each year from excessive exposure to ambient
PM [3]. Currently, among the six major air pollutants,
PM is considered to be the most hazardous to humanhealth [4].
Long-term exposure to PM pollution has been reported to be potentially associated with an increase in
cardiopulmonary mortality [5], impairment in cognitive
functions [6], diabetes mellitus [7], and adverse birthoutcomes [8]. Recently, PM2.5 was found to be related toin vitro toxic potentials, such as oxidative potential, inflammatory response, aryl hydrocarbon receptor agonist
activity, and deoxyribonucleic acid damage [9].
On the other hand, the year 2020 has proven to be
one of the most catastrophic for the global population
because of the coronavirus disease-2019 (COVID-19)
pandemic, which is considered as the greatest challengethat humans have ever faced since World War II [10].
Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) has been identified as being responsible for theoutbreak of COVID-19. On December 31, 2019, theChinese authorities notified the WHO of several cases ofan unusual type of pneumonia in Wuhan City [11]. On
January 30, the WHO declared a worldwide publichealth emergency of international concern and on
March 1, the WHO declared the COVID-19 outbreak as
a global pandemic [11].
While the outbreak was confirmed in mid-January in
the Southeast Asian region, the first case of COVID-19
in Myanmar was confirmed on March 23, 2020 [12].
During the month of March, people were advised to
avoid mass gatherings and campaigns for promoting personal hygiene measures such as frequent hand washingand social distancing were introduced and encouraged
nationwide. On March 29, visits by people from all othercountries were restricted by temporary suspension of issuance of all types of visas, except visas for diplomats
accredited to Myanmar, United Nations official residents
in Myanmar, and crew of ship and aircraft operations toand from Myanmar. From April 18, 2020, the Ministryof Health and Sports (MOHS) enforced some further restrictive measures, such as Stay-At-Home and WorkFrom-Home orders; closure of factories, universities, restaurants, and shops; avoidance of social gathering of
more than 5 people; and night curfew orders from midnight to 4 am [12].
These measures during the COVID-19 pandemic
brought about an unprecedented effect on the global air
quality, and changes in the air quality related to
COVID-19-restrictive measures became a novel and interesting research topic. A thorough review of the literature on the effects of COVID-19-restrictive measures on
the air quality revealed three striking points; (1) the degrees of changes of the PM concentrations were variable,
ranging from noticeable reduction [13–17] or a slight reduction to even an unexpected increase [18–21], ascompared to the PM concentrations recorded before thestart of the COVID-19 pandemic; (2) the changes in the
PM concentrations during different measurement periods during the COVID-19 pandemic could be inconsistent [18, 22]; and (3) even within the same nation
with uniform restrictive measures in place, there could
be unpredictable changes in the PM concentrations indifferent locations with different characteristics [22].
While there are a number of reports regarding the effects of COVID-19-restrictive measures on airborne PM
pollution, we considered it necessary to conduct a local
study for our own country. Accordingly, we chose Yangon city, a metropolitan city that is the most densely
populated part of the metropolitan area of Yangon Region, and also was the epicenter of the COVID-19 outbreak. We previously assessed the ambient PM2.5 andPM10 concentrations in seven townships of the city andrecorded levels higher than the limits stipulated by the
WHO guideline [23, 24]. Recently, according to one

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

report, Myanmar showed a 28.6% reduction of the PM2.5
concentration during an initial short period of community lockdown (March 27 to April 30, 2020) as comparedto the value recorded in the corresponding period in theprevious year, 2019 [25].
Based on the aforementioned three salient points,
we randomly selected three study sites with different
characteristics in Yangon city. We verified thechanges in the ambient PM concentrations during
three periods of 2020 in which COVID-19-restrictive
measures were in place, as compared to the values recorded prior to the onset of the COVID-19 pandemic,
and also as compared to the values recorded duringcorresponding periods of the year in the previous year
2019, and to compare the percent changes among thethree different selected sites. We expected that thefindings of our study would provide useful information for judging the effectiveness of restrictive measures in reducing airborne PM pollution in themetropolitan area of Yangon.

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Material and methods
Study area

For our present study, we selected Yangon city to evaluate the impact of COVID-19-restrictive measures on theambient PM pollution in a metropolitan city of
Myanmar. The city is a commercial and industrial center, and the most heavily populated in the country. It
covers a surface area of nearly 600 kilometer squared,
and is home to over 54 million people, i.e., 10% of thetotal population of the country [26]. It is situated at alatitude of 17.1°N and longitude of 96.1°E, in the southern part of Myanmar, bordered by the Bago Region to
the North and East, the Gulf of Martaban to the South,
and the Ayeyarwady Region to the West. Its population
is growing dramatically, with continuous migration ofpeople from other parts of the country, and the city has
higher number of registered vehicles than all othermajor cities [27]. The city is now recognized as the epicenter of the COVID-19 outbreak in Myanmar, because
the majority of confirmed cases were detected here [12].

Fig. 1 Locations of study sites. A Myanmar map, B Yangon city map, C a crowded semi-commercial area in Mingalar Taungnyunt Township(MGT), D a crowded residential area in South Okkalapa Township (SOK), and E a quiet residential area in Thaketa Township (TKT)

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

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Selection of specific locations

Particulate matter measuring device

We randomly selected three study sites with different
characteristics; the first, a crowded semi-commercial
area in Mingalar Taungnyunt Township (MGT); the second, a crowded residential area in South Okkalapa
Township (SOK); and the third, a quiet residential area
in Thaketa Township (TKT) (Fig. 1).
The study site in the crowded semi-commercial area
(MGT) is surrounded by high-compact buildings in ashort lane named “90th street” that connects two busy
streets, Myanma Gone Yi Street and Daw Thein Tin
Street. Some small businesses, such as computer servicing centers, book shops, and accessories stores, operate in the area. Many shops selling a variety of foods,
including tea shops also line each side of the lane, withhigh local human activity. Moreover, there is usually ahigh traffic volume around this study site, especially during rush hours. The study site in the crowded residential
area (SOK) is close to a busy main road called “Thitsar
Road,” on which traffic congestion can be seen frequently. A small private bank, a small restaurant, twofood shops, and a bus stop are present in the vicinity ofthe sensor site. The study site in the quiet residential
area (TKT) is located in a small lane, Tarma lane, in asmall ward with just a few houses. This quiet residential
area is quite away from traffic and public congestion.

The sensor used to measure the ambient PM concentrations, called Pocket PM2.5 Sensor [PRO], was designed by the National Institute for Environmental
Studies (NIES), Japan, in cooperation with Yaguchi
Electric Co., Ltd., (Miyagi, Japan). A total of three
Pocket PM2.5 Sensor [PRO] were used in this study;
one sensor for each study site. The sensor is small,lightweight (70.7 g), and portable (Fig. 2). The detailed specification and validity assessment of the
Pocket PM2.5 Sensor [PRO] are described in our previous report of a study in which we investigated personal PM2.5 exposures among housewives anduniversity female teaching staff [28]. In brief, thePocket PM2.5 Sensor [PRO] is equipped with a global
positioning system (GPS) and can be used continuously for 45 h, with a range of measurement from 0
to 999 μg m−3. The sensor can measure both PM2.5
and PM10 every second, and the maximum memorystorage is 400 days. The stored data can be downloaded as a comma-separated value (CSV) file. The
validity of the Pocket PM2.5 Sensor [PRO] wasassessed by simultaneously comparing the sensor with
PM-712, Kimoto, a fixed real-time monitor [29] set
up at the Air Quality Research Station, NIES, Tsukuba, Japan.

Fig. 2 Pocket PM2.5 sensor [PRO]. A Dimension and color display of the Pocket PM2.5 sensor [PRO]. B Record of PM concentrations at 8:00 AM on
Monday in SOK showing color variations for different levels of PM concentrations. From left to right; first period, second period, third period, andfourth periodAung et al. Environmental Health and Preventive Medicine(2021) 26:92

Study period

We measured the ambient PM2.5 and PM10 concentrations on four occasions for a duration of one week eachin the year 2020; namely, February 24 to March 2 (first
period), May 10 to May 17 (second period), August 31
to September 6 (third period), and December 7 to December 14 (fourth period). Myanmar, one of the countries of Southeast Asia, has three seasons, namely thesummer season (mid-February to mid-May), rainy season (mid-May to mid-October), and winter season (midOctober to mid-February). Therefore, the first and second periods of measurement were at the start and endof the summer season, the third period fell within therainy season, and the fourth period fell within the winterseason.
At each study site, both the Pocket PM2.5 Sensor[PRO] (Yaguchi Electric Co., Ltd., Miyagi, Japan) and thedata logger (Thermo Recorder, TR-72U, T&D Corp.,
Nagano, Japan) for measuring the ambient temperatureand relative humidity were placed about 3–4 m from theground. In Yangon, the Stay-At-Home order and otherrestrictive measures were implemented in the second
week of April 2020. The first 1-week measurement
period in this study fell before the onset of the COVID19 pandemic, the second period fell during the first waveof COVID-19, the third period fell within the early
period of the major second wave, and the fourth period
fell beyond the peak of the second wave. We also downloaded PM data recorded in 2019 and 2020 in an online
platform,
PurpleAir(https://www2.purpleair.com/),based on measurement using well-calibrated Real-time
Air Quality Monitoring laser particle counters. We collected the PM2.5 and PM10 data for the same periods in2019 and 2020 uploaded by the PurpleAir sensor installed in the campus of the Myanmar Center for Responsible Business, Ahlone Township, Yangon.
Unfortunately, we were unable to use the data before
2019 because our reference PurpleAir sensor was set up
and operated only in February, 2019.
Statistical analysis

The Statistical Package for the Social Sciences (SPSS)
version 26 (IBM Corp., Armonk, NY, USA) was used fordata entry and statistical analysis. Data cleaning andsummarization were effected by checking the descriptive
statistics, including the mean, standard deviation (SD),minimum, maximum, medians, interquartile ranges(IQR), histograms, and box plots. The statistical significance level was set at P < 0.05. Since both the PM2.5 andPM10 data for the four periods showed a positively
skewed distribution, the data are described with box andwhisker plots. We used the non-parametric test, Wilcoxon’s signed-rank test, to compare the ambient PM concentrations among the four periods in each study site.

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Pearson’s correlation was used to access any linear correlation among the PM data from the PurpleAir sensor
and the data from the Pocket PM2.5 sensor [PRO], andalso used for correlation analysis between the hourly PM
concentrations and hourly values of ambient
temperature and relative humidity.

Results
The average values of ambient temperature and relative
humidity recorded during the four periods at the threestudy sites are shown in Table 1. The relative humidity
and ambient temperature differed among the study periods. Our correlation analysis between the PM concentrations and the temperature (r = 0.11, P = 0.003) andrelative humidity (r = − 0.06, P = 0.001) revealed only
weak correlations (r < 0.25) during all the four study periods at all the three study sites, as well as during thecorresponding periods of the previous year (r = 0.19, P =0.004 between PM concentration and temperature; r =− 0.1, P = 0.001 between PM concentration and relative
humidity).
Concentrations of particulate matter

Comparisons of the ambient PM2.5 and PM10 concentrations in the four measurement periods at the three locations are shown in Figs. 3A and B, respectively. At
MGT, in comparison to the ambient PM2.5 concentration during the first period, the concentrations in thesecond, third, and fourth periods were significantly lower(P < 0.001). In the crowded residential area (SOK) also,
the values recorded in the second, third, and fourth periods were significantly lower (P < 0.001) than the value
recorded in the first period. Similarly, in the quiet residential area (TKT) too, the concentrations recorded in
the second, third, and fourth periods were significantly
lower (P < 0.001) than the concentration recorded in thefirst period. Similarly, when the PM10 concentrations
were compared with the value recorded in the first
period, significant reductions (P < 0.0001) were noted in
all the three locations during the three lockdown periods(Fig. 3B). Thus, significant decreases of both ambient
PM2.5 and PM10 concentrations were observed when the
COVID-19-restrictive measures were in force as compared to the values recorded prior to the outbreak of thedisease, irrespective of the study location.
We also determined the trends in the changes of the
PM concentrations during the four periods at the threestudy sites. For all three study sites, a dramatic reductionof the PM2.5 concentration was observed in the second
period followed by an even more pronounced drop in
the third period. In the fourth period also, there was asubstantial reduction of the PM2.5 concentration as compared to the value recorded in first period, but the degree of this reduction was not as high as that in the(2021) 26:92

Aung et al. Environmental Health and Preventive Medicine

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Table 1 Average values of ambient temperature (T) and relative humidity (RH) in the four measurement periods at the three study
sites (2020) and from PurpleAir (2019)
Study site

First period

Second period

Third period

Fourth period

T (°C)

RH (%)

T (°C)

RH (%)

T (°C)

RH (%)

T (°C)

RH (%)

MGT

28.1

50.1

28.2

72.0

30.6

74.7

27.1

46.2

SOK

28.8

41.3

32.7

59.8

29.2

79.2

26.1

60.5

TKT

29.7

37.8

30.5

64.8

30.2

73.8

28.6

58.1

PurpleAir (2019)

33.4

41.9

36.1

47.1

31.6

60.7

28.7

40.1

MGT denotes a crowded semi-commercial crowded area; SOK denotes a crowded residential area, and TKT denotes a quiet residential area. First period: February
24 to March 2, at the start of the summer season; second period: May 10 to May 17, at the end of the summer season; third period: August 31 to September 6,
during the rainy season; fourth period: December 7 to December 14, during the winter season. The first period was pre-COVID-19, and the second, third, andfourth periods were when COVID-19-restrictive measures were in effectsecond and third periods. Similar trends were noted for
the PM10 concentrations at two of the study sites, SOK
and TKT, whereas the lowest concentration was foundin the second period at MGT (Fig. 3).

Percent changes in particulate matter concentrations

We performed further analysis to determine the relative
percent changes (%) of the PM concentrations during
the three periods of measurement after the onset of the
COVID-19 pandemic, using either the measured values
in the first period just prior to the COVID-19 outbreakor the measured values during the same periods in 2019as reference. The percent changes were determined (1)by expressing the concentration difference between
values measured in the first period and those measured
in the second, third, or fourth periods as a fraction of
the value recorded in the first period and multiplying by
100, and (2) by expressing the concentration differences
between the second, third, and fourth periods during the
COVID-19 pandemic and the corresponding periods in2019 as a fraction of the values measured in the corresponding periods in 2019 and multiplying by 100.
As mentioned earlier in the “Materials and Methods”
section, we also collected data on the PM concentrations
from the online platform, PurpleAir. There was a strong
linear correlation between the PM data recorded in PurpleAir (2020) and the data obtained from Pocket PM2.5
sensor [PRO] set at each study site; r = 0.96 at MGT,
0.99 at SOK, and 0.93 at TKT, suggesting that the recorded data using different types of sensors are comparable. Table 2 shows the percent changes in the PMconcentrations in the second, third, and fourth periods
relative to the values recorded during the same periods
in 2019. This comparison revealed marked percent reductions in both PM2.5 and PM10 concentrations during
the second period alone. A minimal reduction or even aslight percent increase was seen during the third period
and a moderate reduction during the fourth period.
Figure 4 shows a comparison of the percent changes
in the latter three periods, that is, May 10 to May 17,
August 31 to September 6, and December 7 to December 14, for the five data sources, namely PurpleAir(2019), PurpleAir (2020), MGT, SOK, and TKT, using
the values measured in the first period (February 24 toMarch 2) as reference. In 2020, during the pandemic, aTable 2 Percent changes in the ambient particulate matter
concentrations relative to the values measured during the same
periods in 2019
Study
sites

Percent changes in PM concentration (%)
Second period

Third period

Fourth period

MGT− 75.4− 18.3− 37.7

SOK− 60.3− 19.1− 33.3

TKT− 62.0− 23.1− 11.3− 83.4+ 0.5− 30.9PM2.5

PM10
Fig. 3 Ambient PM concentrations among four periods of threestudy sites. A PM2.5 concentrations and B PM10 concentrations. Line
within the box: median, cross within the box: mean, box: first andthird quartile, whiskers: non-outlier range, dots: outliers

MGT
SOK

− 64.3+ 26.6− 25.3

TKT− 65.4− 6.5− 13.4(−) Percent reduction; (+) percent increaseAung et al. Environmental Health and Preventive Medicine(2021) 26:92

Fig. 4 Comparison of the percent reductions among the five data
sources. A PM2.5 concentration and B PM10 concentration. The fivedata sources are PurpleAir (2019), PurpleAir (2020), MGT, SOK, and
TKT. Using the period, February 24 to March 2 (first period) as
reference, the percent reductions for the other three study periods,
i.e., May 10 to May 17, August 31 to September 6, and December 7
to December 14, were calculated

significant percent reduction (above 70%) in both PM2.5
and PM10 concentrations were observed at all the threestudy sites in the second and third periods. However, inthe fourth period, the percent reductions of the PM concentrations were lower as compared to those in the preceding two periods. When compared to the data from
PurpleAir (2019), the percent reductions in the secondand fourth periods were lower than the data recorded in
PurpleAir (2020), MGT, SOK, and TKT. However, inthe third period, the percent reductions were almost
equal (round about 80%) among the five data sources,
i.e., the percent reductions in 2019 were similar to thosein 2020.
Daily mean concentrations of particulate matter

Figure 5 shows the variations in the 24-h average concentrations of PM2.5 and PM10 in the four periods at thethree sampling sites. At all three sites, the daily mean
PM2.5 and PM10 concentrations in the first period, i.e.,
before the COVID-19 outbreak/lockdown, were invariably above the WHO-recommended limits (25 μg m−3
for PM2.5 and 50 μg m−3 for PM10) [3]. However, thedaily mean PM2.5 and PM10 concentrations were consistently below the set values on almost all the seven daysof both second and third periods at all the three study
sites. In the fourth period, on most of the days, thePM10 concentrations were below the set limits, while
daily mean PM2.5 concentrations were above the set
limits.

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Discussion
During the first period of measurement, prior to the implementation of the COVID-19-restrictive measures, therecorded PM data at the three study sites were in accordance with our classification; the mean PM2.5 andPM10 concentrations were the highest in MGT, which
was the most crowded area in terms of both vehicularand pedestrian traffic, while they were the lowest in
TKT, the least crowded of the test areas (Fig. 3). However, this ordering of PM concentrations in the threeareas was lost during the period of implementation of
the restrictive measures, and even reversed in MGT, thesite at which the lowest PM concentrations among thethree sites were recorded during the second and fourth
periods of measurement. Some crowded places ordensely polluted metropolitan cities with higher anthropogenic emissions of PM due to high traffic volumes
and crowded local human activities showed a greater degree of reduction of PM concentrations when COVID19-restrictive measures were in force than less urbanized
cities or quiet places [30, 31].
The finding of a significant decrease in PM concentrations during the period in which COVID-19-restrictive
measures were in effect is consistent with many recent
reports worldwide [13–17] and restricted vehicular traffic movement and temporary closure of universities, restaurants, food shops, factories, and industries arecommon possible explanations for such a finding. On
the contrary, only a slight reduction or even an unexpected increase in PM concentrations during lockdown
periods has also been reported [18–21]. These discrepant results could be due to differences in the types of restrictive measures implemented, such as total lockdown,
community lockdown, large-scale social distancing andmovement control order, differences in the enforcement
periods, local meteorological conditions, and the intensity of pre-existing anthropogenic emissions at the respective study sites.
In our study, a noticeable percent reduction (above70%) was observed at the study sites during the second and third periods of measurement, as comparedto the fourth period. Notably, in MGT, a crowded
semi-commercial area showed a consistent degree of
reduction in PM2.5 in the second period (82.9%) andthird period (84.8%) of measurement, which could
have been a result of closure of almost all shops andstores at these sites and prohibition of private vehicular movement on the streets, except for those ofhouse owners in the area during these periods. We
observed inconsistent changes of the PM concentrations among the three measurement periods during
which COVID-19-restrictive measures were in place,and this observation was in line with some previous
reports [18, 22].

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Fig. 5 Mean daily PM concentrations during the four periods at the three study sites. A Mean daily PM2.5 concentration in MGT (upper channel),
SOK (middle channel), and TKT (lower channel). B Mean daily PM10 concentration in MGT (upper channel), SOK (middle channel), and TKT(lower channel)

While some researchers compared the PM datameasured during the lockdown periods with data obtained during a period preceding the lockdown in2020 [32], many investigators compared the PM dataobtained while COVID-19-restrictive measures werein effect with those measured at the same time in
the previous one or more years [18, 33, 34], and thecomparisons were done in terms of percent changesof PM concentrations. It is conceivable that the variations in meteorological parameters during the same
period over different years would be relatively lesspronounced than the variations between the periods
before and during the lockdown within the same
year [33].

In Yangon, Stay-At-Home order, Work-From-Homeorder, and other restrictive measures, including closureof universities, factories, and restaurants allowing takeaway service only, were implemented from the second
week of April 2020. According to the Road Transportation Administration Department (RTAD), in 2020, there
were about 540,000 registered vehicles in Yangon,
among which 350,000 automobiles, that is, over twothirds, were private cars and about 41,000 vehicles were
public transport vehicles [27]. Because of the Stay-AtHome and Work-From-Home orders, there could havebeen a dramatic reduction in the number of both private
and public transport vehicles on the road, resulting in arecognizable reduction of the traffic volume.

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

The percent change in PM concentrations in the second period, that is, May 10 to May 17, were consistently
found to be greatly reduced, regardless of the type ofcomparison (Table 2 and Fig. 4). This period in May is aperiod of transition from the summer to the rainy season
and the percent changes in the PM concentrations werestill high even when the comparison was made with thedata obtained in the corresponding period of 2019, withsimilar meteorological conditions. Therefore, such an
obvious degree of reduction in this second period of
measurement could be directly attributable to the strict
obligation of the citizens to conform to the restrictive
measures during first wave of COVID-19, resulting in a
dramatic reduction in the anthropogenic emissions ofPM.
During the third period of measurement, that is, August 31 to September 6, as described earlier, the extent
of reduction of the absolute PM concentrations was evenmore pronounced (Fig. 3). Moreover, the percent reduction was also high when the comparison was made using
the data obtained during the first period, before the onset of the COVID-19 pandemic, as reference (Fig. 4).
However, the percent changes in this period relative tothe data obtained during the corresponding period of2019 showed only minimal reduction or even a percent
increase (Table 2). Another interesting finding was thatwhen the percent reductions among the five data
sources, namely PurpleAir (2019), PurpleAir (2020),
MGT, SOK, and TKT, were compared, the values were
almost equal for both PM10 and PM2.5 concentrations(Fig. 4). This finding suggests that in 2019, even in theabsence of COVID-19-restrictive measures, an obvious
percent reduction occurred during the period from August 31 to September 6. In fact, the third period fell during the rainy season in Yangon, when precipitation of
PM by rain could occur. Consequently, in the third
period of measurement, when COVID-19-restrictive
measures were in force, seasonal influence could havebeen the predominant factor contributing to the reduction in ambient PM pollution, although reduced anthropogenic emissions due to the restrictive measures
could also have contributed to the reduction.
The fourth period of measurement, namely December
7 to December 14, fell within the winter season. In contrast to the case during the rainy season, thetemperature inversion phenomenon, a favorable condition for ambient PM concentrations, commonly occurs
during the winter season. Moreover, burning of dry
leaves during the winter season could also be a possible
additional source of PM. Such seasonal factors could
have been responsible for the lower percent reductionsin the fourth period as compared to the two preceding
periods of measurement (Fig. 4). However, the percent
reductions could not be greatly determined by seasonal

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pollutant dispersion, because unlike in the third period,
there was an inconsistency in the percent reductions in
the fourth period; the percent reductions of PM2.5 andPM10 in TKT were lower than those in PurpleAir(2019), whereas those in the remaining data sourceswere greater (Table 2 and Fig. 4). A renovation projectat the Thanlyin Bridge, about 7–8 km away from the
TKT site, resumed in November and this emission
source could be a possible reason for the highest PM
concentration and lowest percent reduction in the quiet
residential quiet area. During the second wave of
COVID-19, although the COVID-19-restrictive measures were enforced again, many factories and construction sites, restaurants, and food shops resumed theirbusiness after being cleared to do so according to theguidelines set by the MOHS. Although prohibition ofmass gatherings was still in effect, there was a resurgence of human activities. Therefore, the decline in thepercent reductions in the fourth period could be attributable, at least in part, to some relaxation of the restrictive measures, with a lower degree of compliance withorders by the citizens and in part, by the presence of
weather conditions that favor PM dispersion.
Some studies have also taken into consideration seasonal variations while describing the changes in PM concentrations during the COVID-19 pandemic. In the
study reported by Hashim et al. (2020), the investigators
compared the average PM concentrations in Baghdad,
Iraq, during five periods; the first period before the enforcement of a lockdown, and the remaining four periods during partial or total lockdown. They observedthat the PM2.5 and PM10 concentrations were the lowest
during the first partial and total lockdowns among thefive periods. They speculated the following possible reasons for this finding; the citizens’ compliance with the
lockdown measures during that first lockdown period
contributed to the large decline of PM concentrations
during that period, and the dry hot climate during thesummer resulted in the relative increase of PM concentrations in the subsequent lockdown periods [18]. In areport from Thailand [32], the ambient PM concentrations were compared among three measurement periods;
pre COVID-19, early COVID-19, and while a workfrom-home order was in place. An unexpected increasein the ambient PM concentrations was noted in the early
COVID-19 period, during which only personal hygiene
measures were encouraged, without other strict restrictive measures, and there was also the seasonal transition
from winter to summer.
Meteorological factors, such as the ambient
temperature, relative humidity, wind speeds, precipitation, radiation, and ambient pressure could also exert an
influence on the ambient PM concentrations [35]. In ourstudy, the ambient temperature and relative humidity

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

were measured and their correlations with the PM concentrations were evaluated. In spite of revealing significant level of P value, only weak correlations (r < 0.25)
were noted during all the four periods of three study
sites (r = 0.11, P = 0.003 between PM concentration andtemperature; r = − 0.06, P = 0.001 between PM concentration and relative humidity). A previous study also
showed that during the COVID-19 pandemic, these two
meteorological parameters were only weakly correlatedwith the PM concentrations (r < 0.25), indicating thatthey contributed little to the ambient PM concentrations[36]. Although we found an obvious difference in thetemperature and relative humidity between the COVID19 pandemic year (2020) and the previous year (2019)(Table 1), the aforementioned correlations were also
weak for 2019 data (r = 0.19, P = 0.004 between PM
concentration and temperature; r = − 0.1, P = 0.001

Page 10 of 13

between PM concentration and relative humidity). Thesefindings could be attributable to the contributions ofother meteorological factors rather than the two aforementioned variables to the ambient PM concentrations.
Yangon city indeed suffers from PM-related air pollution. During the period from January 25 to January 29,
2018, we assessed the ambient PM2.5 and PM10 concentrations in seven townships of the city, and found thatthe mean PM concentrations were over the WHO
guideline limits [23, 24]. The average annual PM2.5 concentration (weighted by the population) in the city in2019 was 31 μg m−3, exceeding the annual mean PM2.5
exposure threshold of 10 μg m−3 set by the WHO. In regard to the ranking of regional capital cities of the world
according to the PM2.5 exposure level, Yangon is placed
19th out of 85 capital cities across the world, and ranks
third among cities in the Southeast Asia region [4]. In

Fig. 6 Summarized results. Summarization showing the effect of COVID-19-restrictive measures on ambient particulate matter pollution in
Yangon, Myanmar

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

our study, the daily mean PM2.5 and PM10 concentrations measured in the period prior to the onset of the
COVID-19 pandemic were consistently over the WHOrecommended limits (Fig. 5). Conversely, the mean daily
PM2.5 and PM10 concentrations were below the set
limits on almost all seven days of both the second andthird periods of measurement, when COVID-19restrictive measures were in effect. Therefore, in contrast
to the previous years, Yangon city experienced a profound improvement of PM-related air quality in 2020,
and this appears to be, in all probability, due to the restrictive measures proposed for COVID-19 containment.
Moreover, as compared to previous reports from studies
conducted worldwide, the percent reductions in ourstudy were relatively higher. This could be due to thefact that the city, in which no prior proactive air pollution control measures were in place, experienced a rapid
and effective restriction of anthropogenic emissions for avery first time during the period of enforcement of
COVID-19-restrictive measures (Fig. 6).
The United States Air Quality Index (USAQI) is themost widely used for assessment of the ambient air quality. The average 24-h PM2.5 concentrations are converted into six categories of AQI, where higher values
indicate a higher health risk [4]. In our study, the air
quality during the first period of measurement prior tothe onset of COVID-19 fell into either the unhealthy
category (55.5–150.4 μg m−3) or the category of “unhealthy for sensitive group (USG),” such as children, elderly persons, and patients with cardiovascular disease(35.5–55.4 μg m−3). However, improvement in air quality became apparent during the second and third periodsof measurement, when COVID-19 lockdown was inplace, and the AQI category became moderate (12.2–
35.4 μg m−3) or even good (0–12.1 μg m−3), i.e., the air
quality became satisfactory and posed little or no risk.
Unfortunately, on a few days during the fourth period,
the AQI was categorized as USG.
In addition, as fine particles are more harmful thancoarse particles, higher PM2.5/PM10 ratios may result inserious air pollution, whereas the lesser the ratio, thelesser the possibility of poor air quality [30]. A higher ratio implies predominant contribution of PM2.5, which is
generally ascribed to primary pollution by anthropogenic
emissions, while a lower ratio suggests a greater contribution of coarse particles, which mainly arises from natural sources [37]. In a study from South Korea, after the
implementation of social distancing, the PM2.5/PM10 ratio decreased from 0.66 to 0.4 in Seoul and 0.68 to 0.54
in Daegu city, and this finding was explained by a decrease in anthropogenic emissions [17]. In our study
also, the PM2.5/PM10 ratios declined during the COVID19 measurement periods as compared to the preCOVID-19 values; from 0.82 to 0.79 in MGT, from 0.86

Page 11 of 13

to 0.82 in SOK, and from 0.88 to 0.82 in TKT. This finding indicates that when the COVID-19-restrictive measures were in place, the PM-related air quality improved,
and more specifically, that reducing human activities favors reduction of the anthropogenic sources of PM2.5 rather than PM10. A study in Wuhan city showed that not
only did the mass concentration of PM2.5 reduce, but its
chemical composition also altered during the period of
enforcement of COVID-19-restrictive measures [38]. Although we only assessed the mass concentrations of PM
in this study, we propose to analyze the PM compositions in Yangon cut using a high-volume sampler in the
future [39].

Conclusions
We took advantage of the rare opportunity, during the
COVID-19 pandemic, to investigate the atmospheric PM
response to rapid, widespread anthropogenic emission
reductions. Our results revealed a remarkable reduction
in both the PM2.5 and PM10 concentrations while
COVID-19-restrictive measures were in force in Yangon
city, indicating that these restrictive measures had apositive impact on the ambient PM concentrations. Thechanges in the PM concentrations were considered to belargely attributable to the reduction in anthropogenic
emissions as a result of the restrictive measures in place,but there could also have been seasonal influences. The
scenario of reduction of PM concentrations while restrictive measures were in force during the COVID-19
pandemic highlights the fact that the compliance of citizens with the implementation of environmental policies
on air quality could be very essential for effective reduction of anthropogenic emissions contributing to pollution. Therefore, when devising an action plan for
limiting PM emissions, it is necessary to first raise theawareness of the public about the health risks associatedwith air pollution. According to our findings, the restrictive measures, including the Stay-At-Home order,
were brilliant examples of ways to control the sources ofemission. Thus, frequent implementation of a weekly or
biweekly or Telework policy may be a feasible way of reducing PM pollution and further longitudinal studies todetermine the effects of such short-term application of
restrictive measures on the incidence of PM-related
health problems is also recommended.
Abbreviations
PM: Particulate matter; COVID-19: Coronavirus disease-2019; NIES: National
Institute for Environmental Studies; SARS-CoV-2: Severe acute respiratory
syndrome coronavirus 2; WHO: World Health Organization; MOHS: Ministry of
Health and Sports; MGT: Mingalar Taungnyunt; SOK: South Okkalapa;
TKT: Thaketa; GPS: Global positioning system; CSV: Comma-separated value;
SPSS: Statistical Package for the Social Sciences; SD: Standard deviation;
IQR: Interquartile range; T: Temperature; RH: Relative humidity; USAQI: UnitedState Air Quality Index; AQI: Air Quality Index; USG: Unhealthy for sensitivegroup

Aung et al. Environmental Health and Preventive Medicine(2021) 26:92

Acknowledgements
This study was a joint research based on “Letter of Agreement” between
NIES, Tsukuba, Japan and University of Medicine 1, Yangon, Myanmar. We arethankful to PurpleAir for sharing PM2.5 and PM10 data (2019 and 2020).
Authors’ contributions
OM, T-TW-S, DN, Z-LT, and W-YA designed the study. OM, T-TW-S, and DN
supervised the investigation. DN, AF, TS, and YI performed the validation. SM
managed Software. W-YA and P-M-T-O performed the statistical analysis andwrote and edited the original draft. OM, T-TW-S, DN, and AF supervised themanuscript writing. All authors read and approved the final manuscript.
Funding
This study was partly supported by a research grant (1818AC002) from the
National Institute for Environmental Studies (NIES), Tsukuba, Japan.
Availability of data and materials
The datasets are not opened to the public but are available from thecorresponding author on reasonable request.

9.

10.

11.

12.

13.

Declarations

14.

Ethics approval and consent to participate
This study was a continuation of the study protocol approved by the Ethical
Review Committee, Department of Medical Research (Head Quarter), MOHS,
Myanmar with the approval number (Ethics/DMR/2017/130A/2018).

15.

Consent for publication
Not applicable.

16.

Competing interests
The authors declare that they have no competing interests.

17.

Author details
1
Department of Physiology, University of Medicine 1, Kamayut Township,
11014 Yangon, Myanmar. 2Health and Environmental Risk Division, National
Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506,
Japan. 3Graduate School of Informatics and Engineering, The University ofElectro-communication, Chofu, Tokyo 182-8585, Japan.

18.

19.

Received: 7 June 2021 Accepted: 1 September 2021
20.
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