Uncertainty in the Impact of the COVID-19 Pandemic on Air Quality in Hong Kong, China - MDPI
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
atmosphere
Communication
Uncertainty in the Impact of the COVID-19 Pandemic
on Air Quality in Hong Kong, China
Yuhan Huang 1, * , John L. Zhou 1 , Yang Yu 1, * , Wai-chuen Mok 1 , Casey F.C. Lee 2
and Yat-shing Yam 2
1 Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology
Sydney, Sydney, NSW 2007, Australia; junliang.zhou@uts.edu.au (J.L.Z.); wcmok888@hotmail.com (W.-c.M.)
2 Environmental Protection Department, Hong Kong Special Administrative Region Government,
Hong Kong, China; caseylee@epd.gov.hk (C.F.C.L.); y_s_yam@epd.gov.hk (Y.-s.Y.)
* Correspondence: yuhan.huang@uts.edu.au (Y.H.); yang.yu@uts.edu.au (Y.Y.);
Tel.: +61-(0)-415-040-942 (Y.H.); +61-(0)-431-809-539 (Y.Y.)
Received: 20 July 2020; Accepted: 24 August 2020; Published: 27 August 2020
Abstract: Strict social distancing rules are being implemented to stop the spread of COVID-19
pandemic in many cities globally, causing a sudden and extreme change in the transport activities.
This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and
provides a valuable reference to the policymakers in developing air quality control measures and
projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on
the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring
data collected in January–April 2020 with those in 2017–2019. The results showed that the roadside
and ambient NO2 , PM10 , PM2.5 , CO and SO2 were generally reduced in 2020 when comparing
with the historical data in 2017–2019, while O3 was increased. However, the reductions during
COVID-19 period (i.e., February–April) were not always higher than that during pre-COVID-19
period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant
concentrations in every year. This study implies that one air pollution control measure may not
generate obvious immediate improvements in the air quality monitoring data and its effectiveness
should be evaluated carefully to eliminate the effect of seasonal variations.
Keywords: COVID-19; traffic disruption; roadside and ambient air quality; criteria pollutants
1. Introduction
The COVID-19 pandemic is spreading rapidly worldwide since its first outbreak in Wuhan, China
in December 2019 [1,2]. By 12 August 2020, it has spread to 216 countries, areas or territories, causing
20 million infections and 737,000 deaths [3]. The pandemic has greatly affected nearly every aspect of
human society, such as public health, economy, social activities, politics and the environment. Since
the disease spreads primarily from person to person through small droplets from the nose or mouth
with close contact (e.g., within 1 m) [4], strict social distancing rules have been implemented to stop
the spread of the COVID-19 pandemic, such as working from home, reduced capacity of restaurants
and shops, closure of borders, and limits on large public gatherings. As a result, many countries or
cities have entered into various levels of lockdown shortly after when local transmissions were found.
These lockdowns have greatly reduced the human activities such as road transport, civil aviation and
navigation. Among them, road transport is the most affected sector. Road transport is a major source
of air pollution in urban areas [5–7] and has a severe adverse effect on the public health due to its
close proximity to the people [8,9]. Thus, it is widely perceived that the pandemic lockdowns would
help mitigate the air pollution problem. Although such environmental benefits are not expected to
Atmosphere 2020, 11, 914; doi:10.3390/atmos11090914 www.mdpi.com/journal/atmosphereAtmosphere 2020, 11, 914 2 of 9
be long lasting [10], it offers a unique opportunity to assess how a sudden and extreme change in
human activities could affect the air pollution problem, which provides very useful information to the
policymakers on developing air pollution control measures and projecting their effectiveness [11].
As a global megacity, Hong Kong has been facing serious air pollution problem for many years
and great efforts have been devoted to the control of mobile emissions. However, it is often found
difficult to confirm the air quality improvements due to different emission control programs [12].
Brimblecombe and Ning [12] and Brimblecombe [13] investigated the effect of road blockages during
the 2014 and 2019 street protests on air quality in Hong Kong by using roadside air quality monitoring
stations located near the protest sites. It was found that the improvement on air quality was not
always obvious due to seasonal variations in meteorological conditions and synoptic transport of
pollutants. Since the protests were mostly taken place at designated places, the analysis was only
performed in the nearby roadside air quality stations but not in a wider area. It is worth mentioning
that the ground level air pollution in Hong Kong is largely a local problem and regional air pollutants
transportation may only play an insignificant role. A previous study reported that Hong Kong’s air
pollution still exceeded the World Health Organization (WHO) limit twice even when there was no
wind from Mainland China, although its air pollution was, on average, 15% worse when winds blew
from Mainland China [14].
Hong Kong reported its first two COVID-19 cases on 23 January 2020 [15] and first death on
4 February 2020 [16]. Following that, the city acted swiftly to implement a series of social distancing
rules to stop the spread. This enables us to evaluate how a city-wide traffic disruption would benefit
the air quality. Therefore, this study is conducted to investigate how the COVID-19 social distancing
rules have affected the roadside and ambient air quality in Hong Kong.
2. Methods
As part of the city’s air quality management, the Hong Kong Environmental Protection Department
(HKEPD) is currently deploying 3 roadside and 13 ambient air quality monitoring stations to measure
the criteria air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2 ), ozone (O3 ), sulfur
dioxide (SO2 ), respirable suspended particulates (PM10 ) and fine suspended particulates (PM2.5 ).
The pollutant concentrations are being continuously measured every hour by automatic analyzers.
The 3 roadside stations are placed at 3.0–4.5 m above ground at the commercial and tourist areas
(i.e., Central, Causeway Bay and Mong Kok). The 13 ambient stations are placed 11.0–28.0 m above
ground which are distributed more evenly across the city. The heights of the roadside and ambient
stations are chosen to gather air quality information representative of the exposure of the roadside
pedestrians and the people in a building, respectively. The roadside stations are placed close to roads
and the ambient stations are usually at the rooftop of buildings. More details about their locations,
sensor models, measurement principles and accuracy specifications can be found in [17,18].
Air quality data during 1 January to 30 April 2020 are analyzed in this study. Table 1 lists the
timelines of the key COVID-19 events and social distancing rules in Hong Kong. Since the report of
the first two positive cases in late January and the first death in early February, the city has introduced
a series of control measures to stop the spread of the pandemic. Therefore, the air quality data are
categorized into two groups: the pre-COVID-19 period in January 2020 and the COVID-19 period in
February–April 2020. Historical data collected during the same months in 2017–2019 are also analyzed
to show the yearly variations.
Table 1. Timelines of key COVID-19 events and social distancing rules in Hong Kong.
Date Key COVID-19 Events or Travel Restrictions
23 January 2020 First two cases were confirmed
25 January 2020 An “emergency” warning was declared
28 January 2020 Partial closure of border with mainland China
4 February 2020 First death was reportedAtmosphere 2020, 11, 914 3 of 9
Table 1. Cont.
Date Key COVID-19 Events or Travel Restrictions
5 February 2020 Mandatory 14-day quarantine for those from mainland China
Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 9
25 March 2020 Closure of border to all non-residents from overseas
27 March 2020 Indoor and outdoor public gatherings >4 people were banned
273 March 2020
April 2020
Indoor and outdoor public gatherings >4 people were banned
All bars and pubs were closed
35April 2020
May 2020 All bars and limit
Public gathering pubs eased
were closed
from 4 to 8
5 May
19 June2020
2020 Public gathering limit eased from84toto50
Public gathering limit eased from 8
19
11 June 2020
July 2020 Publicrules
Social distancing gathering limitdue
tightened eased
to afrom 8 to
rise in 50
COVID-19 cases
11 July 2020 Social distancing rules tightened due to a rise in COVID-19 cases
3. Results
3. Results
Figure 1 showed the average pollutant concentrations measured at the 3 roadside air quality
Figure 1 showed the average pollutant concentrations measured at the 3 roadside air quality
monitoring stations during January–April in 2017–2020. The month-to-month variations were relatively
monitoring stations during January–April in 2017–2020. The month-to-month variations were
largerelatively
and there wasand
large no there
clear was
variation
no clear tendency
variation from January
tendency fromto January
April fortoeach
Aprilyear fromyear
for each 2017from
to 2020,
due 2017
to the tochanges
2020, dueintometeorological conditions. This
the changes in meteorological indicates
conditions. that
This a simple
indicates comparison
that of COVID-19
a simple comparison
period (i.e., January 2020) vs. pre-COVID-19 period (i.e., February–April
of COVID-19 period (i.e., January 2020) vs. pre-COVID-19 period (i.e., February–April 2019) 2019) would lead to would
a random
conclusion
lead to a random conclusion on the effect of COVID-19 on air quality. Therefore, comparing 2020 datadata
on the effect of COVID-19 on air quality. Therefore, comparing 2020 data with 2017–2019
in the same
with month (Table
2017–2019 data in2)theis more
same reliable
month (Table due to2)the smaller
is more difference
reliable due tointhemeteorological
smaller difference conditions.
in
meteorological
Generally, the 2020conditions.
CO, SO2 , NO Generally,
2 , PM 10 the
and 2020
PM CO,
2.5 SO 2, NO2, PM10showed
concentrations and PM 2.5 concentrations
reductions when showed
comparing
withreductions
the 2017–2019 whendata,
comparing
except withfor a the
few2017–2019
comparisons data,suchexcept for a2 few
as NO comparisons
in April 2019 andsuch April as2018
NO2 which
in
April 2019 and April 2018 which were actually slightly lower than
were actually slightly lower than the corresponding 2020 data. Specially, the emissions in April 2020 the corresponding 2020 data.
wereSpecially,
generallythe emissions
higher than thein April 2020 year,
previous were although
generally the higher
humanthanemission
the previous year, were
activities although the in
reduced
human emission activities were reduced in April 2020 due to the COVID-19 social distancing. This
April 2020 due to the COVID-19 social distancing. This increase may be because the meteorological
increase may be because the meteorological conditions were not favorable for air movements and
conditions were not favorable for air movements and pollutants dispersion, thus resulting in the higher
pollutants dispersion, thus resulting in the higher pollutant concentrations observed. As shown in
pollutant concentrations observed. As shown in Table 2, during the pre-COVID-19 period (i.e., January),
Table 2, during the pre-COVID-19 period (i.e., January), 2020 data showed reductions of −2% to 7%
2020for
dataCO, showed
17% to reductions
32% for SO2of −2%
, 13% to to
18% 7%forforNOCO, 17% to 32% for SO , 13% to 18% for NO2 , 2.5
2, 14% to 31% for PM10,2 and 19% to 33% for PM
14%whento 31%
for PM ,
comparing
10 and 19% to 33% for
with 2017–2019 in thePM when comparing with 2017–2019 in the same
2.5 same month. While during the COVID-19 period (i.e., February– month. While during
the COVID-19
April), 2020period (i.e., February–April),
data showed reductions of −40% 2020todata
21%showed
for CO, reductions
−42% to 52% −40%
offor SO2to 21% to
, −10% for28%
CO,for −42%
to 52%
NOfor2, −8%SO2to , −10%
37% forto 28%
PM10for , andNO 1%2 , −8%
to 44%to 37%
for PM for2.5PM 10 , andO1%
. Finally, 3 wasto 44% for PM
the only 2.5 . Finally,
pollutant O3 was
that was
constantly
the only pollutant higher
thatinwas
2020constantly
than that in 2017–2019,
higher in 2020withthan24% thattoin39% higher inwith
2017–2019, pre-COVID-19
24% to 39%period higher in
and 1% to 72% higher in COVID-19 period.
pre-COVID-19 period and 1% to 72% higher in COVID-19 period.
1400 10 120
CO SO2 NO2
700 5 60
0 0 0
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020
80 40 80
O3 PM2.5 PM10
40 20 40
0 0 0
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020
Figure 1. Average roadside pollutant concentrations measured at the three roadside air quality
Figure 1. Average roadside pollutant concentrations measured at the three roadside air quality
monitoring stations. All pollutants units are in µg/m3 .
monitoring stations. All pollutants units are in μg/m3.Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 9
Atmosphere
Table2020, 11, 914 changes of roadside emissions of 2020 over 2017–2019 in each month. Negative and 4 of 9
2. Relative
positive percentages indicate emission reductions and increases, respectively.
Table 2. Relative changes of roadside emissions
January Februaryof 2020 over 2017–2019
March in each month. Negative
Apriland
positive percentages indicate emission(COVID-19)
(pre-COVID-19) reductions and increases, respectively.
(COVID-19) (COVID-19)
Pollutants 2020 January
2020 2020 2020 February
2020 2020 2020 March 2020 2020 2020 April
2020 2020
vs.(pre-COVID-19)
vs. vs. vs. (COVID-19)
vs. vs. vs. (COVID-19)
vs. vs. vs.(COVID-19)
vs. vs.
Pollutants 2019
2020 20182020 2017
2020 2019
2020 2018
2020 2017
2020 2019
2020 2018
2020 2017
2020 2019 2020
2020 2018 2020
2017
CO −7%
vs. +2%
vs. −3%
vs. −7%
vs. −21%
vs. −9%
vs. +1%
vs. −13%
vs. −12%
vs. +40%
vs. +4%
vs. −2%
vs.
SO2 2019 2018 2017 2019 2018 2017
−29% −32% −17% 0% −52% −33% +31% −23% −11%2019 2018 2017 2019 2018
+42% −6% −11% 2017
CO2
NO −7% −13%
−18% +2% −13%
−3% −7% −28%
−2% −21% −24%
−9% +1%
−1% −17%−13% −12%
−20% +40%
+10% +4% +5% −2%−6%
−29% −32% −17% −52% −33% +31% −23% −11% +42%
SO2
O3 0%
+39% +24% +27% +46% +48% +20% +8% +6% +1% +53% +72% −11%
−6%
+72%
NO2 −18% −13% −13% −2% −28% −24% −1% −17% −20% +10% +5% −6%
PMO310 −31%
+39% −14%
+24% −18%
+27% −16%
+46% −37%
+48% −26%
+20% +8% −16%
+8% +6% −27%
+1% +6% +72%
+53% −27% +72%
−12%
PM
PM102.5 −33%
−31% −19%
−14% −26%
−18% −20%
−16% −44%
−37% −38%
−26% +8%
−1% −22% −32%
−16% −27% +6%
−9% −27%
−27% −12%
−21%
PM2.5 −33% −19% −26% −20% −44% −38% −1% −22% −32% −9% −27% −21%
Regarding the ambient air quality (Figure 2), CO and NO2 were much lower, PM10 and PM2.5
were slightly lower, SO2 was similar while O3 was higher than their concentrations at roadside (Figure
Regarding the ambient air quality (Figure 2), CO and NO2 were much lower, PM10 and PM2.5 were
1). Similar to the tendencies observed at roadside monitoring stations, Figure 2 showed that all
slightly lower, SO2 was similar while O3 was higher than their concentrations at roadside (Figure 1).
criteria pollutants in 2020 demonstrated reductions when comparing with 2017–2019, except for O3
Similar to the tendencies observed at roadside monitoring stations, Figure 2 showed that all criteria
which generally increased. During the pre-COVID-19 period, as shown in Table 3, 2020 data show
pollutants in 2020 demonstrated reductions when comparing with 2017–2019, except for O3 which
reductions of −7% to 17% foe CO, 30% to 49% for SO2, 16% to 32% for NO2, 23% to 35% for PM10, and
generally increased. During the pre-COVID-19 period, as shown in Table 3, 2020 data show reductions
25% to 36% for PM2.5 when comparing with 2017–2019 data. During the COVID-19 period, the
of −7% to 17% foe CO, 30% to 49% for SO2 , 16% to 32% for NO2 , 23% to 35% for PM10 , and 25% to 36%
corresponding reductions were −21% to 18%, −1% to 48%, −2% to 33%, −16% to 38%, and −4% to 44%,
for PM2.5 when comparing with 2017–2019 data. During the COVID-19 period, the corresponding
respectively.
reductions were −21% to 18%, −1% to 48%, −2% to 33%, −16% to 38%, and −4% to 44%, respectively.
1400 10 120
CO SO2 NO2
700 5 60
0 0 0
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020
80 40 80
O3 PM2.5 PM10
40 20 40
0 0 0
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
2017 2018 2019 2020 2017 2018 2019 2020 2017 2018 2019 2020
Figure 2. Average ambient pollutant concentrations measured at the 13 ambient air quality monitoring
Figure 2. Average ambient pollutant concentrations measured at the 13 ambient air quality
stations. All pollutant units are in µg/m3 .
monitoring stations. All pollutant units are in μg/m3.Atmosphere 2020, 11, 914 5 of 9
Table 3. Relative changes of ambient emissions of 2020 vs. 2017–2019 in each month. Negative and
positive percentages indicate emission reductions and increases, respectively.
January February March April
(pre-COVID-19) (COVID-19) (COVID-19) (COVID-19)
Pollutants 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs.
2019 2018 2017 2019 2018 2017 2019 2018 2017 2019 2018 2017
CO −17% +7% −8% −7% −18% −16% −8% −1% −18% +21% +16% −15%
SO2 −30% −42% −49% −14% −45% −48% +1% −34% −39% −5% −29% −43%
NO2 −32% −17% −16% −8% −33% −24% −12% −20% −23% +2% 0% −12%
O3 +24% +12% +14% +18% +11% −1% +1% −7% −10% +20% +21% +32%
PM10 −35% −23% −29% −5% −38% −32% +2% −19% −35% +16% −30% −18%
PM2.5 −36% −25% −34% −7% −44% −40% −6% −24% −39% +4% −24% −24%
4. Discussion and Implications
The results in Figures 1 and 2 and Tables 2 and 3 showed that roadside and ambient NO2 , PM10 ,
PM2.5 , CO and SO2 in January-April 2020 were generally reduced when comparing with the historical
data in 2017–2019, while O3 was increased. However, comparing with the pre-COVID-19 period
(i.e., January), the reductions were not always higher (emissions were even increased sometimes)
during the COVID-19 period (i.e., February–April) when social distancing rules were introduced
which greatly reduced the human movements, in particular the road transport and civil aviation
sectors. Table 4 shows the monthly road traffic data of total passengers taking the public transport
and the total vehicles through the Cross Harbor Tunnels during the studied periods. In particular,
the Cross Harbor Tunnels link the Kowloon peninsula with Hong Kong Island, which are the two
most important business districts. Therefore, the numbers of vehicles through them largely represent
the traffic activities in Hong Kong. It should be noted that, before the pandemic, Hong Kong had
been experiencing on-going large social unrests since June 2019 which had significantly reduced the
civil aviation sector [19]. The social unrests had also reduced/stopped the traffic in the protest sites,
but might only contribute insignificant reductions in the overall traffic of the whole city because the
traffic could be diverted to other roads. Therefore, the COVID-19 lockdown since February 2020 mainly
reduced the road transport sector in Hong Kong. This is evidenced in the data in Table 4, which show
that road transport has been reduced by 15.7% of total passengers and 7.7% of total vehicles in January
2020, and then reduced obviously higher by 39.6–41.4% and 13.5–21.5%, respectively, in February
to April 2020 when comparing with the same months last year. The small difference in pollutant
reductions between pre-COVID-19 and COVID-19 periods could be explained by the inventories of
anthropogenic emissions in Table 5, which show that road transport and civil aviation are not the
dominant contributors of air pollutants even when they are combined, except for CO. As a result,
the COVID-19 lockdown did not obviously reduce the pollutant concentrations.
Finally, both roadside and ambient O3 concentrations in 2020 were higher than those in 2017–2019.
This is because O3 is a secondary air pollutant which is formed through photochemical reactions of
gaseous precursors (mainly NO2 and volatile organic compounds (VOCs)) in the presence of sunlight.
Higher O3 emissions were usually observed with NO2 reductions which could have changed O3
formation from VOCs sensitive regime to mixed sensitive regime, as reported in previous studies [18,20].
Since NOx (i.e., NO + NO2 ) are involved in both the formation and reduction of O3 , the net formation
of O3 is actually determined by the ratio of NO2 /NOx [18]. Figure 3 shows the average NO2 /NOx ratios
at both the roadside and ambient stations during the studied period. Generally, NO2 /NOx ratios were
higher in April 2020, which explained the higher O3 concentrations observed.
Overall, this study demonstrates that the effect of COVID-19 lockdown on air quality improvement
is not always obvious. There are large seasonal pollutant variations, which suggest that a simple
short-term comparison of pre-COVID-19 vs. COVID-19 periods could draw misleading conclusions on
the effect of COVID-19 on air quality improvement. Taking roadside CO as an example (Figure 4),Atmosphere
Atmosphere2020,
2020,11,
11,x914
FOR PEER REVIEW 66ofof99
lower in February–April than January in 2017, −15% to 20% lower in 2018, 12% to 46% lower in 2019
which
and 12%is to
dominated
19% lower byinroadside transportthere
2020. Although (as shown
was noinpandemic
Table 5), lockdown
it was 6%into2017–2019,
20% lowerCO in
February–April than January in 2017, −15% to 20% lower in 2018, 12% to 46% lower
reductions of February–April vs. January were observed in 2017–2019 and the reduction was even in 2019 and 12%
to 19%inlower
bigger in 2020.
2019 (12% Although
to 46%) there
than that in was
2020no pandemic
(12% to 19%).lockdown in 2017–2019, CO reductions of
February–April vs. January were observed in 2017–2019 and the reduction was even bigger in 2019
(12%Table
to 46%) than that
4. Monthly in 2020
traffic (12%
data of totalto 19%). taking the public transport and total vehicles through
passengers
the Cross Harbor Tunnels during January–April 2017–2020 [21].
Table 4. Monthly traffic data of total passengers taking the public transport and total vehicles through
Traffic Year Tunnels
the Cross Harbor January February
during January–April 2017–2020 [21]. March April
2017 393,374 (+1.7%) 355,731 (+3.7%) 397,004 (+4.2%) 370,591 (−1.2%)
Total
Traffic Year January February
2018 398,100 (+1.3%) 351,335 (−1.1%) 400,049March
(+0.7%) 379,102April
(+2.5%)
passengers
2019
2017 414,391
393,374(+4.0%)
(+1.7%) 357,526
355,731(+1.8%)
(+3.7%) 407,630
397,004(+1.7%)
(+4.2%) 380,383
370,591(+0.3%)
(−1.2%)
Total
(thousands) 2018
2020 398,100(−15.7%)
349,532 (+1.3%) 351,335(−41.0%)
217,999 (−1.1%) 400,049
246,284 (+0.7%)
(−39.6%) 379,102
222,646 (+2.5%)
(−41.4%)
passengers
2019 414,391 (+4.0%) 357,526 (+1.8%) 407,630 (+1.7%) 380,383 (+0.3%)
(thousands) 2017 7,958,585 (+0.5%) 7,147,909 (−0.7%) 8,290,839 (+4.1%) 7,576,014 (−2.5%)
2020 349,532 (−15.7%) 217,999 (−41.0%) 246,284 (−39.6%) 222,646 (−41.4%)
Total 2018 8,066,085 (+1.4%) 7,121,978 (−0.4%) 8,323,358 (+0.4%) 7,724,664 (+2.0%)
2017 7,958,585 (+0.5%) 7,147,909 (−0.7%) 8,290,839 (+4.1%) 7,576,014 (−2.5%)
vehicles 2019
2018 8,257,283
8,066,085(+2.4%)
(+1.4%) 7,027,811
7,121,978(−1.3%)
(−0.4%) 8,382,402
8,323,358(+0.7%)
(+0.4%) 7,696,259
7,724,664(−0.4%)
(+2.0%)
Total
vehicles 2020
2019 7,624,328
8,257,283(−7.7%)
(+2.4%) 6,078,955
7,027,811(−13.5%)
(−1.3%) 6,631,756 (−20.9%)
8,382,402 (+0.7%) 6,041,706 (−21.5%)
7,696,259 (−0.4%)
2020 in7,624,328
Notes: Percentages (−7.7%)
parentheses are the6,078,955 (−13.5%)
changes when 6,631,756
comparing with (−20.9%) 6,041,706
the same month (−21.5%)
last year.
Notes: Percentages in parentheses are the changes when comparing with the same month last year.
Table 5. Inventories of anthropogenic emissions by sectors in 2017 in Hong Kong [22].
Table 5. Inventories of anthropogenic emissions by sectors in 2017 in Hong Kong [22].
Pollutants
SO2 NOx PM10 PM2.5 VOC CO
Sectors Pollutants
Electricity generation 43%SO2 27%NOx PM10
16% PM 2.5
10% VOC
2% CO6%
Sectors
Navigation
Electricity generation 52%43% 37%27% 34%
16% 41%
10% 17%
2% 6%24%
Road transport
Navigation 0%52% 20%37% 10%
34% 13%
41% 19%
17% 24%53%
CivilRoad
aviation
transport 4% 0% 7% 20% 1%
10% 2%
13% 2%
19% 53%8%
Civil aviation
Other combustion 1% 4% 9%7% 16%1% 2%
19% 2%3% 8%10%
Other combustion
Non-combustion N/A1% N/A 9% 16%
22% 19%
15% 3%
57% 10%
N/A
Non-combustion N/A N/A 22% 15% 57% N/A
TotalTotal
(tonnes)
(tonnes) 16,160
16,160 84,960
84,960 4020
4020 3120
3120 25,520
25,520 57,110
57,110
Notes: N/A, not applicable.
Notes: N/A, not applicable.
60% 80%
Roadside Ambient
45% 65%
30% 50%
Jan Feb Mar Apr Jan Feb Mar Apr
2017 2018 2019 2020 2017 2018 2019 2020
Figure
Figure3.3.Average
Averageroadside
roadsideand
andambient
ambientNO /NOxxratios.
NO22/NO ratios.
AAnumber
numberof ofstudies
studieshave
haveusedusedthetheair
airquality
qualitymonitoring
monitoringdata datatotoinvestigate
investigatethe theenvironmental
environmental
benefitsof
benefits ofCOVID-19
COVID-19lockdowns
lockdownsinindifferent
differentcities,
cities,which
whichusually
usuallycompared
comparedthe thepre-COVID-19
pre-COVID-19vs. vs.
COVID-19in
COVID-19 inshort
shortterms
termsand
andreported
reportedsignificant
significantreductions
reductionsof ofsome
somepollutants,
pollutants,ranging
rangingfrom
fromabout
about
10% to over
10% over 50%.
50%. Sharma
Sharmaetetal.al.
[23] compared
[23] compared thethe
concentrations
concentrations of criteria pollutants
of criteria during
pollutants 16 March
during 16
to 14 April
March to 142017–2020 in 22 Indian
April 2017–2020 in cities. Collivignarelli
22 Indian et al. [24] compared
cities. Collivignarelli the criteria
et al. [24] compared pollutants, black
the criteria
carbon, benzene
pollutants, black and ammonia
carbon, benzene dataand
of three 14-daydata
ammonia periods in 2020
of three in Italy,
14-day including
periods the reference
in 2020 in Italy,
period during 7–20 February, the partial lockdown during 9–22 March, and
including the reference period during 7–20 February, the partial lockdown during 9–22 March, the total lockdown during
and
23 March
the to 5 April.
total lockdown Berman
during and Ebisu
23 March to 5 [25] compared
April. the PM
Berman and 2.5 and
Ebisu [25] NO 2 data during
compared the PM82.5January
and NOto 2
data during 8 January to 21 April 2017–2020 in the United States. Chauhan and Singh [26] compared
the PM2.5 data during December–March 2017–2020.effect of meteorological conditions on air pollution is a complicated problem. A number of factors
can affect the air quality in a city, such as the wind speed and direction, temperature, humidity,
rainfall and solar radiation. These factors influence the air movements and thus the dispersion of air
pollutants. Besides the geographical conditions and chemical reactions also have significant effects
on the air2020,
Atmosphere movements
11, 914 and formation of secondary pollutants (e.g., O3, PM2.5 and PM10). All these
7 of 9
factors are highly dynamic which require further investigations on their effects on seasonal variations
in ground level air pollution. This study also implies that one air pollution control measure may not
21 April 2017–2020
necessarily generateinobvious
the United States. Chauhan
improvements in the and Singh [26]
short-term air compared the PM2.5data
quality monitoring dataand
during
the
December–March 2017–2020.
effectiveness should be evaluated carefully to eliminate the seasonal and meteorological effects.
20%
Percentages of concentration changes
10%
0%
-10%
-20%
-30% Feb vs Jan
Mar vs Jan
-40%
Apr vs Jan
-50%
2017 2018 2019 2020
Survey year
Figure
Figure 4.4. Percentages
Percentagesofofroadside
roadsideCO CO concentration
concentration changes
changes of COVID-19
of COVID-19 period
period (i.e., February–
(i.e., February–April)
April)
relativerelative to pre-COVID-19
to pre-COVID-19 period period (i.e., January)
(i.e., January) foryear.
for each each year.
5. Conclusions
This study suggests that such short-term comparisons should be interpreted with caution as the
seasonal
Thisvariations are relatively
study investigated large which
the effect of the could easilysocial
COVID-19 mask distancing
the real changes
rules oncaused by lockdown
the roadside and
ambient air quality in Hong Kong, China. The concentrations of six criteria pollutants (i.e., the
and, thus, could lead to misleading conclusions. Further investigations are needed to explore CO,causes
NO2,
and
O characteristics of the seasonal variations, particularly the meteorological conditions. The effect of
3, SO2, PM10 and PM2.5) collected during January–April 2020 were compared with the same months
meteorological
in conditionstoonthe
2017–2019. According airdevelopment
pollution is aofcomplicated
COVID-19 problem.
pandemicAinnumber of factors
Hong Kong, the can affect
collected
the air quality in a city, such as the wind speed and direction, temperature,
data were categorized into two groups, namely the pre-COVID-19 period (i.e., January 2020) and humidity, rainfall and
solar radiation. These factors influence the air movements and thus the dispersion
COVID-19 period (i.e., February–April 2020). The results showed that the effect of COVID-19 on air of air pollutants.
Besidesimprovement
quality the geographicalwas conditions
not always and chemical
obvious. reactions
Comparing with alsothehave significant
historical data ineffects on the the
2017–2019, air
movements and formation of secondary pollutants (e.g.,
roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally O 3 , PM and PM
2.5 reduced10 ). All these factors
while O3 was increased are
highly dynamic which require further investigations on their effects on
in January–April 2020. The implementation of the social distancing rules had obviously seasonal variations in ground
reduced the
level air pollution. This study also implies that one air pollution control measure may
road transport activities in terms of total passengers taking the public transport and the total vehicles not necessarily
generatethe
through obvious
Crossimprovements
Harbor Tunnels. in the short-term
Despite airreductions
this, the quality monitoring data and the
during COVID-19 effectiveness
period were not
should higher
always be evaluated carefully
than that duringtopre-COVID-19
eliminate the period,
seasonalandandsometimes
meteorological effects. were even higher
the emissions
during the COVID-19 period. In addition, there were large seasonal variations in the monthly mean
5. Conclusions
pollutant concentrations in every year, suggesting that a simple comparison of pre-COVID-19 vs.
This study
COVID-19 investigated
periods would draw the effect of the conclusions
misleading COVID-19 social distancing
on the effect of rules on theon
COVID-19 roadside and
air quality
ambient air quality
improvement. This in Hong
study Kong,that
implies China.
oneThe
air concentrations of six
pollution control criteriamay
measure pollutants (i.e., CO,
not generate NO2 ,
obvious
O , SO
immediate
3 2 , PM and PM
improvements
10 ) collected during January–April 2020 were compared with the same
2.5 in the air quality monitoring data and its effectiveness should be evaluatedmonths
in 2017–2019.
carefully According
to eliminate to theofdevelopment
the effect seasonal andof COVID-19 pandemic
meteorological in Hong Kong, the collected
variations.
data were categorized into two groups, namely the pre-COVID-19 period (i.e., January 2020) and
COVID-19 period (i.e., February–April 2020). The results showed that the effect of COVID-19 on air
quality improvement was not always obvious. Comparing with the historical data in 2017–2019, the
roadside and ambient NO2 , PM10 , PM2.5 , CO and SO2 were generally reduced while O3 was increased
in January–April 2020. The implementation of the social distancing rules had obviously reduced
the road transport activities in terms of total passengers taking the public transport and the total
vehicles through the Cross Harbor Tunnels. Despite this, the reductions during COVID-19 period were
not always higher than that during pre-COVID-19 period, and sometimes the emissions were even
higher during the COVID-19 period. In addition, there were large seasonal variations in the monthly
mean pollutant concentrations in every year, suggesting that a simple comparison of pre-COVID-19Atmosphere 2020, 11, 914 8 of 9
vs. COVID-19 periods would draw misleading conclusions on the effect of COVID-19 on air quality
improvement. This study implies that one air pollution control measure may not generate obvious
immediate improvements in the air quality monitoring data and its effectiveness should be evaluated
carefully to eliminate the effect of seasonal and meteorological variations.
Author Contributions: Y.H. completed the analysis and wrote the manuscript with contributions from all the
co-authors. Conceptualization, Y.H.; formal analysis, Y.H.; writing—original draft preparation, Y.H. and J.L.Z.;
writing—review and editing, Y.H., J.L.Z., Y.Y., W.-c.M., C.F.C.L. and Y.-s.Y.; supervision, J.L.Z. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest. The contents of this paper are solely the
responsibility of the authors and do not necessarily represent official views of the Hong Kong SAR Government.
Data availability: The data that support the findings of this study are available from the HKEPD website at
https://cd.epic.epd.gov.hk/EPICDI/air/station/?lang=en.
References
1. Carducci, A.; Federigi, I.; Verani, M. Covid-19 Airborne transmission and its prevention: Waiting for evidence
or applying the precautionary principle? Atmosphere 2020, 11, 710. [CrossRef]
2. Li, Q.; Guan, X.; Wu, P.; Wang, X.; Zhou, L.; Tong, Y.; Ren, R.; Leung, K.S.M.; Lau, E.H.Y.; Wong, J.Y.; et al.
Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N. Engl. J. Med.
2020, 382, 1199–1207. [CrossRef] [PubMed]
3. WHO. Coronavirus Disease (COVID-19) Pandemic. Available online: https://www.who.int/emergencies/
diseases/novel-coronavirus-2019 (accessed on 12 August 2020).
4. WHO. Q&A on Coronaviruses (COVID-19)—How Does COVID-19 Spread? Available
online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-
a-detail/q-a-coronaviruses (accessed on 16 June 2020).
5. Anenberg, S.C.; Miller, J.; Minjares, R.; Du, L.; Henze, D.K.; Lacey, F.; Malley, C.S.; Emberson, L.; Franco, V.;
Klimont, Z. Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets.
Nature 2017, 545, 467–471. [CrossRef] [PubMed]
6. Huang, Y.; Surawski, N.C.; Yam, Y.S.; Lee, C.K.C.; Zhou, J.L.; Organ, B.; Chan, E.F.C. Re-evaluating
effectiveness of vehicle emission control programs targeting high-emitters. Nat. Sustain. 2020. [CrossRef]
7. Zhou, J.; Tie, X.; Yu, Y.; Zhao, S.; Li, G.; Liu, S.; Zhang, T.; Dai, W. Impact of the emission control of diesel
vehicles on Black Carbon (BC) concentrations over China. Atmosphere 2020, 11, 696. [CrossRef]
8. Zhang, L.; Liu, W.; Hou, K.; Lin, J.; Zhou, C.; Tong, X.; Wang, Z.; Wang, Y.; Jiang, Y.; Wang, Z. Air
pollution-induced missed abortion risk for pregnancies. Nat. Sustain. 2019, 2, 1011–1017. [CrossRef]
9. Kampa, M.; Castanas, E. Human health effects of air pollution. Env. Pollut. 2008, 151, 362–367. [CrossRef]
[PubMed]
10. Guerriero, C.; Haines, A.; Pagano, M. Health and sustainability in post-pandemic economic policies. Nat.
Sustain. 2020, 3, 494–496. [CrossRef]
11. Zhang, R.; Zhang, Y.; Lin, H.; Feng, X.; Fu, T.-M.; Wang, Y. NOx emission reduction and recovery during
COVID-19 in East China. Atmosphere 2020, 11, 433. [CrossRef]
12. Brimblecombe, P.; Ning, Z. Effect of road blockages on local air pollution during the Hong Kong protests and
its implications for air quality management. Sci. Total Env. 2015, 536, 443–448. [CrossRef] [PubMed]
13. Brimblecombe, P. Street protests and air pollution in Hong Kong. Env. Monit. Assess. 2020, 192, 295.
[CrossRef] [PubMed]
14. Neo, K.W. So You Thought Hong Kong’s Pollution Comes from Mainland China? Smart Air. Available
online: https://smartairfilters.com/en/blog/so-you-thought-hong-kongs-pollution-comes-from-mainland-
china/ (accessed on 11 August 2020).
15. Centre for Health Protection. Two Confirmed Imported Cases of Novel Coronavirus Infection in Hong Kong
and the Revised Reporting Criteria. Available online: https://www.chp.gov.hk/files/pdf/letters_to_private_
hospitals_20200123.pdf (accessed on 22 June 2020).Atmosphere 2020, 11, 914 9 of 9
16. The Straits Times. Hong Kong Reports First Death from Coronavirus, 39-Year-Old Had Travelled to
Wuhan. Available online: https://www.straitstimes.com/asia/east-asia/hong-kong-reports-first-death-from-
coronavirus (accessed on 22 June 2020).
17. HKEPD. Air Quality in Hong Kong in 2017. Available online: http://www.aqhi.gov.hk/api_history/english/
report/files/AQR2017e_final.pdf (accessed on 20 July 2020).
18. Huang, Y.; Mok, W.-C.; Yam, Y.-S.; Zhou, J.L.; Surawski, N.C.; Organ, B.; Chan, E.F.C.; Mofijur, M.;
Mahlia, T.M.I.; Ong, H.C. Evaluating in-use vehicle emissions using air quality monitoring stations and
on-road remote sensing systems. Sci. Total Env. 2020, 740, 139868. [CrossRef] [PubMed]
19. CAPA. Hong Kong Traffic Decline Hurts Airlines and Airport. Available online: https://centreforaviation.
com/analysis/reports/hong-kong-traffic-decline-hurts-airlines-and-airport-505593 (accessed on 19 June
2020).
20. Wang, N.; Lyu, X.; Deng, X.; Huang, X.; Jiang, F.; Ding, A. Aggravating O3 pollution due to NOx emission
control in eastern China. Sci. Total Env. 2019, 677, 732–744. [CrossRef] [PubMed]
21. HKTD. Monthly Traffic and Transport Digest. Available online: https://www.td.gov.hk/en/transport_in_
hong_kong/transport_figures/monthly_traffic_and_transport_digest/index.html (accessed on 20 July 2020).
22. HKEPD. Hong Kong Air Pollutant Emission Inventory. Available online: https://www.epd.gov.hk/epd/
english/environmentinhk/air/data/emission_inve.html (accessed on 20 July 2020).
23. Sharma, S.; Zhang, M.; Anshika; Gao, J.; Zhang, H.; Kota, S.H. Effect of restricted emissions during COVID-19
on air quality in India. Sci. Total Env. 2020, 728, 138878. [CrossRef] [PubMed]
24. Collivignarelli, M.C.; Abbà, A.; Bertanza, G.; Pedrazzani, R.; Ricciardi, P.; Carnevale Miino, M. Lockdown
for CoViD-2019 in Milan: What are the effects on air quality? Sci. Total Env. 2020, 732, 139280. [CrossRef]
[PubMed]
25. Berman, J.D.; Ebisu, K. Changes in U.S. air pollution during the COVID-19 pandemic. Sci. Total Env. 2020,
739, 139864. [CrossRef] [PubMed]
26. Chauhan, A.; Singh, R.P. Decline in PM2.5 concentrations over major cities around the world associated with
COVID-19. Env. Res. 2020, 187, 109634. [CrossRef] [PubMed]
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).You can also read
