This study analyzed the nature, mechanisms and drivers
for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China.
Numerical model simulations were conducted for the summer and autumn of
2009–2011. A total of eight HPEs were identified, mainly occurring in
August and September. K-means clustering was applied to group the HPEs into
three clusters based on their characteristics and mechanisms. We found three
HPEs were driven by weak subsidence and convection induced by approaching
tropical cyclones (TC-HPE) and two HPEs were controlled by calm (stagnant) conditions
(ST-HPE) with low wind speed in the lower atmosphere, whereas the remaining
three HPEs were driven by the combination (hybrid) of both aforementioned systems
(HY-HPE). A positive synergistic effect between the HPE and urban heat island (UHI; ∼ 1.1 ∘C increase) was observed in TC-HPE and ST-HPE, whereas no
discernible synergistic effect was found in HY-HPE. Total aerosol radiative
forcing (TARF) caused a reduction in temperature (0.5–1.0 ∘C) in
TC-HPE and ST-HPE but an increase (0.5 ∘C) in HY-HPE.
Introduction
Air pollution and heat waves have been identified as major atmospheric
environmental disasters (Khafaie
et al., 2016; Xia et al., 2018). Air pollution can vary at spatial scales
ranging from local to global (Shi
et al., 2020; M. Y. Wang et al., 2019; Y. Wang et al., 2019; Yang et al., 2019; Yim
et al., 2009, 2014, 2010, 2015). Studies have
reported the significant effects of air pollution on human health
(Chen
et al., 2017; Gu et al., 2018; Liu et al., 2017), ecosystems and
biodiversity (Lovett
et al., 2009; Nowak et al., 2015; Yim et al., 2019b) and weather and
climate (Guo
et al., 2016, 2018; Liu et al., 2018, 2019, 2020).
Heat waves are generally defined as extended periods of elevated
temperatures across the globe varying in frequency, intensity and duration.
Previous studies have revealed the significant impacts of heat waves on
human health and mortality
(Matusick et al.,
2018) and natural systems (Unal et al.,
2013). More seriously, heat wave events are likely to occur more frequently
in future years as a consequence of climate change
(Murari
and Ghosh, 2019; Silva et al., 2016; Wang et al., 2018). Despite their
large spatial heterogeneity, air pollution and heat waves have been shown to
share several common underlying meteorological drivers (Founda
and Santamouris, 2017; Papanastasiou et al., 2014; Zhang et al., 2017).
Although great advances have been made in the study of either air pollution or heat
waves, few studies have comprehensively investigated hot-and-polluted
episodes (HPEs), in which extreme temperature and air pollution occur
simultaneously, causing an even more serious impact on human health due to
their synergic effect (Pinheiro
et al., 2014; Qian et al., 2010; Scortichini et al., 2018). Before this
effect can be comprehensively assessed, fully understanding the driving
mechanisms underlying the formation and remediation of HPEs is imperative.
The processes responsible for HPE formation and intensity vary case by case
(Fan
et al., 2011; Katsouyanni et al., 1993; Ordóñez et al., 2010; Yim,
2020). For instance, Yim (2020) analyzed the
atmospheric conditions in 3D during these events and found that HPEs were
mostly associated with a reduction in both vertical and horizontal wind
velocities. By contrast, Fan et al. (2011) illustrated that approaching tropical cyclones, which may cause
subsidence, weak vertical diffusion and poor horizontal transport, were the
mechanism responsible for the HPEs. To obtain a complete picture of all
possible mechanisms of HPEs, a multi-episodic study is therefore warranted.
The surface energy balance, which modifies and demarcates turbulent energy
fluxes, has been well illustrated to influence heat waves
(Founda and Santamouris, 2017; Li et al., 2015; Li and Bou-Zeid, 2013; Miralles et
al., 2014). For instance, Miralles et al. (2014)
found that soil desiccation led to the modification of turbulent heat fluxes
through the changes of latent and sensible heat fluxes. Li and
Bou-Zeid (2013) and Li et al. (2015) demonstrated the effect of built-up and
vegetated surfaces on the demarcation and modification of turbulent energy.
However, the interaction between surface characteristics and atmospheric
conditions during HPEs, which may exhibit synergies, has yet to be
completely understood. The role of surface characteristics in HPE formation
and development merits further investigation.
Studies are limited in China and few and far between in the Pearl River
Delta (PRD) region.
Previous HPE-related studies in China have been focused mostly on summertime
O3 mechanisms and characteristics
(Gong and Liao, 2019; Lam et al., 2005; N. Li et al., 2018; Shu et al., 2020), while
others have focused on atmospheric drivers for, and interactions between, air
pollution and mortality. However, other studies analyzed the atmospheric
boundary layer characteristics over PRD using measurements and numerical
models to identify boundary layer conditions that could result in HPEs (Fan et al., 2008, 2011; Yim, 2020). Thus far, there has been
limited research on HPE mechanisms in China, particularly in the PRD region, which has been rapidly and substantially urbanized in
recent years (Li et al., 2016).
The urbanization has a significant impact on the regional climate and air
quality through modification of the ecosystem, land surface, atmospheric
and energy processes (Mirzaei
and Haghighat, 2010; Y. Wang et al., 2019, 2020; Xie et al., 2016; Yim et al.,
2019c; Yu et al., 2014). Mirzaei and
Haghighat (2010) identified the ways urbanization modified the surface
cover, climate and energy processes, including the conversion of more
surfaces into urban impervious surfaces; alteration of the local winds;
humidity, temperature and precipitation patterns; and changes in
demarcation in turbulent energy fluxes within the surface and boundary
layer. Furthermore, PRD is susceptible to events associated with the monsoon
and tropical cyclone activities that may cause HPEs (Fan
et al., 2011; H. Li et al., 2018), making the PRD region an ideal location for
assessing HPEs.
This study identified all the HPEs that occurred during 2009–2011 and analyzed
their associated thermodynamic and circulation characteristics.
Representative episodes were examined for a possible synergistic
relationship between the urban/vegetated land covers and the HPEs within the
PRD region (see Sect. S1 and Fig. S2 in the Supplement for details on land cover
characteristics and delineation of urban and vegetated surfaces). This is
expected to contribute to advancing knowledge regarding the factors
responsible for the evolution and sustenance of HPEs as well as the
relationship between HPEs and surface characteristics.
Materials and methodsObservations
This study used meteorological and air quality measurements for model
validation and HPE identification. Hourly air temperature at 2 m above
ground (T2) for the study was obtained from Hong Kong Observatory
(HKO). Hourly mean concentration of coarse particulate matter (PM10)
and ozone (O3) concentrations was obtained from the Environmental
Protection Department (EPD; https://cd.epic.epd.gov.hk/EPICDI/air/station/?lang=en, last access: 14 September 2019).
Hourly data were used for model validation, whereas daily maximum and
mean values were calculated based on the hourly data for HPE identification.
HPE selection
The concept of hot polluted episodes that refer to an episode with
coincident high temperature and air pollution level has been investigated previously (Chan
et al., 2012; Katsouyanni et al., 1993; Ordóñez et al., 2010; Yim,
2020); however, most of the studies have been focused on their compounding
impact on health (Chan et al., 2012) or the
impact of the excess temperature on air pollution levels
(Ordóñez et
al., 2010). Only few studies (Fan
et al., 2011; Yim, 2020) have considered the mechanisms responsible for
their co-occurrence. While Yim (2020) focused
on high temperature and PM2.5 pollution in Hong Kong, this study
focused on high temperature and high PM10 and O3 in the PRD
region. Similar to Yim (2020), this study identified
a HPE based on daily maximum temperature and daily mean of PM10 and
O3. PM10 was used in this study because of the lack of PM2.5
data in the study period, which was June to October in 2009–2011. This study
period was selected because these months represent a period in a year with
the highest daily maximum temperature, which when combined with days with
poor air quality, forms a HPE.
The HPE identification took into account the methods for the traditional
heat wave definition (occurrence for an extended period and threshold) and
a health impact study requiring the use of a critical value (Chan et al.,
2012; Y. Wang et al., 2019). Thresholds for temperature, PM10 and O3
were set as the mean of 50th percentiles of the variables of all
stations, i.e., 31.3 ∘C, 31 and 24 µg/m3, respectively. The thresholds represented the middle value of
their distributions without the interference of outliers in the
distribution. Hence, a HPE was identified if the following conditions are
fulfilled simultaneously:
The daily maximum temperature of Hong Kong Observatory (HKO) station
(22∘18′0′′ N, 114∘10′2′′ E) exceeds the temperature
threshold for 3 consecutive days.
Daily means of PM10 and O3 exceed their thresholds for 3
consecutive days.
Daily maximum air temperature data obtained from the HKO station were
used for the HPE identification for three reasons. First, the temperature
difference among urban stations was marginal such that one urban station
should be fine to represent the overall temperature of urban areas in Hong
Kong. Second, the HKO station is in the downtown area of Hong Kong and thus
serves as a representative urban station in Hong Kong. Third, the critical
temperature was chosen for the HPE identification because epidemiological
studies have reported the risk of health impact above a critical temperature
(28.4 ∘C) which is lower than our temperature criteria, meaning
that the health impact during our identified HPEs should be expected to be
more adverse. While population is concentrated in urban areas, using the HKO
station was considered adequate in this study.
The daily mean data for O3 and PM10 used for HPE identification
were obtained from 14 air quality stations within Hong Kong. These were
operated by the Hong Kong Environmental Protection Department during the
study period. Hong Kong air quality stations were used as proxies to
identify HPEs in the PRD region because there were insufficient data for the
stations in the region during the study period. The PRD regional air quality
monitoring network's annual and quarterly report between 2013 and 2018 showed
that air quality in Hong Kong was always better than or similar to the other
stations in the region (Pearl River Delta Regional Air Quality Monitoring Report, 2021).
Model description and setup
This study employed the Weather Research and Forecasting model
(Skamarock and Klemp, 2008) with Chemistry
(WRF-Chem) version 3.7.1
(Grell et al., 2005), which
is a nonhydrostatic, mesoscale numerical model coupling both meteorology and
chemistry. This model downscaled the meteorology and air quality in three
domains at a downscaling ratio of 3 (Fig. S1a): domain 1 (D1; 27 km) covering the whole of China, domain 2 (D2; 9 km) covering southern
China and domain 3 (D3; 3 km) covering the PRD region. The detailed model
configuration is provided in Sect. S1. The initial condition data
used were provided by NCEP Final Analysis
(https://rda.ucar.edu/datasets/ds083.2/#metadata/detailed.html?_do=y, last access: 14 September 2019) with a 1∘× 1∘ resolution, while the
boundary condition was updated every 6 h from FNL data. The emissions
data for Hong Kong were provided by the Hong Kong Environmental Protection
Department (HKEPD), and the emissions within PRD except Hong Kong were provided
by Zheng et al. (2009). The
emissions outside the PRD region were based on the INTEX-B 2006 regional
emission
inventory
(Zhang et al., 2009). Biogenic emissions were based on Guenther et al. (2006),
and shipping emissions were based on Streets
et al. (2003).
We performed a series of WRF-Chem simulations for each HPE. The performance
of the model was evaluated against observations, which was detailed in
Sect. S2.2. For each HPE, two sets of WRF-Chem simulations were
conducted with a 2 d spin-up period. Two additional days were included
before and after the HPEs in each simulation run. This setting was used to
identify and calculate variations in the variables before, during and after
HPEs. The first set of simulations turned on the aerosol–radiation feedback
option (CTRL), while the second turned off the option (NOFB). The difference
between the two sets of simulations was attributable to the effect of
aerosols on radiation fluxes during a HPE. The model results for all the
CTRL episodic simulations were separated into groups using k-means
clustering, and a representative of each group was characterized based on
the mechanisms responsible for its formation. The mechanisms of
thermodynamic and circulation patterns of HPEs and the synergistic relationship
between HPEs and the urban heat island (UHI) effect were then discussed in detail.
HPE clustering
Based on our identification method, eight HPEs were identified (Table 1). To
enhance our understanding of atmospheric conditions in each HPE, cluster
analysis was performed of meteorological variables
(Stefanon et al., 2012; Tan et al.,
2019) such as T2, sea level pressure (SLP), specific humidity at 2 m,
wind components (u, v) at 10 m, incoming solar radiation at the surface and
geopotential height (GPH) at 500 hPa, which could group these variables from
different times into clusters having the same meteorological conditions. A
modified k-means clustering algorithm (Hartigan and Wong,
1979) function in NCAR command language (NCL) with its center set at random
and iterations set at 1 000 000 was applied for all the aforementioned
meteorological variables.
Results and discussionHPE identification and classification
Due to the different durations of HPEs, mean diurnal variations of the
variables for the study domain during the three periods (before, during and
after) from the model results were analyzed. The dissimilarity was optimized
at three clusters showing the number of groups that the HPEs could be
grouped into. The results of the cluster analysis indicate that the HPEs
were classified based on their formation and characteristics into tropical
cyclone (TC-HPE), stagnant (ST-HPE) and hybrid (HY-HPE), and they are
discussed below. The weather conditions of the TC-HPE were characterized by
lower SLP and GPH but higher T2 and specific humidity. The TC-HPE
typically occurred when a tropical cyclone was approaching the PRD region. A
representative episode (TC-HPErep), occurring from 29 August to
1 September 2010, was selected to further explain the weather conditions,
along with weather charts provided by the Hong Kong Observatory (HKO; see
Fig. S4). In this episode, the tropical storm Lionrock passed
through the PRD region. Its passage caused stagnation and expansion of the
high-pressure system in the lower atmosphere over the north of the region.
This led to an increase in T2 over the region. Midday T2 in Hong
Kong rose from 32 ∘C on 29 August to 34 ∘C on 31 August, which broadly corroborated our previous studies (Yim et al., 2019b) that showed that tropical
cyclones within 1100 km of the region can cause HPEs.
HPEs for the study period. Eight HPEs were identified and
classified into three HPE groups: stagnant (ST), tropical cyclone (TC) and
hybrid (HY).
HPEYearStart End No. ofGroupMonthDayMonthDaydays1200981833TC283091012ST39199213HY49239264HY5201084874ST6829914TC720118268305TC8979104HY
The second group, ST-HPE, was found to be characterized by higher
temperature and lower specific humidity in the PRD region, as demonstrated
by the representative HPE (ST-HPErep) occurring from 4 to 7 August 2010 (see Fig. S5). This group represents a slow-moving weather
system formed by a low-pressure system covering most of China and the
Philippine Sea. This is in contrast to previous studies that have reported a
strong association between high-pressure systems and air stagnation.
However, when Freychet et al. (2017) assessed the
dynamical processes for heat wave formation over eastern China, they found
that a low-pressure system along with northerly flow within the region often
led to heat convergence and an extreme high temperature episode. The
ST-HPErep was observed to have a region-wide slow-moving weather
condition characterized by a wide low-pressure system that covered most of
East Asia for a few days.
The third group, HY-HPE, has a unique profile that was defined by
Karremann et al. (2016).
This group consisted of more than one dominant synoptic condition during an
episode. A representative HY-HPE (HY-HPErep), occurring from 7
to 10 September 2011, was compared with corresponding HKO charts (see Fig. S6). The result confirmed the co-occurrence of more than one large-scale
weather system during the HPE. The result of their characterization
demonstrated that different pressure centers, synoptic conditions or their
combination can be juxtaposed during a weather event and that their gradient
will determine the weather condition of a region.
HPE characterization
This section mainly analyzes the formation mechanism for each HPE group. Due
to the similarities of the members in each group, we discuss the air
quality, energy and circulation characteristics of one representative HPE
from each group.
The model (CTRL) domain-averaged results for air temperature at 2 m
(T2; magenta), cloud fraction (CF; cyan) and incoming shortwave
radiation (blue) at ground level for (a) TC-HPE, (b) ST-HPE and (c) HY-HPE and
PM10 (magenta), O3 (cyan) and outgoing longwave radiation (OLR) at
the top of the atmosphere (blue) for (d) TC-HPErep, (e) ST-HPErep
and (f) HY-HPErep. Time series of vertical velocity (color shading) for
(g) TC-HPErep, (h) ST-HPErep and (i) HY-HPErep. Positive
vertical velocity means downward motion, whereas negative vertical velocity
means upward motion. Black boxes represent the period of HPEs. Days 1 and 2
were before the HPEs, days 3–6 were during the HPEs and days 7 and 8 were
after the HPEs.
TC-HPE
Figure 1a depicts the time series for domain-averaged hourly T2 during
the TC-HPE representative HPE. During the TC-HPErep, T2
increased on day 3 [onset (∼ 30.2 ∘C)], reached a
peak (∼ 32.7 ∘C) on day 5 and decreased on day 7
(∼ 30.8 ∘C). Figure 1g shows the height–time
cross-section of vertical velocity. Positive vertical velocity means
downward motion, whereas negative vertical velocity means upward motion. In
the lower atmosphere, the positive and even relatively small negative
vertical velocity values during the TC-HPErep indicate a weak
subsidence of air masses resulting from an approaching tropical cyclone
(Luo et
al., 2018; Yim et al., 2019a). As shown in Fig. 1a, the cloud fraction was
maintained at approximately 0.40 at the onset of the TC-HPErep and
reduced to 0.20 at 08:00 on day 5. The reduction in cloud fraction was due
to weak convection. These clear-sky conditions caused a significant increase
in incoming shortwave radiation, leading to a remarkable increase in T2
due to greater direct exposure to solar radiation at the surface. Figure 1d
shows that the outgoing longwave radiation (OLR) also increased up to 250 W/m2 at 08:00 on day 5. The situation changed by the end of day 6 due
to the increased upward motion, as indicated by the large negative vertical
velocity shown in Fig. 1g. A greater amount of convective activity led
to an increase in cloud fraction (up to 0.30) and thus a reduction in SW
radiation. Consequently, T2 decreased, signaling the end of the HPE.
During TC-HPErep, the weak vertical motions led to an accumulation of
PM10, as shown in Fig. 1d. The PM10 concentration rose from
approximately 10 µg/m3 on day 3 to >24µg/m3 on day 5. This change can be attributed to the northerly wind
during the episode, as shown in Fig. S7a. Enhanced northerly wind
has been found to significantly enhance transboundary air pollution in the
region (Hou
et al., 2019; Luo et al., 2018; Tong et al., 2018b; Yim et al., 2019a).
During the TC-HPErep, O3 accumulation also occurred. The increase
in insolation due to lower cloud fraction and accumulation of pollutants,
some of which are precursors of O3 formation, led to the increase in
midday O3 concentration from approximately 60 µg/m3 on day 3 to approximately 90 µg/m3 on day 6. The result also
demonstrated that the diurnal variation in O3 concentration was
maintained but the mean concentration increased significantly as the
TC-HPErep got hotter and more polluted before falling at the end of the episode.
ST-HPE
Figure 1b depicts the evolution of the ST-HPErep. T2 increased on
day 3 [onset (∼ 32.7 ∘C)], reached a peak
(∼ 33.3 ∘C) on day 4 and then decreased until the
end of day 6 (∼ 32.1 ∘C). The ST-HPErep was
distinguished by a 5 m/s wind speed in the lower atmosphere from day 1 to 4,
as shown in Fig. S7b. The wind speed reduced to approximately 2 m/s
on day 3, which led to an intense accumulation of heat and a 1.7 ∘C increase in T2. The subsequent increase in wind speed after day 4
(>5 m/s) coincided with the decrease in T2 until the end of
day 6, which marked the end of this episode. Vertical movement during this
event, as shown in Fig. 1h, ranged from -0.1 to 0.2 m/s. The strong
subsidence observed on days 2 and 3 in the middle and lower atmosphere contributed
to the stable atmosphere because the cloud fraction (shown in Fig. 1b) was
lower (∼ 0.20 on day 2 and ≤ 0.1 on day 3). On day 4,
the convective motion (-0.1 m/s) contributed to a steady increase in
cloud fraction to 0.5. Consequently, the increase in cloud cover cut off
insolation (down to approximately 850 W/m2 on day 4 and approximately
680 W/m2 on day 5, as shown in Fig. 1b). However, the cloud could
retain most of the heat on day 4 as the OLR displayed in Fig. 1e decreased
from 210 W/m2 on day 3 to 165 W/m2 on day 4, while the T2
increased by 0.6 ∘C. T2 reduced thereafter by 2.3 ∘C due to the amount of persistent high cloud, which blocked
incoming shortwave radiation and cut off the source of heat. This episode
persisted until the end of the ST-HPErep (day 6).
The calm wind condition also contributed to the deterioration in air
quality, as shown in Fig. 1e. The increase in the concentration of
PM10 to 20 µg/m3 from days 4 to 6 was accentuated by the
change in wind direction to north/northeast, as shown in Fig. S7e.
This result was consistent with a number of findings (Hou
et al., 2019; Luo et al., 2018; Tong et al., 2018a, b; Yim et al.,
2019b) showing that a northerly wind is usually associated with air
pollution in the region as a result of transboundary air pollution. The
increase in O3 concentration displayed in Fig. 1e is attributable to
the increase in precursors and the amount of incoming shortwave radiation
available. The diurnal variation in the time series of O3 is also
indicative of this cumulative trend.
HY-HPE
Compared with the other two groups, HY-HPE did not show an obvious increase
in daily T2 during the episode. As shown in Fig. 1c, the
HY-HPErep (days 3–6) had a mean daily maximum T2 of approximately
30.0 ∘C, with a variation of only ±0.3∘C. The
HY-HPErep showed a relatively large change in horizontal wind speed at
the lower troposphere (Fig. S7c) from <5 m/s on days 1 to 3
to a higher wind speed (≥8 m/s) from days 4 to 8. Similar to the other
two groups, Fig. 1i shows a weak subsidence in this episode due to weak
vertical velocity (from -0.2 to 0.2 m/s), which was accompanied by
weak downward motion (except on day 5). The increased horizontal wind speed
from day 4 contributed to the temperature regime of the HY-HPErep by
offsetting the heat buildup due to the weak vertical motion. This led to
marginal variations in the diurnal temperature between pre-, mid- and
post-HPE days.
The changes in air quality during this episode were influenced primarily by
the horizontal wind direction and speed. This is exemplified by the effect
of the prevailing wind speed and direction on the relatively lower
concentration of PM10 (about 15 µg/m3 at its peak; see
Fig. S7i and f). The prevailing easterly wind suggests a marginal
effect of transboundary air pollution in HY-HPE; the increase in air
pollutant level was thus due to local emissions. The subsequent increase in
wind speed resulted in a dispersion and reduction in the PM10
concentration. However, as the wind direction switched to the north (day 6),
PM10 concentration began to rise due to increases in transboundary air
pollution. Changes in O3 concentration followed the same pattern due to
the availability of precursors and the influence of wind speed and
direction. Consequently, HY-HPE was unable to accumulate heat, PM10
and O3 concentration despite the weak convection.
Synergistic relationship
We examined the synergistic relationship between UHIs and HPEs during these
episodes. This was achieved by scrutinizing the daily midday height–time
series of potential temperatures, which have been found to be a suitable
variable for diagnosing differences in boundary layer characteristics
because it provides insight into its vertical and thermal structures
(Miralles et al., 2014;
Ramamurthy et al., 2017). The effects of land cover on the evolution and
sustenance of the HPE were analyzed by examining UHI intensities during the
HPEs. The UHI intensities were quantified as the difference between the
average urban and rural grids (as shown in Fig. S2). The rural grids are
the average of all the vegetated land use land cover categorizations shown
in Fig. S2. Quantification of potential temperature and sensible and latent
heat fluxes was also carried out using the same method. The effect of the
air quality on the HPEs was also examined, largely through analyzing the
effect of total aerosol radiative forcing (TARF).
TC-HPE
Figure 2a shows the T2 difference between urban and vegetated areas
during TC-HPErep. The results indicate a remarkable 4.2
to 5.5 ∘C difference in T2 between the two types of land
cover due to the UHI effect during the HPE, while the pre- and post-HPE UHI
effect had a lower range of 2.2 to 4.2 ∘C,
indicating the contribution of the HPE to the UHI effect. Figure 2d depicts
the difference between the potential temperature over urban and vegetated
land covers in the PRD region at midday. The temporal distribution in
potential temperature shows an increasing trend in the lower atmosphere
(≥ 1.8 ∘C from days 4 to 6) and upward expansion in height
for warm air beyond 1.2 km (0.3 ∘C on day 5). Persistent daily
heating from incoming solar radiation provided energy that precipitated a
rise in temperature, thereby entrapping and accumulating heat during the
TC-HPErep. For the TC-HPErep, the T2 difference ranged from
0.5 to > 1.5 ∘C, showing that TC-HPE
provided extra heat to the surface, which enabled the urban areas to warm up
to a greater extent than normal. This result indicates that the UHI effect
was enhanced during the TC-HPE.
The model (CTRL) domain-averaged results showing (a–c) difference
of T2 (T2(diff)), sensible heat (SH; SH(diff)), and latent
heat (LH; LH(diff)) between urban and vegetated grids for
TC-HPErep, ST-HPErep and HY-HPErep; (d–f) vertical profile
of potential temperature difference θ (θ(diff)) for
TC-HPErep, ST-HPErep and HY-HPErep at midday for the PRD
region; (g–i) the changes of T2 (ΔT2), CF (ΔCF)
and incoming shortwave radiation SW (ΔSW) at the surface induced by
total aerosol radiative forcing for TC-HPErep, ST-HPErep and
HY-HPErep.
The effect of surface moisture availability, surface energy retention and
transfer for both urban and vegetated surfaces was investigated by analyzing
latent and sensible heat fluxes (Fig. 2a). The largest absolute latent
heat difference between the two types of land cover was between 350 and 400 W/m2. This illustrates the impact of the limited vegetation in the
city. This is buttressed by the 130–200 W/m2 difference, respectively
in the amount of sensible heat for both types of land cover. Although both
the latent and sensible heat fluxes shared similar incoming shortwave
radiation, the continued increase in the temperature can be attributed to
the continued desiccation of the urban areas, leading to faster buildup of
heat and increase in temperature, even as the cloud cover continued to
decrease until day 6, marking the end of the episode.
TARF caused an initial increase in T2 but then a significant reduction
in T2 (> 1.0 ∘C) on day 6 when the PM10
increased to a peak. Figures 1d and 2g show that when PM10 was
increased but below 10 µg/m3, the TARF caused a reduction in
cloud fraction and thus an increase in T2. The reduction in cloud
fraction due to TARF may be due to the aerosol–cloud interaction in which
aerosols served as cloud condensation nuclei, and thus more cloud droplets
were formed with a smaller radius, and the cloud fraction hence decreased
(Liu et al., 2020).
Nevertheless, when the PM10 level exceeded 10 µg/m3, the
aerosol–radiation interaction (aerosol scattering and absorption) may become
dominant, causing a reduction in the amount of solar radiation reaching the
ground level due to the blockage effect of aerosols and thus a temperature
reduction.
ST-HPE
Figure 2b shows the difference in T2 between the two land covers in the
ST-HPErep. The T2 in urban areas was higher by a range of
approximately 3.0–4.1 ∘C during the
ST-HPErep. There was also approximately 0.2 to 1.0 ∘C difference in T2 between the ST-HPErep period and
its pre- and post-HPE periods. Figure 2e shows that heat accumulated at the onset of
ST-HPErep began to decay during the episode. The heat accumulation led
to a maximum difference of about 1.5 ∘C on day 3, which was the
first day of the ST-HPErep and the day with the highest incoming solar
radiation and lowest cloud cover. This contrasts with the pre- and post-HPE periods
which had a minimal difference in their cloud fraction during the episode,
hence highlighting the importance of incoming solar radiation to the HPE.
The sensible and latent heat fluxes shown Fig. 2b indicate that the heat
buildup for this episode started before the onset of this HPE, as they
continuously increased (decreased) for sensible (latent) heat. However, the
changes in the cloud fraction from day 4 and 5 led to the retention of the
accumulated heat and subsequent attenuation of temperature and sensible and
latent heat differences. The potential temperature difference also shows a
positive synergy during the ST-HPErep by maintaining a positive
difference throughout the episode, even though it progressively weakened
over time. These results indicate that the UHI effect was accentuated during
the ST-HPErep, leading to a larger temperature difference between urban
and vegetated surfaces.
The results of the effect of TARF on T2, SWin and CF for ST-HPE
indicate that TARF caused marginal changes during the pre- and post-ST-HPE
periods (Fig. 2h). Like the TC-HPErep, the TARF effect led to a
reduction in approximately 1.5 ∘C when the PM10 reached a
peak on days 4 and 5. The cloud fraction (Fig. 1b) also increased
significantly during this period, thus contributing to the decrease in
T2. As explained in the TC-HPErep, the reduction in T2 could
be mainly due to effect of aerosol–radiation interaction. The TARF effect
on T2 by day 6 led to a 0.5 ∘C increase; this was due to the
slight increase of PM10 concentration (the total level was lower than 8 µg/m3) on that day that reduced the cloud fraction.
HY-HPE
HP-HPErep did not show any significant difference in UHI intensity
between the pre-, mid- and post-HPE periods. Figure 2c shows a similar
magnitude (3.0–3.5 ∘C) during pre- and post-HPE periods. The
daily variation did not display any obvious signs of heat retention in the
atmosphere, indicating that there was no heat accumulation during this
episode. The potential temperature shown in Fig. 1c indicates that heat
generated from heating the surface by the incoming solar radiation could not
be entrained due to its increased wind speed and weak vertical motions,
thereby making it difficult to accumulate heat, an observed pre-requisite
for HPE and UHI synergistic effect. There was therefore no synergistic
relationship between UHI and HPE during the HY-HPE.
The effect of TARF on T2 and OLR in the HY-HPErep, as displayed in
Fig. 2i, shows a variation of -0.5 to 0.5 ∘C in
temperature across the pre-, mid-, and post-HPE periods. The pre-HPE period
shows marginal changes for CF, SWin and T2; however, variations
began to be apparent from day 5. For instance, on the afternoon of day 5,
the PM10 started to increase and reached a peak early on day 6. The
cloud fraction increased in response to the increase in PM10, as did
SWin and then T2. These changes became marginal late on day 6.
Nevertheless, PM10 still maintained a relatively high level, and the
cloud fraction reduced once more, allowing more solar radiation to reach the
ground, causing an increase in T2.
Conclusions
In this study, eight HPEs during 2009–2011 were identified. We found that
TC-HPE was influenced by incoming solar radiation and subsidence, and ST-HPE
was influenced by low wind speed and reduction in cloud cover, while HY-HPE was
influenced by weak vertical transport and increased wind speed.
Consequently, TC-HPE and ST-HPE had a positive synergetic relationship
(∼ 1.1 ∘C) between UHI and HPE because their
characteristic meteorological conditions (weak subsidence, low wind speeds
and reduced cloud cover) accentuated insolation, heat storage and heat
entrainment, while HY-HPE had no discernable relationship. TARF was found to
contribute significantly to the temperature variations during the HPEs,
leading to significant cooling effects on the HPEs (0.5–1 ∘C),
except when upward vertical transport prevailed, particularly during the
daytime.
This study provided further insight into the nature of HPEs at both regional
and local level by analyzing their modes of formation and thermodynamic
characteristics and by investigating the contributions of land use and land
cover (LULC) changes to the nature of HPEs. These findings can aid
policymakers in redesigning and renewing our urban environments to alleviate
the UHI effect, global warming and air pollution. Especially the impact of
aerosol radiative forcing during the HPEs will improve our understanding of
the mechanisms responsible for the co-occurrence of unusually high
temperature and poor air quality in the PRD region, showing enormous
implications for regional climate and health. The next phase of our study
will focus on the regional-scale implications of the effect of HPEs on human
health, the relative contribution of aerosol–radiation and aerosol–cloud
interactions and the combined effect of LULC and aerosols on our climate.
Limitations of the study
This study was limited by access to more
recent air quality emission data; hence the study was conducted for the 2009–2011 period. The use of data from only the Hong Kong area as a proxy for the
region was because of the lack of air quality data from the rest of the
study area. Also, as mentioned in Sect. 2.2, PM10 was used in this
study because of the inadequacy of PM2.5 data in the study period.
Data availability
The in situ meteorology data are provided by
the Hong Kong Observatory at http://www.hko.gov.hk/cis/climat_e.htm (Climatological Information Services, 2021), while the in situ air quality data are provided by the Hong
Kong Environmental Protection Department at https://cd.epic.epd.gov.hk/EPICDI/air/station/?lang=en (Environmental Protection Interactive Centre, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-13443-2021-supplement.
Author contributions
SHLY and ICN designed
the study. ICN did all calculations with support from
SHLY and CYT. ICN wrote the paper with
support and editing from SHLY, JG and CYT.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The views expressed in this paper are those of the
authors and do not necessarily reflect the views or policies of the Hong
Kong government or any of its agencies or parastatals.
Acknowledgements
This work is supported by the Early
Career Scheme of Research Grants Council of Hong Kong (grant no.
CUHK24301415). The in situ meteorology and air quality observations are
provided by the Hong Kong Observatory (http://www.hko.gov.hk/cis/climat_e.htm) and Hong Kong
Environmental Protection Department (https://cd.epic.epd.gov.hk/EPICDI/air/station/?lang=en).
Financial support
This research has been supported by the Early Career Scheme of Research Grants Council of Hong Kong (grant no. CUHK24301415).
Review statement
This paper was edited by Zhanqing Li and reviewed by three anonymous referees.
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