Heatwaves (HWs) paired with higher ozone (O3)
concentration at the surface level pose a serious threat to human health. Their
combined modulation of synoptic patterns and urbanization remains unclear.
Using 5 years of summertime temperature and O3 concentration
observation in Beijing, this study explored potential drivers of compound
HWs and O3 pollution events and their public health effects. Three
favorable synoptic weather patterns were identified to dominate the
compound HWs and O3 pollution events. These weather patterns
contributing to enhance those conditions are characterized by sinking air
motion, low boundary layer height, and high temperatures. Under the
synergy of HWs and O3 pollution, the mortality risk from all
non-accidental causes increased by approximately 12.31 % (95 %
confidence interval: 4.66 %, 20.81 %). Urbanization caused a higher risk of HWs and O3 in urban areas than at rural stations. Particularly, due to
O3 depletion caused by NO titration at traffic and urban stations, the
health risks related to O3 pollution in different regions are
characterized as follows: suburban stations > urban stations
> rural stations > traffic stations. In general,
favorable synoptic patterns and urbanization enhanced the health risk of
these compound events in Beijing by 33.09 % and 18.95 %, respectively.
Our findings provide robust evidence and implications for forecasting
compound HWs and O3 pollution events and their health risks in
Beijing or in other urban areas all over the world that have high concentrations
of O3 and high-density populations.
Introduction
Climate warming and rapid urbanization have led to increases in the
frequency and duration of extreme high-temperature
episodes (Lehner
et al., 2018; Li et al., 2020; Meehl and Tebaldi, 2004; Wang et al., 2021b;
Yang et al., 2017). Such prolonged extreme high-temperature exposure can
induce an increase in the morbidity and mortality due to cardiovascular and
respiratory diseases, posing a serious threat to human health (Patz et al., 2005; Xu et al., 2016).
Therefore, extreme high-temperature events are recognized as one of the
most serious types of meteorological disaster worldwide. However, high
temperatures during summer heatwaves (HWs) frequently co-occur with serious O3
pollution; for instance, significantly increased O3
concentrations were observed in the UK and France during the August
2013 HW event (Lee
et al., 2006; Vautard et al., 2005, 2007). High concentrations of O3
exposure stimulate the human respiratory system, damage lung cells,
and aggravate other chronic lung diseases (WHO, 2021), therefore posing a great threat to human health. Consequently, residents may suffer
from dual health risks caused by both high temperatures and O3
exposure in summer. Although extreme high-temperature events have received extensive
attention from academia and society, research on health risks aroused by
O3 pollution associated with high temperature has been neglected. As a
result, health risks to humans that are consistently exposed to the outdoors during hot days might be greatly underestimated.
As a continuous extreme case of high-temperature weather in summer, HWs have previously been shown by numerous epidemiological studies
to cause significantly higher overall deaths than non-heatwave (NHW) periods (Conti
et al., 2005; Fouillet et al., 2006). Subsequently, many scholars launched
investigations into the relationship between high-temperature exposure and mortality (Abbas
and Mylene, 2005; Huang et al., 2015; Zhang et al., 2017), and they
found that when the temperature was higher than a certain threshold, the mortality rate increased with the increase of temperature.
Most studies suggested that there were a U-, V-, W-, or J-shaped nonlinear
change relationship between daily mortality and daily temperature (Goggins
et al., 2012; Huang et al., 2015; Zhang et al., 2017). Similar studies on
O3 concentration and mortality have also been conducted (Atkinson
et al., 2012; Gu et al., 2018; Pope et al., 2016). In particular, some
epidemiological evidence showed that the coefficient of the O3
concentration–response relationship for mortality in summer was higher with
respect to other seasons (Pattenden et al.,
2010; Pope et al., 2016), suggesting that the health effects and mortality
related to O3 pollution were exacerbated by high temperatures.
Therefore, the significant increase in O3 concentrations during
the summertime is also greatly responsible for the increase in excess mortality;
that is, high temperatures and O3 exhibit a joint impact on public
health (Hertig et al., 2020;
Katsouyanni et al., 1993; Pattenden et al., 2010). Numerous previous studies
have been devoted to the individual impacts of a single extreme
high-temperature or air pollution event on human health (Ma
et al., 2015; Ning et al., 2020; Wang et al., 2020; Wong et al., 2013; Xu et
al., 2016). However, with the co-occurrence of extreme HW and O3
pollution events in summer becoming more frequent, it is imperative to
reveal the underlying mechanisms of extreme HW–O3 compound events and
to improve the level of risk assessment related to extreme events in urban
areas (Hertig et al., 2020; Sartor et
al., 1995).
Together with the rapid development of economic globalization and
urbanization, human activities have induced frequent occurrences of both extremely high surface air
temperature and air pollution issues (Chen
et al., 2022; Chew et al., 2021; Li et al., 2016; Lolli et al., 2018a; Luo
and Lau, 2018, 2019; Meehl et al., 2007; Rastogi, 2020; Wang et al., 2007;
Yang et al., 2020). Particularly, HWs paired with the urban heat island
(UHI) effect expose urban residents to more sustained extremely high
temperatures (Chew
et al., 2021; Jiang et al., 2019; Tan et al., 2010; Wang et al., 2017; Zong
et al., 2021b). Meanwhile, rapid urbanization induced many more emissions of
hydrocarbons and nitrogen oxides into the atmosphere from traffic vehicle
and industries; the rising concentrations of these precursors coupled with
high temperature and intense solar radiation during HWs can accelerate
the photochemical reaction rate and generate more O3 (Sillman,
1999; Yim et al., 2019; Zanis et al., 2000). As a result, urban residents
may face more severe stresses from both heat and O3 pollution.
However, note that the improvement of economic level, medical infrastructure,
and air-conditioning utilization associated with urbanization can alleviate
the health burden of the human body in the face of high temperature and
O3 exposure to a certain extent (Bai
et al., 2016; Kovach et al., 2015; Li et al., 2017). Therefore, it can be
concluded that there still are some uncertainties in affecting the excess
mortality of high temperature and O3 pollution. To sum up, clarifying
the formation mechanism of HW–O3 compound events and quantifying their
health risks to urban residents are important scientific issues that warrant
further investigation.
Beijing, the capital of China, is the second largest city in the country,
with a permanent population of 21.89 million. It is not only one of the
fastest developing metropolises in China in recent decades, but also a
typical heat island
city (Ren
et al., 2007; Wang et al., 2017; Yang et al., 2013, 2022). Taking Beijing as
a typical example, therefore, this study focuses on the health risks of
extreme HW–O3 compound events during summertime of 2014–2019 and
comprehensively investigates the role of synoptic weather patterns and
urbanization in these compound events based on surface observation and
reanalysis data. Then, the contributions of weather types and urbanization
to the excess mortality induced by combined heat and O3 stress were
quantified according to an established health assessment model. The
findings are expected to provide a scientific reference for the monitoring
and forecasting of summertime HW–O3 compound events and their health
risks from the perspective of synoptic patterns and urbanization in
high-density megacities.
Data and methodsData
Ground-level O3 observation data during summertime (June–August) of
2014–2019 were retrieved from the Beijing Municipal Ecological and
Environmental Monitoring Center. After quality control, and excluding
stations with a missing value rate for the O3 concentration of more
than 10%, 31 air quality stations (AQSs; including 11 for urban stations,
11 for suburban stations, 3 for traffic stations (road monitoring
stations for traffic air quality), and 6 for rural stations) are
ultimately used in this study. In order to better assess the relationship
between O3 pollution and the meteorological variables, we selected 29
automatic weather stations (AWSs) closest to the environmental monitoring
stations from the high-density AWS network. For specific geographic location
information, see Fig. 1 and Table 1. Hourly 2 m air temperature, relative
humidity (RH), daily maximum temperature (Tmax), and 10 m wind
speed (WS) of these 29 AWSs were obtained from the National Meteorological
Information Center of the China Meteorological Administration, and then heat
index (HI) was retrieved as shown in Rothfusz (1990), as given in Eq. ():
HI=-42.379+2.04901523×T+10.14333127×RH-0.22475541×T×RH-0.00683783×T2-0.05481717×RH2+0.00122874×T2×RH+0.00085282×T×RH2-0.00000199×T2×RH2,
where T indicates the temperature (unit: ∘F), and RH (unit: %) indicates
relative humidity.
In addition, we also used the hourly geopotential height (GH), boundary
layer height (BLH), wind vector, vertical velocity, and temperature fields
to further analyze the weather type and local boundary layer characteristics
under the joint occurrence of HW and O3 pollution (fifth major global
reanalysis produced by the European Centre for Medium-Range Weather
Forecasts, with a spatiotemporal resolution of 0.25∘).
(a) Geography of Beijing. (b) Distribution of AWSs and AQSs in Beijing (superimposed on the built-up area data for 2015
from digital elevation model data).
The location information and station type of AQSs; the
corresponding AWSs are the closest matching weather station among 295 AWSs.
∗ indicates that the underlying surface of the observing station is covered by vegetation.
MethodsCompound HW and O3 pollution events
An HW event is usually characterized by the daily maximum temperature
reaching or exceeding a certain threshold (it can be a relative value or an
absolute threshold) for several consecutive days (Ngarambe et al., 2020). In this
paper, we selected 33 ∘C (which corresponds to the 90th
percentile of Tmax during 2014–2019 in Beijing) as the threshold for
Tmax lasting for 3 d or more to determine an HW event; otherwise, it
was a NHW event. Moreover, precipitation has a certain
regulating effect on urban pollution and high
temperature (Lolli
et al., 2018b; Roth, 2007; Zhao et al., 2014; Zheng et al., 2020); in particular, the occurrence of precipitation during the day inhibits the
photochemical reaction of O3 production (Yu
et al., 2020; Zhang et al., 2015; Zhao and Wang, 2017). Here a daytime
precipitation event (accumulated precipitation ≥2 mm during 07:00–19:00 LST) was excluded to avoid the impact of precipitation on compound HW and
O3 pollution events. O3 pollution was identified as when the MDA8
O3 concentration exceeded 160 µg m-3, which is in accordance
with the Ambient Air Quality Standards issued by the Ministry of Ecology and
Environment of the People's Republic of China. Based on the above criteria,
84 d of co-occurring HW and O3 pollution events during 2014–2019
were finally obtained.
Weather type classification
The T-mode principal component analysis (T-PCA) is an improved mathematical
method to classify the circulation pattern, which has a low dependence on
preset parameters and has advanced temporal and spatial stability of
classification (Huth et al., 2008).
Consequently, T-PCA has been widely used in the studies of atmospheric
circulation effects of extreme weather (Liu
et al., 2019; Miao et al., 2019; Yang et al., 2018, 2021; Zhang and
Villarini, 2019; Zong et al., 2021a). It decomposes the original data matrix
into the product of the principle component matrix and the load matrix (two
low-dimensional matrices), then rotates the first r (r≤n) principal
components with larger variance contributions obliquely, and finally obtains
the synoptic patterns and classifications of each time according to the
magnitude of the load (Huth, 2000). Here,
T-PCA was applied in COST733class software to classify the 850 hPa GH field of the
joint occurrence of HW and O3 pollution events, and the number of
classifications was determined based on the explained cluster variance; more
specific details on T-PCA were introduced in our previous study (Zong et al., 2021a). As for the categorical data, we mainly
focused on the domain (110–125∘ E, 32–47∘ N), including Beijing, associated with these 84 d of
compound events during summertime (June–August) 2014–2019.
Excess mortality
In epidemiology, the relative risk (RR) is usually used to evaluate the
intensity of the association between exposure and disease, which refers to
the ratio of the incidence of the exposed group to the incidence of the
non-exposed group (Chen
et al., 2018; Pope et al., 2016). The RR is calculated by Eq. ():
RRi=expβi∗ΔXi,
where i indicates the risk factor (high temperature or O3
concentration), βi denotes the coefficients of the exposure response
function between the risk factor i and total mortality through nonlinear
regression (Cao
et al., 2021; Du et al., 2020; Gu et al., 2018), and ΔXi is the
difference between the risk factor i and its reference health threshold. The
excess risk (ER) is calculated by Eq. ():
ERi=RRi-1×100%.
Previous studies indicated that there were distinctly different magnitudes
of human morbidity and mortality caused by high temperature and O3
overexposure over various geographic regions (Huang
et al., 2015; Ma et al., 2015; Wang et al., 2020; Yin et al., 2017). For
instance, Huang et al. (2015)
revealed that for a 1 ∘C increase above the minimum mortality
temperature, the daily mortality increased by 1.04 % (95 % confidence
interval (CI): 0.90 % to 1.18 %), 1.25 % (95 % CI: 0.71 % to 1.79 %), 1.19 % (95 %
CI: 0.79 % to 1.58 %), and 1.38 % (95 % CI: 0.54 % to 2.23 %) in nationwide,
central China, eastern China, and south China, respectively. Here, we refer
to the coefficients of exposure response function (β) for the high
temperature as suggested by Liu
et al. (2021) and those for O3 concentration as suggested by Yin et al. (2017) in
northern China. In detail, Liu
et al. (2021) investigated the mortality caused by high temperature in 84
cities in China from 2013 to 2016 and found that for every 1 ∘C
increase in the daily Tmax above 31.5 ∘C, the largest RR of
mortality caused by high temperature in northern China was 1.002 (95 % CI:
1.001, 1.004). According to Eq. (), we can deduce that βTmax=0.997 % (95 % CI: 0.996 %, 0.999 %); note that RR is equal to 1 when Tmax=31.5∘C. For O3 exposure, a
10 µg m-3 increase in MDA8 O3 was related to an increase in
the total daily mortality of 0.39 % (95 % CI: 0.04 %, 0.75 %) in
northern China during the warm season (Yin et al., 2017);
that is, βOzone=0.39 % (95 % CI: 0.04 %, 0.75 %).
Since the two models have removed mutual influence, the final joint ER
is the sum of the ERs of both high temperature and O3.
Urbanization and Synoptic contribution rates
To estimate the impact of urbanization and weather patterns on compound HW
and O3 pollution events, we further determined their contribution rates
to the excess mortality of compound events. With reference to Ma and Yuan (2021) and Yang et al. (2017), the urbanization effect is
calculated by Eq. ():
ΔERi,urbanization=ERi,urban-ERi,rural,
and the contribution rate is calculated by Eq. ():
CRi,urbanization=ΔERiERi,urban×100%,
where i indicates risk factor (high temperature or ozone pollution), ER is
excess mortality, and CR is the contribution rate.
Similarly, we also defined synoptic effects as
ΔERi,synoptic=ERi,synoptic-ERi,average
and the contribution rate as
CRi,synoptic=ΔERiERi,synoptic×100%,
where i, ER, and CR are the same as in Eq. ().
ResultsCompound HW–O3 pollution events and associated public health in Beijing
Figure 2 shows the time series of the HW, NHW, O3 pollution, and
precipitation days and the interannual and intraseasonal variations of HW
and O3 pollution days. For interannual variation, the total number of days of
O3 pollution in summer was relative stable, while the total number of days of HW
increased slightly. For intraseasonal variation, O3 pollution was the
most serious in June, while HW events were most frequent in July.
Obviously, higher O3 pollution levels (>160µg m-3) were always accompanied by most HW periods (approximately 79.2 %
of HW days) in Beijing (Figs. 2a and 3b), which were mainly in the middle
of summer. In addition, note that there was an increase in the maximum
duration of HW events and the number of HW–O3 paired days during
the summertime of 2014–2019 (Fig. 3), especially in 2019, when the longest HW event lasted for 10 d, resulting in more extreme and enduring dual
heat and O3 stresses to residents. As shown in Fig. 4, relative to
NHW days, MDA8 O3 increased significantly on HW days, exceeding 160 µg m-3 across all stations, with an average of 189.35 µg m-3. Both surface O3 concentration and MDA8 O3 concentration
in Beijing showed significant differences (P<0.001) through
analysis of variance among three conditions (Table S1 in the Supplement).
(a) Time series of synoptic weather types, in which the black dots indicate
O3 pollution that occurred on that day. Interannual (b) and
intraseasonal (c) variations in summertime O3 pollution and HW days.
(a) Maximum number of days of HW events each year. (b) Proportion
of O3 pollution during HW events each year.
Distribution of MDA8 O3 under (a) HW, (b) NHW, and (c) precipitation periods (superimposed on built-up area data for 2015; black
and green circles represent urban and suburban stations, respectively).
In general, the difference in O3 concentration was mainly due to
meteorological conditions and the precursors' emission paired with
photochemical reactions in the boundary layer. We further investigated the
diurnal variation for surface air temperature (T), RH, HI, BLH, and WS under
HW, NHW, and precipitation periods (Fig. 5), and these five variables also
showed significant differences (passed the Kruskal–Wallis test with a result of 0.001; for
more details, see Table S2) in the three periods. For HW days, HI raised more
by increased air temperature, and although the RH was relative lower, people
still suffered from higher apparent temperature than actual air temperature.
Under HW conditions, solar radiation reaching the ground heats the
atmosphere, increasing the near-surface temperature. Warmer air convection
promotes atmospheric instability, with increased WS and higher BLH. It is
clear that the meteorological variables in the daytime were significantly
different during HW periods with respect to NHW periods (Fig. 5; Zong et al., 2021b). Similarly, hourly
O3 concentrations also showed significant differences under different
meteorological conditions and reached peak levels in the afternoon on HW days
(Fig. 5f). Note that the contribution of local and regional emissions
(transport of pollution between urban and rural areas) to air quality at a
city scale should be focused on (Thunis et al., 2021),
as it can also induce urban–rural differences. We assumed that the
intraseasonal differences in precursor emissions can be ignored, and the diurnal variation differences in NO2, CO, and O3 among
different stations are further
compared (Fig. 6 and Table S3). CO and NO2 levels were
higher at traffic stations than urban and suburban stations due to enhanced
emission from vehicles, and the lowest CO and NO2 levels appeared at
rural stations. Generally speaking, high precursor levels are supposed to
correspond to high resultant levels, but the lowest O3 levels were
found at traffic stations, followed by rural stations, then urban and
suburban stations. Since automobile exhaust in the traffic and urban
stations also caused heavy NO emission (Colvile et al., 2001), ambient O3 can
be titrated by NO via the reaction NO +O3→NO2+O2 (Gao
et al., 2020; Murphy et al., 2007; Sillman, 1999); this process in turn led
to higher NO2 levels and the loss of O3 in traffic and urban
areas. As for rural stations, low pollutant emissions may be the primary
reason for the lower O3 levels. Note that although the CO and NO2
emissions were significantly higher at urban stations than those of suburban
stations, there was less difference in O3 concentrations between these
two types of station, which may be due to O3 consumption induced by
titration at urban stations or more biogenic volatile organic compounds (BVOCs) at suburban stations.
This is because the difference in O3 concentrations between the
rural and the suburban stations was the largest in the afternoon, while the
difference in CO and NO2 levels was the smallest, indicating that
anthropogenic emissions have less impact in suburban areas, coupled with
more than half of suburban stations being covered by vegetation, leading to
more BVOC emissions.
The diurnal variation of (a) air temperature, (b) RH, (c) HI, (d) BLH, (e) WS, and (f)O3, under HW, NHW, and precipitation periods
(shading indicates standard deviation; P<0.001 shows statistical significance).
The diurnal variation of (a) CO, (b) NO, and (c)O3, at
different stations (shading indicates standard deviation; P<0.001
shows statistical significance).
Moreover, the high temperatures on HW days not only induce a higher public
risk related to high-temperature exposure, but also increase mortality
related to O3 exposure. During HW periods, high temperatures and strong
solar radiation accelerate the rate of the photochemical reaction that
produces O3 (Pu
et al., 2017; Sun et al., 2017), favoring the production and accumulation
of O3, thereby aggravating health risks. The health risks related to
both O3 and high temperature have greatly increased during HW days for
all types of station. Specifically, for all stations, HWs have increased the ER
caused by high temperatures and O3 by 3.867 % (90 % CI: 3.863 %,
3.875 %) and 7.9 % (90 %CI: 0.78 %, 15.78 %), respectively (Table 2). The high-temperature risks were mainly manifested as follows: urban
stations > traffic stations > suburban stations
> rural stations, but the health risks aroused by O3
exposure in different underlying surface stations were more difficult to
quantify due to the complexity of O3 photochemical reactions. As
mentioned above, urbanization-enhanced NO or CO titration reduced more
O3 loss in urban areas, which was more pronounced over traffic
stations. For suburban stations, the abundant BVOCs emitted by
vegetation also contributed to O3 generation, and BVOC emissions
were enhanced more, especially on hot days (Ma et al.,
2019; Trainer et al., 1987; Wang et al., 2021a). As a result, O3
exposure risks in Beijing were mainly characterized by suburban stations
> urban stations > rural stations > traffic
stations. Urbanization seems to have increased the ER induced by both high
temperatures and O3 exposure. In details, summertime HW, O3, and
compound ER increased by 1.67 %, 0.20 %, and 1.89 %, respectively,
compared to rural stations. Note that urbanization has alleviated O3
pollution to a certain extent, and the health risk of O3 at stations
with developed transportation was even lower than that of rural stations.
RH, temperature, HI, MDA8 O3, O3 concentration, and ER of
HW and O3 pollution compound events for mortalities in different
station types, associated with different weather conditions.
Note: Ur–Ru: urban–rural. Bold numbers indicate groups with
greater ER.
Role of synoptic weather pattern and urbanization
To further clarify the mechanism underlying the joint occurrence of
HW–O3 events in Beijing, three favorable synoptic weather patterns
were identified as follows: (1) Type 1, characterized by the western Pacific
subtropical high being located in the southeast of Beijing with prevailing
southwesterly winds; (2) Type 2, controlled by a high-pressure system
accompanied by weak southerly winds; and (3) Type 3, a low-vortex located
over northeast Beijing with prevailing northwesterly winds (Fig. 7a–c;
for detailed HW–O3 date and type, see Table S4). Additionally, vertical
cross-sections of the potential temperature and wind vectors at 14:00 LST
under the three patterns are shown in Fig. 7e–f. Under Type 1, a low
boundary layer paired with weak vertical motion favors pollutants'
accumulation. In addition, the prevailing southwesterly wind may blow pollutants
from the upwind direction to Beijing, and the northern mountains block the
pollutants from continuing to be transported in the downward wind direction,
causing the pollutants to gather in Beijing. For Type 2, a lower BLH and
vertical convection together regulate the transportation and accumulation of
O3 in the boundary layer. Under Type 3, there is a valley–plain wind
circulation in the boundary layer, and the strong downdraft over urban areas
and the higher boundary layer cause the lowest MDA8 O3 concentrations
among the three weather types.
(a–c) 850 hPa GH (contours) and υν wind (vectors)
patterns related to HW and O3 pollution compound events based on
objective classification (grey outline represents Beijing, and the number in
the upper-right corner of each panel indicates the frequency of occurrence
of each pattern). (d–f) Vertical cross-sections of the potential
temperature (contours) and wind vectors (synthesized by ν and scaled
ω, ω scaled by 100) averaged between 116.0 and
117.0∘ E, associated with the synoptic patterns (solid purple lines
mark the BLH, grey contours mark the topography, and the green dot marks
the location of Beijing).
Overall, Type 1 tends to be associated with the highest excess mortality
caused by O3, and Type 3 is related with the highest excess mortality
caused by HWs. For excess mortality induced by the HW–O3 compound
events, Type 1 (12.59 %) > Type 3 (12.05 %) > Type 2 (11.66 %). Although there is marginal difference in the HW–O3
compound ER under the three weather types, the mechanisms of the three types
are quite different. Under the modulation of weather circulation and
boundary layer meteorological elements, Type 1, Type 2, and Type 3 were
associated with high O3 and intermediate Tmax exposure,
intermediate O3 and low Tmax exposure, and low O3 and high
Tmax exposure (Fig. 8 and Table 3), respectively. Therefore, the
synoptic weather pattern plays an important role in regulating the formation
mechanism of HW–O3 compound events, which also further leads to it
having a significant impact on morbidities and deaths caused by HW–O3
compound events.
Schematic illustration of the mechanism of HW and O3 compound
pollution events under different synoptic weather patterns (height of the
icon indicates the size of each variable).
RH, temperature, HI, MDA8 O3, O3 concentration, and ER of
HW and O3 compound pollution events for mortalities at different
station types, associated with different synoptic patterns.
Note: Ur–Ru: Urban–Rural. Bold numbers indicate groups with
greater ER.
To sum up, urbanization shows a positive regulation on health risks of
O3 and high temperature under different synoptic weather patterns. In
particular, HWs extended the urban–rural air temperature difference (the UHI
effect) in Beijing (Table 2), which was also found in our previous study (Zong et al., 2021b). That is, the
urbanization effect on health risk associated with heat exposure was
amplified during hot days. But for O3 pollution, urbanization and
anthropogenic activities have significantly increased the emission of
pollutants. On the one hand, it promotes photochemical reactions to generate
O3 during HW days. On the other hand, the titration of NO and CO in
cities can deplete O3. Under Type 1, the strong southerly airflow
caused the horizontal transportation of O3 and its precursors from the
southwest (urban) to the northeast (suburban and rural), which is favorable
for the accumulation of pollutants under the topography of Yanshan
(to the north of Beijing). Therefore, urban–rural differences in O3
concentration were narrowed, and the risk related to O3 exposure in the
suburban areas was the greatest under this weather pattern. Type 3 is
mainly dominated by the northerly airflow at 850 hPa and the southerly wind
at the lower level; the transport of local circulation has a weak adjustment
to the urban–rural difference of O3. However, the BLH difference
between urban and rural areas (north–south difference) should be responsible
for the decrease in urban–rural difference of health risk induced by O3
concentration. For the stable weather and lower BLH under Type 2, the
difference in O3 concentration between urban and rural areas was the
largest. Based on statistical analysis, the contribution rates of
urbanization to the excessive mortality caused by high temperatures and
O3 exposure were 45.68 % and 5.05 %, respectively, while 80.2 1%
and 13.9 % were caused by the synoptic pattern, respectively. In summary,
urbanization and the synoptic pattern respectively contributed 18.95 % and
33.09 % to the total HW–O3 excess mortality (Table 4).
Contribution rate of urbanization and synoptic weather pattern to ER.
In addition to heatstroke, heat exhaustion, heat fainting, heat cramps,
and other diseases, high temperature during HW days can also lead to
increased mortality of residents. Several studies have proposed that the
mortality because of respiratory diseases, cardiovascular diseases, and
cardiopulmonary diseases induced by high temperature and O3 exposure is
particularly relevant (Chen
et al., 2018; Du et al., 2020; Hu et al., 2019). Therefore, patients with
preexisting conditions, such as respiratory and cardiovascular diseases, should
pay more attention and limit outdoor activity during heatwaves and on O3-polluted days. Furthermore, demographic and socio-economic factors related
to the level of urbanization, including age structure, education, and
healthcare services, occupational types, and air-conditioning use, also
greatly affect the exposure response function of high temperature and
O3. For instance, females, the elderly, and people with lower education have suffered significantly higher health risks from
overexposure of high temperature and O3 than the average population (Huang
et al., 2015; Yin et al., 2017; Zhang et al., 2017). However, this study
mainly considers mortality by all causes for all the population caused by
high temperature and O3 exposure. Health risks for special groups such
as the elderly, children, and patients with cardiovascular and respiratory
diseases should be higher than our results. Consequently, especially during
synoptic weather patterns that can cause paired HW and O3 pollution
events, the responsible departments should strengthen the risk management of
extreme compound events such as HW and O3 pollution, establish an early
warning system, configure emergency plans, strengthen the health precautions
of respiratory and cardiovascular diseases, as well as the elderly and other
vulnerable groups, and protect public health.
To date, there is no exact consensus on the urbanization effects on the risk of
concurrent high temperature and O3 exposure. Previously, a common
perception was that urban residents were more prone to the risks of heat effects
in the context of global warming and the UHI effect (Clarke,
1972; Goggins et al., 2012; Heaviside et al., 2017). Indeed, air temperature
is one of the dominant reasons for the change in excess mortality caused by
compound HW and O3 events. In terms of urban areas, the higher
density of buildings, roads, and population and the greater heat capacity, anthropogenic heat, and temperature of urban areas are significantly higher than
those of rural areas (Roth,
2007; Stewart and Oke, 2012). The heatwaves increase the urban and rural
areas' temperature difference, as well as the maximum temperature difference,
so urban residents may be exposed to a higher temperature environment. However,
the urban–rural difference in O3 concentration modulated by HW days is
inconsistent with that in temperature. Since urban O3 can be consumed
by NO titration (Gao
et al., 2020; Murphy et al., 2007; Sillman, 1999), urbanization alleviates
the ozone exposure risk of residents to a certain extent. But suburban
forests emit additional BVOCs that generate O3 during hot days (Ma
et al., 2019; Wang et al., 2021a; Werner et al., 2020), resulting in O3
pollution slightly lower in urban areas than suburban areas (Gao et
al., 2020). Based on the regional exposure response function model, urban
areas suffer from higher mortality related to high temperature, while
suburban areas experience higher public mortality associated with O3
pollution. Overall, there is little difference in the risk of O3
exposure from urbanization. It should be highlighted that urbanization has also
brought some positive aspects. For example, a better economic level and
medical conditions can help to prevent more deaths to a certain extent;
a high air-conditioning utilization rate can also effectively reduce heat
exposure; and a reduction in the proportion of highly exposed people
engaged in agriculture, forestry, and animal husbandry in urban areas also
greatly reduces the risk of outdoor high-temperature and O3
overexposure. As a result, rural residents are more vulnerable to facing the
dual high temperature and O3 stress, and their exposure response
function coefficients may also be higher than those of urban residents (Hu
et al., 2019; Kovach et al., 2015; Li et al., 2017; Williams et al., 2013;
Xing et al., 2020; Zhang et al., 2017). This also means that under
co-occurring heatwaves and ozone-polluted weather patterns, vulnerable
groups in the suburbs should be warned of the risks of outdoor activity and
so limit their exposure to the pollutants.
Regarding the paired HW–O3 events, though we moved a step forward
in exploring the role of synoptic weather pattern and urbanization, there are
still some limitations in our study. As mentioned earlier, in a specific
area, the health risks faced by residents adjusted by different levels of
urbanization may be quite different. Moreover, the high-temperature and
O3 compound health risk model for special populations (e.g., patients
with cardiovascular and respiratory diseases, the elderly, and children)
can also be further established and analyzed. Therefore, in future
research on compound climate and pollution health impacts, it is necessary to
consider a more refined discussion in a city based on the level of
urbanization and different population groups.
Conclusions
In this study, the complex mechanism of co-occurring HW–O3 events in
the boundary layer in Beijing was systematically investigated by combining
meteorological observations, environmental monitoring observations, and
reanalysis data, and the regulatory role on health risks induced by such
compound events was explained from the perspective of the synoptic pattern
and urbanization.
The Beijing area not only experienced a stronger UHI effect during the
summertime high-temperature HWs, but it was also often accompanied by more
serious O3 pollution. In the period under study, the max temperature
Tmax and MDA8 O3 concentrations during HW days were
∼4.21∘ and ∼37.98µg m-3
higher than those on NHW days, respectively, excluding rainy daytime days.
When people are exposed to the dual stress of high temperatures and O3
pollution during HW–O3 days, the increase in Tmax and MDA8
O3 concentrations is associated with an 12.31 % (95 % CI: 4.66 %,
20.81 %) higher excess mortality from all non-accidental causes. Three
favorable synoptic weather patterns dominate such compound events and
were identified as follows:
Type 1. This is a high-pressure system located in the
southeast of Beijing and accompanied by southwesterly winds, under which the
weak downdraft and relative stable boundary layer weaken the vertical mixing
of O3 and induce heavy O3 pollution, consequently meaning that
people consistently experience high health risks.
Type 2. Under this pattern, Beijing is under the influence of a high-pressure system, accompanied by weak
southerly winds and sinking airflow in the boundary layer that favors
O3 transport together with its precursors. This translates into a lower
excess mortality compared to Type 1.
Type 3. This is a
low-pressure system located in the northeast of Beijing, accompanied by
northwesterly winds. Under this type, people endure stronger heat stress
owing to higher temperatures and lower RH, but the higher BLH and large
atmospheric environment capacity alleviate O3 exposure to a certain
extent, which results in a decrease in O3 concentration and ER compared with the other two patterns.
Overall, the unfavorable weather types contributed ∼33.09 % to the excess mortality attributed to the HW–O3 compound events. In addition, urbanization has also exacerbated the combined health risks of
high temperature and O3 pollution, which contributed ∼18.95 %. During the co-occurring HW–O3 days, urbanization greatly
affected the increase in high temperatures and related excess mortality
risks in urban areas, which were significantly higher than those in rural
areas. However, O3 pollution and its health risks in urban areas were
slightly higher than those in rural areas, and the urbanization effect was
attenuated due to the reaction of O3 and NO. Note that O3
pollution and its health risks in suburban areas were quite prominent due to
less O3 depletion and more BVOC emissions.
In summary, our findings help to better understand the formation mechanism
of HW–O3 compound events in Beijing, with robust supporting evidence
from the perspective of synoptic patterns and urbanization. Our results also
suggest that the forecasting of identified synoptic patterns could help to avoid
exposure of compound HW–O3 events. However, the urbanization effect
has a different regulatory effect on HWs and O3, meaning that high
temperatures and O3 exposure are deserving of the establishment of a
more refined health model that takes into account the differences between
urban and rural areas.
Data availability
The datasets that are analyzed and used to support the findings of this
study are publicly available. The ground-level O3 observation data from the Beijing Municipal Ecological and Environmental Monitoring Center was collected at 10.5281/zenodo.5703735 (Zong, 2021). The hourly meteorological data can be obtained from the National
Meteorological Information Center of the China Meteorological Administration
(http://data.cma.cn/, last access: 20 November 2021; CMA, 2021). The ERA5 hourly data on pressure levels from the European Centre for Medium-Range Weather Forecasts are available at (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview, last access: 20 November 2021; ECMWF, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-6523-2022-supplement.
Author contributions
YY and HX designed the research. LZ, YY, and HX developed and wrote the manuscript. LZ and YY collected and analyzed the data. HX, ZS, ZZ, XL, GN, YL, and SL provided useful comments. All the authors contributed to the revision of the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the editor and Sergio Ibarra and one anonymous referee for their constructive comments.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 42175098).
Review statement
This paper was edited by Eduardo Landulfo and reviewed by Sergio Ibarra and one anonymous referee.
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