The Pearl River Delta (PRD) region in southern China has been subject to severe ozone (O3) pollution during daytime and anomalous nocturnal O3 increase (NOI) during nighttime. In this study, the spatiotemporal variation of NOI events in the PRD region from 2006 to
2019 is comprehensively analysed, and the role of vertical transport in the
occurrence of NOI events is quantified based on observed surface and
vertical O3 and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) dataset. The results show that the average annual frequency of NOI events in the whole PRD region during the 14 year period is estimated to be 53 ± 16 dyr-1, with an average of 58 ± 11 µgm-3 for the nocturnal O3 peak
(NOP) concentration. Low-level jets (LLJs) are the main meteorological
processes triggering NOI events, explaining on average 61 % of NOI events. Annual NOI events exhibit an upward trend before 2011 (4.70 dyr-1) and a downward trend thereafter (-0.72 dyr-1), which is consistent with the annual variation of LLJs (r=0.88, p<0.01). Although the contribution of convective storms (Conv) to NOI events is relatively small with an average value of 11 %, Conv-induced NOI events steadily increased at a rate of 0.26 dyr-1 during this 14 year period due to the impact of urbanisation. Seasonally, a relatively higher frequency of NOI events is
observed in spring and autumn, which is consistent with the seasonal pattern of LLJs and maximum daily 8 h average (MDA8) O3. Spatially, NOI events are frequent in the eastern PRD, which agrees well with the spatial distribution of the frequency of LLJs and partially overlaps with the distribution of MDA8 O3 concentration, suggesting that vertical transport plays a more important role in NOI events than daytime O3 concentration. The Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model and the observed vertical O3 profiles are further applied to illustrate the mechanisms of NOI formation caused by LLJs and Conv. The results confirm that both LLJs and Conv trigger NOI events by inducing downdrafts with the difference being that LLJs induce downdrafts by wind shear, while Conv by compensating downdrafts. Through observational and modelling analysis, this study presents the long-term (2006–2019) trends of NOI events in the PRD region and quantifies the contribution of meteorological processes for the first time, emphasising the importance of vertical transport, as well as daytime O3 concentration for the occurrence of NOI events.
Introduction
As a secondary pollutant, surface ozone (O3) is formed via photochemical reactions involving nitric oxide (NOx) and volatile organic compounds (VOCs) in the presence of sunlight. Therefore, O3 shows significant diurnal variation, with concentration peaks observed during daytime (Kleinman et al., 1994; Zhang et al., 2004). During nighttime, O3 production ceases owing to the absence of sunlight, and dry deposition and NO titration (Eq. R1) remove O3 directly from the atmosphere, leading to relatively low O3 concentrations at night (Jacob, 2000; Brown et al., 2006).
NO+O3→NO2+O2
However, O3 concentrations do not always remain at low levels during
nighttime, and frequent nocturnal O3 increase (NOI) events have been
observed in various countries in Asia, Europe, North America, etc. in
different topographies (plains, valleys, mountains, etc.) (Kuang et al.,
2011; Kulkarni et al., 2013; Klein et al., 2019; Udina et al., 2019; Zhu et
al., 2020). Kulkarni et al. (2015) found that NOI events were observed
around 03:00 (LT) in the UK, with concentrations as high as 118 µgm-3, which was much higher than the monthly average daytime O3
concentration (69 ± 10 µgm-3). Yusoff et al. (2019)
also reported that frequent NOI events were observed in some cities in
Malaysia, and the annual trend of nocturnal O3 concentration was found
to be on the increase based on 11 years of ground-based measurements. High
nocturnal surface O3 concentrations have adverse effects on crops and
vegetation, leading to plant water loss, stomatal sluggishness, and
reduction in plant production (Caird et al., 2007; Cirelli et al., 2016;
Yue et al., 2017), as well as on human health (Kurt et al., 2016;
Carré et al., 2017).
Because there is no photochemical production of O3 at night, NOI events
are likely to be due to meteorological processes (Salmond and
McKendry, 2002). It has been widely recognised that low-level jets (LLJs)
are one of the most important meteorological processes that cause NOI events
(Salmond and McKendry, 2002; Kuang et al., 2011; Sullivan et al., 2017).
After sunset, radiative cooling and subsequent weakened turbulence result in
a stratified nocturnal boundary layer (NBL) with an altitude of 400–500 m
(Stull, 1988; Sugimoto et al., 2009; Fan et al., 2022). A residual layer
(RL) exists above the NBL, which contains residual O3 produced during
daytime. When an LLJ occurs during nighttime, it can break the delamination
between the NBL and the RL by wind shear and bring the O3 from the RL
to the surface, leading to an accumulation of ground-level O3. An
analysis of aircraft data from California has shown that LLJs promote the
mixing between the NBL and the RL and transport O3 from the RL to the
surface, leading to NOI events (Caputi et al., 2019). Convective
storms (Conv) are another meteorological process that contributes to NOI
events, especially at the Equator and in tropical areas that have a higher
frequency of convection (Prtenjak et al., 2013; Zhu et al., 2020; Wu et
al., 2020). Dias-Junior et al. (2017) revealed that downdrafts induced by
Conv play an important role in triggering NOI events in the Amazon region of
Brazil based on 1 year of observations. Jain et al. (2007) noted
that NOI events in India are often accompanied by thunderstorms and stable
boundary layer conditions. Other meteorological processes that are highly
dependent on topography, such as sea–land breezes and mountain–valley
breezes, also contribute to NOI events (Salmond and McKendry, 2002; Nair
et al., 2002). Seibert et al. (2000) pointed out that
nocturnal O3 concentrations are elevated during foehn events in the
Eastern Alps.
Therefore, NOI events are not an exception and can occur worldwide as a
result of certain meteorological processes (LLJs, thunderstorms, foehn,
etc.) (Hu et al., 2013; Caputi et al., 2019; Klein et al., 2019; Udina et
al., 2019; Shith et al., 2021). LLJs and Conv are important factors
influencing the generation of NOI events; however, their relative
contribution to NOI events has not yet been quantified. Most previous
studies have focused on the analysis of a single NOI event or NOI events at
limited monitoring sites for short periods (Jain et al., 2007; Hu et al.,
2013; He et al., 2021). Consequently, it is of great importance to
investigate the long-term trends of NOI events on a larger scale to further
quantify the impacts of meteorological processes, such as LLJs and Conv, on
NOI events.
Summary of the dataset used in this study.
DescriptionPeriodSitesTemporalSpatialPurposeresolutionresolutionObserved O3 data2006–201916 sites1 h–Spatiotemporal analysis of NOIand NOP, model performanceObserved vertical O32019Dongguan12 min–Analysis of an NOI event causeddatasuperstationby ConvObserved meteorological8–15 September 20179 sites1 h–Model performancedataObserved cloud-top2019Gridded data1 h0.1∘Indicator of the occurrence oftemperature (CTT) dataconvectionERA5 reanalysis dataset2006–2019Gridded data1 h0.25∘Definition of LLJs and Conv
In China, ground-level O3 pollution has deteriorated
in recent years, especially in the Pearl River Delta (PRD) region (Wang et al., 2017). Liao et al. (2021) investigated ozonesonde profiles recorded in Hong Kong during 2000–2019 and indicated that O3 concentrations in the lower troposphere have increased substantially at a rate of 0.618 ppbvyr-1, indicating a continuous deterioration of O3 pollution in the PRD region over the last 20 years. The PRD region is the first urban agglomeration in China to change its main pollutant from particulate matter with an aerodynamic diameter of less than or equal to 2.5 mm (PM2.5) to O3. Numerous studies in the PRD region have investigated the daytime O3 characteristics, such as the long-term trends (Xue et al., 2014; Li et al., 2022), the nonlinear response of O3 to precursor emissions (Lu et al., 2010; Mao et al., 2022), the source apportionment of O3 (Shen et al., 2015; Liu et al., 2020), and the relative contributions of precursor emissions and meteorology to O3 (L. Yang et al., 2019; Chen et al., 2020). In terms of nighttime O3, Tong and Leung (2012) observed a double-peak pattern of diurnal O3 variation in Hong Kong during 1990–2005 and found that nocturnal O3 peaks are sometimes higher than daytime maxima. He et al. (2021) studied an NOI event in the city Shaoguan in Guangdong province and found that nocturnal mountain–valley breezes from the Nanling Mountains transported O3 from the RL to the surface. However, studies on the spatiotemporal distribution of nocturnal O3 concentration in the PRD region and the factors influencing it are still lacking. There is an urgent need to comprehensively study the characteristics of NOI events in the PRD region, as it is frequently affected by special meteorological processes (such as LLJs and Conv) that favour NOI events due to its special topography with the coast to the south and the mountains to the north. In addition, high population densities and an increasing number of people active at night in the PRD region make NOI events an important potential risk to human health (Kurt et al., 2016; Carré et al., 2017; C. Yang et al., 2019; Zhang et al., 2021).
In this study, the long-term trends and spatial distribution of NOI are
presented via in situ hourly O3 concentration data collected from 16
air quality monitoring sites in the PRD region during 2006–2019. In
addition, the relative contributions of LLJs and Conv to NOI events are
quantified based on the ERA5 reanalysis dataset. Finally, the observed
vertical profile of O3 and the Weather Research and Forecasting (WRF)
model coupled with the Community Multiscale Air Quality (CMAQ) model are
applied to further elaborate the impacts of LLJs and Conv on the selected
typical NOI events. This study provides a comprehensive analysis of NOI
events and the meteorological factors influencing them in the PRD region
over a 14 year period for the first time, expanding our knowledge of the
meteorological role in NOI events.
Data and methodsData sources
The dataset used in this study is summarised in Table 1. In brief, the
observed hourly O3 concentrations at the 16 air quality monitoring
sites in the PRD region from 2006 to 2019 are provided by the Guangdong–Hong
Kong–Macau Pearl River Delta Regional Air Quality Monitoring Network (HKEPD,
2017) (Fig. 1). More detailed information of these sites can be found in
Table S1 in the Supplement. The observed hourly O3 data were used for subsequent NOI and
NOP analyses and evaluation of O3 simulations.
Model domains and locations of 16 air quality monitoring stations (purple dots), nine meteorological stations (blue triangles), and the Dongguan superstation (red triangles). The figure on the right shows the elevation of the terrain (m). The 16 air quality monitoring stations are Nanchengyuanling (NCYL), Jinjiju (JJJ), Huijingcheng (HJC), Luhu (LH), Wanqingsha (WQS), Tianhu (TH), Tap Mun (TM), Tsuen Wan (TW), Tung Chung (TC), Xiapu (XP), Jinguowan (JGW), Donghu (DH), Liyuan (LY), Tangjia (TJ), Chengzhong (CZ), and Zimaling (ZML). The nine meteorology sites are Dongguan (DG), Shunde (SD), Guangzhou (GZ), Huiyang (HY), Taishan (TS), Shenzhen (SZ), Zhuhai (ZH), Gaoyao (GY), and Zhongshan (ZS).
The vertical distribution of O3 concentrations observed at the Dongguan
superstation (23.02∘ N, 113.79∘ E) in 2019 is also used
to investigate the impact of Conv on a particular NOI event. The vertical
profile of O3 is measured using an O3 lidar (model: LIDAR-G-2000).
The detection height of the O3 lidar is 3 km, with a vertical spatial
resolution of 7.5 m and a temporal resolution of 12 min.
The observed meteorological variables at the nine meteorological sites (Fig. 1)
in the PRD region are obtained from the Chinese National Meteorological
Centre (CNMC, http://www.cma.gov.cn/, last access: 10 February 2022), including temperature (T2), relative humidity (RH) and wind
speed (WS10). The observed meteorological data were used to evaluate the
performance of the model. More detailed information of the nine meteorological
sites can be found in Table S2.
To investigate the impacts of meteorological processes on NOI events, the
ERA5 reanalysis dataset (https://cds.climate.copernicus.eu/cdsapp#!/home, last access:
10 February 2022) provided by the European Centre for Medium-Range Weather
Forecasts (ECMWF) is used in this study. The ERA5 reanalysis dataset, which
currently covers the period from 1979 to present, is provided on regular
latitude–longitude grids at approximately 0.25∘×0.25∘ and up to 1 h frequency. Vertically, ERA5 resolves the
atmosphere using 137 levels from the surface to an altitude of 0.01 hPa. The
performance of ERA5 has been evaluated in previous studies and has been shown to
be adequate for further analysis (Olauson, 2018; Hersbach et al., 2020).
The ERA5 reanalysis dataset includes wind speed, precipitation, temperature,
and vertical wind velocity. Since the ERA5 reanalysis dataset was gridded,
the nearest-neighbour interpolation method is used to obtain site-specific
meteorological variables at the 16 air quality monitoring sites.
The observed cloud-top temperature (CTT) data for 2019 obtained from the
Fengyun 2G satellite (http://satellite.nsmc.org.cn/, last
access: 31 August 2022) are used to indicate the occurrence of convection. The CTT data
cover the East Asia region with a spatial resolution of 0.1∘ and
a temporal resolution of 1 h.
Definition of NOI and NOP
For our analysis, we define a nocturnal O3 increase (NOI) event as
O3 concentrations peaked at night (from 21:00 to 06:00 LT the next
day), with an increase in levels of at least 10 µgm-3 compared to
the previous hour (includes 20:00 LT) and a decrease of less than 10 µgm-3 in the next hour (includes 07:00 LT). The corresponding nighttime
peak concentration of O3 is referred to as the nocturnal O3 peak
(NOP) (Zhu et al., 2020). In this study, based on the above observed
hourly O3 data at the 16 air quality monitoring sites, NOI events are
identified at each site, yet only one NOI event is recorded per night,
regardless of how many NOI events occur in a single night. In addition, the
regional values of NOI and NOP from the 16 air quality monitoring sites were
averaged.
Definition of LLJs and Conv
Low-level jets (LLJs) and convective storms (Conv) are defined in this study
based on the above site-specific ERA5 reanalysis dataset. According to
Banta et al. (2002) and Hodges and Pu (2019), LLJs are defined
as when vertical wind speed maxima occur below 800 hPa and exhibit a decrease
of at least 1.5 ms-1 at vertical levels both above and below the
levels of the maxima. We assume that LLJs cause downdrafts because of the
vertical wind shear the jets induce, which creates mechanical turbulence.
Conv is defined by the following criterion: the mean K index (KI) is greater
than 30 ∘C within 3 h prior to an NOI event (George, 1960;
Johnson, 1982). The KI is calculated as follows:
KI=(T850-T500)+Td850-(T700-Td700),
where T850, T700, and T500 are the temperature (∘C)
at 850, 700, and 500 hPa, respectively, and Td850 and
Td700 are the dew point temperature at 850 and 700 hPa,
respectively.
Model configurations.
ModelPhysical processParameterisation schemeReferenceWRFMicrophysicsLinLin et al. (1983)Longwave radiationRRTMGIacono et al. (2008)Shortwave radiationRRTMGIacono et al. (2008)Surface layerMonin–ObukhovMonin and Obukhov (1954)Planetary boundary layerMYJNakanishi and Niino (2006)Cumulus parameterisationGrell-3Grell and Dévényi (2002)Land surfaceNoah land surface modelChen and Dudhia (2001)CMAQGas-phase chemistrySAPRC 07Carter (2010)Aerosol chemistryAERO6Carlton et al. (2010)
Cloud-top temperature (CTT) was also introduced as an indicator of the
occurrence of convective systems and further used to evaluate the
applicability of KI. The lower the CTT, the higher the probability of
a convection event. According to the work of Ai et al. (2016), CTT lower than
-35 ∘C indicates the occurrence of convection. We randomly selected
10 nights with KI>30∘C (Table S3) and 10 nights with
KI<30∘C (Table S4) and examined the corresponding CTT
values. In the cases with KI>30∘C, the CTT values were
lower than -35 ∘C in 10 out of 10 nights (Table S3). And the spatial
distribution of CTT showed that they had a distinct circular area with a lower
value over the selected sites, indicating the occurrence of convective
systems (Fig. S1 in the Supplement). For the cases with KI<30∘C, 6 out of
10 nights were with CTT higher than -35 ∘C, while the rest of the 4 nights
had no CTT data due to cloudless weather (Table S4). The spatial
distribution of CTT did not show the features of a convective system (Fig. S2), suggesting that convection was not observed for the selected 10 cases
with KI<30∘C. The above results suggest that the KI>30∘C criterion is a valid metric to capture the
occurrence of convection.
In this study, an NOI event at each air quality site was classified into
four categories: caused by LLJs only, caused by Conv only, caused by LLJs
and Conv (LLJs+Conv) at the same time, and caused by other factors.
Trend analysis
In this study, the nonparametric Mann–Kendall (M–K) test (Mann, 1945) is
used to determine the statistical significance (p values) associated with
the annual trends of NOI, NOP, MDA8 O3, LLJs and Conv, etc. A
significance level of p<0.05 was used to test the significance of
the interannual trend. The magnitude of a given trend is calculated by the
nonparametric Theil–Sen (T–S) estimator (Sen, 1968). The advantage of the
M–K test and the T–S estimator is that they do not require prior assumptions of the statistical distribution for the data and are resistant to outliers. The M–K test and the T–S estimator have been widely used in previous O3 trend studies (Wang et al., 2019; Lu et al., 2020; Li et al., 2022).
WRF–CMAQ model configuration
Due to the lack of observed vertical profiles of wind speed, the WRF–CMAQ
model is employed to investigate the effects of LLJs on a selected NOI
event. The NOI event induced by LLJs that occurred at the Nancheng Yuanling
(NCYL) site in Dongguan on 13–14 September 2017 is selected as a typical
case. The simulation was conducted during 6–14 September by using the
WRF–CMAQ–IPR model with the first 2 d used as model spin-up to eliminate the
impact of the initial meteorological conditions (IC) (Jiménez et al., 2007).
The Weather Research and Forecasting model (WRFv3.9.1) is used to provide
meteorological inputs to drive the Community Multiscale Air Quality (CMAQ
v5.3.1) model. The IC
and boundary conditions (BC) are provided by the National Centers for Environmental Prediction (NCEP) Final Analyses (FNL) dataset, with a spatial resolution of 1∘×1∘ and a temporal resolution of 6 h. The main physics options used for the WRF model are shown in Table 2. Two nested domains are used in the WRF simulations, with 38 vertical layers from the surface to 100 hPa. Figure 1 shows the two nested modelling domains, with spatial resolutions of 27km×27 km, and 3km×3 km for the coarse (D01) and inner (D02) domains, respectively. D01 covers most regions of China and D02 covers the whole PRD region.
The CMAQ model is used to simulate the O3 concentrations in the PRD
region. The SAPRC07 and AERO6 aerosol modules are used for gas-phase and
particulate matter chemical mechanisms, respectively (Carter, 2010; Wyat
Appel et al., 2018). The chemical IC and BC for D01 are derived from a
global chemical transport model, the Model for Ozone and Related Chemical
Tracers, version 4 (MOZART4) (Emmons et al., 2010), and those for D02 are
provided by the simulated results from D01. The anthropogenic emissions for
D01 are based on the 2016 Multi-resolution Emission Inventory for China,
which has a grid resolution of 0.25∘×0.25∘ (Zheng et al., 2018). Those used for D02 are based on the 2017 high-resolution emission inventory of the PRD region with a grid resolution of 3km×3 km (Zhong et al., 2018), which includes the emission sectors of agriculture, biomass combustion, incineration, dust, industrial processes, non-road, solvent, storage, transportation and waste disposal. Biogenic emissions are calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 that was integrated into the CMAQ model (Guenther et al., 2006; Wang et al., 2011).
In order to interpret the underlying atmospheric mechanisms for NOI events,
the Integrated Process Rates (IPR) analysis tool embedded in the WRF–CMAQ
model is used to identify and quantify the contribution of various physical
and chemical processes to O3. The processes include horizontal
transport (HTRA), vertical transport (VTRA), gas-phase chemistry (CHEM), dry
deposition (DDEP), and cloud processes (CLDS). Horizontal transport is the
sum of horizontal advection and diffusion, and vertical transport is the sum
of vertical advection and diffusion. More details on the IPR analysis tool
can be found in previous work (Liu et al., 2010; Wang et al., 2010).
Model evaluation
The WRF–CMAQ simulation results are evaluated by comparison with available
ground-based observed O3 and meteorological data. Statistical metrics including mean value (Obs‾ and Sim‾), mean bias (MB), normalised mean bias (NMB), normalised mean error (NME), root mean square error (RMSE), correlation coefficient (r), and index of agreement (IoA) are calculated as follows to evaluate model performance.
2MB=Obs‾-Sim‾,3NMB=∑i=1n(Simi-Obsi)∑i=1nObsi×100,4NME=∑i=1n|Simi-Obsi|∑i=1nObsi×100,5RMSE=1n∑i=1n(Simi-Obsi)2,6r=∑i=1n(Simi-Sim‾)(Obsi-Obs‾)∑i=1n(Simi-Sim‾)2∑i=1n(Obsi-Obs‾)2,7IoA=1-∑i=1n(Simi-Obsi)2∑i=1n(|Simi-Obs‾|+|Obsi-Obs‾|)2.
The evaluation protocols of the U.S. Environmental Protection Agency (EPA,
2017) are used to evaluate the performance of the meteorological parameters.
The simulated results were accepted when the statistics met the criteria
listed as follows: MB≤±0.5∘C and IoA≥0.8 for simulated T2; MB≤±5 % and IoA≥0.6 for simulated
RH; and MB≤±0.5ms-1, RMSE≤2.0ms-1, and IoA≥0.6
for simulated WS10. The evaluation protocols of the Ministry of
Environmental Protection of China (MEE, 2015) are used to evaluate the performance of O3 and the simulated results were acceptable if the statistics met the criteria listed below: -15%<NMB<15 %, NME<35 %, and r>0.4.
Results and discussionGeneral characteristics of NOI events
The average annual frequency of NOI events in the 16 sites across the whole
PRD region from 2006 to 2019 is estimated to be 53 ± 16 dyr-1,
with an average annual NOP concentration of 58 ± 11 µgm-3.
LLJs are the primary factor causing NOI events, accounting for about 61 %,
followed by the combination of LLJs and Conv (LLJs+Conv) with a value of
16 %, while the corresponding value is 11 % for Conv (Fig. 2). The
remaining 12 % of NOI events that cannot be explained by LLJs and Conv may
be related to other meteorological processes, such as mountain–valley
breezes and sea–land breezes (Sousa et al., 2011; He et al., 2021).
The average relative contribution of different meteorological
processes to NOI events during 2006–2019.
Long-term trends of NOI events
As depicted in Fig. 3a, the regional-average annual frequency of NOI events
increased from 38 ± 18 dyr-1 in 2006 to as high as 67 ± 18 dyr-1 in 2011 at a rate of 4.70 dyr-1 (p<0.01) and
gradually decreased after 2012 at a rate of -0.72 dyr-1 (p<0.05). A similar annual trend is observed for the frequency of total
downdrafts (sum of LLJs, LLJs+Conv, and Conv) (Fig. 3b). The frequency of
total downdrafts increased at a rate of 4.02 dyr-1 (p<0.01)
before 2012 and decreased at a rate of -0.53 dyr-1 (p<0.05)
thereafter, which is significantly positively correlated with NOI events,
with a Pearson correlation coefficient (r) of 0.96 (p<0.01). Among
the total downdrafts, LLJs exhibit a similar pattern with NOI events
(r=0.89, p<0.01), further suggesting that LLJs are the
predominant driver. Conv presents a continuously increasing trend over the
whole 14 year period, with a rate of 0.26 dyr-1 (p<0.01), and
the frequency of LLJs+Conv does not show obvious variation.
Regional-average annual trends of (a) frequency of NOI events; (b)
frequency of total downdrafts (black), LLJs (orange), LLJs+Conv (green), and
Conv (red) that can induce NOI events; (c) NOP concentrations; and (d)
MDA8 O3 concentrations in the PRD region during 2006–2019. The units of
k (Sen's slope) are d-1yr-1 in (a, b) and µgm-3yr-1 in (c, d). Linear trends significant at the 95 % confidence
level are illustrated with dashed lines. The error bars indicate the range
of deviations for the 16 air quality sites.
Both the frequency of NOI and LLJs present increasing trends before 2012 and
decreasing trends thereafter, which was likely related to urbanisation.
Previous studies have shown that urbanisation has large effects on the
frequency of LLJs by changing surface conditions (roughness and soil
moisture) and further affecting the turbulence and geostrophic wind speed
(McCorcle, 1988; Fast and McCorcle, 1990; Kallistratova, 2008; Nikolic et
al., 2019; Ziemann et al., 2019). Kallistratova (2008) and Nikolic et al.
(2019) pointed out that negative correlation was found between urban areas
and the frequency of LLJs. During 1987–2017, the urban areas in the PRD
region grew at an average rate of 8.82 %yr-1 (C. Yang et al., 2019)
and reached a maximum urban land expansion growth rate of 6.66 % during
2010–2015 (Zhang et al., 2021). Therefore, the trends for the frequency
of NOI and LLJs were quite different during these two periods (2006–2011 and
2012–2019).
Although the percentage of NOI events caused by Conv alone is relatively
small compared to those caused by LLJs (Fig. 2), it is noteworthy that the
frequency of Conv-induced NOI events was on the increase during the 14 year
period (Fig. 3b), which is also mainly related to the rapid urbanisation in
the PRD region in recent years (C. Yang et al., 2019; Zhang et al.,
2021). Surface roughness increase due to city expansion led to the greater
frequency and intensity of convection in the form of enhanced mechanical
turbulence (Li et al., 2021), thus an increase in the frequency of
Conv-induced NOI events. The role of Conv in the occurrence of NOI events is
expected to amplify in the future if the urbanisation trend in China
continues (Seto et al., 2012; Marelle et al., 2020).
In contrast to the annual trend of NOI frequency, the nocturnal O3 peak
(NOP) value shows an upward trend during 2006–2019, with a slower growth
rate of 0.54 µgm-3yr-1 (p<0.05) before 2015 and a
faster growth rate of 4.76 µgm-3yr-1 (p<0.01)
thereafter (Fig. 3c). The maximum daily 8 h average (MDA8) O3 mixing
ratio exhibits a similar pattern to NOP, with an increase rate of 0.39 µgm-3yr-1 (p<0.05) before 2015 and 9.21 µgm-3yr-1 (p<0.01) thereafter (Fig. 3d). NOP is
significantly positively correlated with MDA8 O3, with r up to 0.88
(p<0.01). This implies that daytime O3 concentration levels
potentially affect NOP concentrations. The variations of NOP and MDA8
O3 during the two periods (2006–2015 and 2016–2019) are more likely
related to the change in precursor emissions. The continuous increase in the
emissions of anthropogenic VOCs and NOx resulted in the gradual
increase of O3 concentrations between 2006 and 2012 (Ma et al.,
2016; Li et al., 2017; Zhong et al., 2018; Liao et al., 2021). However,
since the implementation of the Air Pollution Prevention and Control Action Plan
(APPCAP) in 2013, NOx emissions were dramatically decreased by 21 % in
2017 compared to 2013 (Feng et al., 2019; L. Yang et al., 2019). The weakening
of NO titration caused by the dramatic decrease in NOx emissions and
the continuously increasing VOC emissions due to the lack of controls
became important drivers of the sharp rise in O3 since 2015 (Li et al.,
2019; Mousavinezhad et al., 2021; Li et al., 2022). Furthermore, the
decreasing PM2.5 levels and the increasing atmospheric oxidising
capacity in the PRD region in recent years have also been considered
important contributors to accelerated O3 growth during 2016–2019
(Gong et al., 2018; Li et al., 2019; Han et al., 2019). Consequently, NOP
and MDA8 O3 present a slower increase rate before 2015 and higher
increase rate thereafter.
Seasonal variations of NOI events
NOI events exhibit obvious seasonal variation (Fig. 4a), with relatively
higher frequency observed in spring (18 ± 4 dyr-1) and autumn
(20 ± 5 dyr-1) and lower frequency in summer (14 ± 3 dyr-1) and winter (16 ± 3 dyr-1). LLJs are the dominant
inducer of NOI events in spring, autumn, and winter (Fig. 4b), while in
summer, the dominant factors are LLJs+Conv and Conv, because convective
activity is more intense during summer (Chen et al., 2014). Given
that LLJs can enhance turbulence below the jet and create favourable
formation conditions for Conv (Trier et al., 2017; Du and Chen, 2019),
most Conv events preferentially occur on days when LLJs exist in the PRD
region (Chen et al., 2014), which makes LLJs+Conv the main
contributor in summer.
Seasonal variation of (a) frequency of NOI events; (b) frequency
of total downdrafts (black), LLJs (orange), LLJs+Conv (green), and Conv
(red) that can induce NOI events; (c) NOP concentrations; and (d) MDA8
O3 concentrations in the PRD region during 2006–2019. The error bars
indicate the range of deviations for the 16 air quality sites.
In terms of NOP (Fig. 4c), relatively higher concentrations are observed in
spring and autumn, with values of 59 ± 12 and 66 ± 10 µgm-3, respectively, while the concentration in summer
is the lowest (44 ± 7 µgm-3). The MDA8 O3 has a
similar seasonal variation to NOP except in winter (Fig. 4d); it is high in
spring (111 ± 15 µgm-3) and autumn (120 ± 13 µgm-3) and low in summer (88 ± 15 µgm-3). In winter,
surface MDA8 O3 was the lowest (86 ± 12 µgm-3), while
NOP remained at relatively high levels (56 ± 10 µgm-3).
This is because the higher O3 concentrations in the lower troposphere
in winter allow more O3 to be transported downward during the NOI
period, resulting in a higher NOP concentration in winter, as shown by the
seasonally observed vertical O3 profile at the Dongguan superstation
(Fig. 5). As illustrated in Fig. 5, higher O3 concentrations are
observed at 200 to 750 m altitude in winter than in summer. A similar result
was also observed in Hong Kong (Liao et al., 2021). This is
mainly due to the typical Asian monsoon circulation, which brings clean
marine air to the lower troposphere of the PRD region in summer and dilutes
polluted air masses inland, while it brings pollutant-laden air from
mainland China in winter resulting in higher O3 concentrations over the PRD region (Wang et al., 2009).
Seasonally averaged vertical distribution of O3
concentrations at the Dongguan superstation from the surface to an altitude
of 3000 m in 2019. Spring (Spr.): March–May, Summer (Sum.): June–August,
Autumn (Aut.): September–November, Winter (Win.): December–February.
Diurnal variation of (a) the frequency of NOI events, (b)
the frequency of total LLJs (black) and the LLJs that can induce NOI events
(orange), and (c) NOP concentrations during 21:00–06:00 (LTC) in the PRD region
during 2006–2019. The error bars indicate the range of deviations for the 16
air quality sites.
Spatial distribution of annual average (a) NOI event frequency
(points) and LLJs frequency (contours) and (b) MDA8 O3 concentrations.
Diurnal variation of NOI events
Distinct diurnal variation is observed in NOI events (Fig. 6a) with an
increasing trend from 21:00 to 03:00 LT (UTC+08:00) and a decreasing trend thereafter. It is estimated that about 60 % of the events occurred in the middle of the night (11:00–03:00 LT). The LLJs that can induce NOI events show a similar diurnal variation to the frequency of NOI events and often occur around midnight. However, the frequency of total LLJs differs from the frequency of LLJs that can induce NOI events (Fig. 6b), as it increases steadily from 21:00 to 00:00 LT and remains stable after 00:00 LT. This suggests that LLJs are not the only factor that determines whether an NOI event can develop, and O3 concentration in the RL can also affect the development of an NOI event. As the sun sets and the daytime boundary layer fades away, the O3 produced during daytime remains at a relatively high level in the RL during 21:00–03:00 LT. During this period, the occurrence of LLJs tends to increase the probability of NOI events. After 03:00 LT, the O3 concentrations in the RL decreased due to horizontal transport to a downwind area and vertical transport (e.g. LLJs, convection, O3 dry deposition process) during 21:00–03:00 LT, which reduced the amount of O3 that can be transported downward. Hence, even though the frequency of the total LLJs is relatively high after 03:00 LT, the lower O3 content in the RL results in less O3 being transported downward to form an NOI event, which ultimately decreases the frequency of NOI events. As illustrated in Fig. 6c, the trends of NOP concentrations from 21:00 to 06:00 LT also reflect the fact that O3 concentrations in the RL are higher during 21:00–00:00 LT and lower during 00:00–06:00 LT. Therefore, the development of an NOI event is influenced by the combination of a downdraft induced by meteorological processes and the level of O3 concentrations.
Spatial distribution of NOI events
As most NOI events are caused by LLJs, LLJs are taken as an example to
explore the role of meteorological processes in the spatial distribution of
NOI events. The spatial distribution of the average annual frequency of NOI
events and LLJs in the PRD region from 2006 to 2019 is shown in Fig. 7a, and
the spatial distribution of MDA8 O3 concentrations obtained by
Kriging's interpolation method is shown in Fig. 7b. Obvious geographical
variations are observed for NOI events, with a higher frequency in the
eastern PRD region, coupled with a higher frequency of LLJs, although the
MDA8 O3 concentrations are relatively lower in these regions. In the central PRD region, despite the highest MDA8 O3 concentrations, the frequency of NOI events was the lowest, implying a more important role of
vertical transport induced by meteorological processes in the formation of
NOI events. At the three sites located in the southern part of the PRD
regions (Tangjia– TJ, Tsuen Wan – TW, Tung Chung – TC) the frequency of NOI events was the highest, while the frequency of LLJs was not. This is because these three sites were also affected by non-LLJ (=Conv+(LLJs+Conv)+Other) processes with comparable contributions of LLJs and non-LLJs to the NOI events. And the
contributions of LLJs (60 %–70 %) were higher than those of non-LLJs at the rest of the sites (Table S5).
Causative analysis of NOI events: convective storms trigger
In order to elaborate on the underlying atmospheric mechanisms for Conv-induced NOI events, a distinct NOI event associated with Conv observed at the Nancheng Yuanling (NCYL) site in Dongguan on 3–4 September 2019 is taken as a typical example. The vertical O3 profile data observed at the Dongguan superstation are used to represent the vertical O3 distribution at the NCYL site during this NOI event, since the distance between these two stations is only 3 km.
The KI remains above 36 ∘C (Fig. 8a), and the vertical velocity shows continuous updraft trends at 1–3 km altitude from 14:00 to 23:00 LT (Fig. 8b), indicating a high possibility of convection. Although the magnitude of vertical velocity was relatively low, it has also been found in previous studies (Ploeger et al., 2021). In addition, the spatial distribution of CTT shows that the CTT value at 18:00 LT over Dongguan was around -70 ∘C (Fig. 9a), which was lower than the criterion (-35 ∘C) for the occurrence of a
convection process. The results of KI, the vertical velocity, and the CTT indicate that the possibility of a convection process is high. Thus, the precipitation that occurred during 16:00–20:00 LT was a convective precipitation. The effect of rainfall on O3 removal is relatively small after sunset, because wet deposition of O3 occurs through the removal of the precursors HNO3 and H2O2 by water vapour under solar radiation, which is indirect and rather peripheral, and the effect of heterogeneous processes on O3 removal is weak (Jacob, 2000; Awang et al., 2015; Zhu et al., 2020). Therefore, the unconsumed O3 remains stable in the RL. As illustrated in Fig. 8c, higher O3 concentrations are found in the RL after 18:00 LT, reaching around 200 µgm-3. At 21:00 LT, a strong updraft suddenly appears above 1.5 km (Fig. 8b), and a distinct area of low CTT values (around -66 ∘C) could be observed over Dongguan (Fig. 9b), confirming the occurrence of an updraft. The updraft subsequently caused a strong compensating downdraft below 1 km at 22:00–23:00 LT (Fig. 8b). The downdraft then breaks through the stable nocturnal boundary layer and transports O3 from the RL to the surface (Fig. 8c). Hence, an NOI event occurs at 23:00 LT, with O3 concentrations increased from 45 µgm-3 at 22:00 LT to 59 µgm-3 at 23:00 LT. Although the modelled downdraft that occurred at 22:00–23:00 LT (Fig. 8b) was around half an hour later than the observed O3 intrusion into the nocturnal boundary layer (Fig. 8c) due to the model errors, the modelled results can still generally capture the occurrence of convection processes.
(a) Hourly variations of KI (brown line), O3 concentrations
(blue line), and hourly precipitation amount (blue bar); (b) vertical wind
velocity with positive and negative values related to updrafts and
downdrafts; and (c) vertical profile of O3 concentrations at the NCYL site
in Dongguan on 3–4 September 2019.
Spatial distribution of cloud-top temperature (CTT) at (a) 18:00 LT and (b) 21:00 LT on 3 September 2019.
Causative analysis of NOI events: LLJs trigger
Another typical NOI event induced by LLJs that occurred at the NCYL site in
Dongguan on 13–14 September 2017 was simulated using the WRF–CMAQ–IPR model, because no vertical profiles of wind speed were observed. Model performance was first evaluated. Comparisons of the simulated meteorological parameters with observations for the nine sites in the PRD region during 8–14 September 2017 are shown in Fig. S3, with statistical indices reported in Table S6. The results show that WS10 was reasonably well simulated, as the regional average of MB, RMSE, and IoA met the EPA criteria mentioned in Sect. 2.6. The simulated regional average of RH and T2 were slightly overestimated (MB =-6.1 %) and underestimated (MB = 1.4 ∘C), respectively, while they performed well at the Dongguan site, where both the MB (RH =-1.0 %, T2 = 0.5 ∘C) and IoA (both are 1.0) met the EPA criteria mentioned in Sect. 2.6. The MB of the simulated WS10 at the Dongguan site was slightly underestimated (MB=0.7ms-1) with the RMSE and IoA meeting the EPA criteria.
Comparisons of simulated O3 with hourly observations during
8–14 September 2017 are shown in Fig. S3, with statistical indices reported in Table S7. The simulated O3 showed a good performance in the PRD region, with NMB (-4.8 %), NME (17.7 %), and r (1.0) meeting the MEE criteria, while it was slightly underestimated at the NCYL site in Dongguan but still met the MEE criteria (NMB=-12.7 %, NME=29.8 %, r=0.8). Therefore, the simulation results of the meteorological parameters and O3 are reasonable and reliable for further analysis.
Figure 10a shows the time series of simulated O3 concentrations and the contributions of different processes to surface O3 concentrations at the NCYL site in Dongguan during 13–14 September 2017. During the night on 13 September, the O3 concentration increased from 45 µgm-3 at 21:00 LT, reached the peak of 85 µgm-3 around 22:00 LT and dropped to 15 µgm-3 at 00:00 LT. Before 21:00 LT, the magnitude of the negative contribution of chemical processes to O3 was greater than the positive contribution of vertical transport, resulting in net O3 depletion. This suggests that gas-phase chemistry processes such as NO titration are the main pathway for O3 loss at night. At 21:00 LT, the vertical and horizontal transport contribution increased abruptly by 48 and 27 µgm-3, respectively, while the chemical depletion remained constant. At this point, the net O3 concentration turned from loss to production (51 µgm-3). In terms of vertical distribution (Fig. 10b), a positive contribution of both vertical and horizontal transport can
be found at the surface, while vertical transport became negative in the
upper layers and horizontal transport remained positive, indicating the
occurrence of a downdraft. In addition, the wind profile showed a typical
LLJ characteristic (Fig. 10b), with a maximum wind speed of about 12 ms-1 at 1 km altitude and a wind speed difference of more than 3 ms-1 above and below. Figure 11 further presents the process of vertical transport during an NOI event. Compared to normal days, the nocturnal boundary layer during the NOI event was more unstable and turbulent, with significant upward and downward transport. At around 1 km, there was a straight stream over the NCYL site during the NOI event (Fig. 11b). This suggested that LLJs broke the stable structure between the nocturnal boundary layer and the RL and enhanced the strength of turbulence
(Caputi et al., 2019). The LLJ-induced turbulence promoted mixing between the upper and lower layers and continuously transported O3 from the upper layer to the surface, causing an unusual surge in O3 at the
surface and leading to an NOI event. As a result, the LLJ process contributed as much as 40 µgm-3O3 from the upper layer to the surface during this NOI event.
Contribution of individual processes to (a) hourly O3 concentration near the surface during 13–14 September 2017 and (b) vertical O3 concentration at 21:00 LT on 13 September 2017. VTRA: vertical transport, the net effect of vertical advection and diffusion; HTRA: horizontal transport, the net effect of horizontal advection and diffusion; CHEM: gas-phase chemistry; CLDS: cloud processes; DDEP: dry deposition; NET: the net change in O3 due to all atmospheric processes.
Vertical profiles of O3 concentrations at 21:00 LT during (a) a normal day (12 September 2017) and (b) an NOI event (13 September 2017). Red triangles represent the NCYL site in Dongguan, contours represent O3 concentrations (µgm-3), and black lines and arrows indicate vertical airflow and its direction.
Correlation between (a) the afternoon's MDA8
O3 concentration and the following night's NOP
concentration and (b) the NOP concentration and the following afternoon's MDA8
O3 concentration.
Comparison and prospects
Zhu et al. (2020) identified an NOI event frequency of 16–19 dyr-1
in the summers from 2014 to 2015 in Beijing, China, with nocturnal O3
maxima ranging from 45 to 85 µgm-3, which is comparable to our
result of NOI frequency (14 ± 3 dyr-1) and slightly higher than
our NOP concentration (44 ± 7 µgm-3) in summer.
Sousa et al. (2011) analysed nocturnal O3 maxima
events (maxima higher than the average nocturnal O3
concentration of 10 µgm-3) during 2005–2007 in northern Portugal and found that the
frequencies of nocturnal O3 maxima were between 40 % and 50 % in
urban areas and 15 % in rural areas, which is higher than our NOI
frequency (53 dyr-1, 14.5 %). Other studies focusing on short-term
nocturnal O3 maxima cases found that NOP concentrations were 30–50 µgm-3 in the lower Fraser Valley, British Columbia, Canada
(Salmond and McKendry, 2002), 20–60 µgm-3 in Senegal
(Grant et al., 2008), and 40–80 µgm-3 in North
America (Kuang et al., 2011; Hu et al., 2013; Sullivan et al., 2017),
values that are comparable to our results (58 ± 11 µgm-3).
Our study emphasises the importance of meteorological processes, as well as
daytime O3 concentration in the occurrence of NOI events, implying that
higher NOP may occur during a severe daytime O3 pollution period under
the effect of meteorological processes. The occurrence of NOI events is
likely to impact the O3 levels on the following day, which makes
O3 prevention more complex and challenging (Ravishankara, 2009;
Sullivan et al., 2017). However, the relationship between NOI events and the
following daytime O3 pollution remains unclear and controversial.
Kuang et al. (2011) and Sullivan et al. (2017) revealed
that NOI events led to a higher increasing rate of O3 and worse air
quality on the following day, while Klein et al. (2019) and
Caputi et al. (2019) observed lower O3 levels during the
daytime following NOI events. To further explore the relationship between
the daytime MDA8 O3 and nighttime NOP in the PRD region, we display the
correlation between the MDA8 O3 and the following night's NOP
(shorthand MDA8–NOP) (Fig. 12a) and the NOP and the following MDA8 O3
(shorthand NOP–MDA8) (Fig. 12b), respectively. The results show that MDA8
O3 was positively correlated with NOP with a correlation coefficient of
0.63 (p<0.01) and 0.56 (p<0.01) for MDA8–NOP and NOP–MDA8,
respectively, suggesting an interplay between daytime O3 and NOP in the
PRD region.
Conclusion
In this study, based on observed surface and vertical O3 data, ERA5 datasets and WRF-CMAQ simulations, the spatial and temporal characteristics of NOI events are comprehensively presented and the role of vertical transport in NOI events in the PRD region from 2006 to 2019 is further quantified.
The average annual frequency of NOI events is estimated to be 53 ± 16 dyr-1
from 2006 to 2019, with an annual average of 58 ± 11 µgm-3 for nocturnal O3 peak (NOP). LLJs are the dominant factors
causing NOI events (61 %), followed by the combination of LLJs and Conv
(LLJs+Conv) with a value of 16 %. The high correlation between NOI
events and the frequency of LLJs in the annual trend (r=0.89, p<0.01) supports the important influence of LLJs on the occurrence of NOI
events. Although the contribution of Conv to NOI events is relatively small,
Conv-induced NOI events steadily increased at a rate of 0.26 dyr-1
during this 14 year period due to the impact of urbanisation. Moreover, the
significant positive correlation between NOP and maximum daily 8 h average
(MDA8) O3 in annual (r=0.88, p<0.01) and seasonal trends
(r=0.80, p<0.01) and a higher NOI frequency (60 %) during the
first half of the night imply that daytime O3 concentrations are also
an important factor influencing the formation of NOI events.
Two typical NOI events caused by LLJs and Conv further
demonstrate that downdrafts from enhanced turbulence are the direct cause of
NOI events, as these can transport O3 from the RL to the surface. The
difference is that LLJs induce downdrafts by a fast-moving air mass
enhancing shear below, whereas Conv induce downdraft by compensating
downdrafts.
This study emphasises the importance of vertical transport induced by LLJs, Conv, and daytime O3 concentration in the formation of NOI events
and highlights the key role of vertical transport in linking daytime and
nighttime O3 pollution. This study provides not only a new perspective
and better understanding to reconceptualise the role of meteorology in
daytime and nighttime O3 pollution but also a reference for
other regions with ground-level O3 pollution.
Data availability
The observed hourly meteorological data at the nine sites across the PRD region can be downloaded from http://www.cma.gov.cn/ (CMA, 2022). The ERA5 reanalysis dataset can be downloaded from https://cds.climate.copernicus.eu/cdsapp#!/home (Fifth-generation European Centre for Medium-Range Weather Forecasts, 2022). The vertical O3 profile data and the in situ hourly O3 data at 16 stations across the PRD region from 2006 to 2019 are available upon request from the corresponding author Weihua Chen.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-453-2023-supplement.
Author contributions
YW and WC designed the research. YW did the data analysis and simulation work and prepared the draft with support and editing from WC. YY and QX contributed to data analysis. SJ and XW contributed to paper revision.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors gratefully acknowledge the AirQuip (High-resolution Air Quality Information for Policy) Project funded by the Research Council of Norway, the Collaborative Innovation Center of Climate Change, Jiangsu province, China, and the high-performance computing platform of Jinan University. The authors would like to thank Dani Caputi (University of California, Davis) and the other anonymous referee for their thoughtful comments and efforts towards improving the paper.
Financial support
This research has been supported by the Key-Area Research and Development Program of Guangdong Province (grant no. 2020B1111360003), the National Natural Science Foundation of China (grant nos. 42121004, 42230701, 41905086, 41905107, 42077205, and 41425020), the Science and Technology Projects in Guangzhou (grant no. 202102080141), the Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (grant no. 2019B121205004), and the national Key Research and Development Program of China (grant no. 2019YFE0106300).
Review statement
This paper was edited by Jerome Brioude and reviewed by Dani Caputi and one anonymous referee.
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