In the summer of 2017, heavy ozone pollution swamped most of the North China Plain (NCP), with the maximum regional average of daily maximum 8 h ozone concentration (MDA8) reaching almost 120 ppbv. In light of the continuing reduction of anthropogenic emissions in China, the underlying mechanisms for the occurrences of these regional extreme ozone episodes are elucidated from two perspectives: meteorology and biogenic emissions. The significant positive correlation between MDA8 ozone and temperature, which is amplified during heat waves concomitant with stagnant air and no precipitation, supports the crucial role of meteorology in driving high ozone concentrations. We also find that biogenic emissions are enhanced due to factors previously not considered. During the heavy ozone pollution episodes in June 2017, biogenic emissions driven by high vapor pressure deficit (VPD), land cover change and urban landscape yield an extra mean MDA8 ozone of 3.08, 2.79 and 4.74 ppbv, respectively, over the NCP, which together contribute as much to MDA8 ozone as biogenic emissions simulated using the land cover of 2003 and ignoring VPD and urban landscape. In Beijing, the biogenic emission increase due to urban landscape has a comparable effect on MDA8 ozone to the combined effect of high VPD and land cover change between 2003 and 2016. In light of the large effect of urban landscape on biogenic emission and the subsequent ozone formation, the types of trees may be cautiously selected to take into account of the biogenic volatile organic compound (BVOC) emission during the afforestation of cities. This study highlights the vital contributions of heat waves, land cover change and urbanization to the occurrence of extreme ozone episodes, with significant implications for ozone pollution control in a future when heat wave frequency and intensity are projected to increase under global warming.
In recent decades, China has been facing severe air pollution issues,
particularly for the winter PM
Tropospheric ozone is closely related to both anthropogenic emissions and
biogenic emissions, including volatile organic compounds (VOCs) and nitrogen
oxides (
Besides emissions, tropospheric ozone is also closely related to meteorological conditions, such as heat waves (Gao et al., 2013; Fiore et al., 2015; Otero et al., 2016), low wind speed and stagnant weather (Jacob and Winner, 2009; Sun et al., 2017; Zhang et al., 2018). Weather conditions concomitant with heat waves, including high temperature, low wind speed and little cloud coverage, may enhance ozone production (Jaffe and Zhang, 2017; Pu et al., 2017; Sun et al., 2019). At the same time, such meteorological conditions also promote emissions of BVOC and ozone formation (Zhang and Wang, 2016). Using a global model, Fu and Liao (2014) suggested a slight to moderate increase in biogenic isoprene west and north of Beijing due to land cover and land use alone and an even more obvious increase when meteorological changes are considered. In the summer of 2017, heat waves swept over the majority of the NCP, providing an excellent opportunity to investigate how the heat wave may have modulated BVOC emissions and subsequent severe ozone events in the NCP. Observation data and modeling are used to delineate various factors contributing to enhanced biogenic emissions and elevated ozone concentrations. More details of the data and model are provided in the “Methods” section.
The distribution of observed data is shown in Fig. 1. For instance, the
meteorological observations used in this study such as daily maximum
temperature, daily mean wind speed and daily total precipitation were obtained
from the China Meteorological Data Service Center (CMA,
Distribution of observational sites over the NCP. Blue dots: daily maximum temperature daily mean wind speed at 10 m and daily total precipitation from the China Meteorological Administration (CMA); red dots: ozone monitoring sites from the China National Environmental Monitoring Centre; black hexagon: hourly temperature at 2 m (T2), specific humidity at 2 m (Q2), wind speed (WS10) and direction (WD10) at 10 m from MADIS; green box: urban area of Beijing. B, H and T represent Beijing, Hebei Province and Tianjin, respectively.
For modeling the meteorological conditions, WRF V3.8.1 is used in this
study. The domain is centered at 34
For modeling atmospheric chemistry, the widely used Community Multi-scale
Air Quality (CMAQ) model (Byun and Ching, 1999; Byun and
Schere, 2006), with the latest version 5.2, was used in this study. The
major gas phase chemistry was represented by Carbon Bond version 6
(CB6) and Aerosol Module Version 6 (AERO6) aerosol module. Initial and boundary conditions were from the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)
(Emmons et al., 2010). A dynamical
downscaling tool was developed in this study to link the Mozart output to
CMAQ, based upon the package of Mozart to WRF-Chem
(mozbc:
The anthropogenic emissions of air pollutants in China were estimated by
Tsinghua University, detailed in previous studies (Wang et al., 2014;
Zhao et al., 2013, 2017, 2018) and updated based on the Multiresolution
Emission Inventory for China (MEIC,
The biogenic emissions were calculated by the Model of Emissions of Gases
and Aerosols from Nature version 2.1 (MEGAN; Guenther et al., 2006, 2012). MEGAN input data includes three components: plant
functional type (PFT), leaf area index (LAI) and emission factors (EFs).
There is a total of 19 emission species including isoprene and terpenes derived from more than 100 emission compounds. For each of the 19 species,
the emission rates
Compared with the previous version 2.0 with only four PFTs, there are 16 types of PFTs represented in the new MEGAN version (Guenther et al., 2006, 2012), allowing for more accurate estimations of PFT-differentiated emission factors. PFT and LAI data were from the MODIS MCD12Q1 (Friedl et al., 2010) and MCD15A2H datasets (Myneni et al., 2015), respectively. The eight vegetation types in MODIS were apportioned to the 16 PFT types in MEGAN 2.1 based on the temperature zone. For example, MODIS has only one type of broadleaf deciduous trees, while MEGAN 2.1 has three: broadleaf deciduous tropical, temperate and boreal trees. The broadleaf deciduous trees in MODIS are mapped onto the three MEGAN types based on the latitudinal boundaries of the tropical, temperate and boreal zones, with detailed mapping information provided in Table S4 in the Supplement. Monthly mean LAIs were used in this study. The meteorological conditions used to generate biogenic emission in MEGAN were provided by the WRF simulation.
The Technical Regulation on Ambient Air Quality Index (HJ633-2012) defines
six classes of ozone-related pollution based on the daily maximum 8 h ozone
concentration (MDA8). Classes I and II are clean conditions (MDA8 less than
82 ppbv), class III (82–110 ppbv) indicates slight pollution, class IV
(110–135 ppbv) represents medium pollution, and classes V and VI are severe
pollution conditions with MDA8 higher than 135 ppbv. Utilizing the observed
MDA8 from the China National Environmental Monitoring Centre (
The number of severe ozone pollution days (MDA8 greater than 110 ppbv) during the summer of 2014–2017 over the NCP.
Correlation between MDA8 ozone and daily maximum 2 m temperature (Fig. 3) shows statistically significant values for all 4 years, confirming the
significant impact of temperature on ozone. However, the correlation in 2017
is obviously higher than in the other 3 years, and the regression slope of
4.21 ppbv
The correlation between summer MDA8 ozone and daily maximum
2 m temperature (
To further delve into the meteorological factors modulating the ozone
variations in the summer of 2014–2017, the time series of summer MDA8 ozone
is shown in Fig. 4, along with daily maximum temperature, wind speed and
daily total precipitation. From Fig. 4d, the two long-lasting ozone episodic
events (event 1: 14–21 June; event 2: 26 June–3 July) occur during heat
waves concomitant with stagnant (calm or low wind speed), dry (little or no
precipitation) air and strong solar radiation (not shown), conducive to
ozone formation and accumulation. During the first 3 d of these two
high-ozone episodic events, the regional mean daily maximum temperature is
32.3
Time series of observed MDA8 ozone (red lines; based on
sites from the China National Environmental Monitoring Centre; red points in
Fig. 1), daily maximum temperature at 2 m (blue lines), daily mean wind speed
at 10 m (green lines) and daily total precipitation (yellow bars) over the NCP
(based on sites from CMA; blue dots in Fig. 1) during the summer from 2014
to 2017. The regional precipitation was set to zero for a certain day if
less than 15 % (9 sites) of the total sites (58 sites) with daily total
precipitation greater than 1 mm. The horizontal blue dashed lines in each
panel denote 31
The correlation between summer MDA8 ozone and daily maximum VPD during 2014–2017 over the NCP. Regional mean was calculated from the observational sites over the NCP, so each data point corresponds to a regional mean value of MDA8.
Biogenic emissions contribute importantly to ozone formation. The MEGAN
model has been widely used to simulate biogenic emissions in air quality
modeling studies (Guenther et al., 2012), but recent
research suggested that biogenic emissions may be underestimated in the
model for several reasons:
water-stressed impact on biogenic emissions. Zhang and
Wang (2016) found that two high-ozone events in the US were associated
with excess isoprene release due to dry and hot weather conditions that
induced water stress in plants. The increased vapor pressure deficit (VPD;
the pressure difference between saturation vapor and ambient vapor) drives
the release of more isoprene, but the VPD effect on biogenic emissions has
not been taken into consideration in MEGAN 2.1, so the subsequent influence
of biogenic emissions on ozone may be largely underestimated.
Zhang and Wang (2016) suggested a doubling of daily biogenic
isoprene when the daily VPD reaches 1.7 kPa or greater. It should be noted
that this parameterization was based upon the observed information over the US; more tests may be needed in future when applying it to areas besides the US. The
monthly mean VPD spatial distribution in June 2017 (Fig. S3) as well as the
high correlation between observed MDA8 ozone and VPD (Fig. 5; with time
series shown in Fig. S4) suggest enhanced isoprene emission in the NCP, so we
will test this VPD mechanism using model simulations. Please note that in
the latest version MEGAN 3 (Jiang et al., 2018), a new
approach was developed to quantify the drought effect on the isoprene
emissions based on both photosynthesis and water stress, yielding a general
reduction of monthly mean isoprene emission across the globe, including
northern China. The impact of changes in isoprene emissions, based on the
new method, on ozone formation deserves further evaluation in future. Effect of changes in land cover on biogenic emissions. As
reflected by the much higher emission factor, biogenic isoprene emission is
enhanced in broadleaf forest relative to other land cover types such as
needleleaf forest, shrub, grass or crop (Table 2 in Guenther et al., 2012). In the NCP, broadleaf tree is the dominant land cover type, and its
coverage has been increasing dramatically since the 1970s, primarily as a result of the Three-north Protection Forest System project. For example,
based on Moderate Resolution Imagine Spectroradiometer (MODIS) land use data
(Friedl et al., 2010), the coverage of broadleaf deciduous temperate trees
nearly doubled from 2003 to 2016 over the NCP (Fig. 6a–c). This has
resulted in a substantial increase in isoprene emissions between 2003 and
2016 (Fig. 6), particularly north of Beijing, Hebei and Tianjin, where
the increase is more than 200 %. Combining point (a) described above,
the underestimation of biogenic emission due to changes in land cover may be
exaggerated in years with high temperatures and high VPD. It is vital to
quantify the effect of land cover changes on biogenic emissions such as
isoprene and the subsequent impact on ozone formation. Impact of urban landscape on biogenic emission. The land use type
cataloged in the MODIS MCD12Q1 product (Friedl et al., 2010) does not
take into consideration urban green spaces, which may lead to a 15 %
underestimation of total BVOC emissions in 2015 over Beijing (Ren et al.,
2017). Generally, urban ozone production is highly sensitive to VOC
emissions (Xing et al., 2011; Liu et al., 2012). Bell and Ellis (2004) found a doubling of ozone in urban areas relative to rural areas for
the same percentage increase in biogenic emissions. The impact of biogenic
emission from urban landscape on urban ozone formation has not been
considered in previous studies. For a sensitivity analysis, we added a 15 %
increase in the total BVOC emissions in Beijing to investigate their impact
on urban ozone formation. These emissions were distributed evenly in the
urban core area of Beijing as the increase in biogenic emissions from urban
landscape were only available for Beijing.
Spatial distribution of broadleaf deciduous trees in 2003
The comparison of isoprene concentrations between model
simulations and observations in Beijing. The black dots represent the
observed data from various contributions to the literature, whereas the hollow triangles (in
black, red, green and blue) represent the model simulations for the four
cases described above (cases 2–5). For each observational dataset, the
corresponding reference number is given after the site name in
To elucidate the mechanism modulating the ozone events discussed above, the regional meteorology and air quality model WRF/CMAQ was used to conduct simulations from 8 June to 4 July 2017. The WRF simulations generally meet the benchmark standard for meteorological variables (Table S3). For air quality simulations, five scenarios were designed, with biogenic emissions ignored in the base case. Compared to the base case, case 2 adds biogenic emission associated with the land cover of 2003 and cases 3, 4 and 5 are the same as case 2 except for the inclusion of the VPD effect, both VPD and land cover of 2016, and VPD and land cover of 2016 combined with the effect of urban green spaces, respectively. To validate the reasonableness of adding the biogenic emission, we first evaluate the simulated isoprene concentration, one of the most important species closely related to ozone formation, from WRF/CMAQ among different cases. Since there is a lack of observed ambient isoprene concentration during this study period, the data available (mostly over Beijing) from the literature were retrieved and used as cross comparison with the model results (Fig. 7). From Fig. 7a, b, the observed mean isoprene concentration ranges from 0.4 to 1.6 ppbv at various sites of Beijing. By taking into consideration isoprene emissions from VPD, land cover of 2016 and urban green spaces (case 5) the model simulations yield the best performance, with isoprene concentration of 0.8 ppbv to 1.4 ppbv. However, the other cases (with isoprene concentrations of 0.1 ppbv to 0.2 ppbv) substantially underestimate the isoprene concentrations. Therefore, the isoprene emissions from urban green spaces (comparing case 5 and case 4) in Beijing play a vital role in the isoprene concentrations, which subsequently affect the ozone formation which will be further evaluated and discussed below.
Since the effect of urban landscape was only applied to Beijing in case 5,
we use case 4 (combination of VPD and land cover change effects) (referred
to as B_MDA8) as the reference. Therefore, we first compare
MDA8 ozone in case 4 with observations. To facilitate the comparison,
observational data were interpolated to the model grids, and reasonable
performance is achieved with an MFB/MFE (mean fractional bias and mean fractional error) of
MDA8 ozone evaluation over the NCP from 8 June to 4 July in 2017. NMB, NME, MFB and MFE represent normalized mean bias, normalized mean error, mean fractional bias and mean fractional error, respectively.
Zooming into the two ozone episodic events (14–21 June, 26 June–3 July), the mean MDA8 values of case 4 are 98.02, 108.89, 95.75 and 98.98 ppbv for the NCP, Beijing, Hebei and Tianjin, respectively, during the heat wave periods (14–21 June 2017; 26 June–3 July 2017), whereas the MDA8 ozone value for the case (case 1) without biogenic emission are 87.15, 93.06, 84.78 and 89.65 ppbv for the corresponding region. The ozone increment from case 2 to case 5 (as well as observations; magenta stars in Fig. 9a) relative to case 1 is shown in Fig. 9a for these regions. Including biogenic emission based on the land cover of 2003 (case 2) yields an extra mean MDA8 ozone of 7.84 ppbv (8 % of B_MDA8), 9.96 ppbv (9 % of B_MDA8), 7.86 ppbv (8 % of B_MDA8) and 6.99 ppbv (7 % of B_MDA8) for the NCP, Beijing, Hebei and Tianjin, respectively (yellow bars in Fig. 9a), compared to case 1. Including the VPD effect (case 3) adds an extra mean MDA8 of 1.71 ppbv in the NCP compared to case 2, and the enhancement is highest in Beijing (3.08 ppbv) (green bars in Fig. 9a). Additional MDA8 ozone enhancement is simulated by including the effect of land cover change (increase in natural broadleaf forest; Fig. 6a–c; case 4), i.e., an extra MDA8 of 1.32 ppbv in the NCP relative to case 3, with the highest contribution of 2.79 ppbv in Beijing (blue bars in Fig. 9a). The urban landscape (case 5) in Beijing yields an extra 4.74 ppbv or 4 % of MDA8 compared to case 4, almost doubling the effect of VPD and land cover change in Beijing. The larger percentage increase in MDA8 ozone (41 % from Fig. 9a, which is shown in Fig. 9b as well) due to urban landscape relative to the prescribed 15 % increase in BVOC emission in Beijing supports the notion of an amplified MDA8 ozone response in urban areas because of the high sensitivity of ozone to VOC emissions, which well matches observational data (magenta star).
Biogenic contribution to MDA8 ozone during the heat wave
periods (14–21 June; 26 June–3 July), shown by the individual
A schematic diagram of the impact of biogenic emission on ozone formation. N-BVOC refers to natural biogenic emission, P-BVOC refers to the biogenic emission from planted forest and in this study represents the increase in forest coverage. U-BVOC refers to urban BVOC generated from urban green spaces. The red thick upward arrows indicate that extra VOCs may be induced by the heat waves.
To further illustrate the contributions of BVOC to MDA8, Fig. 9b shows the
contribution of biogenic emissions (Bio_emis, based on land
cover of 2003), VPD, land cover change and urban landscape (or urban green)
to MDA8 as a fraction of the MDA8 of B_MDA8 (left
In order to demonstrate whether changes in land cover and VPD play any role during normal ozone conditions, we conducted another sets of simulations (the same as cases 2–4 discussed above) from 8 June to mid-July in 2016, a similar period as in 2017. The mean MDA8 ozone concentrations over the NCP during this entire period in 2017 for case 2 is 79.03 ppbv, and statistical significant enhancement (1.34 ppbv) was achieved in case 3. In comparison to case 3, the land cover change in case 4 shows a statistically significant increase as well (1.13 ppbv). As expected, looking at the entire period in 2016 (8 June–4 July), a statistically significant (and even higher in comparison to 2016) increase was achieved in case 3 (1.55 ppbv) compared to case 2 (90.11 ppbv) and case 4 (1.23 ppbv) compared to case 3. Therefore, the land cover and VPD may be applied in both episodic events and conditions with normal ozone concentrations.
Herein the mechanisms for ozone enhancement are summarized in the schematic of Fig. 10. Both natural and anthropogenic emissions contribute to ozone formation. Because of the Three-north Protection Forest System project, natural forest north of Beijing has more than tripled in area coverage compared to 2003, leading to an increasing trend in biogenic emissions. Under heat wave conditions, biogenic emissions may be further enhanced through the effect of VPD in addition to the effect of temperature. For urban areas, even more biogenic emissions may be emitted from urban landscapes. All these mechanisms for increasing biogenic emissions could enhance ozone formation, particularly over urban areas such as Beijing.
The mechanisms contributing to the severe ozone pollution events in the summer of 2017 in the NCP were investigated. Two severe tropospheric ozone pollution events occurred in the NCP during the periods of 14 to 21 June and 26 June to 3 July. We provided support for the roles of the observed meteorological conditions including high temperature and stagnant dry weather, which favor high ozone concentrations. More importantly, the influence of biogenic emissions on ozone formation was investigated in more detail by incorporating important biogenic emission factors that are typically ignored in regional model simulations. Biogenic emissions based on the land cover of 2003 yields an extra mean MDA8 ozone of 7.84 ppbv for the NCP. Including the VPD effect and land cover change adds 1.71 and 1.32 ppbv of ozone in the NCP. These contributions are even larger in Beijing, with VPD adding 3.08 ppbv and land cover change adding 2.79 ppbv. Most notably, biogenic emissions from urban landscape (i.e., green spaces) have so far not been considered in ozone regional modeling studies to our knowledge. By adding this source in the urban area of Beijing, substantial ozone enhancement was simulated, bringing the WRF/CMAQ simulation of MDA8 closer to observations. The urban landscape in Beijing yields an extra 4.74 ppbv of MDA8, comparable to the combined effect of VPD and land cover change in Beijing. Together, the combined effect of VPD, land cover change and urban landscape doubles the effect of biogenic emission calculated based on the land cover of 2003 and not including the VPD and urban landscape effects. Please note that although the urban isoprene emission from landscape in Beijing only accounts for 15 % (Ren et al., 2017), the location of the emissions may play a much larger role in contributing to the urban isoprene concentration. As was shown in Fig. 6, most of the isoprene emissions from the forest in Beijing are located in the rural area, which is relatively far from the urban area. Considering the short lifetime of isoprene, it may not be as efficient as the urban isoprene emission resulting from urban landscape directly in modulating the isoprene concentrations. Therefore, the urban isoprene emission may play a much more significant role in urban photochemical reactions compared to the isoprene emissions from the forest over the rural areas.
The BVOC emissions from urban green spaces are projected to increase by more than twice in 2050 due to urban area expansion (Ren et al., 2017). Together with the more frequent heat waves projected for the future (Gao et al., 2012; Zhang et al., 2018), the impact of biogenic emissions on ozone pollution in the NCP will likely play an increasingly important role in ozone pollution and should be taken into considerations in future air quality management plans to address issues of air quality and health. The effect of urban green spaces was only considered in Beijing in this study as we lack the data to parameterize this effect in other regions. Considering the substantial effect of urban green spaces on urban ozone formation, it is vital to evaluate similar effects in other cities where ozone pollution is a concern.
The observational or reanalysis data are available from the website provided in the paper and the WRF-CMAQ and MEGAN model output can be accessed by contacting Yang Gao (yanggao@ouc.edu.cn).
The supplement related to this article is available online at:
YG came up with the original idea, and YG, YW and CL designed the research. MM conducted all the analysis; SZ, LRL, SW, BZ, XC, HS, TZ, LS, XY and HW wrote the paper.
The authors declare that they have no conflict of interest.
This research was supported by grants from the National Key Project of MOST (2017YFC1404101), National Natural Science Foundation of China (41705124, 21625701, 41722501, 91544212), Shandong Provincial Natural Science Foundation, China (ZR2017MD026) and Fundamental Research Funds for the Central Universities (201941006, 201712006). Y. Wang was supported by the National Science Foundation Atmospheric Chemistry Program. PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.
This research was supported by grants from the National Key Project of MOST (2017YFC1404101), National Natural Science Foundation of China (41705124, 21625701, 41722501, 91544212), Shandong Provincial Natural Science Foundation, China (ZR2017MD026) and Fundamental Research Funds for the Central Universities (201941006, 201712006).
This paper was edited by Alex B. Guenther and reviewed by two anonymous referees.