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  • View in gallery

    Average number of exceedance days per year for (a) Tw, (b) O3, and (c) PM2.5; (d) locations of the sites in the BTH, PRD and YRD regions.

  • View in gallery

    Trend of (a) Tw, (b) O3, and (c) PM2.5 exceedance days. Both the angle and color are showing the negative trends (i.e., downward sloping arrows in blue color) and positive trends (i.e., upward sloping arrows in red color).

  • View in gallery

    Trend of cooccurrence of Tw, O3, and PM2.5 exceedance days over China. Cooccurrence of (a) Tw and O3, (b) PM2.5 and O3, (c) Tw and PM2.5, Tw, and (d) PM2.5 and O3. Both the angle and color are showing the negative trends (i.e., downward sloping arrows in blue color) and positive trends (i.e., upward sloping arrows in red color).

  • View in gallery

    Pooling trends of cooccurrence of Tw, O3, and PM2.5 exceedance days in the (a) BTH and (b) YRD regions.

  • View in gallery

    Pooling trend of independent and joint occurrence of Tw, O3, and PM2.5 exceedance days in (a) BTH and (b) YRD.

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Amplified Upward Trend of the Joint Occurrences of Heat and Ozone Extremes in China over 2013–20

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  • 1 Department of Geography, Hong Kong Baptist University, Hong Kong, China;
  • | 2 Department of Atmospheric Sciences, Texas A&M University, College Station, Texas;
  • | 3 Department of Geography, Hong Kong Baptist University, Hong Kong, China;
  • | 4 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China;
  • | 5 State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China;
  • | 6 Atmospheric Chemistry Observation and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado;
  • | 7 Department of Geography, and State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, and Hong Kong Branch of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Hong Kong University of Science and Technology, Hong Kong, China
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Abstract

Climate change and air pollution are two intimately interlinked global concerns. The frequency, intensity, and duration of heat waves are projected to increase globally under future climate change. A growing body of evidence indicates that health risks associated with the joint exposure to heat waves and air pollution can be greater than that due to individual factors. However, the cooccurrences of heat and air pollution extremes in China remain less explored in the observational records. Here we investigate the spatial pattern and temporal trend of frequency, intensity, and duration of cooccurrences of heat and air pollution extremes using China’s nationwide observations of hourly PM2.5 and O3, and the ERA5 reanalysis dataset over 2013–20. We identify a significant increase in the frequency of cooccurrence of wet-bulb temperature (Tw) and O3 exceedances (beyond a certain predefined threshold), mainly in the Beijing–Tianjin–Hebei (BTH) region (up by 4.7 days decade−1) and the Yangtze River delta (YRD). In addition, we find that the increasing rate (compared to the average levels during the study period) of joint exceedance is larger than the rate of Tw and O3 itself. For example, Tw and O3 coextremes increased by 7.0% in BTH, higher than the percentage increase of each at 0.9% and 5.5%, respectively. We identify same amplification for YRD. This ongoing upward trend in the joint occurrence of heat and O3 extremes should be recognized as an emerging environmental issue in China, given the potentially larger compounding impact to public health.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Meng Gao, mmgao2@hkbu.edu.hk

Abstract

Climate change and air pollution are two intimately interlinked global concerns. The frequency, intensity, and duration of heat waves are projected to increase globally under future climate change. A growing body of evidence indicates that health risks associated with the joint exposure to heat waves and air pollution can be greater than that due to individual factors. However, the cooccurrences of heat and air pollution extremes in China remain less explored in the observational records. Here we investigate the spatial pattern and temporal trend of frequency, intensity, and duration of cooccurrences of heat and air pollution extremes using China’s nationwide observations of hourly PM2.5 and O3, and the ERA5 reanalysis dataset over 2013–20. We identify a significant increase in the frequency of cooccurrence of wet-bulb temperature (Tw) and O3 exceedances (beyond a certain predefined threshold), mainly in the Beijing–Tianjin–Hebei (BTH) region (up by 4.7 days decade−1) and the Yangtze River delta (YRD). In addition, we find that the increasing rate (compared to the average levels during the study period) of joint exceedance is larger than the rate of Tw and O3 itself. For example, Tw and O3 coextremes increased by 7.0% in BTH, higher than the percentage increase of each at 0.9% and 5.5%, respectively. We identify same amplification for YRD. This ongoing upward trend in the joint occurrence of heat and O3 extremes should be recognized as an emerging environmental issue in China, given the potentially larger compounding impact to public health.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Meng Gao, mmgao2@hkbu.edu.hk

Global warming and ambient air pollution are two leading global public health concerns, driven by anthropogenic emissions of greenhouse gases and air pollutants from fossil fuel uses (IPCC 2014). It was estimated that the increase in global temperature would result in additional 250,000 deaths each year between 2030 and 2050 (Watts et al. 2015), while a recent assessment attributed 4.2 million premature deaths per year to ambient air pollution exposure (Cohen et al. 2017; WHO 2020). Climate change and air pollution are also intimately interlinked (Dean and Green 2018). A warming climate could directly alter meteorological variables, such as temperature, precipitation, and wind (Sanderson et al. 2011), and thus further affects physical and chemical processes of air pollution [e.g., ozone (O3) and particulate matter ≤ 2.5 μm, (PM2.5)] over multiple spatiotemporal scales (Ebi and McGregor 2008; Kinney 2008; Xu et al. 2018). Climate change is also likely to indirectly change particulate matter (PM) levels by modulating the natural emission from the occurrences of wildfires and dust storms (Dean and Green 2018).

Compared to the mean conditions of weather and air pollution, extreme weather and air pollution events, despite rare occurrences, can pose greater threats to human health and induce larger devastation to ecosystems and economy (IPCC 2012; Zhang et al. 2020). More concerning is that extreme air pollution episodes and heat waves often occur simultaneously because they can be driven by some common meteorological conditions. For example, heat waves, droughts, and peak ozone episodes are usually associated with stagnant high pressure systems (low precipitation, low wind speeds, sufficient solar radiation, etc.) that tend to accumulate heat and ozone precursors in a certain location. Moreover, complex interactions and feedbacks could happen to exacerbate extreme conditions. For example, high temperature during heat waves enhances biogenic emissions of volatile organic compounds (BVOCs) to increase production of O3 and secondary organic aerosols (Karl et al. 2003). Under drought stress, stomatal uptake by plants is inhibited to reduce water loss, leading to a weaker dry deposition of O3 and thus its higher surface concentrations (Gerosa et al. 2009; Lin et al. 2020).

Given that heat waves (Beniston 2004; Meehl and Tebaldi 2004; Stott et al. 2004; Fischer et al. 2007; Cowan et al. 2014; Schär 2016; Hoegh-Guldberg et al. 2018) and air pollution episodes (Mickley et al. 2004; Tagaris et al. 2007; Wu et al. 2008; Gao et al. 2017; Rieder et al. 2015; Schnell et al. 2016; Doherty et al. 2017; Schnell and Prather 2017) may aggravate over the coming decades, it is of great significance to analyze the historical trends of cooccurrence of heatwave and air pollution extremes, which would shed lights on the fidelity of their future projections. Another imperative to understand the cooccurrence of heatwave and air pollution extremes is driven by the recognitions that the simultaneous exposure to both air pollution and heat wave may amplify the health consequences beyond the sum of individual effects (Basu 2009; Dear et al. 2010; Kan et al. 2012; Li et al. 2014; Ren et al. 2008; Stafoggia et al. 2008; Q. Wang et al. 2020; Willers et al. 2016; Zanobetti and Peters 2015).

Over the recent decade, air pollution, particularly the high PM2.5 levels, have raised wide concerns in China (Gao et al. 2020a,b; Liang et al. 2017), and the State Council of China announced its strictest plan, the Air Pollution Prevention and Control Plan, in September 2013 (Zhang et al. 2019) to reduce the level of air pollutants. Since then, a decreasing trend of PM2.5 levels have been found in both satellite and ground-level observations (Lin et al. 2018; Y. Wang et al. 2020; Wang et al. 2021). Despite of the overall decreasing trend, PM2.5 concentrations during some pollution episodes can still exceed the threshold recommended by the World Health Organization (WHO) or local standards adopted in China (Y. Wang et al. 2020). Notably, while the concentrations of most primary pollutants have been decreasing in response to the emission control plan, surface O3 concentrations have been increasing in several populated regions of China (Liu and Wang 2020; Lu et al. 2020; Y. Wang et al. 2020), and are projected to increase (Zhu and Liao 2016). Nevertheless, the variability and recent trend of the joint frequency of all three detrimental environmental stressors (PM2.5, O3, and heat extremes) have not been extensively explored in China. Here we present a series of spatiotemporal analyses based on various sources of observations from 2013 to 2020 (“Methods” section). The observationally based results here would be crucial to enhancing environmental protection measures and informing public health policies in the future (Chen et al. 2018; Xu et al. 2020).

Methods

Data sources of PM2.5, O3, and temperature.

Nationwide observations of hourly PM2.5 and O3 concentrations from year 2013 to 2020 were obtained from the China National Environmental Monitoring Center (CNEMC) network. Starting from 2013 in 74 major cities, the CNEMC network now consists of more than 1,600 monitoring sites, covering 367 cities in China. PM2.5 and O3 were reported in units of micrograms per cubic meter (μg m−3). Daily mean values of PM2.5 were calculated from the hourly record. Daily maximum 8-h averages (MDA8) of O3 were calculated as well. Hourly temperature and corresponding dewpoint temperature were taken from the ERA5 reanalysis dataset by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al. 2020). Both temperature and dewpoint temperature from ERA5 were sampled at CNEMC sites to examine cooccurrences.

Definition of heat extremes using wet-bulb temperature.

Previous studies suggested that a combination of temperature and humidity is a better metric to assess heat-related health risks (Kovats and Hajat 2008; Mora et al. 2017; Xu et al. 2020), as the human body is less able to cool itself efficiently by sweating under high humidity conditions. We adopted the wet-bulb temperature (Tw) in this study as the metric to define occurrences of heat waves (Sherwood 2018). The calculation of Tw assumes light wind speed and moderate radiation (Knutson and Ploshay 2016; Willett and Sherwood 2012), and thus only accounts for temperature (T) and humidity measures. In this study, we computed Tw using Stull’s (2011) method:
Tw=Tatan[0.151977(100×RH+8.313659)1/2]+atan(T+100×RH)atan(100×RH 1.676331)+0.00391838(100×RH)3/2×tan(0.023101×100×RH)4.686035,
where Tw denotes the wet-bulb temperature (°C), T the temperature (°C), RH the relative humidity. Because ERA5 provides dewpoint temperature only, RH was calculated by the following equation:
es=e0×exp[LυRw(1T01T)],edew=e0×exp[LυRw(1T01Tdew)],RH=edewespespedew×100%,
where e0 represents the reference water vapor pressure (611 Pa), and es and edew signify the water vapor pressure at saturation and at dewpoint temperature, respectively. The term T0 refers to the reference temperature (273 K), Tdew denotes the dewpoint temperature, Lυ is the latent heat of water vaporization from liquid to gas (2.5 × 106 J kg−1), and Rw represents the specific gas constant for water vapor (461.5 J kg−1 K−1). Following Xu et al. (2020), we adopted daily average Tw ≥ 25°C as the threshold for heat extremes.

Definition of air pollution extremes.

We used the air quality standard of China (Zhao et al. 2016) for PM2.5 and O3, namely, 75 and 160 μg m−3, as the cutoff values of exceedance. The days when daily mean Tw, daily mean PM2.5, or MDA8 value for O3 were higher than corresponding cutoff values were marked as exceedance days for each metric. The days when two or more metrics exceed thresholds simultaneously were further marked as cooccurring extreme days. The numbers of exceedance days were summarized by months for further trend analyses.

In addition to number of exceedance days (i.e., frequency of extreme events), we also considered the duration and severity of these extremes (Xu et al. 2020). Duration was defined as the number of successive days of extreme events. The severity was defined as the difference between the long-term average and the corresponding levels within the exceedance days only.

Statistical method for trend analyses.

Previous studies have shown that heat waves and O3 extremes often occur in warm seasons while PM2.5 is typically more severe in cold seasons in China (Jia et al. 2017; Lu et al. 2020; Zheng et al. 2005), we therefore quantify the trend of Tw and O3 during warm seasons only (six months from April to September), and for PM2.5 we quantified the trend across the entire year. For the cooccurrence of Tw, O3, and PM2.5, we also used data during warm seasons. We assess the trends of monthly exceedance frequency (i.e., days per month) for heat waves, PM2.5, and O3 from 2013 to 2020, explicitly accounting for seasonal cycles and autocorrelation (Chandler and Scott 2011; Lu et al. 2020), as detailed below.

Trend analyses were performed by constructing a generalized linear regression equation with periodic functions accounting for seasonal variation and an autoregression term accounting for autocorrelation within the study period, as follows:
yt=b+kt+αcos(2πMC)+βsin(2πMC)+ARt,
where yt represents the exceedance frequency for the metrics of Tw, PM2.5, and O3 in month t, t denotes the index of month during the study period of 8 years (ranging from 1 to 48 for Tw and O3, or from 1 to 96 for PM2.5 alone), b denotes the intercept, k is the linear trend coefficient, α and β are coefficients of periodic functions, M is the month index in each year (ranging from 1 to 6 for Tw and O3, or from 1 to 12 for PM2.5 alone), C is the length of the seasonal cycle (6 for Tw and O3, or 12 for PM2.5 itself), and ARt is the autoregression term for yt. A nonparametric Mann–Kendall (M-K) test was performed to test the significance of linear trends.

Pooling to derive regional trend.

Previous studies showed that the spatial distribution of O3 concentrations vary greatly across different regions in China (Lu et al. 2018). Beijing–Tianjin–Hebei (BTH) region, Yangtze River delta (YRD) and Pearl River delta (PRD) region are three major urban clusters with distinct pollution patterns (Liu et al. 2018; Ma et al. 2019). In this study, we calculated the aggregated/pooled trend of exceedances for Tw, O3, and PM2.5 as well as their joint occurrences in these three megacity clusters in China (BTH, YRD, and PRD).

However, the methods to generate regional trend in previous studies using observation data from monitoring sites seem arbitrary as each monitoring site may have depicted different and even opposite trends. A synthetical statistical algorithm is thus needed to standardize the calculation of regional trend. Here we propose a pooling method to aggregate the trends calculated from all individual sites within a specific region. The site-specific local trend, notated as ki in the “Statistical method for trend analyses” section, are then pooled to estimate the average trend (Kr) representing a specific region following the equation of
Kr=i=1nkiwii=1nwi,
where n is the number of sites within the region, and wi is the weighting factor for each site i, defined as follows, similar to meta-analysis (Lipsey and Wilson 2001), where the standard error SEi represents the uncertainty of estimating ki
wi=1/SEi2.
The average trend Kr is approximately normally distributed (Sánchez-Meca and Marín-Martínez 2010), and its sample variance could be defined as
Var(Kr)=1/i=1nwi.

Results and discussion

Spatiotemporal variations and long-term trend of Tw, O3, and PM2.5 exceedances.

Humidity has critical effects on human body’s reaction to temperature (Liu et al. 2014) as the human body is not able to cool itself by sweating under high humidity. Using temperature only may underestimate the severity of heat waves, especially in humid regions (Russo et al. 2017). In this study, we adopted 25°C as the threshold of heat extremes as proposed by Mora et al. (2017), and note that 25°C at a typical RH of 40% is very close to daily max temperature of 35°C (Xu et al. 2020). Over the southeastern coastal regions, Tw exceedance days could reach as high as 150–180 days annually (Fig. 1a and Fig. ES1 in the online supplement; https://doi.org/10.1175/BAMS-D-21-0222.2), suggesting high frequency of heat waves there. The average Tw in China displays a slightly increasing trend, rising from 13.5°C [standard deviation (SD): 5.6°C] in 2013 to 13.8°C (SD: 5.5°C) in 2020 (Fig. ES2). Moreover, the severity of high Tw extremes could reach up to 3°C (Fig. ES3) and the events (mean duration) could last about two months in southernmost part of China (Fig. ES4). Previously, Ding et al. (2010) and Wei and Chen (2011) reported a significant increase in heat waves across the nation during recent decades, except for a slight decrease in central China. In the shorter period of the last decade as examined here, however, the trend of Tw exceedance can go in both directions across China (Fig. 2). Overall, a clear positive trend could be found in mideastern and northeastern regions, on average, at rates of up to 1.4 days decade−1. In contrast, a decreasing trend, around 1.0 days decade−1 on average, is found in coastal and central areas of China.

Fig. 1.
Fig. 1.

Average number of exceedance days per year for (a) Tw, (b) O3, and (c) PM2.5; (d) locations of the sites in the BTH, PRD and YRD regions.

Citation: Bulletin of the American Meteorological Society 103, 5; 10.1175/BAMS-D-21-0222.1

Fig. 2.
Fig. 2.

Trend of (a) Tw, (b) O3, and (c) PM2.5 exceedance days. Both the angle and color are showing the negative trends (i.e., downward sloping arrows in blue color) and positive trends (i.e., upward sloping arrows in red color).

Citation: Bulletin of the American Meteorological Society 103, 5; 10.1175/BAMS-D-21-0222.1

Occurrences of O3 exceedance were concentrated in the BTH, YRD, and PRD (locations marked in Fig. 1d), where intense human activities are located (Fig. 1b). Exceedance days exhibited a general increase over 2013–20 (up to 6.0 days decade−1, Fig. 2b), in line with the variations of O3 levels (Figs. ES5 and ES6). Despite extremely high levels of MDA8 O3 (i.e., >30 μg m−3 above the threshold value) becoming less frequent, a modest exceedance (approximately 15–30 μg m−3above the threshold values) was observed in more sites in recent years (Fig. ES7). The increase in the mean duration of O3 extremes (Fig. ES8) also highlighted the nationwide spread of O3 pollution, among which BTH area showed the most significant growth, consistent with previous studies (G. Li et al. 2017). The BTH is severely polluted with respect to PM2.5, and mean exceedance days generally reached over 60 days (Fig. 1c). The number of exceedances of PM2.5 reached a daunting 150 days per year in 2013–16 (Fig. ES9), which improved gradually since 2015 (He et al. 2020; Y. Wang et al. 2020; Xue et al. 2020), with both lower PM2.5 levels (Fig. ES10) and lower severity observed (Fig. ES11). PM2.5 exceedance days decreased at the rate of more than 10 days decade−1, with the largest decreasing trend observed in BTH area (Fig. 2c).

Changes in the joint exceedance of Tw, O3, and PM2.5 .

Figure 3 displays the trends of joint exceedance frequency of Tw, O3, and PM2.5 over the 2013–20 period. Here, we identify an alarming trend of cooccurrence of Tw and O3 extremes. High Tw and O3 extremes tend to increase in the study period, especially in the BTH and YRD regions (at a rate up to 4.0 days decade−1). Among exceedance days, mean duration and severity of Tw and O3 cooccurrence, we observe similar spatiotemporal pattern, in which the rising trend of Tw and O3 is larger individually than jointly (absolute changes, Figs. ES13 and ES14), and most of the upward trend is observed in BTH and YRD regions driven by the cooccurrence in midsummer (June and July, figures not shown).

Fig. 3.
Fig. 3.

Trend of cooccurrence of Tw, O3, and PM2.5 exceedance days over China. Cooccurrence of (a) Tw and O3, (b) PM2.5 and O3, (c) Tw and PM2.5, Tw, and (d) PM2.5 and O3. Both the angle and color are showing the negative trends (i.e., downward sloping arrows in blue color) and positive trends (i.e., upward sloping arrows in red color).

Citation: Bulletin of the American Meteorological Society 103, 5; 10.1175/BAMS-D-21-0222.1

The cooccurrence of O3 extremes during heat waves has long been recognized in developed countries (Filleul et al. 2006; Lee et al. 2006), and the underlying reason behind the combination of the two risk factors may partially be their common favorable weather patterns. For example, atmospheric blocking was reported to enhance the probability of cooccurrences of O3 and heat extremes (Otero et al. 2022). Under a warming climate, amplified atmospheric blocking events are likely to lead to more frequent joint occurrences of heat and O3 extremes (Nabizadeh et al. 2019). During heat waves, the stagnant condition, controlled by an anticyclone with a sinking airflow, may lead to less cloud cover (Pu et al. 2017) and weaker surface winds (K. Li et al. 2017), both of which are favorable for O3 formation (Pyrgou et al. 2018). Besides, previous review has indicated that high temperatures could play a catalytic role in promoting chemical reactions of O3 formation and enhancing natural emissions of O3 precursors; temperature is also associated with other synoptic patterns such as blocks and stagnation (Lu et al. 2019; Wang et al. 2017).

In addition, as important O3 precursors, changes in anthropogenic emissions of NOx, CO, and volatile organic compounds (VOCs) could also play a role in the observed patterns (Logan 1985; Lu et al. 2018; Qu et al. 2014). Previous numerical experiments suggested that NOxreductions and aerosol control measures were the major cause for enhanced O3 in Beijing, while reductions of NOx and increase in VOCs emissions contributed to O3 increase in Shanghai (Liu and Wang 2020). Accordingly, effective strategies of VOCs emission control should be also considered in high priority (He et al. 2022).

The cooccurrence of Tw, PM2.5, and O3 exceedance days had been decreasing at majority of sites, among which the greatest decreasing trend was observed in BTH (Fig. 3d). The trend of duration of these coextremes also showed a similar pattern (Fig. ES14). We observe that although PM2.5 increased at a small number of sites (Fig. 2c), the joint occurrence of PM2.5 and O3 is found to decrease at nearly all sites (Fig. 3b). This is possibly associated with the fact that elevated PM2.5 levels would reduce O3 levels due to aerosols’ influences on O3 photochemistry and heterogeneous chemistry (Chen et al. 2020; Li et al. 2019).

In addition to the augmented cases (absolute changes) of cooccurrence of Tw and O3, we ascertain in this study that the cooccurrence of Tw and O3 have been increasing at higher percentage rates than the individual pace of each. As shown in Table 1, the exceedance days of Tw, and O3 increased by 1.0% and 8.2% decade−1, respectively, while the joint exceedance of Tw and O3 showed an augmented increase by 10.8% decade−1. Such an enhancement in the joint occurrences might be due to the abovementioned interaction between temperature and O3 formation. Additionally, these numbers also indicate that although the cooccurrence of Tw and O3 extremes was relatively rare in most cities, they have become more common in the recent years at a disproportionately larger rate.

Table 1.

Average trends in percentage per decade (calculated with respect to the mean levels of each metric over the study period).

Table 1.

Regional trend in BTH, YRD, and PRD.

Among the three regions, BTH showed the highest downward trend (−13.0 days decade−1) in PM2.5 exceedances, followed by YRD (−7.7 days decade−1) and PRD (−4.6 days decade−1) (Fig. ES15). Opposite trends were identified for O3 exceedances, with BTH increasing at 11.4 days decade−1, YRD increasing at 5.5 days decade−1, and PRD increasing at 1.7 days decade−1. The exceedance trends of Tw were also positive, despite with a relatively smaller magnitude (0.9 days decade−1 for BTH, 3.8 days decade−1 for YRD, and 4.3 days decade−1 for PRD) (Fig. ES15).

Since the cooccurrences of Tw, O3, and PM2.5 were relatively rare in the PRD region, next we only report results for the BTH and YRD. In the BTH, the cooccurrence of Tw and O3 increased at 4.7 days decade−1 (or relatively at 7.0% decade−1) while all other combinations exhibited decreasing trends (Figs. 4 and 5a). Similar patterns are found in the YRD (Figs. 4 and 5b). Similarly, increasing trends of Tw and O3 severity and extreme duration were also identified in these two regions (Figs. ES16 and ES17). In BTH, we observe also that the exceedance days of Tw and O3 coextremes increased by 7.0%, higher than the percentage of each of them (0.9% and 5.5%, respectively, Table 1). Same amplification is also identified for the YRD.

Fig. 4.
Fig. 4.

Pooling trends of cooccurrence of Tw, O3, and PM2.5 exceedance days in the (a) BTH and (b) YRD regions.

Citation: Bulletin of the American Meteorological Society 103, 5; 10.1175/BAMS-D-21-0222.1

Fig. 5.
Fig. 5.

Pooling trend of independent and joint occurrence of Tw, O3, and PM2.5 exceedance days in (a) BTH and (b) YRD.

Citation: Bulletin of the American Meteorological Society 103, 5; 10.1175/BAMS-D-21-0222.1

Interpretation of the amplified trends.

As there is no census on the definition of heat waves around the globe. Previous studies that adopted various definitions of heat waves have revealed differences of effect estimation under different definitions (Chen et al. 2015; Kent et al. 2014). Our study found that absolute changes in the rising trend of Tw and O3 is larger individually than jointly while the percentage rates showed the opposite pattern. This counterintuitive result may be partially due to the small number of cooccurrence as we used the mean values of each metrics to derive the percentage change. In addition, the uncertainty of percentage change might also exist when using other definitions of heat wave. But our sensitivity analysis (Figs. ES21 and ES22) revealed that the direction and significance remain robust when using different threshold values. The amplified trend of Tw and O3 we observed might be associated with multifactors, such as urban growth, anthropogenic heat, and PM2.5 reduction. In addition, heatwave trends were also suggested to be associated with the local hydroclimate (Liao et al. 2018). Further investigations are needed to understand the contributions of different factors to the observed amplified trend of Tw and O3. Another limitation of this study is that we used the fixed-effect model to obtain the average trend estimates in specific regions. The fixed-effect model made an assumption that the weight of the trend at each site is simply determined by the corresponding variance residuals (lower indicating better model performance) of trend regression model. Other factors such as geographical and meteorological conditions (such as elevation and wind speed) of each site cannot be considered.

Conclusions

In the trend pooling analyses, we used a strategy to assess the overall trend of a particular region. The results are not sensitive to outliers in the time series of data. We first followed the trend analyses method proposed and used in previous studies (Chandler and Scott 2011; Cochrane and Orcutt 1949; Weatherhead et al. 1998), and then we combined the trend within regions by using a standard error-based weighting method. The results are consistent with previous studies. For example, contrasting trends of PM2.5 and surface O3 concentrations were observed among all of the three regions (Y. Wang et al. 2020). In addition, we also found that the severity of ozone pollution (difference between mean concentration and its threshold value) was also on the rise.

BTH, YRD, and PRD are the three major city clusters in China and several studies have indicated that, in urban areas of these region, ozone formation is mainly VOC limited or mixed limited (Geng et al. 2009; Qu et al. 2014; Shao et al. 2009). For mixed-limited regions, it has been suggested that both decreasing NOx levels and increasing VOCs levels could enhance ozone pollution (Lu et al. 2018). Furthermore, dealing with warming temperature and ozone pollution may have some cobenefits due to the relationship between temperature and ozone formation as discussed above as well as the fact that tropospheric ozone is a potent greenhouse gas. Therefore, cooperation in policies regarding warming climate and urban ozone pollution is warranted and further studies are needed to quantify the effect of emission control measures on both climate change and air pollution.

We conclude that China has achieved success in mitigating particulate matter pollution, as reduction in average concentration level, and in the frequency, duration, and severity of exceedance events have been observed. However, widespread ozone pollution and warming temperatures as well as the less-recognized cooccurrence of these two conditions are on the rise across the country. These two damaging factors for public health and ecosystems (Chen et al. 2007; Rossati 2017) should be seen as an emerging alarming issue. Further investigation on both aspects is needed to develop control strategies that effectively mitigate the ongoing trend and avoid undesired consequences.

Acknowledgments.

We acknowledge the European Centre for Medium-Range Weather Forecasts (ECWMF) for providing the ERA5 reanalysis datasets. This study was supported by grants from Research Grants Council of the Hong Kong Special Administrative Region, China (Project HKBU22201820 and HKBU12202021), National Natural Science Foundation of China (42005084), and Natural Science Foundation of Guangdong Province (2019A1515011633).

Data availability statement.

All the data presented can be accessed through contacting the corresponding authors.

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