Climate variability or anthropogenic emissions: which caused Beijing Haze?

Meteorological data used here to define a haze day were obtained from homogenized station observations collected by the National Meteorological Information Center of China (http://data.cma.cn/), including relative humidity, visibility and weather phenomenon records. Consecutive records during winter (December, January and February; DJF) from 1980 to 2018 at 20 stations in the Beijing region are used. Pei et al (2018) adjusted visibility data to maintain their consistency before analysis, i.e. the visibility observations since 2013 were transformed to be comparable to the early man-made observations. In this study, a haze day at each station is defined if a haze weather phenomenon is recorded with daily mean visibility <10 km and daily mean relative humidity <90%, and excludes sand, dust, snow and other weather phenomena influencing visibility. A haze day in the Beijing region is defined if there is at least one (threshold number of N = 1) station with haze on the day (Pei et al 2018). We calculated the correlation between the series of haze days defined with N = 1 and 2, and that with climatic variables, anthropogenic emissions and the associated MLR results, as shown in table S1, available online at stacks.iop.org/ERL/15/034004/mmedia. The results indicate that the different series of haze days (defined with N = 1 and 2) have similar interannual variability, although the increasing trend of the haze day series for N = 1 is relatively minor. The correlation coefficients between the (detrended) series of haze days (N = 1 and 2) and meterological variables for the period 1980–2017 are all significant at the α = 0.01 level. When meteorological conditions and total amount of anthropogenic emissions are considered in the MLR model, the variance explained about the N = 2 series does not show notable differences with those about the N = 1 series. Therefore, using a different threshold number (N) of stations to define a haze day in Beijing has little influence on the conclusions of this paper. The intensity of a haze day here is measured by average visibility over the Beijing region when haze occurs. The climatological mean of the average visibility over the Beijing region during winter is around 20 km; and the average visibility during the haze days is much smaller than 20 km. To ensure a meaningful positive intensity value, we define the intensity of haze as .. is the average visibility over the Beijing region (20 sites) when haze occurred (unit: km). The frequency of haze is the number of the haze days during a period (a month or a winter).

Climatic conditions corresponding to Beijing haze in winter include local meteorological factors and the associated large-scale atmospheric circulation factors. Homogenized station data are used to depict local meteorological conditions over the Beijing region, including relative humidity and surface wind speeds, collected from the National Meteorological Information Center of China (http://data.cma.cn/) for the period 1980–2018. Monthly atmospheric analysis, including meridional and zonal wind speeds, sea level pressure (SLP) and air temperature, used for depicting large-scale atmospheric features for winter hazes in Beijing, are from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) for the period 1980–2018, of 2.5° resolution (Kalnay et al 1997).

To obtain long-term changes in anthropogenic emissions in China, two datasets of the emissions of particulate matter and its precursors are used: the Emission Database for Global Atmospheric Research (EDGAR, http://edgar.jrc.ec.europa.eu/) and the Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org). Eight chemical species, including both gaseous and aerosol species, are used in calculating the total amounts of anthropogenic emissions: sulfur dioxide (SO₂), nitrogen oxides (NO_x), nonmethane volatile organic compounds (NMVOCs), ammonia (NH₃), particulate matter with diameter less than or equal to 10 μm (PM₁₀), particulate matter with diameter less than or equal to 2.5 μm (PM_2.5), black carbon (BC) and organic carbon (OC). OC, BC, PM_2.5 and PM₁₀ are the primary Particulate Matter (PM) emissions from industry and other sources (Zhang et al 2013, Hu et al 2015). SO₂, NH₃ and NO_x are the emissions of gas precursors for secondary inorganic aerosols (SIA) and NMVOCs for the secondary organic aerosols (SOA) (An et al 2019). During wintertime heating period in Beijing, in addition to primary PM, the contribution of SOA and SIA are found to be the most important to the chemical composition and sources of PM_2.5 when severe haze episodes occurred (Huang et al 2014, Wang et al 2016).

During severe haze episodes in Beijing, the regional transport of pollutants from cities southeast of Beijing, including Tianjin and Hebei province (BTH), play an important role (Zheng et al 2015). Therefore, chemical species over the BTH region (36–42 °N, 114–119 °E, figure 1(a)) are necessary for calculating anthropogenic emissions influential to Beijing Haze. The time series of total amounts of anthropogenic emissions and each chemical species over the BTH region based on the EDGAR and MEIC during the period 1980–2011 and 2010–2017 are shown in figures 1(b) and (c), respectively. Reconstruction of the normalized time series of each species (colors) and total amounts of anthropogenic emissions (black) in BTH for the period 1980–2017 are shown in figure 1(d), consisting of the normalized time series from EDGAR for the period 1980–2011 and that from MEIC for the period 2012–2017. Statistical analyses indicate that all species, except for OC, exhibit significant positive trends for the period 1980–2011 (table S2). However, for the period from 2010 to 2017, particularly since 2012, most of the species decreased markedly, except for NMVOCs, as a consequence of active clean air policies implemented in recent years (Zheng et al 2018). Time series of total amounts of anthropogenic emissions indicated a significantly increasing period in 1980–2012 and a notable decreasing period in 2012–2017. More than 80% of the total variance of the time series can explained by the trends.

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In this paper, the MLR model was applied to link the observed haze frequency and intensity (predictant) in Beijing and meteorological/climatic indices and anthropogenic emissions (predictors). Through correlation analysis, several factors are selected to reproduce the observed haze frequency and intensity. The equations of the MLR models are listed in table 1.

Based on homogenized observational data in Beijing for the period 1980–2017, we found that the frequency of winter haze exhibits strong interannual variability without any significant trend (black curve in figure 2(a)), consistent with previous studies (Chen and Wang 2015, Mao et al 2019). However, the time series of haze intensity exhibits a significant positive trend from 1980 to 2012, reaches a peak in 2012 and then notably decreases to a lower point in 2017 (black curve in figure 2(b)). During the two periods, more than 50% of the total variance of the intensity series can be explained by the trends. From 1980 to 2012, the average visibility during haze days decreased by 24%, suggesting intensifying winter haze during this period. The most polluted winter was 2012 (2012/Dec–2013/Feb), with an average visibility of 10.6 km when haze occurred. As pointed out in many studies, serious haze episodes repeatedly occurred in northern China during January 2013, which is the most severe haze pollution events in the observational history (Huang et al 2014, Zhang et al 2014). After this year, the haze intensity turns to an unprecedented negative trend, with the average visibility of haze episodes increasing by 34%. The haze intensity for the winter of 2017 (Dec 2017 to Feb 2018) was 4.5, with an average visibility of 15.5 km, which was the highest value in the past 20 years.

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Local meteorological conditions over the Beijing region play important roles in the formation and development of haze episodes, such as weak surface wind speeds, prevailing southerly flows, high relative humidity, and inversion in the boundary layer (Wu et al 2017). Through statistical analysis, we found that surface wind speeds (Ws) and relative humidity (RH) over the Beijing region are significantly correlated with the number of winter haze days during 1980–2017, with correlation coefficients of −0.54 and 0.64, respectively, both significant at the α = 0.01 level (table 1). It is consistent with former studies that surface wind speeds are generally very low (only 1–2 ms⁻¹) when winter hazes frequently occur in the BTH, unfavorable for the dispersion of air pollutants and conducive to the accumulation of pollutants (Wu et al 2017). The regional mean winter wind speed series in Beijing indicates a significant negative trend during 1961–2008, as a result of both urbanization and large-scale climate change in the region (Li et al 2011, Zhang et al 2018). Abundant moisture over the Beijing region creates favorable humidity conditions for Beijing hazes, linked to the hygroscopicity and scatter of particles (Wang et al 2014) and the efficiencies of secondary pollutants formation (Wang et al 2016), leading to lower visibility (Wu et al 2017). We also examined a suite of other local meteorological variables, such as near-surface air temperature, but its regional averages are not as highly correlated with the haze frequency.

As proposed by Pei and Yan (2018), four large-scale factors are selected to depict large-scale atmospheric conditions associated with the frequency of Beijing hazes in winter, which are the Siberian High Intensity (SHI), the meridional wind speeds anomaly at 850 hPa over northern China (V850), the difference in zonal wind speeds at 500 hPa (U500) and the vertical difference in air temperature (ΔT), respectively (figure S1). Physical mechanisms about how these large-scale atmospheric circulation anomalies influenced frequency of winter haze days in Beijing are discussed in Pei and Yan (2018). The correlation coefficients between the above four large-scale atmospheric circulation factors and the number of winter haze days in Beijing for the period 1980–2017 are listed in table 1, all significant at the α = 0.01 level. However, these four variables are not independent, SHI, V850, U500 and ΔT are correlated with correlation coefficients over the 1980–2017 period ranging between 0.41 and 0.83. Thus, these favorable large-scale atmospheric circulation factors co-occur to provide a conducive setting to frequent haze events in the wintertime.

The MLR model was applied to link the number of winter haze days in Beijing (predictant) and climatic indices or anthropogenic emissions (predictors). The multiple correlation coefficient is 0.77 (table 1), based on six climatic factors (four large-scale atmospheric circulation indices and two local meteorological indices), significant at the α = 0.01 level (red in figure 2(a)). This result suggests that 59.3% of the total variance in the number of winter haze days during 1980–2017 can be explained by climatic variability. When both climatic variability and total amounts of anthropogenic emissions are considered in the MLR model, the variance explained does not increase, suggesting that changes in emissions have little effect on the variability of the frequency of Beijing hazes. Emissions of each pollutants are also calculated individually in the MLR models; the variance explained does not increase as well, as shown in table S3. When large-scale atmospheric circulation factors (4 indices) and local meteorological factors (2 indices) are considered in MLR, the correlation coefficients are 0.71 and 0.66 (brown and blue in figure 2(a)), respectively. Over 50% of the total variance of the frequency of winter haze days in Beijing can be explained by large-scale atmospheric circulation factors only. Therefore, climatic conditions, particularly large-scale atmospheric circulation factors, are dominant in determining the number of haze days in winter, while changes in anthropogenic emissions have little effect. It is consistent with former conclusions about changes in the frequency of haze days in China and the associated influencing factors (e.g. Wang et al 2015, Zhang et al 2015, Mao et al 2019).

Based on statistical analysis, it is found that surface wind speeds and relative humidity are highly correlated with the haze intensity, with correlation coefficients of −0.67 and 0.56, respectively, both significant at α = 0.01 (table 1). The anomalous southerly flows give rise to the accumulation of pollutants and moisture over Beijing, with reduced surface wind speeds, which is conducive to the accumulation of air pollutants (Wu et al 2017). Abundant moisture over the Beijing region enhances aerosol scattering and secondary aerosol formation, resulting in the accumulation of pollutants (Wu et al 2019, An et al 2019).

Figure S2 depicts correlation coefficients between anomalous variables of atmospheric circulation from the near-surface to the upper troposphere and the intensity of haze days in Beijing in winter from 1980 to 2017. Only one large-scale factor, that is V850, is highly correlated with the changes in haze intensity in Beijing, with a correlation coefficient of 0.53, significant at α = 0.01 (table 1). Anomalous southerlies imply weakening of the northerly winds or even a reversal to southerly flow in the region (figure S2(b)), which is favorable for the transport of warm, moist air and pollutants to the Beijing region. Abundant and warm moisture is necessary for the formation and maintenance of haze episodes because it is favorable for efficient secondary inorganic aerosol transformation and hygroscopic growth of haze particles (Wang et al 2016). Meanwhile, influenced by anomalous southerlies, large amounts of air pollutants are transported to the Beijing region, leading to a significant reduction in visibility (Tang et al 2016).

On one hand, when climatic indices (V850, surface wind speeds and relative humidity) are considered in the MLR model, the multiple correlation coefficient is 0.72 (red in figure 2(b)), statistically significant at the 0.01 level. It suggests that 51.8% of the total variance in haze intensity can be explained by climatic conditions. However, its linear trends during the two periods (1980–2012 and 2012–2017) are weaker than those of the observed haze intensity, indicating that the changes in climatic conditions alone are not enough to explain the observed trends of haze intensity during these periods. On the other hand, changes in total amounts of anthropogenic emissions are highly correlated with haze intensity in Beijing, with a correlation coefficient of 0.74 (green in figure 2(b)), suggesting that changes in anthropogenic emissions can explain 54.8% of the variance of haze intensity in the past decades. When both changes in anthropogenic emissions and climatic conditions are considered in the MLR model, the multiple correlation coefficient is as high as 0.86. Furthermore, the trends of the reconstructed haze intensity are highly consistent with the observed. Correlation coefficients between each species of anthropogenic emissions and time series of haze intensity are shown in table S2, indicating most are highly correlated, except for NH₃ and NMVOCs. Emissions of each pollutant are incorporated individually in the MLR models, suggesting the uncertainties of each species of emissions in MLR results (table S3). Therefore, both changes in emissions and meteorological conditions are responsible for the changes in haze intensity (pink in figure 2(b)), which explain 74% of the total variance of haze intensity for the period 1980–2017. In details, about half of the total variance of the haze intensity can be explained by climate variability (mainly for interannual variations), and about half by the changing emissions (mainly for the trends).