Characteristics of the secondary water-soluble ions in a typical autumn haze in Beijing

Environmental Pollution 227 (2017) 296e305

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Characteristics of the secondary water-soluble ions in a typical autumn haze in Beijing* Lili Xu a, Fengkui Duan a, **, Kebin He a, *, Yongliang Ma a, Lidan Zhu a, Yixuan Zheng a, Tao Huang b, Takashi Kimoto b, Tao Ma a, Hui Li a, Siqi Ye a, Shuo Yang a, Zhenli Sun a, Beiyao Xu c a

State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China Kimoto Electric Co. Ltd, Funahashi-Cho, Tennouji-Ku, Osaka 543-0024, Japan c College of Resources and Environmental Sciences, China Agricultural University, Beijing 100094, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 October 2016 Received in revised form 25 April 2017 Accepted 26 April 2017

Four haze episodes (EPs) were observed in October 2014 in Beijing, China. For better understanding of the characteristics and the formation mechanisms of PM2.5 (particulate matter with an aerodynamic diameter
2.5 mm), especially secondary water-soluble inorganic species in these haze events, hourly concentrations of PM2.5, sulfate, nitrate, and ammonium (SNA) were measured in this study. Concentrations of gaseous pollutants and meteorological parameters were also measured. The average concentration of PM2.5 was 106.6 ± 83.5 mg m3, which accounted for around 53% of PM10 (particulate matter with an aerodynamic diameter
10 mm) mass. Nitrogen dioxide (NO2) concentration was much higher than that of sulfur dioxide (SO2) since October is a non-heating month. SNA is the most abundant secondary water-soluble inorganic species and contributed to 33% of PM2.5 mass concentration. Sulfur oxidation ratio (SOR) was much higher than nitrogen oxidation ratio (NOR). NOR and SOR increased with elevated PM2.5 levels and heterogeneous processes seemed to be the most plausible explanation of this increase. Relative humidity (RH), which is of great influence on aerosol liquid water content (ALWC), played a considerable role in the formation of secondary inorganic aerosols, accelerated the secondary transformation of gaseous precursors, and further aggravated haze pollution. The positive feedback loop associated with high aerosol levels and low planetary boundary layer (PBL) height led to the evolution and exacerbation of heavy haze pollution. Fire maps and 48-h air mass backward trajectories supported the significant impact of biomass burning activities and regional transport on haze formation over Beijing in October 2014. © 2017 Published by Elsevier Ltd.

Keywords: Haze Secondary water-soluble ions Heterogeneous processes Biomass burning

1. Introduction With rapid economic growth and accelerated urbanization in recent years, the consumption of fossil fuels including coal, diesel, oil, and natural gas has been increasing in China. The emissions of atmospheric pollutants such as sulfur dioxide, nitrogen oxides (NOx ¼ NO þ NO2), and fine particulate matter (PM2.5) continue to

*

This paper has been recommended for acceptance by Eddy Y. Zeng. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (F. Duan), [email protected] (K. He). http://dx.doi.org/10.1016/j.envpol.2017.04.076 0269-7491/© 2017 Published by Elsevier Ltd.

increase, having potential effects on climate change (Booth and Bellouin, 2015; Sun et al., 2006; Wang et al., 2014b), visibility (Che et al., 2014; Ma et al., 2011), and human health (Araujo et al., 2008; Peacock et al., 2011; Wu et al., 2010). The pollution characteristics, formation mechanisms, and effects of PM2.5 on the environment and public health have been studied extensively in recent years (Shaughnessy et al., 2015; Zhou et al., 2016; Zhu et al., 2011). In particular, Beijing, as the capital city and the political and cultural center of China, has attracted great attention because of its heavy air pollution problem. PM2.5 is composed of organic matter, inorganic ions, elemental carbon, mineral dust, and other substances. Previous studies showed that sulfate, nitrate, and ammonium (SNA) are the major water-soluble inorganic species (Cao et al., 2012; Yao et al., 2002;

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Zhao et al., 2013), accounting for about 25e30% of PM2.5 in Beijing (He et al., 2001). During the most severe air pollution days in January 2013, the contribution of SNA to PM2.5 was higher than 50% (Quan et al., 2014; Zheng et al., 2015a), suggesting strong secondary formation activity. Beijing is characterized with short autumn, traditionally includes two months, September and October. The air quality in October is normally much worse than in September. According to the air quality index (AQI) data from China's Ministry of Environmental Protection (http://www.zhb.gov.cn/), heavily polluted (200 < AQI
300) and severely polluted (AQI > 300) days accounted for 36% of the 31 days in October 2014 in Beijing. However, there have been only few studies (Liu et al., 2013; Nakamura et al., 2005) focusing on the autumn haze pollution and its formation mechanism. Therefore, our work will focus on the heavy haze occurred in October to illustrate in detail the possible source and formation pathway of secondary ions. Based on the continuous hourly observations of PM2.5 and gaseous pollutants, and their chemical compositions, we investigated the temporal variations of PM2.5 concentrations and identified four heavy EPs in October 2014. Furthermore, we analyzed the characteristics of SNA at different PM2.5 and aerosol liquid water content levels, and discussed the sources and formation processes of SNA in Beijing.

2. Experimental methods We carried out online hourly ambient observations from October 1st to October 31st, 2014 in Tsinghua University, located between North 4th Ring Road and North 5th Ring Road to the northwest of Beijing. There are no large industrial pollution sources around the sampling site and instruments were installed on the roof of Weilun Building on campus (40.00 N, 116.32 E) (Fig. 1) which is about 15 m above ground level. In our previous work, concentration and chemical species of PM2.5 at this site demonstrated similar characteristics with those of the typical downtown site, Chegongzhuang (CGZ). Therefore, our observation site can

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represent an urban environment in Beijing. Hourly average mass concentrations of PM2.5 were monitored continuously by a dichotomous monitor, i.e. PM-712 (Kimoto Electric Co., Ltd., Japan), at a flow rate of 16.7 L min1. Hourly filter samples can be collected simultaneously on Teflon filter for laboratory analysis of chemical compositions. Water-soluble inorganic 2þ ions (NO 3 , SO4 ) and acidity (H ) were monitored simultaneously by a continuous dichotomous aerosol chemical speciation analyzer (Model ACSA-08) (Kimoto Electric Co., Ltd., Japan). The measurement procedure has been described in detail in previous studies (King and Kester, 1989). It is clear that the online observed data of 2NO 3 and SO4 have good correlation with the data measured from filter samples by traditional method (ion chromatography, IC), suggesting the data used in the following discussion were reasonable. In addition, ammonium (NHþ 4 ) concentration was calculated based on the following formula where n refers to mole number:  2þ n(NHþ 4 ) ¼ n(NO3 ) þ 2  n(SO4 )  n(H )

(1)

It should be noted that NHþ 4 can not be always assumed to be in equilibrium with sulfate and nitrate fully neutralized (Kim et al., 2015). For data validation, we further measured the NHþ 4 by IC analysis of the simultaneous hourly collected samples by PM-712 as mentioned in our previous work (Duan et al., 2016). The comparison between online-based calculated NHþ 4 and offline measured 2 NHþ 4 was shown in Fig. S2. Strong consistence can be seen with R of þ 1.00 and slope of 0.95, indicating that our calculated NH4 data are reasonable and can be used in the following discussions. Organic carbon (OC) and elemental carbon (EC) concentrations in fine particles were measured by a Sunset Model 4 SemiContinuous Carbon Analyzer (Beaverton, OR, USA). The concentrations of SO2, NO2, NO, CO, and ozone (O3) were determined by the integrated gas monitor (SA, NA, CA and OA, Kimoto Electric Co., Ltd., Japan). Meteorological parameters including temperature, RH, wind speed (WS), and wind direction (WD) were acquired via an automatic meteorological observation instrument (Milos 520, VAISALA Inc., Finland). RH exerts a great influence on aerosol water

Fig. 1. Observation site in Tsinghua University, Beijing (40.00 N, 116.32 E).

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content, together with the concentration of hygroscopic aerosol species. ALWC was calculated using the aerosol thermodynamics model E-AIM (Extended Aerosol Inorganic Model, http://www.aim. env.uea.ac.uk/aim/aim.php) (Clegg et al., 1998). PBL height was simulated using the Weather Research & Forecasting (WRF) Model. 3. Results and discussion 3.1. General characteristics In general, average concentrations of PM2.5 and PM10 during observation period were 106.6 ± 83.5 mg m3 and 221.7 ± 156.2 mg m3, respectively. PM2.5 levels were close to the values reported during an autumn episode (EP) of 2011 in the Yangtze River Delta (100 mg m3) (Hua et al., 2015), but higher than the values observed in urban and rural areas of Miyun, Beijing (91.61 and 66.70 mg m3, respectively) (Gao et al., 2015). The maximum hourly concentrations of PM2.5 and PM10 was 322.2 mg m3 and 775.0 mg m3 observed at 16:00 on 9 October and 0:00 on 26 October, respectively. The peak PM2.5 value was lower than 469 mg m3 observed on the campus of Beijing Normal University in October 2014 (Yang et al., 2015) and higher than 220 mg m3 monitored at Peking University in September 2011 (Liu et al., 2013). Although the pollution was heavy, the maximum PM2.5 loading was still much less than 680 mg m3 measured during a haze episode in January 2013 (Wang et al., 2014a). We define EPs as continuous periods with daily PM2.5 mass concentrations over 75 mg m3 (Grade II National Ambient Air Quality Standard, GB3095-2012) and the other time except EPs during observation period is clear days (Cs). According to this definition, four typical EPs including EP1 (7e11 October), EP2 (17e20 October), EP3 (22e25 October), EP4 (29e31 October) and

four Cs (C1-C4, see Fig. 2a) were analyzed in this study. PM2.5 and PM10 concentrations during EPs were much higher than on clear days (Fig. 2a). EP1, EP2, and EP3 presented similar patterns and increase rates of hourly particulate matter concentrations. The increase rate of PM2.5 concentrations during EP4, however, was much lower than the other three episodes. The decrease rates were similar in all episodes and pollutants were removed within a few hours. During the observation period, PM2.5 to PM10 ratio (Fig. 2b) varied between 0.05 and 0.77, with the average value of 0.44. The fine fraction during EP1 was 49%, which increased 5% compared with C1 (44%). While this fraction increased 24%, 12% and 19% for the other three episodes, respectively. The increase of ratio PM2.5/ PM10 can be a good indicator of the rise of secondary aerosol relatively to total aerosol. Other sources like biomass burning at the regional scale might account for the observed smaller fine fraction growth during EP1. SNA in PM2.5 plays an important role in ambient air quality and visibility. Daily averaged concentrations of SNA and carbonaceous components are presented in Fig. 2c. OC, EC and SNA accounted for 28%, 7% and 33% of PM2.5 mass concentration, respectively. The  þ mean concentrations of SO24 , NO3 , and NH4 during EPs were 17.13, 3 21.17, and 11.82 mg m , respectively, which were approximately 3.43, 3.75, and 3.31 times the concentrations during clear days, respectively. Fig. 3 shows the trends of trace gases (O3, NOx, SO2, and CO) and PM2.5 loadings during observation period. The concentrations of trace gases except for O3 displayed obvious increase before and at the beginning of every episode. Given that the heating systems are not in use in October in Beijing, the concentration of SO2, which is emitted mainly from coal burning, was much less than that of NO2, which is mainly emitted from vehicle exhaust. The average mass concentrations of PM2.5, NOx, SO2, and CO in EPs were 166.3, 163.8,

 Fig. 2. (a) Time series of hourly PM10 and PM2.5 concentrations (b) Time series of PM2.5 to PM10 ratio (c) Time series of daily average mass concentrations of SO24 , NO3 , predicted NHþ 4 , OC and EC in PM2.5. “C” and “EP” mean clear and haze episode days, respectively.

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Fig. 3. Time series of observed gaseous pollutants (O3, NOx, SO2, CO) and PM2.5 in October 2014, Beijing. “C” and “EP” mean clear and haze episode days, respectively.

24.3, and 1790 mg m3, respectively, about 2.9, 1.1, 0.6, and 1.4 times higher than those on clear days. PM2.5 and CO increased significantly, while SO2 showed a smaller increase due to no supply of heating in October. The average mass concentration of O3 was 31% lower in hazy days than on clear days. O3 concentration reached its peak shortly after noon and its minimum value in early morning hours. In general, PM2.5 loading was so high that it degraded atmospheric visibility and further reduced O3 levels during the heavy pollution period. Furthermore, the daily average concentrations of all gases reached their maximum value on 18 October and this was largely due to the meteorological conditions on that day, which will be discussed in Section 3.2. 3.2. Meteorological conditions Meteorological conditions have important effects on the concentrations of air pollutants. Fig. 4 illustrates the temporal variations of PM2.5 concentrations and major meteorological parameters (PBL height, wind speed, wind direction, temperature, and RH) in October 2014. Vertical diffusion is an important process affecting the variability of the surface concentrations of air pollutants. PBL height, which affects the vertical dispersion of pollutants, is mainly driven by thermal (air temperature) and dynamic (wind speed) factors (Pal et al., 2015; Yang et al., 2015). A reduction of atmospheric convection due to low wind speed and low solar radiation deduces PBL height and worsen air quality, since air pollutants can accumulate near the ground level (Zhang et al., 2009). During this study, PBL height was generally high at the end of one haze event, but dramatically decreased at the beginning of next event. For instance, the PBL height (Fig. 4a) was 2185 m at 10:00 on 12 October at the end of EP1, which was 5.6 times of the height at 10:00 on 17 October (390 m) at the beginning of EP2. Correspondingly, the concentration of PM2.5 on 12 October was only 5.4% of that on 17 October. The increase in the amount of particles (especially light-

scattering particles like sulfate, nitrate, and organic carbon) tends to restrict the growth of the PBL height by decreasing the solar radiation, while the depressed PBL would in turn weaken the vertical diffusion of pollutants, causing increased contamination at the ground level. Consequently, this positive feedback loop (more aerosols/ lower PBL height/ more aerosols) causes the haze conditions to persist over a long period of time or worsen (Quan et al., 2013). Another important factors are wind speed and wind direction (Fig. 4b). The variations in the wind speed pattern impact the nearsurface aerosol levels (Zhang et al., 2009). During hazy days, wind speeds were often less than 1 m s1 (with a frequency of 74%), and the mean wind speeds during hazy and clear days were 0.8 m s1 and 1.4 m s1, respectively. These results confirm that the horizontal transport of pollutants from the source regions to the other locations was very weak and the low wind speed was an important factor in heavy pollution formation (Sun et al., 2013; Tian et al., 2014). In the south of Beijing, there are Hebei, Shandong, Henan and other provinces with huge amounts of steel, coal and other factories. It tends to cause pollution of Beijing area when south wind blows. Whereas northwest and north wind, which is mostly caused by Mongolia - Siberian high pressure, will greatly promote the dilution and diffusion of pollutants especially when wind speed is high. RH plays an important role in the evolution and transformation of atmospheric aerosol, and aerosol water content is basically dependent on atmospheric RH and aerosol chemical composition. Water content of aerosol in an atmosphere increases with high RH (Fig. S5), which leads to reduction of visibility in the region affected (Tsai and Kuo, 2005). High aerosol liquid water level can dissolve more pollutants and accelerate chemical reaction, which further increase secondary aerosol. PM2.5 loading and RH increased rapidly during haze episodes and PM2.5 concentration often exceeded 150 mg m3 (except for EP4). The average RH during hazy and clear

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Fig. 4. Time series of observed PM2.5 and meteorological parameters in October 2014, Beijing. “EP” means haze episode days.

days were 63.5% and 45.9%, respectively, suggesting that PM2.5 and RH were major factors contributing to severe haze events The temporal variations of RH and temperature showed opposite trends (Fig. 4c). The mean temperature was 14.9  C during the entire month of October 2014. The relatively low air temperatures before the haze episodes favored the partitioning of semi-volatile components and ammonium salts into the particle-phase, and worked against the development of the PBL height, thus exacerbating air pollution. Notably, the average PM2.5 mass concentration on 18 October (EP2) was 138.1 mg m3, i.e. 3.6 times the concentration on 16 October (C2). Daily average concentrations of all gaseous pollutants also reached the maximum on 18 October, which was largely due to the unfavorable weather conditions that day. RH continued to increase on 18 October with an average value of 46.9%, which was 1.6 times the value on 16 October. The PBL height decreased by 84% from 519 m on 16 October to 283 m on 18 October. Wind speed decreased by 4.5 times in only 8 h (from 1.1 m s1 at 23:00 on 17 October to 0.2 m s1 at 7:00 on 18 October). All of these weather conditions jointly led to gas concentration peaks on 18 October. Although the average PBL heights in EP2 and EP3 were 20% and 14% lower than that in EP1, respectively, the PM2.5 concentrations during EP1 were 27% and 20% higher than that in EP2 and EP3, respectively. Compared to EP2 and EP3, the increase in the PM2.5 loading in EP1 is more likely from the regional transport of the pollutants rather than merely the compression of the PBL height.

3.3. Secondary formation Using the Air Quality Index, we divided PM2.5-polluted conditions into four categories: clean (C) (PM2.5
75 mg m3), slightly polluted (S) (75 mg m3250 mg m3) conditions, where PM2.5 represents hourly PM2.5 mass concentrations. The average ratio of NO2/SO2

progressively increased from clean (4.93), slightly polluted (5.10), polluted (5.48) to heavily polluted (5.96) conditions. The ratio of 2NO 3 /SO4 , however, increased from C (1.49) to S (2.03) condition and decreased from S to P (1.40) condition and was comparable between P and H (1.37) cases. The decline of the ratio from S to P and then to H level can be attributed to the greater increase in the sulfur oxidation rate than the nitrogen oxidation rate under heavy polluted circumstances. In order to evaluate the importance of the secondary production of water-soluble inorganic species on air pollution levels, EC-scaled 2concentrations (Zheng et al., 2015b) for NO 3 and SO4 in PM2.5 were used to exclude the effect of atmospheric physical processes (dilution/mixing conditions) on the variation of observed pollutant 2levels. We used NO 3 /EC and SO4 /EC ratios to estimate the sec2ondary production of NO and SO 3 4 (Fig. 5). When the haze evolved from the clean stage to the polluted stage, the average NO 3 /EC and 2SO24 /EC varied differently. That was, SO4 /EC ratio increased obviously from 1.64 to 2.11, while NO 3 /EC almost unchanged, 2.06 and 2.09, respectively. It suggested a higher increased atmospheric conversion for sulfate than for nitrate. To evaluate the degree of the 2secondary conversion from NO2 and SO2 to NO 3 and SO4 , NOR and SOR were calculated based on the following formula.

  n NO 3 ;  NOR ¼ nðNO2 Þ þ n NO 3

  n SO2 4   SOR ¼ nðSO2 Þ þ n SO2 4

(2)

The average NOR and SOR during the whole measurement campaign were 0.10 and 0.25, respectively; while the period when NOR and SOR were above 0.10 and 0.20 accounted for 46% and 51% of the whole study period, respectively. This result suggests strong secondary formation of both nitrate and sulfate. The average NOR increased about 114% in polluted (0.15) and heavily polluted conditions (0.14) compared to that on clean ones (0.07). The mean value of SOR and its growth in heavily polluted cases (0.41) compared to that on clean ones (0.16) were much higher than that

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2Fig. 5. Variation of NO 3 /EC, SO4 /EC, NOR and SOR with pollution levels. “C”, “S”, “P”, “H” refer to “clean”, “slightly polluted”, “polluted” and “heavily polluted”, respectively. In the box-whisker plots, the whiskers and boxes indicated the 95th, 75th, 50th (median), 25th and 5th percentiles, respectively. The small box inside every box-whisker plot was the mean value. Normalized X in column 3 refers to the average mass concentration of X in any pollution level scaled by its mean concentration in clean condition.

of NOR. The increase in both NOR and SOR implies enhanced conversion from NO2 and SO2 to their corresponding particle phases along with the evolution and accumulation of heavy haze. Furthermore, SOR increased more rapidly than NOR. The elevated oxidation ratios of nitrogen and sulfur in severe haze period can be attributed to heterogeneous reactions, given that the solar radiation and the photochemical capacity were weakened by the in2crease of ambient aerosol loading. Concentrations of NO 3 and SO4 increased more significantly than their gaseous precursors. NO 3 and SO24 concentrations increased by 5.7 and 7.1 times from clean to heavily polluted case, respectively; while there was only a 2.7fold and 1.8-fold increase in NO2 and SO2 concentrations. These results also suggested the enhanced secondary transformation of NO2 and SO2 during serious haze events. PM2.5 mass concentration increased notably with enhanced RH levels especially at RH<75% and remained almost steady at higher RH levels (Fig. S4). When water vapor condenses into an aqueous solution as RH increases, hygroscopic growth occurs (Martin, 2000). Although it has been confirmed that a large proportion of organic matter is water-soluble (Saxena and Hildemann, 1996), inorganic salts such as sulfates, nitrates, and chlorides dominate the water uptake by ambient aerosol particles (Boreddy et al., 2016). Moreover, ALWC on the surface of the particles is critical for heterogeneous reactions occurring in the atmosphere (Mogili et al., 2006). The hygroscopic growth of particles can affect light scattering and absorption properties of the particles, which in turn affect air quality, visibility, Earth's radiation, and the climate. Therefore, the hygroscopic growth of particles because of the increase in RH can play a more important role than the photochemical reactions in heavy haze formation. NOR and SOR are generally used to infer the degree of atmo2spheric secondary transformation of NO2 and SO2 to NO 3 and SO4 . Fig. 6a presents the variations in NOR and SOR as a function of RH. NOR increased rapidly when RH was below 60% and remained relatively constant when RH was higher than 60%. Since particulate nitrate could be generated via both homogeneous reaction of NO2 and hydroxyl radical, and heterogeneous hydrolysis of N2O5 on humid aerosol surface (Pathak et al., 2009), constant values or even small decrease of NOR at higher RH levels were likely due to the weakened photochemical intensity (low O3 concentration at elevated RH levels). As for the sulfate production, SOR presented an evident positive

correlation with RH (R2 ¼ 0.64) and RH appeared to have a minor influence on SOR for RH levels below 35%: the average SOR at RH<25% was less than 0.10 and only increased by 1% from RH ¼ 25% to RH ¼ 35%. However, as RH increased, SOR increased rapidly and was up to 0.49 at RH ¼ 80%e90%. Normally, the aqueous-phase oxidation of sulfate is faster than the gas-phase oxidation (Wang et al., 2016), and the aqueous-phase oxidation is subject to the droplet pH and oxidants like hydrogen peroxide, ozone, and oxygen (catalyzed by metals) (Shen et al., 2012). Since O3 concentration when RH>60% was 16.4 mg m3 (7.6 ppb), too low to provide adequate oxidizing capacity, photochemical formation of sulfate was expected to be trivial. Instead, the aqueous-phase oxidation played a more important role at high RH. The escalation of RH facilitated the uptake of NO2 and SO2, which were converted to nitrate and sulfate by heterogeneous processes. The generation of  SO24 and NO3 can enlarge particle size, enhance hygroscopicity, and increase the water absorption. The increased water facilitates the gas-liquid-solid reactions of NO2 and SO2 on particles and increases the hygroscopicity of the particles once again. The increase in particle size can lead to the intensification of light scattering, which can ultimately induce the occurrence of haze. Therefore, these processes form a positive feedback mechanism that accelerates the secondary conversion of gaseous precursors and the formation of haze pollution. As shown in Fig. 6b, along with the increase of ALWC, NOR at first increased and then was almost steady and finally dropped, which was similar with the pattern between NOR and RH. Contrarily, SOR kept growing with enhanced ALWC except when ALWC was up to 200 mg m3. In the meanwhile, the variation of sulfate concentration with ALWC (Fig. S6) behaved almost in the same pattern with SOR. This result revealed elevated secondary transformation and further the crucial effects of heterogeneous chemistry of sulfate. As mentioned above, both nitrate and sulfate can be produced through homogeneous and heterogeneous reactions. Nitrate is predominantly formed by the gas-phase reaction of NO2 and OH radicals during the day and by heterogeneous reactions of nitrate radical (NO3) at night (Seinfeld and Pandis, 2006). SO2 can also be converted into sulfate through gas-phase and aqueous-phase reactions. The diurnal variations of NOR and SOR (Fig. 7a and b) in this study showed that during the day, NOR demonstrated a strong O3 concentration dependency and reached the maximum value shortly after noon, but SOR was much lower during the day than

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Fig. 6. Relationship between NOR, SOR, and (a) RH (b) ALWC color-coded by O3 concentration. The data were also binned according to (a) RH and (b) ALWC, and the mean (black circles) and standard deviation (lower and upper whiskers) were shown for each bin.

2Fig. 7. Diurnal variations of NOR, SOR (dot), and O3 concentrations (dash dot line) color-coded by (a) RH and (b) ALWC. (c) Diurnal variations of NO 3 , SO4 and their precursor gases concentration.

at night. In contrast, although the concentration of O3 at night was the lowest in the whole day, SOR reached its highest level and NOR exhibited an obvious growth trend along with enhanced RH and ALWC levels in very early morning hours. These results

suggest that both photochemical and heterogeneous reactions make significant contribution to the secondary transformation of NO2 and SO2. The photochemical reactions had a greater influence on NOR than on SOR in October 2014, and the heterogeneous

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chemistry in ALW had more dramatic impact on SOR than on NOR. SO2 is a soluble gas which can be scavenged effectively in fog or wet aerosol particles. Fog and high RH haze events usually occur at night and persist until the temperature rises up in the morning, leading to low nighttime SO2 concentrations. During hazy days, SO2 concentration was characterized by a nocturnal minimum (Fig. 7c). While sulfate concentration at night didn't decline so much as SO2 did, instead, it went up after 7:00 p.m., leading to the nocturnal maximum of SOR as a consequence. H2O2, O3 and OH radicals are the major oxidants for SO2 in the aqueous phase together with oxygen (catalysed by metals). Although we didn't have H2O2 data, high NO2 levels happened when concentrations of O3 (below 20 mg m3) and OH radicals were very low at night. Recently published study has proposed the concept of “haze chemistry” and found that NO2 was the most important oxidant in Beijing during heavy haze events. They also found that the sulfate production rates of NO2 pathway in the presence of ALW was much higher than that involving other oxidants like O3 and H2O2 (Cheng et al., 2016). Given the highest and the following relatively high NO2 concentration since 8:00 p.m., we considered that the most important aqueous oxidant of sulfate production at night in hazy days might be NO2. 3.4. Biomass burning and regional transport CO is a by-product of combustion and can be used as an indicator of biomass burning (Kato et al., 1999; Zhang et al., 2015). CO/ NOx ratio can be used to separate biomass burning activities from other emission sources (Huang et al., 2012). In this study, the mean CO/NOx ratios during EP2, EP3, and clear days were close to

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each other, 11.0, 11.9, and 11.0, respectively, while the mean value reached 13.9 during EP1. During EP1, average concentrations of SO2 and NOx were 20.43 and 138.38 mg m3, much lower than in EP2 (28.98 and 182.71 mg m3), EP3 (22.99 and 163.78 mg m3), and EP4 (26.38 and 181.16 mg m3). However, CO had an average concentration of 1786 mg m3 in EP1, which was similar to the values to EP2, EP3, but higher than the value in EP4. Biomass burning process can generate large amounts of CO while it produces relatively smaller amounts of SO2 and NOx. The emission factors of CO calculated using the measurements of various kinds of crops in Eastern China are much higher than the emission factors of SO2 and NOx (Li et al., 2007; Zhang et al., 2008), indicating that SO2 and NOx are not the predominant gaseous pollutants originated from biomass burning in this region. The results reinforced the hypothesis of a biomass burning contribution during EP1 discussed in Sect. 3.1. The harvest of maize around Beijing usually occurs in early October (Sun et al., 2013) and biomass burning during autumn harvest is one of the dominant sources of atmospheric organic aerosol in North China Plain (NCP). Fire maps (https://firms. modaps.eosdis.nasa.gov/firemap/) for NCP and the eastern region of China on 6 and 7 October are presented in Fig. 8(a1-a2) Among the four haze episodes, only EP1 displayed abundant fire points, likely caused by open biomass burning activities during that time. On 6 and 7 October, there were a total of 362 fire points of straw burning over China and 36, 206, and 38 fire points were in the provinces of Hebei, Henan and Shandong, respectively. The 48-h air mass backward trajectories ending in Beijing during EP1 calculated by the HYSPLIT model - online version (http://www.ready.noaa.gov/HYSPLIT.php) further clarified how

Fig. 8. Fire maps in NCP and the eastern region of China (a1) on 6 October. (a2) 7 October, and (b) backward trajectories from Beijing for EP1. The red ovals and the orange points in a1 and a2 are the positions of Beijing and the fire points, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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biomass burning activities and the regional transport of pollutants affected the haze formation in Beijing (Fig. 8b). The backward trajectories were started at 20:00 (LST) on 11 October and a new trajectory was started every 12 h. Except for the trajectory started at 20:00 (LST) on 11 October at the end of the first episode, the other seven trajectories all originated from the south and southeast directions, specifically from Hebei, Henan, Shandong, and Anhui provinces, in agreement with the locations of the major fire points. Air masses carrying intense aerosol loadings passed over these neighboring areas around Beijing with a low speed at fairly low altitudes and exacerbate the accumulation of air pollutants and the formation of severe haze. For example, OC mass concentration on 6 and 7 October increased by 63% and 168% as compared to 5 October, respectively, affirming the significant influence of biomass burning and regional transport on haze formation over Beijing. 4. Conclusions Compared with the air pollution in winter, haze pollution in autumn has not drawn enough attention in North China. In this work, we investigated the temporal variations of PM2.5, SNA loadings, and trace gases, along with the meteorological parameters and air mass backward trajectories, then discussed the physical and chemical processes leading to the haze formation in October 2014 in Beijing. We found that PM2.5 hourly concentrations varied dynamically from 0.8 mg m3 to 322.2 mg m3 with an average of  106.6 ± 83.5 mg m3 for the entire sampling campaign. SO24 , NO3 , þ NH4 together accounted for about 33% of the total PM2.5 mass concentration. Except for O3, the concentrations of gaseous pollutants presented an increasing pattern before and during every episode. The concentration of SO2 was much lower than that of NO2 because heating systems are not in use in October in Beijing. CO/ NOx ratio for EP1 suggests that a significant biomass burning activity contributed to the measured pollutant levels. The nitrogen and sulfur oxidation ratios increased along with PM2.5 concentrations during severe haze periods, probably because of the heterogeneous reactions. SOR presented a distinct positive correlation with RH (R2 ¼ 0.64) and RH had a significant influence on SOR when RH>35%. Both photochemical and heterogeneous reactions were in favor of the secondary transformation of NO2 and SO2 while photochemical reactions had a greater influence on NOR in October 2014 and heterogeneous reactions had a greater effect on SOR. Meteorological parameters also played an important role in the formation of haze events through both physical and chemical interactions. Low PBL height, high RH and low wind speed accelerated the accumulation of particles and prevented the elimination of haze pollution. Fire maps and the 48-h air mass backward trajectories suggested significant influence of biomass burning from regional transport to the haze formation over Beijing. The finding in this work will not only be helpful for understanding the haze formation in autumn, but also has implication for the government to make targeted air pollution control policy in this season. Notes The authors declare no competing financial interest. Acknowledgements This work was supported by the National Research Council of Science and Technology Support Program of China (2014BAC22B01), by National Natural Science Foundation of China (81571130090).

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