Insights into submicron particulate evolution, sources and influences on haze pollution in Beijing, China

Atmospheric Environment 201 (2019) 360–368

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Insights into submicron particulate evolution, sources and influences on haze pollution in Beijing, China

T

Lihui Han∗, Xin Xiang, Hailiang Zhang, Shuiyuan Cheng, Hongmei Wang, Wei Wei, Haiyan Wang, Jianlei Lang Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China

A R T I C LE I N FO

A B S T R A C T

Keywords: NR-PM1 Mass concentration Diurnal cycle Haze Influencing factors Source contribution

The real-time continuous measurements of non-refractory submicron aerosol (NR-PM1) species including organics (Org), sulfate (SO42−), nitrate (NO3−), ammonium (NH4+) and chloride (Cl−) in winter from 10 December 2015 to 30 January 2016 in Beijing were performed with an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). The average concentration of NR-PM1 was 81.24 μg m−3, with the mean concentration in heavy haze days being 220.00 μg m−3, ∼3 times higher than that in light haze days and ∼15 times higher than that in clean days. Org was the most significant component of NR-PM1 species, accounting for 53% of the total NR-PM1 for the entire study. SO42− was also a significant component, accounting for 23% of the total NR-PM1. However, NO3−, NH4+ and Cl− composition together accounted for 25% of the total NR-PM1. All NR-PM1 species presented remarkably diurnal cycles in haze days, characterized by the highest concentrations occurring at midnight, and the lowest concentrations occurring at daytime. Note that the sulfur oxidation ratios were higher than the nitrogen oxidation ratios for the entire study, especially during the haze periods. The formation of sulfate was mainly affected by relative humidity (RH), while that of nitrate was more associated with NH3. Heterogeneous oxidation of NO2 on the surfaces of aerosol particles might be a significant pathway of nitrate formations during haze periods. NR-PM1 was mainly from secondary chemical reactions contributing 46.1%, vehicle emissions contributing 22.3%, coal combustion contributing 16.1%, and biomass burning contributing 15.6% in clean days. However, compared to the clean-day source contributions, the haze-day secondary source contribution to NR-PM1 increased to 66.8%, indicating that NR-PM1 in haze days was dramatically dominated by the secondary pollutants.

1. Introduction Atmospheric aerosol particulate matter is a very important component in the atmosphere, especially fine particles, and exerts highly significant impacts on radiation balance (Jacobson, 2001) and atmospheric visibility (Zhang et al., 2012), but also has extremely detrimental effects on human health (Chow et al., 2006). As the capital of China with a population of over 1300 million, Beijing, with the international exchanges of politics, economics and culture, as well as the rapid development of urbanization, has become a worldwide famous metropolis, but has experienced serious airborne particulate pollution, because of the rapid increases in both energy consumption and vehicle quantities. Although a series of atmospheric pollution control measures have been implemented in recent years, and atmospheric particulate pollution has already been alleviated to a certain extent, atmospheric fine particulate pollution episodes such as haze often happened due to

∗

adverse meteorological conditions and high pollutant emissions from anthropogenic activities, especially in winter. For example, there occurred haze events of 29 days in Beijing from December 2015 to January 2016, of which about more than half of these days were severe polluted days. It could be shown that Beijing has been suffering from serious atmospheric fine particulate pollution in recent years, especially submicron particulate matter PM1 with aerodynamic diameter ≤1 μm. PM1 is a dominant component of fine particulate matter PM2.5, accounting for about 70%, and has attracted much attention owing to more serious effects on the environment and human health in recent years. Lots of studies on characteristics, sources, and formation mechanisms of atmospheric fine particulate matter PM2.5 in Beijing, based on filter measurements, were carried out in the past decade (He et al., 2001; Sun et al., 2006; Han et al., 2016), but there were a few studies on PM1. So far, there are only several studies on characteristics of nonrefractory PM1 (NR-PM1) mass and chemical composition and sources

Corresponding author. E-mail address: [email protected] (L. Han).

https://doi.org/10.1016/j.atmosenv.2018.12.045 Received 31 March 2018; Received in revised form 21 November 2018; Accepted 25 December 2018 Available online 08 January 2019 1352-2310/ © 2019 Published by Elsevier Ltd.

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in the vacuum system, vaporizes with 100% efficiency, and is well focused by the aerodynamic lens. Since calibration of IEs for all ambient species is not possible, RIEs (relative ionization efficiency of species) which is the ionization efficiency of species relative to the ionization efficiency of the NO3 moiety of calibration with NH4NO3 particles is presented. However, it is noted that in the ACSM direct measurements of single particles are not possible due to the slow detection electronics (Ng et al., 2011). In fact, IE calibration of the ACSM is based upon measuring an instrument response factor (RF) using NH4NO3 aerosol with 300 nm mobility diameters. RFNO3 is measured in units of amps of NO3 signal (sum of NO+ and NO2+) per μg/m3 of sampled aerosol and is proportional to the ionization efficiency of NO3 (Ng et al., 2011). RFNH4 is also measured in units of amps of NH4 signal (sum of NH+, NH2+ and NH3+) per μg/m3 of sampled aerosol. RIENH4 is the ratio of RFNH4/RFNO3. So in this study, the RIE values usually used in the ACSM ambient concentration calculations are 6.89 and 0.7 for NH4 and SO4 respectively which were determined with pure ammonium nitrate and ammonium sulfate particles during the IE calibrations, while default 1.1, 1.3 and 1.4 for NO3, Cl and Org respectively.

of its organic aerosol (OA) in PM1 during 2011–2015 in the northern part of Beijing using an Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) or an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). NR-PM1 mainly comprises organics (Org), sulfate (SO42−), nitrate (NO3−), ammonium (NH4+) and chloride (Cl−), of which Org is major fraction of NR-PM1 species, and nitrate plays an important role in the high particulate matter pollution, due to the high relative humidity and excess gaseous ammonia (Sun et al., 2012). NR-PM1 species increase linearly with the change of relative humidity RH at low RH levels (< 50%), and the increase rates of most aerosol species are reduced at high RH levels (> 50%) (Sun et al., 2013a). Source emission, secondary formation, regional transport and stagnant meteorological conditions are major factors driving the formation and evolution of haze during wintertime (Sun et al., 2013b, 2015; Tang et al., 2016). It has been proved that atmospheric particulate pollution is generally severer in the southern than in the northern Beijing due to largely different economic development levels and pollution source emissions between them (Han et al., 2017), especially southeastern Beijing, situated in the downwind area of Beijing during wintertime. In order to better and more comprehensively characterize and understand submicron aerosol in Beijing, it is significant to investigate the atmospheric submicron particles in the southeastern Beijing and further reveal significant formation mechanisms of haze pollution. However so far, there is no related report on NR-PM1 species in the southeastern Beijing. In this study, an Aerodyne ACSM was deployed to have a real-time continuous measurement of NR-PM1 and its species during wintertime from 10 December 2015 to 30 January 2016 at a typical urban site in the southeast Beijing, and to investigate the chemical characteristics, evolution processes and sources of NR-PM1, and to further discuss formation mechanisms of its secondary species and impacts on haze pollution. The research will provide important scientific theoretical evidences for deeply understanding the formation mechanisms of haze and then controlling atmospheric pollution.

2.2. ACSM data analysis The ACSM data were analyzed for the mass concentrations and composition with the ACSM standard data analysis software written in Igor Pro. A collection efficiency (CE) of aerosol particles was presented mainly due to the effects of particulate bounce at vaporizer on measurement of particulate species. Since the ACSM instrument uses the identical aerodynamic lens and vaporizer design used in Aerodyne Aerosol Mass Spectrometer (AMS) instrument, CE values are similar to those observed in AMS measurement (Ng et al., 2011). For most field studies, CE = 0.5 is found to be representative with data uncertainties usually within ± 20% (Middlebrook et al., 2012). CE actually varies depending on the acidity of aerosol particles, aerosol composition, and particle phase water (Matthew et al., 2008). Since the aerosol particles were dried by a Nafion dryer before sampling into the ACSM system in this study and were overall neutralized in Beijing (Huang et al., 2010; Sun et al., 2010), the particle phase water and acidity played minor roles in affecting CE values. Middlebrook et al. (2012) developed a technique based on the measured inorganic constituents such as the ammonium nitrate in NR-PM1 to estimate the collection efficiency (CE), and suggested a CE of ∼45–50%. If calculated with a CE of 0.5, the NRPM1 mass concentrations of ∼50% of the data points in this study would exceed the related PM2.5 mass concentrations at Tiantan in Beijing state-controlled monitoring stations near the BJUT sampling site obtained from China national environmental monitoring centre, indicating that a CE of 0.5 was not suitable for this study because the significant organic components (∼53% of NR-PM1) could impact on the complete efflorescence of particles in the drier, reduce the particle bounce effect and increase the particle collection efficiency (Sun et al., 2016). Although the ratio of NR-PM1/PM2.5 is flexible, the ratio is often close 1 with a range of 0.64–0.74 (Sun et al., 2013b). Sun et al. (2016) suggested that a CE of 0.8 could be chosen based on the comparison of the NR-PM1 measurements and PM2.5 measured independently. If a CE of 0.8 was chosen in this study, the average ratio (0.69) of NR-PM1 mass loadings to PM2.5 mass measurements at the Tiantan state-controlled monitoring site is consistent with the results (0.74) in previous studies (Sun et al., 2013b). In addition, the m/z dependent ion transmission efficiency (TE) corrections of the quadrupole are also needed for the ACSM mass concentration calculations because the TE value decreases sharply as a function of m/z beyond a low m/z range (m/z < 50) in which it is relatively constant 1. The TE correction is performed with the internal standard naphthalene situated into the detection chamber of ACSM. The detailed corrections for the TE have been shown in Ng et al. (2011). Positive matrix factorization (PMF) is a multivariate factor analysis model widely used in air pollution source apportionment. The PMF (v

2. Experimental 2.1. Sampling The NR-PM1 species including Org, SO42−, NO3−, NH4+ and Cl− were measured in situ from 10 December 2015 to 30 January 2016 by an Aerodyne ACSM at Beijing University of Technology (BJUT), which is located between the southeastern 3rd and 4th ring road in Beijing. The sampling site is located on the roof of the 7th floor in the Building of College of Environmental and Energy Engineering, about ∼20 m height, and without any stationary pollution sources nearby. The ACSM is specially planned to measure the mass and composition of non-refractory submicron particle for long-term periods (Ng et al., 2011). The ACSM can determine sample and background values by alternating sample flow between ambient air and particle-filtered air which is controlled by a automated 3-way switching valve. The sample flow into the ACSM instrument through an aerodynamic particle focusing lens is approximately 85 cc/min, which is fixed by a 100 μm diameter critical orifice mounted at the entrance of the aerodynamic lens. The mass analyzer employed in ACSM has a scan range of 10–150 amu. During this study, the ACSM was operated at a time resolution of approximate 15 min. The detailed descriptions for ACSM have been given in Ng et al. (2011). All the data in this study are reported in Beijing Standard Time. In order to assure data quality, ionization efficiency (IE, in units of ions/molecule) of the ACSM should be calibrated so as to convert the measured mass spectra ion signals to aerosol mass with DMA-size-selected (Dm = 300 nm) pure ammonium nitrate particles using a combination of differential mobility analyzer (DMA) and condensation particle counter (CPC) (Ng et al., 2011). Ammonium nitrate is used as the calibration aerosol because it has minimal fragmentation (NO+, NO2+, NH+, NH2+, NH3+), does not leave residual background levels 361

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loading (< 52 μg m−3), low AQI (< 100), high visibilities (> 10 km) and favorable meteorological conditions for the dispersion of atmospheric pollutants such as high wind speed WS (∼2.07 m/s) with prevailing northerly and northwesterly winds, low relative humidity RH. The light haze days showed higher NR-PM1 mass (52–104 μg m−3), higher AQI (100–200), lower visibilities (5–10 km), as well as low WS (∼1.47 m/s) with dominating northeasterly and easterly winds, and higher RH which are unfavorable factors for the dispersion of air pollutants. The heavy haze days displayed much higher NR-PM1 mass concentrations (> 104 μg m−3), high AQI (> 200), much lower visibilities (< 5 km), as well low WS (∼1.21 m/s) from prevailing easterly, northeasterly and southwesterly winds and high RH unfavorable for the dispersion of air pollutants. The average NR-PM1 concentration was 220.00 ( ± 82.80) μg·m−3 with a range of 104.20–453.45 μg m−3 in heavy haze days, being ∼3 times higher than that (75.95 ± 14.44 μg m−3) in light haze days and ∼15 times higher than that (14.71 ± 13.11 μg m−3) in clean days, indicating that the stagnant meteorological factors with low WS and high RH are in favor of the accumulations and secondary chemical formations of submicron particles. The average diurnal cycles of NR-PM1 mass concentrations during the clean periods and haze periods for the entire study are shown in Fig. 3, usually, in clean days, showing a flat pattern with a small peak mass concentration at 20:00 in the evening, likely due to cooking and driving home. However, NR-PM1 mass loadings in haze days often presented a pronounced diurnal cycle with the highest peak concentration of 185.75 μg m−3 occurring at 23:00 during nighttime, then a gradual decrease at a rate of 7.21 μg m−3·h−1 after 23:00, a small peak mass concentration occurring between 12:00 and 14:00 in the early afternoon, and the lowest mass loading of 97.31 μg m−3 occurring at 15:00 in the afternoon, then a linear increase at a rate of 14.01 μg m−3·h−1 after 17:00 in the evening, which could be likely due to the relative high temperature, intensity solar radiation and high O3 concentrations between 12:00 and 14:00 being in favor of the atmospheric photochemical reactions of precursors from anthropogenic activities, the shallower boundary layer and inversion layer easily formed after sunset favorable for the accumulations of particles from enhanced anthropogenic activities such as driving home and dinner in the evening, and the low temperatures, high RH, disappearing inversion layer, and a few anthropogenic activities after midnight favorable for the diffusion and reductions of atmospheric pollutants. These suggest that the anthropogenic activities could accelerate the formations of haze days owing to unfavorable meteorological factors.

5.0) algorithm was applied in this study to data obtained with the ACSM, including 14 species such as NR-PM1, SO42−, NO3−, NH4+ and Cl−, and significant fragment ions such as m/z 27, 29, 41, 43, 44, 55, 57, 60 and 69 which could be considered as the representative source tracer ions, to recover potential components from different sources and processes. The chosen 14 species, due to their signal-to-noise ratios being greater than 2, were all strong. The uncertainties of their data inputted into the PMF model were calculated according to the following equations. If the concentration was less than or equal to the method detection limit (MDL) provided, the uncertainty (Unc) was calculated using a fixed fraction of the MDL

Unc =

5 × MDL 6

If the concentration was greater than the MDL provided, the calculation was based on a user provided fraction of the concentration and MDL

Unc =

(Error Fraction × Concentration)2 + (0.5 × MDL)2

In running the PMF algorithm, 3–10 factors were tried and optimized for several times, and 5 factors were finally determined to explain the source apportionment reasonably. At the moment, the results of the PMF analysis were stable and most of the residual values were distributed between −3 and +3 (Wang et al., 2015). And then possible rotations could be explored by varying FPEAK value between −1 and +1 to find that the impact of the variation of FPEAK value on the result of the PMF analysis was not obvious. Therefore, the result of the PMF analysis with FPEAK = 0 was chosen (Reff et al., 2007). 2.3. Meteorology data and atmospheric pollutant concentrations The hourly average meteorology data including relative humidity, temperature and wind speed during this study were obtained from China air quality online monitoring and analysis platform (https:// www.aqistudy.cn/). The hourly average mass concentrations of atmospheric pollutant such as sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and PM2.5, and air quality index AQI were obtained from the website of China national environmental monitoring centre (http:// 106.37.208.233:20035/). 3. Results and discussion 3.1. Mass concentrations of NR-PM1

3.2. Mass concentrations of NR-PM1 species

The time series of 1-h-average mass concentrations of NR-PM1 during the period from 10 December 2015 to 30 January 2016 is shown in Fig. 1. The average concentration of NR-PM1 during this period was 81.24 ( ± 91.61) μg·m−3 with a range from 1.90 to 453.45 μg m−3, higher than those of 66.8 μg m−3 and 34.1 μg m−3 during winter periods of 2012 and 2014 (Sun et al., 2013b; Zhang et al., 2016a), respectively, and 49.9 μg m−3 in summer 2011 (Sun et al., 2012), slightly lower than that of 89.3 μg m−3 during the same period of 2013 with a record-broking haze episode (Zhang et al., 2014), as shown in Table 1, indicating that the airborne submicron aerosol pollution level is still severe during wintertime in Beijing. The NR-PM1 mass loadings measured by the ACSM tracked well the PM2.5 mass concentrations at the Tiantan state-controlled mornitoring site near the BJUT sampling site with the correlation coefficient r of 0.93, as shown in Fig. 2, and accounted for ∼69% of PM2.5 mass concentrations, suggesting that the NR-PM1 is the significant fraction of PM2.5. In addition, the NR-PM1 mass concentrations varied very remarkably and were strongly associated with meteorological factors during the entire study, as shown in Fig. 1 and Table 2, with three typical days including clean days, light haze days and heavy haze days based on Technical Regulation on Ambient Air Quality Index (on trial) (HJ 633–2012). The clean days presented low NR-PM1 mass

The average mass concentrations and fractions of all NR-PM1 species in Beijing in winter from 10 December 2015 to 30 January 2016 are shown in Table 1, of which Org was the most significant component with the average concentration being 42.74 ( ± 48.58) μg·m−3 and accounting for 53% of the total NR-PM1 for the entire study, SO42− was also a significant component with the average concentration being 18.38 ( ± 25.91) μg·m−3 and accounting for 23% of the total NR-PM1, however, NO3−, NH4+ and Cl− composition with the average concentrations of 8.87 ( ± 8.87) μg·m−3, 7.22 ( ± 8.03) μg·m−3 and 4.02 ( ± 4.93) μg·m−3, accounted for 11%, 9% and 5% of the total NR-PM1, respectively. The average concentrations of all NR-PM1 species detected here are all higher than corresponding those observed in winter 2014 (Zhang et al., 2016a,b), respectively, as shown in Table 1, and very close to related those reported in winter 2013 with the record-broking haze event (Zhang et al., 2014). It is noted that the average concentrations of Org component in winters from 2012 to 2016 were generally higher than that in summer 2011, as shown in Table 1, and their contributions to the NR-PM1 mass loading were more than 50%, higher than that of 40% in summer 2011. The average concentrations of inorganic species like SO42− and Cl− in winters 2012–2016, similar to 362

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Fig. 1. Time series of (a) temperature(T) and relative humidity (RH); (b) wind speed (WS) and wind direction (WD); (c) AQI and visibility(V); (d) NR-PM1 and its species mass concentrations.

Org, were often higher than those in summer 2011, and their mass fractions were typically more than 20% and 4%, higher than those in summer, respectively. However, the average mass concentrations of NO3− in winters 2012–2016, except winter 2013 with a record-broking haze episode, were lower than that in summer 2011, with their contributions to the NR-PM1 mass being also lower than that in summer. Compared to summer 2011, NH4+ average mass concentrations in winters 2012–2016 hardly present obvious trend, but its mass fractions were all evidently lower than that in summer 2011. These results suggest that the wintertime NR-PM1 is dramatically characterized by Org, SO42− and Cl− species, the summertime NR-PM1 is mainly characterized by Org, NO3− and NH4+ species, and the average concentration of Org component in winter is higher than that in summer, likely owing to the intensified emissions of coal combustion during the heating period in winter and their secondary chemical reactions. The average concentrations of all NR-PM1 species in three pattern days are shown in Table 2. The average concentration of Org component in heavy haze days was 110.85 ( ± 49.68) μg·m−3 with a range of 35.70–266.02 μg m−3, ∼3 times higher than that in light haze days,

Fig. 2. Correlation between NR-PM1 and PM2.5 mass loadings.

Table 1 Mass concentrations and fractions of NR-PM1 and its species in this study and previous studies. Season

Winter

Summer a

2016 2014 2013 2012 2011

NR-PM1

Org

μg·m−3

μg·m−3/%a

81.24 34.1 89.3 66.8 49.9

42.74/52.61 20.1/59 44.7/50 34.4/52 20.0/40

SO42-

NO3−

NH4+

Cl−

References

18.38/22.63 6.8/20 19.6/22 9.3/14 9.0/18

8.87/10.92 3.4/10 12.5/14 10.9/16 12.4/25

7.22/8.89 2.4/7 8.9/10 8.6/13 8.0/16

4.02/4.95 1.4/4 3.6/4 3.5/5 0.5/1

this study Zhang et al. (2016a) Zhang et al. (2014) Sun et al. (2013b) Sun et al. (2012)

Mass concentration/mass fraction. 363

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showed the largest increase in all NR-PM1 species in heavy haze days, indicating that the meteorological factors like lower WS and high RH during heavy haze periods are more favorable for the formation of SO42−. The average diurnal profiles of all NR-PM1 species during clean periods and haze periods for the entire study are shown in Fig. 3. Org component, remarkably similar to NR-PM1, displayed a flat pattern with a small peak concentration at 20:00 in clean days, and a pronounced diurnal cycle in haze days such as the highest concentration of 100.90 μg m−3 occurring at 23:00 during nighttime, then a slow decrease at a rate of 4.30 μg m−3·h−1 after 23:00, a small peak at 12:00–14:00 in the early afternoon, and the lowest value of 44.53 μg m−3 occurring at 15:00 in the afternoon, then a gradual increase at a rate of 9.14 μg m−3·h−1 after 17:00 in the evening. SO42− component, also very similar to NR-PM1, presented a very flat pattern in clean days, and also an obvious diurnal variation with the highest concentration of 41.27 μg m−3 at 23:00 in nighttime, then a slow decrease at a rate of 0.62 μg m−3·h−1 after 23:00, the lowest value of 23.63 μg m−3 at 11:00 in the morning, and a small peak at 12:00–14:00 in the early afternoon, then a linear increase with a growth rate of 2.65 μg m−3·h−1 after 17:00 in the evening in haze days. However, both NO3− and NH4+ showed the same diurnal cycles in clean and haze days, with a relatively flat pattern with two tiny peak concentrations at 16:00 in the afternoon and 20:00 in the evening in clean days, and the highest mass concentrations of 17.76 and 15.17 μg m−3 at 23:00 during nighttime, then slow decreases at the rates of 0.62 and 0.55 μg m−3·h−1 after 23:00, the lowest values of 11.56 and 9.16 μg m−3 at 9:00 and 10:00 in the morning, and small peak concentrations at 12:00–14:00 in the early afternoon, then slow increases at the rates of 0.48 and 0.63 μg m−3·h−1 after 15:00 in the afternoon, respectively, in haze

Table 2 Summary of meteorological factors and mass concentrations of gaseous pollutants, NR-PM1 and its species in three typical days. Factors

Clean days

Light haze days

Heavy haze days

Temperature (°C) Relative humidity (%) Wind speed (m/s) Visibility (km) SO2 (μg·m−3) NO2 (μg·m−3) O3 (μg·m−3) NR-PM1 (μg·m−3) Org (μg·m−3) SO42− (μg·m−3) NO3− (μg·m−3) NH4+ (μg·m−3) Cl− (μg·m−3)

−3.5 30.6 2.1 15.4 11.69 45.78 30.11 14.71 8.65 2.37 1.81 1.35 0.53

−2.8 55.1 1.5 5.9 35.42 86.01 6.54 75.95 37.42 16.18 9.98 7.62 4.75

−1.4 84.4 1.2 2.8 12.79 114.33 3.92 220.00 110.85 57.55 22.80 19.09 9.72

and ∼13 times higher than that in clean days. The average concentrations of secondary inorganic species such as SO42−, NO3− and NH4+ in heavy haze days were 57.55 ( ± 28.02) μg·m−3 with a range from 11.75 to 139.65 μg m−3, ∼4 times higher than that in light haze days, and ∼24 times higher than that in clean days; 22.80 ( ± 5.07) μg·m−3 with a range of 9.11–34.97 μg m−3, ∼2 times higher than that in light haze days, and ∼13 times higher than that in clean days; and 19.09 ( ± 6.49) μg·m−3 with a range of 8.19–42.42 μg m−3, ∼3 times higher than that in light haze days, and ∼14 times higher than that in clean days, respectively. The average Cl− mass concentration in heavy haze days was 9.72 ( ± 5.54) μg·m−3, having a range of 1.98–34.07 μg m−3, ∼2 times higher than that in light haze days, and ∼18 times higher than that in clean days. It has been seen that SO42−

Fig. 3. Average diurnal profiles of meteorological factors, O3, NR-PM1 species, SOR and NOR during haze periods and clean periods for the entire study. 364

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In addition, SOR was tightly related to NH4+ and NO2 in haze days, as presented in Table 3, with their correlation coefficients being 0.62 and 0.40 during daytime, and 0.74 and 0.47 during nighttime, respectively, indicating that atmospheric NH3 and NO2 also play the significant roles in the sulfate formations, which is consistent with a previous study by Wang et al. (2016). It is noted that the correlation coefficients between SOR and SO2/O3 during daytime and nighttime were negative, as illustrated in Table 3, likely due to the sulfate formations reducing atmospheric SO2 and O3 mass concentrations. Overall, the sulfate formations are positively affected by RH, NH3 and NO2 with the order of RH > NH3 > NO2, and negatively influenced by SO2 and O3 at day and night during wintertime. It is noted that the impact levels of the factors above on sulfate formations are generally much higher during nighttime than daytime, and haze periods than clean periods. Heterogeneous chemical reactions of SO2 on the surfaces of aerosol particles might be a significant pathway of sulfate formations during haze periods, which is consistent with our previous result (Han et al., 2007).

days, which could be likely related to the better correlation between NO3− and NH4+ of NR-PM1 species. The diurnal cycle of Cl− in haze days was slightly different from NO3− and NH4+, with the highest concentration of 10.66 μg m−3 occurring at 23:00 in nighttime, then a slow decrease at a rate of 0.54 μg m−3·h−1 after 23:00, a very small peak at 12:00 noon, the lowest concentration of 3.80 μg m−3 at 15:00 in the afternoon, then a linear increase with a growth rate of 1.08 μg m−3·h−1 after 17:00 in the evening. It should be noted that all NR-PM1 species demonstrated outstandingly similar diurnal cycles which were characterized by the highest concentrations occurring at midnight, then gradual decreases at the rates following the order of Org > SO42− > NO3− > NH4+ > Cl− after midnight, the lowest values occurring at daytime, and small peaks in the early afternoon, then linear increases at the rates following the order of Org > SO42− > Cl− > NH4+ > NO3− in the evening in haze days, and flat patterns with the tiny peaks in the evening in clean days. These results indicate that the diurnal profiles of all NR-PM1 species are intensively affected by inversion layer related to temperature, low WS and relatively high RH during nighttime and solar radiation related to temperature, photochemical oxidant O3, relatively higher WS and lower RH during daytime, and anthropogenic activities.

3.3.2. Effects on the nitrate formation NO3− is also an important component of NR-PM1 secondary species, and its formation mechanisms on fine particles remain unclear. Nitrogen oxidation ratio NOR defined as NOR = n-NO3-/(n-NO3- + nNO2), of which n refers to the molar concentration, could also display the oxidation degree of nitrogen such as the conversion of NO2 to NO3− (Zhang et al., 2016a,b). The average diurnal profiles of NORs in clean and haze days for the entire study are shown in Fig. 3. In clean days, NOR exhibited an obvious diurnal variation for the entire study, with the NORs during daytime usually being higher than those during nighttime and the highest NOR occurring at 14:00 in the afternoon, likely due to the intense solar radiation during daytime accelerating the OH radical formation and conversion of NO2 to gaseous HNO3 which further reacts with ammonia to form ammonium nitrate. These reactions could lead to high NORs. However, NO3 formed from the reaction between NO2 and O3 during nighttime reacts with NO2 to produce N2O5 which reacts with H2O to form NO3− (Seinfeld and Pandis, 2006), as shown below:

3.3. Impacts on the formations of sulfate and nitrate 3.3.1. Effects on the sulfate formation SO42− is an important species of NR-PM1, as mentioned above, and also a significant component of secondary species, but its formation is quite complicated because of influences of multiple variables. Sulfur oxidation ratio SOR defined as SOR = n-SO42-/(n-SO42- + n-SO2), of which n refers to the molar concentration, could dramatically express the degree of oxidation of sulfur (Zhang et al., 2016a,b). In order to further investigate the formation mechanisms of sulfate, the average diurnal variations of SOR during clean and haze periods for the entire study could be calculated, as shown in Fig. 3. SOR in clean days presented a relatively flat pattern, far lower than that in haze days due to the lower RH (RH < 30%) in clean days. Note that SOR values at nighttime were usually a little higher than those at daytime, which could be likely owing to night-time oxidants such as nitrate radical NO3 and O3, as well as relatively high RH favorable for the conversion of SO2 to SO42−, being consistent with correlations of SOR with RH and O3, as shown in Table 3, and the correlations between SOR and RH/O3 at nighttime were basically better than those at daytime. SOR in haze days showed a pronounced diurnal cycle, with SORs at nighttime being much higher than those at daytime. One of the reasons was due to high RH that facilitated the heterogeneous reactions of SO2 at nighttime. This is consistent with the better correlations between SOR and RH with r = 0.82 and 0.86 during daytime and nighttime in haze days, respectively, shown in Table 3.

Factor

Haze periods

Clean periods

SOR

NOR

SOR

NOR

Daytime

RH SO2 NO2 O3 NH4+

0.82 −0.50 0.40 −0.51 0.62

0.62 0.05 0.55 −0.55 0.82

0.61 −0.34 0.26 −0.31 0.33

0.40 0.61 0.50 −0.15 0.83

Nighttime

RH SO2 NO2 O3 NH4+

0.86 −0.68 0.47 −0.43 0.74

0.76 −0.26 0.50 −0.55 0.82

0.75 −0.42 0.31 −0.50 0.67

0.64 0.39 0.62 −0.54 0.92

(1)

NO2+ NO3→N2O5

(2)

N2O5+H2O→2HNO3

(3)

The reactions could result in lower NORs. It is shown that the photochemical reaction of NO2 is likely a significant pathway of nitrate formation in clean days. Furthermore, NOR presented obvious correlations with RH, NH3, SO2 and NO2 with an order of NH3 > SO2 > NO2 > RH at day, as shown in Table 3, indicating that nitrate formations are mainly influenced by NH3 > SO2 > NO2 > RH at day. Then NOR displayed better correlations with RH, NH3, SO2 and NO2 with an order of NH3 > RH > NO2 > SO2, and negative correlation with O3 at night, suggesting that nitrate formations are mainly influenced by NH3 > RH > NO2 > SO2, and negatively by O3 at night in clean days. Compared with clean days, haze-day NOR presented a contrary diurnal variation, with NORs during nighttime being higher than those during daytime, mainly because weak solar radiation and relatively lower RH during daytime in haze days are unfavorable to the NO3− formation, but relatively rich NO3 and high RH during nighttime are favorable for the NO3− formation. For example, NO2 can react with H2O absorbed on the surface of fine particle to produce HNO3 and HNO2 during nighttime (Han et al., 2014), of which HNO3 further reacts with alkali as NH3/CaCO3 to produce NO3−, as shown below:

Table 3 Correlation coefficients between SOR/NOR and main factors during daytime and nighttime for the haze periods and clean periods. Time

NO2+O3→NO3+O2

365

NO2+H2O→HNO3+HNO2

(4)

HNO3+NH3→NH4NO3

(5)

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HNO3+ CaCO3→CaCO3+CO2+H2O

(6)

Note that NOR showed a good correlation with RH, i.e. r = 0.62 and 0.76 during daytime and nighttime, respectively, as shown in Table 3, indicating RH plays an important role in the nitrate formations at day and night, and then heterogeneous oxidation of NO2 on the surfaces of aerosol particles might be a significant pathway of nitrate formations during haze periods. Besides RH, NOR also exhibited distinct correlations with NH4+ and NO2 at day and night during haze periods, as shown in Table 3, with the correlation coefficients being 0.82 and 0.55 during daytime, and 0.82 and 0.50 during nighttime, respectively, suggesting that NH3 and NO2 showed important impacts on the NO3− formation, which was obviously related to basic NH3 neutralizing acid HNO3 to produce NH4NO3, as well as NO2 acting as precursors of O3 and nitrate. Moreover, NOR presented negative correlation with O3, likely due to the nitrate formations reducing atmospheric O3 mass concentrations. It is clear that the nitrate formations are mainly influenced by NH3, RH, and NO2 with an order of NH3 > NO2 > RH at day, and an order of NH3 > RH > NO2, as well as negatively by O3 at night in clean days. It is noted that the photochemical reactions of NO2 are likely a significant pathway of nitrate formations in clean days. However in haze days, nitrate formations are largely influenced by NH3, RH and NO2, with an order of NH3 > RH > NO2, and negatively by O3 at day and night. Heterogeneous reactions of NO2 on the surfaces of aerosol particles might be a significant pathway of nitrate formations during haze periods.

Fig. 4. Source profiles of NR-PM1 resolved during clean periods.

3.4. Source apportionment of NR-PM1 The NR-PM1 species measured by the Aerodyne ACSM include Org, SO42−, NO3−, NH4+ and Cl−, of which Org is the major fraction accounting for 53%, as shown in Table 1, and often called organic aerosol OA. OA often consists of hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), coal combustion OA (CCOA), and oxygenated OA (OOA). In ACSM mass spectra of OA, m/z 27, 29, 41, 43, 44, 55, 57 and 60 are often significantly representative source tracer fragment ions, among which m/z 27, 41 and 55, typical of cycloalkanes CnH2n-1, and m/z 29, 43 and 57, typical of alkanes CnH2n+1 (McLafferty and Turecek, 1993) are mainly characterized by fragment ions such as C2H3+, C3H5+ and C4H7+, and C2H5+, C3H7+ and C4H9+, respectively, largely from hydrocarbon, representing HOA which mainly comes from vehicle emissions (Sun et al., 2013b; Zhang et al., 2005a, 2005b); m/z 55 and 57 with a higher ratio of m/z 55/57 are mainly characterized by fragment ions as C3H3O+, C4H7+ and C3H5O+, C4H9+, respectively, representing COA mainly from cooking emission (Sun et al., 2013b); m/ z60 is mainly fragment ion of polyhydroxylic compounds such as levoglucosan, and often considered to be a tracer ion of BBOA (Alfarra et al., 2007; Aiken et al., 2009); however, m/z 60 with a much lower fraction of m/z 60 is also considered to be a tracer ion of CCOA together with chloride due to the tight correlations between CCOA and two combustion tracers as m/z 60 and chloride, respectively (Sun et al., 2013b; Sun et al., 2014a); and m/z44 is mainly characterized by ion of CO2+ basically from carboxyl group, typical of oxygenated OA (OOA) (Zhang et al., 2005a, 2005b). PMF analyses of ACSM species, i.e., SO42−, NO3−, NH4+, Cl− and NR-PM1 together with the typical fragment ions such as m/z 27, m/z 29, m/z 41, m/z 43, m/z 44, m/z 55, m/z 57, m/z 60 and m/z 69 resolved five factors in both clean days and haze days for the entire study, as shown in Figs. 4 and 5. In clean days, the factor 1 was mainly characterized by m/z 60 with the highest explained variation (EV) of 57.34%, although m/z 27, m/z 29, m/z 41, and m/z 55 related to HOA from vehicle-related source, and m/z 44 mainly associated with OOA also displayed higher EVs of 36.2%, 38.0%, 30.8%, 31.5% and 29.9% in the factor 1, respectively, they were here largely associated with BBOA, because firstly, m/z 27, m/z 29, m/z 41 and m/z 55 showed very tight correlations with m/z 60,

Fig. 5. Source profiles of NR-PM1 resolved during haze periods.

such as 0.94, 0.93, 0.94, 0.94, respectively; secondly, m/z 57, due to a higher ratio of m/z 55/57 (4.93), typical of COA mainly from cooking emissions, had very low EV of 8.42% in the factor 1, and Cl−, a significant indicator of coal combustions (Yao et al., 2002; ,b), presented a quite low EV of 0.00%; thirdly, m/z 44 showed better correlation with m/z 60, i.e., 0.78, and is related to biomass comprising lots of hydroxyl groups, indicating that the factor 1 represented BBOA, contributing 15.6% to NR-PM1. The factor 2 was characterized by SO42− with the highest EV of 74.60%, suggesting that the factor 2 was related to secondary chemical reactions, contributing 22.6% to NR-PM1. Cl− showed the highest EV of 73.71% in the factor 3, and better correlation of 0.89 with m/z 60 with higher EV of 42.7%, illustrating that the factor 3 was 366

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related to coal combustion and contributing 16.1% to NR-PM1. The factor 4 was dominated by NO3−, NH4+ and m/z 44 with high EVs of 67.5%, 54.7% and 54.0%, respectively, verifying that the factor 4 was highly related to secondary photochemical reactions and the contribution to NR-PM1 was 23.5%. In the factor 5, m/z 55, m/z 57 and m/z 69 showed high EVs of 42.1%, 57.6% and 49.9%, respectively, being surrogates of HOA due to a lower ratio of m/z 55/57 (0.97 < 1), however, SO42−, NO3−, NH4+ and Cl− displayed very low EVs of 5.0%, 0.0%, 8.0% and 0.0%, respectively, indicating that the factor 5 presented HOA from vehicle emission, contributing 22.3% to NR-PM1. As shown above, NR-PM1 composition was mainly originated from four significant sources, i.e., secondary chemical reactions, contributing 46.1% to NR-PM1, vehicle emissions dominated by HOA with the contribution of 22.3% to NR-PM1, coal combustion, contributing 16.1% to NR-PM1, and biomass burning characterized by BBOA with the contribution of 15.6% to NR-PM1 in clean days for the entire study. In haze days, as presented in Fig. 5, the factor 1 was characterized by NO3− and m/z 44 with high EVs of 54.0% and 56.8%, indicating that the factor 1 was related to secondary photochemical reactions of gaseous pollution matter from vehicle emissions, contributing 22.0% to NR-PM1. The factor 2 was dominated by NH4+ presenting the highest EV of 56.5%, illustrating that the factor 2 was also related to secondary chemical reactions of NH3 from NH3 emissions and with the contribution of 8.0% to NR-PM1. SO42− showed the highest EV of 68.6% in the factor 3, suggesting that the factor 3 was also related to secondary chemical reactions of SO2 mainly from coal combustion and with the contribution of 36.7% to NR-PM1. The factor 4 was characterized by m/ z 41, m/z 43, m/z 55, m/z 57 and m/z 69 with high EVs of 42.9%, 41.6%, 43.7%, 53.8% and 47.7%, respectively, but SO42−, NO3−, NH4+ and Cl− presented very low EVs of 4.2%, 10.4%, 0.0% and 0.0%, respectively, implying that the factor 4 was tightly related to HOA from vehicle emissions and with the contribution of 20.6% to NR-PM1. In the factor 5, Cl− and m/z 60 showed high EVs of 55.1% and 39.6%, respectively, and better correlation of 0.91, indicating that the factor 5 was mainly associated with coal combustion, contributing 12.6% to NRPM1. Compared to clean days, the source contributions to NR-PM1 changed obviously in haze days for the entire study, of which the secondary source contribution to NR-PM1 increased from 46.1% to 66.8%, implying that NR-PM1 in haze days was dramatically dominated by the secondary pollutants.

(4)

(5)

(6)

(7)

higher than that in clean days, indicating that SO42− mass concentration increased fastest in severe haze days. The average diurnal profiles of all NR-PM1 species, similar to NRPM1, presented flatter patterns in clean days, and remarkably diurnal cycles in haze days, which were characterized by the highest concentrations occurring at midnight, then gradual decreases at the rates following the order of Org > SO42− > NO3− > NH4+ > Cl− after midnight, the lowest concentrations occurring at daytime, and small peaks in the early afternoon, then linear increases at the rates following the order of Org > SO42− > Cl− > NH4+ > NO3− in the evening. The sulfur oxidation ratios were higher than the nitrogen oxidation ratios for the entire study, especially during the haze periods. The sulfate formations are positively affected by RH, NH3 and NO2 with the order of RH > NH3 > NO2, and negatively influenced by SO2 and O3 at day and night during wintertime. The impact levels of these factors on sulfate formation are generally much higher during nighttime than daytime, and haze periods than clean periods. Heterogeneous chemical reactions of SO2 on the surfaces of aerosol particles are a significant pathway of sulfate formations during haze periods. Nitrate formations are mainly influenced by RH, NH3, and NO2 with an order of NH3 > NO2 > RH at day, and an order of NH3 > RH > NO2, as well as negatively by O3 at night in clean days. Photochemical reactions of NO2 are likely a significant pathway of nitrate formation in clean days. However in haze days, nitrate formations are largely influenced by RH, NH3 and NO2, with an order of NH3 > RH > NO2, and negatively by O3 at day and night. Heterogeneous reactions of NO2 on the surfaces of aerosol particles are a significant pathway of nitrate formations during haze periods. In clean days, the secondary source contributed 46.1% to NR-PM1, vehicle emissions characterized by HOA contributed 22.3% to NRPM1, coal combustion contributed 16.1% to NR-PM1, and biomass burning dominated by BBOA contributed 15.6% to NR-PM1. Moreover, compared to the clean-day source contributions, the haze-day secondary source contribution to NR-PM1 increased to 66.8%, indicating that NR-PM1 in haze days was dramatically dominated by the secondary pollutants.

Acknowledgements

4. Conclusion

This work was supported by the Ministry of Environmental Protection Special Funds for Scientific Research on Public Causes (No. 201409003-4), the project of China Scholarship Council (No. 201406545022), and the project of Beijing Municipal Education Commission (No. PXM2016_014204_001029_00205967_FCG).

(1) The average concentration of NR-PM1 was 81.24 μg m−3 during this study period, with the mean concentration in heavy haze days being 220.00 μg m−3, ∼3 times higher than that in light haze days and ∼15 times higher than that in clean days. The average diurnal cycles of NR-PM1 mass concentrations for the entire study showed a relatively flat pattern in clean days, and a pronounced diurnal cycle in haze days with the highest peak concentration occurring at midnight, then a gradual decrease at the rate after midnight, the lowest concentration occurring at daytime, and a tiny peak in the early afternoon, then a linear increase at the rate in the evening. (2) Org was the most significant component of NR-PM1 species with the average concentration being 42.74 μg m−3 and accounting for 53% of the total NR-PM1 for the entire study, SO42− was also a significant component with the average concentration being 18.38 μg m−3 and accounting for 23% of the total NR-PM1, however, NO3−, NH4+ and Cl− components with the mean concentrations of 8.87 μg m−3, 7.22 μg m−3 and 4.02 μg m−3, respectively, together accounted for 25% of the total NR-PM1. (3) The average concentrations of NR-PM1 species in heavy haze days were generally 2–4 times higher than those in light haze days, and 13–24 times higher than those in clean days, among which the mean concentration of SO42− in heavy haze days was 24 times

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