pH of Aerosols in a Polluted Atmosphere: Source Contributions to

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pH of Aerosols in a Polluted Atmosphere: Source Contributions to Highly Acidic Aerosol Guo-liang Shi, Jiao Xu, Xing Peng, Zhimei Xiao, Kui Chen, Yingze Tian, Xinbei Guan, Yinchang Feng, Haofei Yu, Athanasios Nenes, and Armistead G. Russell Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05736 • Publication Date (Web): 17 Mar 2017 Downloaded from http://pubs.acs.org on March 18, 2017

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Environmental Science & Technology

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pH of Aerosols in a Polluted Atmosphere: Source

2

Contributions to Highly Acidic Aerosol

3 *

4

Guoliang Shi †,§ *, Jiao Xu †, Xing Peng †, Zhimei Xiao ‡, Kui Chen ‡,

5

Yingze Tian †, Xinbei Guan §, Yinchang Feng †, Haofei Yu §, Athanasios Nenes ||,

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Armistead G. Russell § *

*

7 8 9 10 11 12 13 14 15 16 17 18

†

State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China ‡

Environmental Monitoring Center of Tianjin School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0512 §

||

Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332

*Corresponding Author: Phone: +86 22 23507962. Fax: +86 2223503397. E-mail: [email protected], [email protected]

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1

ACS Paragon Plus Environment


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ABSTRACT

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Acidity (pH) plays a key role in the physical and chemical behavior of PM2.5. However, understanding of how specific PM sources impact aerosol pH is rarely considered. Performing source apportionment of PM2.5, allows a unique link of sources pH of aerosol from polluted city. Hourly water-soluble (WS) ions of PM2.5 were measured online from December 25th 2014 to June 19th 2015 in a northern city in China. Five sources were resolved including secondary nitrate (41%), secondary sulfate (26%), coal combustion (14%), mineral dust (11%) and vehicle exhaust (9%). The influence of source contributions to pH was estimated by ISORROPIA-II. Lowest aerosol pH levels were found at low WS-ion levels, then increased with increasing total ion levels, until high ion levels occur, at which point the aerosol becomes more acidic as both sulfate and nitrate increase. Ammonium levels increased nearly linearly with sulfate and nitrate until approximately 20 µg m-3, supporting that the ammonium in the aerosol was more limited by thermodynamics than source limitations, and aerosol pH responded more to the contributions of sources such as dust than levels of sulfate. Commonly used pH indicator ratios were not indicative of the pH estimated using the thermodynamic model.

2


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36 37 38

TOC art

39 40

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■INTRODUCTION

42

PM2.5 (Particulate matter with aerodynamic diameter less than or equal to 2.5 µm) is of

43

concern in many cities across the world1, 2. Numerous studies have found that PM2.5 can have

44

substantial adverse effects, direct and indirect, on human health, atmospheric visibility,

45

ecosystem health and global climate change3-6. Acidity, i.e. pH, is one of the key

46

characteristics of PM2.57-9. Acidity plays an important role in the physical and chemical

47

behavior of PM2.5, and has important implications in human and ecosystem health, secondary

48

aerosol formation, aerosol hygroscopic property, and acid rain10-13.

49

Despite its importance, the pH of PM is mostly unknown, as it cannot be directly

50

measured. Studies often use pH proxies based on ion balances and molar ratios, which

51

however can be subject to considerable uncertainty7. An emerging body of evidence suggests

52

that pH acts counter to established thinking derived from these pH proxies9. Given this and the

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key role pH plays, it is important to characterize the emission sources, behavior, and formation

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mechanisms of constituents that regulate particle pH levels14. Some studies report the sources

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(such as soil, coal or secondary sources) may link to atmospheric acidity change15, 16, though,

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most of the works were the laboratory studies, and between a single source and acidity. Source

57

impact analysis of detailed, ambient observations is lacking.

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Water-soluble ions (WS-ions) are the key drivers of particle pH – as they regulate both the

59

aerosol water uptake, as well as the balance of ions that exist in the aerosol aqueous phase7, 8.

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WS-ions are a major component of PM2.517, 18 and often account for 30 percent or more of the

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PM2.5 mass19, 20. We relate the influence of different sources on WS-ions and aerosol acidity, 4


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62

using observations from a detailed field campaign. High temporally resolved (1-h) online

63

aerosol ions and gas concentrations were measured in Tianjin, China. Then, source

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apportionment (Multilinear Engine: ME2)21 and thermodynamic (ISORROPIA-II) 22 modeling

65

were employed to link sources to the acidity properties of the aerosol. Tianjin was selected as

66

the study area as a polluted city experiencing strong impacts of pollution and acid deposition23,

67

24

.

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■Methods

69

Sampling. The sampling campaign was conducted at a sampling site located in a residential

70

region of Tianjin (a northern city in China), and approximately 200 m away from a densely

71

travelled major road. Samples were collected at both ground level and 22 m above sea level.

72

Water soluble ions (AIM, URG Corporation, URG9000B) of PM2.5 and trace gases (online

73

instruments from Thermo Electron Inc.) in ambient air were measured by commercially

74

available ambient monitors at hourly temporal resolution. The same instrument has been

75

successfully deployed in several other field campaigns, with further details can be found at

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Zhou et al. (2007)25 and Wu et al. (2007) 26. As a brief summary, the instrument is consisted of

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two components: a particle collection unit and two ion chromatographs (IC) for chemical

78

analysis. The sample inlet was equipped with a PM2.5 sharp-cut cyclone and samples were

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collected at a flow rate of 3 L min-1. In this work, sampling duration was from December 25th

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2014 to June 2nd, 2015. During the sampling period, centralized residual heating (using coal

81

combustion) were effective from December 1st to March 15th. The URG9000B instrument is

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capable of measuring mass concentrations of major inorganic ions in aerosols, including five

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major cations (Na+, K+, NH4+, Ca2+, Mg2+) and five anions (SO42-, NO3-, Cl-, F-, NO2-).

84

Methanesulphonic acid (20 mM) was used for cation analysis, and a mixed solution of sodium 5



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carbonate (0.08 mM) and sodium bicarbonate (0.01 mM) was used for anion analysis. Both

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ICs were operated in isocratic elution at a flow rate of 0.5 mL min-1. In addition to the above

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mentioned ions, other gases species including SO2 and NO2 were also measured

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simultaneously to help better understanding the formation of secondary species.

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Source apportionment: Multilinear Engine 2 (ME2). In this work, ME2 (Multilinear engine

90

2) was applied to analyze the possible sources of WS-ions. ME2 is a method based on Positive

91

Matrix factorization (PMF), but is more flexible than PMF for solving multilinear and

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quasi-multilinear problems and allows the use of pulling factors27,28. The purpose of factor

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pulling is to force known source markers to become dominant species in the solution29.

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Particle pH Calculation. pH levels were calculated using the ISORROPIA-II model9.

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ISORROPIA-II is a thermodynamic equilibrium model for K+-Ca2+-Mg2+-NH4+- Na+- SO42--

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NO3-- Cl−- H2O aerosol system22. It is widely used in regional and global atmospheric models,

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and has been successfully applied in numerous studies to evaluate particle pH levels7-9, 30.

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ISORROPIA-II computes the particle liquid water content in equilibrium with the ambient

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relative humidity and considers the nonidealities between all dissolved major ions in solution.

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In the calculations of pH here, the hydrogen ion activity is assumed to be unity9. Further

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descriptions of this model can be found elsewhere7, 9, 22.

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Aerosol water content was also calculated using ISORRPIA based on the ionic

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concentrations and the measured relative humidity.

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significant fraction of the PM2.5 in China, organic acid ions have a lower hygroscopicity than

105

the major inorganic ions, and their impact on water content will not significantly change

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pH7,9,31,32.

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While secondary organic aerosols are a

In this work, hourly pH and ∆pH values were calculated, where ∆pH is the difference 6


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between the calculated pH of the aerosol sample minus what the aerosol pH would be at the

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same RH and T of the sample, but if the sample had a composition equal to the average of all

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the samples (see equation (4) of the main text). This is done to reduce the impact of RH and T

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on pH, as RH and T can be correlated with source contributions.

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For pH, hourly concentrations of WS-ions (K+, Ca2+, Mg2+, NH4+, Na+, SO42-, NO3-, Cl−),

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gas-NH3, RH and T were used as the input dataset. “forward” type and “metastable” phase

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state were used.

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∆pH is calculated as follows9, 22:

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∆pH(t)=pH(C(t),T(t),RH(t))-pH(̅ ,T(t),RH(t))

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where, C(t) is the chemical composition of the sample at time t, T(t) is the temperature at time

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t, RH is the relative humidity at time t, and ̅ is the average composition of the aerosol over

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the entire sampling period. This step is taken to remove the strong RH and T dependence of

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aerosol pH when assessing source impacts.

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Evaluate the ion balance of aerosol. Except for chemical composition information,

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information on PM2.5 acidity is also important, since acidity is a key parameter that affects the

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hygroscopic growth, toxicity, heterogeneous reactions on PM2.5, as well as neutralizing

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process of acid rain33, 34. Ion balance calculations were useful for studying the acid-base

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balance of aerosol. The ratios of AE (anion equivalent) and CE (cation equivalent) could be

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also used to indicate the acidity of atmospheric aerosol35, 36. The calculation of particulate

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anion and cation equivalent are as follows35, 36:

(1)

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AE = [ N O 3− ] 62 + [ SO 42 − ] 48 + [ C l ] − 35.5

(2)

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CE = [ NH4+ ] 18 + [Ca2+ ] 20 + [K + ] 39 + [Mg 2+ ] 12 + [ Na+ ] 23

(3) 7



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where [NO3-], [SO42-], [Cl-], [NH4+], [Ca2+], [K+], [Mg2+], [Na+] represent the mass

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concentrations of these ion species in the PM2.5 samples. In prior studies, an AE/CE ratio

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larger than 1 has been used to indicate acidic particle condition and a ratio of lower than 1

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ratio suggests more alkaline condition10, 35, 36, though thermodynamic modeling and source

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apportionment find a more complex relationship.

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■ RESULT AND DISCUSSION

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WS-ion Concentrations. Hourly concentrations of PM2.5 WS-ions species were collected

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using commercially available ambient ion monitor (AIM, URG Corporation, URG9000B),

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with levels ranging from 2.8 µg m-3 to 402 µg m-3 and an average level of 37 µg m-3. In this

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work, sampling was conducted from December 25th 2014 to June 2nd, 2015, and includes both

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a period with and without residential heating (using coal combustion) (Table 1). Ions with the

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highest average concentrations were: NO3- (10.7 µg m-3), followed by SO42-, NH4+, Cl-, K+, F-,

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Na+, Ca2+ and Mg2+ (Figure 1, Table S1).

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concentrations during the heating period (13.5, 10.2 and 8.7 µg m-3, respectively), and lower in

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summer time (non-heating period, 7.6, 7.4 and 4.8 µg m-3, respectively). Further details and

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statistical analysis of WS-ions observations can be found in Supporting Information.

NO3-, SO42-, and NH4+ experience higher

8


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Concentrations (µ µg m-3)

20

NH4+ Ca2+

A

0 60 40

SO42NO3-

B

20 0 10

AE/CE pH

C 5

0

147 148 149 150 151 152

0 2014/12/25

500 2015/1/21

1000 2015/2/15

1500 2015/3/10

2500 2015/5/29

2000 2015/5/13

3000 2015/6/20

Figure 1. Concentration variations of dominant WS-ion species (A) NH4+, Ca2+; (B) SO42-, NO3-and (C) AE/CE, pH during the sampling period (1-hour resolution), from December 25th 2014 to June 2nd, 2015.

Table 1 Contribution and percentage of sources during the sampling periods

Coal Dust Vehicle Secondary nitrate Secondary sulfate

Entire period -3 µg m (%) 5.0 (14) 3.9 (11) 3.2 (9) 15.0 (41) 9.4 (26)

Heating period -3 µg m (%) 7.4 (17) 4.0 (9) 4.3 (10) 18.9 (42) 9.8 (22)

Non-Heating period -3 µg m (%) 2.4 (9) 3.7 (14) 1.8 (7) 10. 7 (39) 8.8 (32)

153 154

During the heating period, observations of total anion equivalents (AE) and total cation

155

equivalents (CE), when plotted against each other, were clustered into two groups, with a

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relatively larger branch under the 1:1 line and another branch above the line (Figure S2). A

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similar pattern was also found for the non-heating period, with one group relatively close to

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the 1:1 line, and the other group above the 1:1 line (indicating a more frequently the ion

159

balance was shifted more often to a surplus of H+, which was not directly measured). Overall, 9



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the average AE/CE ratio for all samples was 0.97, indicating that the collected PM is slightly

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alkaline (carbonate was also not measured). This finding agrees with other studies in northern

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China23, 37.

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Source Analysis for WS-ions. The hourly WS-ions data were analyzed using the Multilinear

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Engine 2 source apportionment model (ME2) to help identify potential source impacts38. Five

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factors were resolved, with a Q value of 22515 (Qmain= 21208, Qaux= 1307, Qaux/Qmain=6%),

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which is close to the theoretical Q value (20737), indicating satisfactory model performance21,

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29

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exhaust14, 41, and secondary sulfate and nitrate42, 43 (Figure S3), which similar to findings of

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other studies of cities in northern China, including Beijing44, 45, Tianjin46 and Shijiazhuang47.

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Detailed discussion of factor identification is in Supporting Information.

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. Extracted factor profiles were linked to: coal combustion39, 40, mineral dust41, vehicle

Factor contributions show significant daily and seasonal variation (Figure S5). On

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average, the secondary nitrate factor represented the largest contributor, 41%, to total WS-ions;

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followed by secondary sulfate (26%), coal combustion (14%), mineral dust (11%) and vehicle

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exhaust source (9%) (Table 1). The absolute contribution (µg m-3) of each source factor was

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higher in heating period than the non-heating period. On the other hand, the percentage

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contributions (%) of two periods showed different patterns. The fraction from coal combustion

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was higher in the heating period (17% vs 9%), due to increased coal consumption for heating.

178

Dust was lower in the heating period (9% vs 14%), as expected (e.g., from snow and rain

179

reducing dust emissions). Secondary nitrate had a higher percentage contribution during the

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heating period (42% vs 39%) while secondary sulfate shows the opposite pattern (22% vs 32%

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in non-heating). The lower sulfate was explained by the reduced oxidation rate of SO2 to 10


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sulfate and greater wet deposition48.

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factor. Biomass burning is used for heating in rural areas when coal (which contributed to

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SO2 emissions) is used in the cities, leading to increased emissions of oxidized sulfur and

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potassium at a similar time.

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burning plumes and wood smoke episodes, potentially due to increased emissions and

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conversion31,49.

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Impact of Sources on AE and CE. Source impacts on WS-ions levels was analyzed using

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regression source contribution against AE, CE, pH, and Neutralization ratio ( Rneutral ) (see

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Table S2 and S3 for details). Regression analysis of AE and CE associations with source

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factors find that the sensitivity of CE to dust was higher than AE, compared to other sources

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(Supporting Information), consistent with results from other studies50,51. Secondary sulfate had

193

high coefficients for both AE and CE, which can be explained by the NH4+ associated with the

194

factor (Figure S3).

Elevated levels of potassium were found in the sulfate

Also, elevated levels of sulfate have been found in biomass

195

Focusing on ammonium, sulfate and nitrate, we found that ammonium increased nearly

196

linearly with the total amount of sulfate and nitrate equivalents up until about 1 µeq m-3 (about

197

18 µg m-3), a high level.. As explained in Weber et al.9, ammonia gas deposits quickly via wet

198

and dry deposition, and has a short atmospheric lifetime, therefore ammonia are determined by

199

a pseudo-steady state its sources and sinks (but not aerosol partitioning). As more sulfate and

200

nitrate are formed, the ammonia levels respond, and the amount of aerosol ammonium is

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determined largely by the levels of nitrate and sulfate and thermodynamic equilibria48, 52, and

202

much less so by ammonia availability, even in highly polluted areas during smog episodes.

203

Thermodynamic modeling found that ppb levels of ammonia are still present at the high 11



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sulfate levels found here.

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Impact of Sources on Particle pH. Hourly particle pH and ∆pH (see Method) values were

206

calculated by ISORROPIA-II model in the forward mode (Eq 4)9, 22, which can be referred to

207

the method section. This step was taken to remove the strong RH and T dependence of aerosol

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pH when assessing source impacts. The pH calculation assumed that the aerosol is internally

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mixed: though it is recognized that aerosol composition is size dependent and particle

210

composition will vary even for similarly sized particles, so some particles will be more and

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some less acidic. Calculated bulk pH values ranged between 0.33 and 13.6, with an average of

212

4.9 (standard deviation = 1.4).

213

The dependence of ∆pH on source contributions was found to be:

214

∆pH = 0.10 + 0.051 Coal + 0.095 Dust − 0.070 Vehicle − 0.0038 SN − 0.032 SS

215

(R2=0.16)

216

Here, we focus on the impact of sources on pH (Detailed information in Table S4). Dust had

217

the largest coefficient overall, positive, indicating that particulate pH increases from dust

218

contributions, as found before50,51. Vehicle emission, which was largely characterized by their

219

impact on gas phase species, but also had sulfate, had a relatively large, and negative, impact

220

on pH due to the sulfate component (China does not use ultralow sulfur fuels). Secondary

221

nitrate had a negative, but small coefficient, indicating a low sensitivity to nitrate levels48. We

222

also used pH and 10pH for regression (Table S4). Source contributions to the Neutralization

223

Ratio ( Rneutral = [NH4+]/(2[SO42-]+[NO3-]); all in mol m-3), which is sometimes used to indicate

224

the degree of neutralization of sulfate and nitrate by ammonia (as a pH proxy), was:

225

Rneutral = 1.95 + 0.064 Coal + 0.051 Dust − 0.093 Vehicle − 0.037 SN + 0.00047 SS

(SN: secondary nitrate; SS: secondary sulfate)

(4)

(5) 12


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This fit had a R2 of 0.05, showing a weaker relationship between sources and Rneutral than with

227

pH or AE and CE. It does, however, reflect the weak dependence of Rneutral on secondary sulfate,

228

which is consistent with Guo et al.7 and Weber et al. 9, whom show that molar ratios were not

229

strongly correlated with pH hence less suitable as a pH proxy than previously thought7, 9.

230

Aerosol pH and source impacts can be further understood by analysis of the source

231

contributions to AE and CE, and the AE/CE ratio (Figure S6). pH dependence on AE/CE-pH

232

for the heating and non-heating periods overlap and show a generally negative relationship.

233

High AE/CE ratios suggest abundance surplus of H+ 35,51, which was not measured. Plots of

234

AE vs pH and CE vs pH for the two periods showed a triangular shape (Figure 2 and S7). The

235

non-heating period tended to have an aerosol with a lower pH level than the heating period,

236

which was somewhat counter intuitive given the increased emissions from heating. However,

237

the rate of SO2 oxidation is higher during the summer, leading to increased sulfate (Table 1)48,

238

and dust levels were lower.

239

14

A

12

heating

10

Region C

10

B

12

heating non-heating

pH

14

pH=7 line

8 6 4 2

8

pH

Region B

0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

AE

6 14

Region A

4

12

C

non-heating

10 8

pH

2

0

6 4

0.0

0.5

1.0

1.5

2.0

AE

2.5

3.0

3.5

4.0

2 0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

AE

240 241 242 243

Figure 2. (A) Anion equivalents (AE) (mol m-3) vs. pH levels, for two periods. The non-heating period (B) has a relatively lower pH levels than the heating period (C). Region A (pH < 3, low pH region), Region B (3 < pH < 6, moderately low-pH ) and Region C (pH > 6)

244 13



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The influence of sources on pH as a function of WS-ions loadings leads to a slightly

246

convex association, with the highest aerosol pH occurring at moderate loadings. To illustrate

247

this, the pH ranges were divided into three regions: Region A (pH < 3, low pH region), Region

248

B (3 < pH < 6, moderately low-pH ) and Region C (pH > 6) (Figure 3, S8). In the low pH

249

region (pH7 were either dust-rich

289

or secondary nitrate-rich. Finally, coal had higher percentage contributions (%) in heating

290

period than in non-heating period, due to extensive coal combustion in residential heating55.

291

Figure S9 describes the relationship of pH, the concentration of NO3- and the estimated source

292

contribution (%). In the low-pH region, NO3- is typically low while in Region B, NO3-

293

increased along with increasing pH; but in the high-pH region, NO3- reduced while pH

294

increased56, because the samples in this region were dust-rich. The relationship between pH

295

and Rneutral ([NH4+]/(2[SO42-]+[NO3-]), mol m-3) leaded to low r2 (0.01) (Figure S10),

296

indicating that this ratio was a poor indicator of aerosol pH.

297

16


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Heating period

Coal contribution (%) Region C

A

B

Dust contribution (%)

50 40

Region B

C

60

Vehicle contribution (%)

70

50

30

pH

80

40 30

20 20

Region A

10

10

0

D Heating period

0

0

SN contribution (%) Region C

50

50

Region B

30

30

70

E

SS contribution (%)

70

F Dust-rich

pH

Region A

10

non-Heating period

0

AE/CE

298

SN-rich

10 0

AE/CE

299 300

Figure 5. AE/CE-pH plots with fractional source contributions for (A) Coal combustion, (B) Dust, (C) Vehicles,

301 302 303 304 305

Region A (pH < 3, low pH region), Region B (3 < pH < 6, moderately low-pH ) and Region C (pH > 6 is more neutral and alkaline). The contribution (=(Si/Ssum x 100%)) is the percentage contribution

306

The combined source apportionment and thermodynamic modeling of highly time-resolved

307

aerosol composition data identified the drivers of pH in this polluted atmosphere21, 22. Of the

308

five source categories identified, mineral dust and mobile sources (which includes road dust)

309

had the most influence on pH, both decreasing acidity50,51. The dependence on secondary

310

sulfate and nitrate was much smaller at these levels. Aerosol pH tended to be low, except when

311

there was a significant amount of dust-like sources, and vehicles were found to increase

312

aerosol acidity. Surprisingly, the most acidic aerosols were found during cleaner conditions,

313

and overall, there was a tendency towards stronger acidity at the two ends of the PM2.5 levels,

314

i.e., in either the cleaner or the most heavily polluted conditions. This was due to multiple

315

factors.

316

often in the summer when photochemical processing (e.g., the reaction with the OH radical)

(D) Secondary nitrate (SN), (E) Secondary sulfate (SS) and (F) Dust-rich and SN-rich areas for high pH level.

(%) of i’th source category to the sum of the source impacts on water soluble ions.

Periods of lower pollution tended to occur during periods of greater dilution and

17



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317

increased the rate of oxidation SO2 to SO42, 57, while periods of high PM tended to occur

318

during periods of lower stagnation when NOx levels are also elevated, decreasing OH. Periods

319

with higher PM2.5 concentrations also tended to have higher levels of dust, the alkaline

320

components of which increased aerosol pH. These periods also have higher levels of

321

potassium, e.g., from biomass burning, and biomass burning has been found to lead to less

322

acidic aerosol than from atmospheric sulfate production31. Cleaner periods also occur when

323

the water content of the aerosol is relatively low (Figure S11), which increased aerosol acidity.

324

At very high levels of sulfate production, the level of alkaline components in relationship to

325

the sulfate was lower, and pH tended to decrease as sulfate levels increased further. This study

326

provides experimental evidence at high pollutant loadings that pH is relatively insensitive to

327

sulfate and nitrate levels because aerosol water is increased by a similar amount (Figure S11),

328

but is mainly responsive to the presence of dust and biomass burning, supporting the findings

329

of Weber et al.9 and Bougiatioti et al.31. Further, ammonia emissions were such that

330

ammonium increased with sulfate and nitrate to very high levels, and thus do not appear to be

331

a limiting factor, affecting pH, emphasizing that aerosol pH is controlled by both

332

thermodynamic and physical buffering.

333

ACKNOWLEDGEMENTS

334

This study was supported by the National Key Research and Development Program of China

335

(2016YFC0208500, 2016YFC0208505), Special Scientific Research Funds for Environment Protection

336

Commonweal Section (Nos. 201509020, 201409003), the Tianjin Research Program of Application

337

Foundation and Advanced Technology (14JCQNJC0810), Tianjin Natural Science Foundation 18


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(16JCQNJC08700) and the Blue Sky Foundation. This research was also supported, in part, by USEPA

339

grant R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the

340

official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or

341

services mentioned in the publication.

342

SUPPORTING INFORMATION

343

The Supporting Information details the concentration, existing forms and source analysis for

344

WS-ions. Besides, impacts of sources on AE and CE were also described in detail in

345

Supporting Information.

346

19



347

References

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