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Natural Silicon Isotopic Signatures Reveal the Sources of Airborne Fine Particulate Matter Dawei Lu, Qian Liu, Miao Yu, Xuezhi Yang, Qiang Fu, Xiaoshan Zhang, Yujing Mu, and Guibin Jiang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06317 • Publication Date (Web): 28 Dec 2017 Downloaded from http://pubs.acs.org on December 30, 2017

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Environmental Science & Technology

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ARTICLE

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Natural Silicon Isotopic Signatures Reveal the Sources of

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Airborne Fine Particulate Matter

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Dawei Lua,b, Qian Liua,b,d,*, Miao Yua, Xuezhi Yanga,b, Qiang Fuc, Xiaoshan Zhanga, Yujing

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Mua, Guibin Jianga,b,*

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a

State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China b

8 c

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d

University of Chinese Academy of Sciences, Beijing 100190, China

China National Environmental Monitoring Center, Beijing 100029, China

Institute of Environment and Health, Jianghan University, Wuhan 430056, China

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Corresponding author: Prof. Qian Liu or Prof. Guibin Jiang. 18 Shuangqing Road, Haidian

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District, Beijing 100085, China. Tel: +86-10-62849124. Email: [email protected] (Q. Liu);

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[email protected] (G. Jiang).

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Keywords: particulate matter; silicon; stable isotope; aerosol; source tracing

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ACS Paragon Plus Environment


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Abstract

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Airborne particulate pollution is a critical environmental problem affecting human health

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and sustainable development. Understanding of the sources of aerosol particles is of extreme

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importance for regional air pollution control. Here we show that natural Si isotopic signature

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can be used as a new tool to elucidate the sources of fine particulate matter (PM2.5). Through

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the analysis of Si isotopic composition (δ30Si) of PM2.5 and its primary sources collected in a

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typical pollution region–Beijing, we recognized the direct source tracing ability of Si isotopes

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for PM2.5. The different primary sources of PM2.5 had different Si isotopic signatures. The

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δ30Si value of PM2.5 ranged from -1.99‰ to -0.01‰ and showed a distinct seasonal trend

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(isotopically lighter in spring/winter and heavier in summer/autumn). The variations in δ30Si

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of PM2.5 revealed that Si-isotopically light sources were important sources for Beijing’s

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severe haze pollution and that coal burning was a major cause for the aggregating haze

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weather in spring/winter in Beijing. We also analyzed several typical haze events by using Si

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isotopic signatures. As the first study on the natural Si isotopes in the atmospheric

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environment, this study may reveal an important tool to advance the particulate pollution

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research and control.

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1. Introduction

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In developing countries hundreds of millions of people are suffering from severe haze

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pollution caused by airborne fine particulate matter (PM2.5). PM2.5, with aerodynamic

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diameter smaller than 2.5 µm, can affect the Earth’s radiation budget, impact regional to

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global weather, and enter the human lungs and the bloodstream, causing a wide range of

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diseases and a significant reduction of life expectancy.1, 2 PM2.5 can be directly emitted from

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natural or anthropogenic sources (primary particles) or formed secondarily in the atmosphere

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from gaseous precursors (secondary particles).3 Understanding of the sources and dynamics

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of PM2.5 is of extreme importance for haze pollution control. However, till now, the factors

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controlling the high levels of PM2.5 present during severe haze events in typical pollution

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regions such as China remain poorly understood.4 The contributions of different sources to

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the severe haze pollution are still highly controversial, making it difficult to develop efficient

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pollution control policies.

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Silicon (Si) is the second most abundant element in the Earth’s crust with three stable

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isotopes (28Si,

29

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biogeochemical cycle of Si that is linked to the global carbon cycle via the chemical

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weathering of silicate rocks,5-7 whereas other applications are rare. Furthermore, although the

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Si cycle in continental and marine environments has been well elucidated, the Si isotopic

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compositions in atmospheric environment remain unreported. We notice that Si is a major

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element in PM2.5.8 More importantly, Si has some exceptional properties that make it different

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from other elements. In contrast to other major elements in PM2.5 (i.e., C, O, N, and S) that

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are more reactive and may be prone to isotope fractionation during complex atmospheric

Si, and

30

Si). Natural Si stable isotopes have been used to quantify the

3



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processes,9-11 Si has only one valence state (Si4+) and does not form volatile compounds

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readily in terrestrial systems, so the potential of isotope fractionation of Si in the atmosphere

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is limited by its low volatility, chemical inertness, and invariant bonding environment.12 This

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suggests that the Si isotopic signatures may be maintained during the emission and growth

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process of PM2.5. Therefore, we hypothesize that Si isotopic composition can be used as a

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unique tool to unravel the information on the sources of PM2.5.

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Here we investigated for the first time the natural abundance and stable isotopic

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composition of Si in PM2.5 and its primary sources. The samples were collected during 2003

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and 2013 around Beijing, China (see Fig. S1). This region is important in that it has

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experienced extremely severe haze pollution in the past decades.13 The 2013 is a typical year

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when the annual mean PM2.5 concentration in Beijing reached an unprecedentedly high

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level14 and the 2003 was selected for a comparison. We show that natural Si signatures can be

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used as a new tool to elucidate the sources of PM2.5. The seasonal variations in Si isotopic

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composition of PM2.5 reveal the major causes for the severe haze events. Our results provide

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new insights into the Beijing’s severe haze pollution; more importantly, the methodology

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developed herein is also applicable to other developing countries that are suffering from air

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pollution to help them develop effective pollution control strategies.

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2. Materials and Methods

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2.1. Chemicals and reagents

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The Si isotope standard NIST SRM-8546 and the standard material of urban atmospheric

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particulate matter (NIST-1648a) were purchased from the National Institute of Standards and 4


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Technology (Gaithersburg, MD). The secondary Si isotope standard IRMM-017 was

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purchased from the Institute for Reference Materials and Measurements (GEEL, Belgium).

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The element calibration standard solution was purchased from Agilent (Santa Clara, CA).

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The rare earth element calibration standard GSB 04-1789-2004 was purchased from the

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National Testing Center of Nonferrous Metals and Electronic Materials Analysis (Beijing,

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China). Sodium hydroxide was from Beijing Ruikai Co. (Beijing, China). Nitric acid was

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purchased from Merck (Darmstadt, Germany). Hydrochloric acid was from Beijing

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Chemicals Works (Beijing, China). Hydrogen peroxide was from Sinopharm Chemical

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Reagent Co. (Shanghai, China). Ultrapure water (18.3 MΩ·cm) produced from a Milli-Q

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Gradient system (Millipore, Bedford) was used throughout the experiments.

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2.2. Sampling and characterization of PM2.5

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PM2.5 samples were collected around the Beijing region, China (Fig. S1) in random days

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(n = 100) of haze weather in 2003 and 2013. For PM2.5 samples of 2003, ca. 110 m3/day of air

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was collected on polypropylene membrane filters (Ø = 90 mm) using a medium-volume air

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sampler (Beijing Geological Instrument Co., China) at a flow rate of 77.6 L/min. For PM2.5

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samples of 2013, ca. 80-100 m3/day of air was collected onto Whatman 3-5 Teflon membrane

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filters (Ø = 47 mm, Maidstone, UK) by using a low-volume air sampler (Partisol 2025i,

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Thermo Fisher, USA) at a flow rate of 16.7 L/min. The mass of the collected PM2.5 was

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measured by the giant gravimetric balance method.15 The characterization of PM2.5 samples

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was performed on a Hitachi S-3000N scanning electron microscope (Tokyo, Japan) equipped

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with an energy dispersive X-ray spectroscope (EDX) operating at an accelerating voltage of

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15 kV. 5



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2.3. Sampling of primary sources of PM2.5

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We selected seven sources as the major primary sources of PM2.5 according to the

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literature reported,14, 16-19 including coal burning, industrial emission, biomass burning, urban

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fugitive dust, soil dust, construction dust, and vehicle emission. The samples of these sources

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were collected around the Beijing region (Fig. S1) following the previously reported

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methods.20-26 Briefly, the coal burning samples were collected from burning of honeycomb

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briquette and lump coal, which are the main fuels used around the Beijing region. The coal

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burning fly ashes were first drawn through a dilution system,20 and then the formed

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particulate matter was collected onto polypropylene membrane filters by an impactor at a

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flow rate of 36 L/min. The industrial emission samples were collected from a steel plant and a

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power plant in Tangshan, Hebei, China. The emitted dusts were collected by air sleeves or

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electrostatic filters equipped in the chimneys. The biomass burning samples were the burnt

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straws collected in Daxing and Tongzhou District, Beijing. The soil dust samples were

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collected at different depths of soil (5 and 30 cm) and at different directions in the Beijing

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region. The urban fugitive dust samples were collected at a height of 20 m by using an SYC-3

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auto-sampler (Laoshan Instruments Co., Qingdao, China). The construction dust samples

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were gathered at several construction sites in the Beijing city and mainly consisted of fine

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sands, cement dusts and brick dusts, and were then sieved through a Tyler 200 mesh sieve.

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The vehicle emission samples were collected by sampling the air onto Whatman Teflon

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membrane filters (Ø = 102 mm, Maidstone, UK) in the middle section of the Tanyugou

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Tunnel (3455 m in length) in Changping District of Beijing using a high-volume air sampler

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(Echo Hi-Vol, Tecora Co., Milan, Italy) at a flow rate of 280 L/min. Since only motor 6


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vehicles are permitted to pass through the tunnel and that the concentration of PM2.5 outside

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the tunnel was very low (< 15 µg/m3), the collected airborne solids inside the tunnel could

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represent the vehicle emission. This approach has also been used in previous studies.25

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2.4. Sample preparation

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For the measurements of Si concentration and isotopic composition, the samples were

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first placed in a silver crucible and dried in a muffle furnace at 1000 K for 10 min. For the

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samples collected on polypropylene membrane filters, the filters were sheared into strips and

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heated together with the samples. Afterward, the samples were digested using the alkali

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fusion method with NaOH.27, 28 Briefly, the samples were mixed with solid NaOH at a ratio of

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1:20 in a silver crucible, and then the mixture was heated in a muffle furnace at 1000 K for 10

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min followed by cooling down to room temperature. The obtained “fusion cake” was

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dissolved with 2 mL of water and stored in the dark for 24 h. Then, an HCl solution was

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added to the crucible to make the final pH of the solution to be ~2. The solution was stored in

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acid-free centrifuge tubes for the isotope ratio measurement. The blank polypropylene and

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Teflon membrane filters and solid NaOH have also been analyzed using the same procedures

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to ensure that they caused no interference to the Si isotope ratio measurement.

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To eliminate the interference from sample matrix, the samples were purified by using the

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cation exchange chromatographic method as reported previously.27, 28 Briefly, the cation resin

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(Dowex 50WX8, 200-400 mesh) in H+ form were activated for 12 h and filled to a 1.8 mL

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resin bed in a BioRad column. Then the resin was rinsed with HCl, HNO3 solution, and water

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until the pH of the eluate was close to neutral. Afterwards, 1 mL of the sample solution was

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loaded to the 1.8 mL of acid-cleaned resin and then eluted with 2 mL of water. The cation 7



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exchange resin could remove cationic species from the Si species, because the Si in solution

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are mostly in the form of non-ionic Si(OH)4 and anionic H3SiO4-.27 The recovery of the

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column purification step obtained with the standard reference material IRMM-017 was >95%.

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The whole sample pretreatment procedures were also tested using the standard reference

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material NIST-1648 (urban atmospheric particulate matter), and the recovery of Si was in the

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range of 89.9-96.2%.

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2.5. Measurement of Si concentration

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The concentration of Si in PM2.5 and its primary source samples was measured on an

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Agilent 8800 inductively coupled plasma mass spectrometer (Santa Clara, CA, USA). The

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clean blank membrane filters have also been analyzed using the same procedures to subtract

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the background signals from the samples.

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2.6. Measurement of Si isotope ratios

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The stable Si isotopic composition was measured by multi-collector inductively coupled

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plasma mass spectrometry (MC-ICP-MS)27 on a Nu Plasma II MC-ICP-MS (Wrexham, UK)

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equipped with 16 Faraday cups coupled to a DeSolvation Nebulizer System (DSN-100)

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working in medium-resolution mode. All samples were diluted with HCl solution (pH ~2) to

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a Si concentration of ~300 ng/mL to obtain the intensity of 28Si of ~2 V (the intensities of 29Si

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and 30Si were ~0.11 and ~0.08 V). The samples were injected by using a PFA nebulizer at a

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flow rate of 70 µL/min in dry mode. The optimized parameters of the instrument and the

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Faraday cup configuration are shown in Table S1. The intensity of

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blank HCl solution (pH 2), the polypropylene and Teflon membrane filters, and solid NaOH

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was all less than 0.04 V. This intensity was negligible compared to the intensities of the 8


28

Si obtained with the

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samples (~2 V) and caused no interference to the Si isotope ratio measurement. The

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procedure blank intensities of the Faraday detector noise were subtracted in the electrostatic

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analyzer for 30 s prior to each measurement. Three parallel measurements were made for all

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samples (n = 3). A rinse with HCl solution (pH 2) for 120 s after each measurement was used

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to reduce the background intensity to < 0.03 V. The Si isotopic composition in a sample was

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expressed as a δ value (δ30Si and δ29Si) relative to a standard material (NIST SRM-8546) as

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follows: Si/

Sisample

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Si = Si/

Si

− 1 × 1000

(1)

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! Si = "#

− 1 × 1000

(2)

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The mass bias was corrected based on a standard-sample-standard bracketing method as

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described previously.29 The δ value of a sample was calibrated based on the mean values of

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the isotope ratios in two adjacent standard measurements. The method has been validated

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using two standard reference materials, NIST SRM-8546 and IRMM-017. A δ30Si value of

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-0.08 ± 0.11‰ (mean ± 2SD, n = 40) was obtained in a NIST SRM-8546 solution with a Si

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concentration of 300 ng/mL. The δ30Si value of IRMM-017 was measured to be -1.38 ± 0.18‰

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(mean ± 2SD, n = 36). This value was very close to the previously reported results,28 proving

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that our method was highly accurate and precise.

standard "# Si/

Sisample Si/

Si standard

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3. Results and Discussion

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3.1. Abundance and isotopic composition of Si in PM2.5

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As shown in Fig. 1a, the daily PM2.5 concentration in 2013 ranged from 1.6 to 530 µg/m3

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with an annual mean value of up to 106.4 µg/m3 (n = 360). Weather with heavy haze (PM2.5 > 9



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200 µg/m3) was much more frequent in spring/winter than in summer/autumn. Scanning

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electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) measurements

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show that PM2.5 is highly diverse in morphology and elemental composition, in which Si is a

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ubiquitous element (Fig. S2). No seasonal trend was observed in total Si in PM2.5 (Fig. 1b) or

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in abundance of Si in PM2.5 (Fig. 1c). Note that the total Si in PM2.5 is expressed as SiPM2.5

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relative to air volume (in µg/m3) and the Si abundance is given as mass fraction (in %). For

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comparison, we also measured the SiPM2.5 for 2003, which was overall higher than that for

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2013 (P < 0.001; Fig. S3) and showed a peak value in April (Fig. 1b). This result was

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consistent with the decreasing frequency of sand-dust weather in past decades in Beijing,30, 31

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because the most common constituent of sand is silica (usually in the form of quartz).

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We then measured the Si isotopic composition of PM2.5 in 2003 and 2013. The data for

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all samples produced a mass-dependent line (Fig. 1d). The annual mean δ30Si value showed

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no significant difference between 2003 and 2013 (P = 0.52; see Fig. S3). Notably, as shown

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in Fig. 1e-f, the δ30Si value changed dramatically over different months. A similar seasonal

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trend was observed for 2003 and 2013, i.e., the Si in PM2.5 was isotopically lighter in

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spring/winter and heavier in summer/autumn (see Fig. S4 for P values). It was interesting to

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note that in both 2003 and 2013 the ranges of δ30Si value in spring/winter were much wider

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than those in summer/autumn, indicating that the source contributions to PM2.5 in

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summer/autumn were more stable than those in spring/winter. Specifically, the δ30Si ranges in

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spring/winter in 2013 were wider than those in 2003 (Fig. 1e-f), suggesting that the primary

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sources of PM2.5 in Beijing were becoming more complex from 2003 to 2013.

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3.2. Si isotopic signatures of primary sources of PM2.5 10


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To link PM2.5 to its sources via Si, we investigated both Si abundance and isotopic

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signatures of seven major primary sources of PM2.5 collected around the Beijing region.14, 16,

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19, 32

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Si (Fig. 2a). Soil, construction, and urban fugitive dusts contained high abundance of Si (>

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10%), coal burning was mid-Si-abundant (8.1%), and biomass burning, industrial emission,

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and vehicle emission were low-Si-abundance sources (< 1%).

Elemental analysis demonstrated that all primary sources contained a certain content of

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Notably, these sources showed different Si isotopic signatures (Fig. 2b). The δ30Si values

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of soil, construction, and urban fugitive dusts were in the range of -1.0‰ to 0.5‰ (n = 64).

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This range was narrower than the δ30Si range in soils at a global scale6 but close to the natural

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Si isotopic signatures of soils and natural endogenous rocks and sands (-1.1‰ to 0.7‰).7 The

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soil dusts collected at different depths and directions had an identical Si isotopic composition

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(P > 0.05; Fig. S5). The δ30Si in particles from biomass burning was also in this range (-0.9‰

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to 0.1‰, n = 10) due probably to the plant uptake of Si from soil.33, 34 However, the δ30Si in

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particles from coal burning was remarkably negative (-1.2‰ to -3.4‰, n = 11) probably

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resulting from the long-term evolution from the phytoliths.35 The δ30Si of industrial emission

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was also negative (-0.9‰ to -1.8‰, n = 8). Lastly, the δ30Si of vehicle emission ranged from

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0.8‰ to 1.2‰ (n = 3), which was consistent with that of petrol and diesel used in vehicles

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(2.8‰ to 3.9‰, n = 3). All Si isotope data for the sources could be described by a

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mass-dependent line (Fig. S6). The different Si isotopic signatures of primary sources of

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PM2.5 met the prerequisite for using Si isotopic signature to trace the source of PM2.5.

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Considering the different Si isotopic signatures of primary sources and limited potential

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of Si isotope fractionation, the variations in Si isotopic composition of PM2.5 were able to 11



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directly reflect the changes in primary sources. In both 2003 and 2013, the Si in PM2.5 in

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spring/winter was isotopically lighter (all monthly mean δ30Si values < -1.0‰) than in

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summer/autumn (Fig. 1e-f). This result clearly indicated that the contributions of

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Si-isotopically light sources (i.e., coal burning and industrial emission with δ30Si < -1.0‰;

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Fig. 2b) in spring/winter were higher than those in summer/autumn. Note that severe haze

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weather in spring/winter was much more frequent than in summer/autumn (Fig. 1a) and that

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the contribution of industrial emission should be relatively steady throughout the year.

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Therefore, the aggravation of haze pollution in spring/winter should mainly result from coal

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burning. This conclusion was evidenced by the fact that coal burning is a major manner of

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heating in spring and winter around the Beijing region36 and was also consistent with the

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result obtained in a recent report.37

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3.3. Correlation of Si isotopic composition of PM2.5 with the pollution level

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The correlation analysis of Si isotopic composition with the PM2.5 level also unraveled

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the important sources of pollution. We investigated the correlation of both Si abundance in

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PM2.5 and isotopic composition of PM2.5 with the PM2.5 concentration in 2013. From Fig. 3a,

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no significant correlation of Si abundance in PM2.5 with the PM2.5 concentration could be

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found. However, if dividing the Si abundance data into three groups in terms of PM2.5

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pollution level (PM2.5 < 100, 100–200, and > 200 µg/m3; Fig. 3b), it was clear that heavy

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haze weather (PM2.5 > 200 µg/m3) tended to have a low content of Si in PM2.5 (< 1.1%).

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Regarding to Si isotopic composition (Fig. 3c), we found that the δ30Si value of PM2.5 was

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strongly correlated with the PM2.5 concentration. The δ30Si value decreased linearly with the

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PM2.5 concentration increasing (r = 0.66, P < 0.001). If the Si isotope data were also divided 12


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into three groups in terms of PM2.5 pollution level (Fig. 3d), it could be seen that the δ30Si

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value was significantly different at different pollution levels (P < 0.001), indicating that

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different primary sources were responsible for different levels of pollution. All tested PM2.5

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samples in heavy haze weather (> 200 µg/m3) yielded a δ30Si value of < -1.0‰; while with

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the improvement of air quality (PM2.5 < 100 µg/m3) the Si was enriched in the heavier isotope

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(30Si). This suggested that the Si-isotopically light sources, i.e., coal burning and industrial

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emission, played a critical role in the heavy haze weather in the Beijing region.

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In addition, we found that the δ30Si of PM2.5 appeared to be heavy isotope-deficit with

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the increase of atmospheric concentration of SO2 and NOx (P < 0.001; Fig. S7), because

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Si-isotopically light sources (i.e., coal burning and industrial emission) were usually

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accompanied by the emission of high concentration of SO2 and NOx. We also found that the

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δ30Si of PM2.5 did not show any dependence on the relative humidity (P = 0.487) but showed

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a tendency to be enriched in PM2.5 at high temperature (Fig. S8), which well matched the

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seasonal trend in δ30Si of PM2.5 (see Fig. S4).

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3.4. Analysis of typical haze events

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To further demonstrate the potential of Si isotopes in source tracing, we analyzed two

270

typical haze events in Beijing during 2013 (Fig. 4). In the haze event during Feb 23 to 25 (Fig.

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4a), the burst of PM2.5 from 42.7 to 401.4 µg/m3 within 34 h was accompanied by only a

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slight decrease in both abundance and isotopic composition of Si (P > 0.2; Fig. 4b-c),

273

suggesting that there was no significant change in the primary source input and thereby the

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rapid growth of PM2.5 might mainly result from a secondary formation process in the

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atmosphere.3, 4 13



276

In the second haze event during Mar 5 to 8 (Fig. 4d), two haze episodes were observed

277

with quite different variations in abundance and isotopic composition of Si. Similar to the

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haze event described in Fig. 4a-c, the first haze episode (Mar 5-6) might also result from a

279

secondary formation process. The slightly positive shift in δ30Si during this episode (P < 0.1;

280

Fig. 4f) could be explained by the effect of low-Si-abundance and isotopically heavy sources

281

(e.g., vehicle emission; see Fig. 2). However, in the second haze episode (Mar 7), the PM2.5

282

steeply decreased to 127.6 µg/m3 due to a strong wind,38 and then rose again to 255.9 µg/m3

283

(Fig. 4d). The dramatic increase in abundance of Si and δ30Si (Fig. 4e-f) suggested that it was

284

mainly caused by high-Si-abundance and Si-isotopically heavy sources. According to Fig. 2,

285

these sources could be soil, construction, or urban fugitive dust. Thus, the second haze

286

episode should mainly result from a direct emission of PM2.5 from primary sources. These

287

results indicated that the Si isotopic signature not only is useful to analyze PM2.5 pollution

288

over a period of time, but also can be used to monitor individual haze events.

289

3.5. Environmental implications

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Source tracing and apportionment of PM2.5 is critical for the development of pollution

291

control policies. Until now, the sources of PM2.5 in typical pollution regions are still poorly

292

understood due partly to the lack of proper tracers. This study demonstrates that the natural Si

293

isotopes provide a unique tool to trace the sources of PM2.5. Unlike other isotopes of major

294

elements in PM2.5, Si isotopic composition can directly reflect the information on the primary

295

sources of PM2.5 due to the chemical inertness of Si and different Si isotopic signatures of the

296

primary sources. Furthermore, from the analysis of typical haze events, Si isotopic

297

composition can provide extra information on the sources to the Si concentration and offers 14


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evidence to distinguish the primarily emitted from the secondarily formed PM2.5, thus making

299

it a versatile tool for PM2.5 research.

300

Specifically, this study reveals that the Si-isotopically light sources were important

301

sources for Beijing’s severe haze pollution and demonstrates that coal burning was a major

302

cause for the aggregating haze weather in spring/winter in the studied years in Beijing.

303

Therefore, more stringent control policies on emission from coal burning should be effective

304

to mitigate the Beijing’s severe haze pollution in spring/winter. The methodology developed

305

herein may also be applicable to other developing countries that are suffering from air

306

pollution to help them develop effective pollution control strategies. Furthermore, this study

307

also suggests that stable isotope compositions may be better tracers in source apportionment

308

of PM2.5 than normally used concentrations of elements. Several measures may further

309

improve the reliability of the result, such as increasing the size and representativeness of

310

primary source samples. Considering the inter-regional transport of PM2.5, inclusion of

311

primary sources of adjacent cities/regions in the investigation may also help obtain more

312

accurate results, which will be considered in our future studies.

313

15



314

Acknowledgements

315

This work was financially supported by the Chinese Academy of Sciences (No.

316

XDB14010400, QYZDB-SSW-DQC018), the National Basic Research Program of China

317

(2015CB931903, 2015CB932003), and the National Natural Science Foundation of China

318

(No. 91543104, 21377141, 21422509). We thank Dr. Man Teng, Dr. Chenglong Zhang,

319

Pengfei Liu, and Chaoyang Xue for their help with collecting PM2.5 and vehicle emission

320

samples, and thank Prof. Yong Cai for helpful comments on the manuscript. We also thank

321

Qifeng Li for processing the GIS data.

322

Supporting information

323

Supporting experimental details, additional discussion, supporting tables, supporting

324

figures, and references for SI. The Supporting information is available free of charge on the

325

ACS Publications website.

326

Competing financial interests

327

The authors declare no competing financial interests.

328

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Figures

436

437

Fig. 1. Abundance and stable isotopic composition of Si in PM2.5. a, Daily mean PM2.5

438

concentration in 2013. The error bars represent standard deviations (1SD) from 24

439

measurements in a day (n = 24). b, Monthly mean total Si in PM2.5 in 2003 and 2013. The

440

error bars represent 1SD from samples in a month (n = 2–9). c, Si abundance in PM2.5 at

441

different days in 2013. The error bars represent standard deviations (1SD) from 24

442

measurements in a day (n = 24). d, Three-isotope plot showing δ29Si versus δ30Si for all PM2.5

443

samples collected in 2003 and 2013. The black line represents the mass-dependent

444

fractionation line. The error bars represent 1SD from three parallel measurements (n = 3). e,f,

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ranges of δ30Si value of PM2.5 in different months of 2003 (e) and 2013 (f). In e and f, the

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horizontal lines in the bars represent the mean values for each month.

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447 448

Fig. 2. Si abundances (a) and stable isotopic signatures (b) of primary sources of PM2.5.

449

a, The error bars represent 1SD of measurements from the group. The mean abundances of Si

450

in the sources are labeled in the parentheses. b, Si isotopic signatures of primary sources of

451

PM2.5 collected around the Beijing region.

23



452 453

Fig. 3. Correlation of Si in PM2.5 with the PM2.5 pollution level in 2013. a, Plot of Si

454

abundance in PM2.5 versus PM2.5 concentration. The error bars represent 1SD from 24

455

measurements in a day (n = 24). b, Grouping of Si abundance in PM2.5 according to PM2.5

456

concentration. *P = 0.522; **P < 0.05. c, Plot of δ30Si versus PM2.5 concentration. The error

457

bars on x-axis represent 1SD from the PM2.5 measurements in a day (n = 24), and those on

458

y-axis represent 1SD from three parallel measurements of Si isotope ratios (n = 3). d,

459

Grouping of δ30Si values according to PM2.5 concentration. *P < 0.001; **P < 0.001.

460

24


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461 462

Fig. 4. Analysis of typical haze events by using Si isotopic signatures. a,d, Monitoring of

463

PM2.5 concentration in two haze events during Feb 23, 2013 to Feb 25, 2013 (a) and Mar 5,

464

2013 to Mar 8, 2013 (d). b,c, Daily mean abundance of Si in PM2.5 (b) and δ30Si values of

465

PM2.5 (c) in the first haze event (a). The yellow and orange bars in b and c correspond to the

466

episodes marked in the same colors in a. *P = 0.553, and **P = 0.203. The error bars

467

represent 1SD from the PM2.5 measurements (b) or three parallel measurements of Si isotope

468

ratios (c). e,f, Daily mean abundance of Si in PM2.5 (e) and δ30Si values of PM2.5 (f) in the

469

second haze event (d). The yellow, orange and red bars in e and f correspond to the episodes

470

marked in the same colors in d. In e, *P = 0.666, **P < 0.01. In f, *P = 0.093, **P < 10-4.

471

The meanings of error bars are the same as in b and c.

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