Chemical characteristics and source apportionment of PM10 during Asian dust storm and non-dust storm days in Beijing

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<ul><li><p>lable at ScienceDirect</p><p>Atmospheric Environment 91 (2014) 85e94Contents lists avaiAtmospheric Environment</p><p>journal homepage: www.elsevier .com/locate/atmosenvChemical characteristics and source apportionment of PM10 duringAsian dust storm and non-dust storm days in Beijing</p><p>Qingyang Liu a,b,*, Yanju Liu a,c,*, Jianxin Yin d, Meigen Zhang e, Tingting Zhang a</p><p>aBeijing Center for Physical and Chemical Analysis, Beijing 100089, ChinabCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinacBeijing Milu Ecological Research Center, Beijing 100076, ChinadDivision of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences, The University of Birmingham,Edgbaston, Birmingham B15 2TT, United Kingdome State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029, Chinah i g h l i g h t s Two dust storm events were monitored on March 28th and April 28th, 2012. The PMF model was used to calculate soil contribution to PM10. Higher lead isotopic ratios in PM10 were observed in the DS days. The dry lakebed region may serve as a dust transport pathway for the two DS events.a r t i c l e i n f o</p><p>Article history:Received 29 November 2013Received in revised form25 March 2014Accepted 27 March 2014Available online 27 March 2014</p><p>Keywords:Chemical characteristicsDust stormPM10Source apportionmentLead isotopePMF model* Corresponding authors. Beijing Center for PhysBeijing 100089, China.</p><p>E-mail addresses: (Y. Liu).</p><p> 2014 Elsevier Ltd. All rights reserved.a b s t r a c t</p><p>To study the chemical characteristics of Asian dust storm, airborne particulate matter PM10 (particleswith aerodynamic diameter smaller than 10 mm) was collected at two sites in Beijing from March to May2012. Water soluble ions, metals, organic carbon and elemental carbon were analyzed. Two dust storm(DS) samples were also collected during the sampling period on March 28th (DS1) and April 28th (DS2).Backward trajectory results showed that both events were originated from Inner Mongolia and Mongolia.A receptor model, positive matrix factorization (PMF) was applied to calculate the soil emission differ-ences between DS and non-DS days. Five emission sources were identified that contribute to PM10,including soil dust, vehicular emission, industrial emission, metal processing and secondary ions. ThePMF estimated contributions of dust aerosols to PM10 were in the range of 31%e40% during DS days,which were far greater than that contribution (10%e20%) from local soil dust only during non-DS days.Furthermore, lead isotopic composition analyses in PM10 in Beijing and the soil samples from InnerMongolia Plateau and Zhangbei Plateau were conducted. Higher lead isotopic ratios (206Pb/207Pb,206Pb/208Pb) in PM10 were observed in DS days than non-DS days, and those ratio compositions werefound to be similar to those observed in the dry lakebed soil samples collected from Inner MongoliaPlateau and Zhangbei Plateau, which indicate that the dry lakebed region served as a dust transportpathway of those two DS events.</p><p> 2014 Elsevier Ltd. All rights reserved.1. Introduction</p><p>Asian dust (Yellow sand) storms commonly originate from aridareas in China and Mongolia, where strong surface winds upliftical and Chemical Analysis,</p><p>(Q. Liu), liuyanju@hotmail.mineral particles into the middle troposphere (Jayaratne et al.,2011; Shen et al., 2009). Springtime winds carry a portion of thisdust across the Chinese mainland to East Asia and North America,which increase the ambient PM10 concentrations during March,April and May (Tan et al., 2012; Wang et al., 2005, 2011). Simulatedspringtime dust emission results for the period 1960e2002 indi-cated that Asian dust mainly originated from 10 source areas in thedeserts of Mongolia, northern China, and Kazakhstan (Zhang et al.,2010b; Gallon et al., 2011; Hoffmann et al., 2008; Ho et al., 2003).</p><p>;</p></li><li><p>Q. Liu et al. / Atmospheric Environment 91 (2014) 85e9486Both observations and simulations showed that the Inner MongoliaAutonomous Region (Inner Mongolia) in China is one of the mostimportant source regions for Asian dust and is responsible forapproximately 31% of the total dust production (Jayaratne et al.,2011; Li et al., 2009; Kim et al., 2008; Onishi et al., 2012; Tanet al., 2012). Observational studies have explored dust source areaof Inner Mongolia, including deserts, sandy lands of grass lands,agricultural soil, etc (Tan et al., 2012; Zhang et al., 2010a, 2010b; Xieet al., 2005).</p><p>To date, source regions responsible for long-range transporteddust have only been qualitatively identified using concentrationratios of selected major elements (e.g. Al, Fe, Ti), backward airtrajectories, satellite data and isotopes (Hoffmann et al., 2008;Ashbaugh et al., 2003; Bozlaker et al., 2013; Shen et al., 2009;Song et al., 2007). Chen et al. (2008) found that Songnen Plain,located at the eastern extension of Inner Mongolia, was a potentialsource region for Asian dust storms using carbon isotopic compo-sition in atmospheric dust carbonates. Yang et al. (2009) exploredthe northeastern Tibetan Plateau, which may serve as one of thesource regions for the atmospheric dust in Beijing in April 2006,based on SreNd isotopic results. In addition, transported dustcontributions have been calculated by comparisons of the compo-sition of samples from remote locations not impacted by localemission sources and that of locally generated materials (Zhanget al., 2010a; Cao et al., 2011). Recently, Bozlaker et al. (2013)have quantified the contribution of long range transported dustto particulate matter using the chemical mass balance receptormodel.</p><p>Lead isotopes can serve as powerful source-area fingerprints (Biet al., 2013), as they have distinct 206Pb/207Pb and 206Pb/208Pb ra-tios, depending on their geological origins (Cheng and Hu, 2010).Cheng et al. (2010) have summarized the regional characteristics oflead isotope ratios at several urban sites in Asia. Coal combustioncan be a lead source and its contribution can alter the lead isotoperatios in the atmosphere, whereas industrial emissions wereidentified asmajor sources of lead in the atmosphere in Asia (Chenget al., 2010; Mukai et al., 2001). In addition, Erel et al. (2006) hasnoticed an increasing trend of 206Pb/207Pb isotopic composition inPM during the dust storm days.</p><p>In this study, particulate matter (PM10) samples during DS andnon-DS days were collected in two urban sites in Beijing to inves-tigate their chemical characteristics, including water soluble ions,metals, lead isotopic compositions, organic carbon (OC) andelement carbon (EC). To clarify the differences of the dust contri-butions between the dust storm and non-dust storm days, positivematrix factorization (PMF) receptor model was applied to calculatethe source contribution. Based on the backward air trajectory re-sults, soil samples were collected from possible source regions,including Inner Mongolia Plateau and Zhangbei Plateau. Lead iso-topic data were also discussed in order to aid the source appor-tionment results in the sampling areas.</p><p>2. Method</p><p>2.1. Aerosol and soil sampling</p><p>The sampling site S1 (N 3997091000, E 11621030000) was selectedas an urban background site on the roof of an office building with aheight of 20m above groundwithin Institute of Botany, the ChineseAcademy of Sciences. The site was located at about 30 km north-west of Tiananmen Square surrounded by unused land withnearest (about 500 m) anthropogenic pollution arising from a lighttrafficked road. The sampling site S2 was located near a busy trafficline (latitude of N 3956050007, longitude of E 11618010008), with thesampling equipment set up on the roof of an office building at aheight of 30 m above ground and a distance about 30 m from theheavy trafficked west 3rd ring road (Fig. S1). 24-h PM10 aerosolsamples were collected onto 90 mm diameter quartz microfiberfilters (QMA, Whatman) at sites S1 and S2 in spring 2012 (March10theMay 10th), using medium volume sampling equipment at aflow 100 L min1. Filters were replaced daily at 10:00 a.m. Beijingtime through the whole sampling period.</p><p>Twenty-three soil samples were collected from possible sourceregions, including Inner Mongolia Plateau and Zhangbei Plateauusing stainless steel shovels. In addition, three soil sub-samplesfrom each sampling site were collected as duplicate samples. Allsoil samples were stored in airtight polyethylene bags and trans-ferred back to the laboratory. Before analysis, they were air-driedand passed through a 75 mm sieve.</p><p>2.2. PM mass concentration</p><p>PM10 mass concentration was calculated as the filter mass dif-ference before and after sampling at unit sampling volume. Filterswere heated for 4 h at 550 C and preserved in desiccators for 24 hbefore pre-sampling weighing using a balance (CP225D, with ac-curacy of 0.01 mg, made in Sartorius, Germany), located in aweighing room controlled at a relative humidity of around 35% anda temperature of 20 2 C. After sampling, all filters were equili-brated in the weighing room for at least 24 h before post weighing,and then theywere divided into quarters using stainless steel cutterfor further chemical component analysis.</p><p>2.3. Chemical analysis</p><p>A 15 cm2 cut piece of the quartz fiber filter samplewas extractedwith 20 mL de-ionized water, filtered and analyzed for major ionschloride, sulfate, nitrate, sodium, potassium, magnesium, calciumand ammonium using a Dionex model ICS-2000 ion chromatog-raphy system (Yin and Harrison, 2008; Yin et al., 2010). The sampleconcentrations were calibrated with a series of mixed standards ofknown concentration (0.01e10 mg mL1).</p><p>One quarter of the filter samples and the soil samples weredigested with HNO3 H2O2 (2:0.5) by microwave assisted diges-tion (CEM Co., MARS), and diluted with Mill-Q water (18.2 MU cm,Millipore). A suite of 8 tracer elements (Al, Cu, Fe, Pb, Ni, V, Zn andMn) in the filter samples were quantified using Inductively-Coupled Plasma Mass Spectrometry (Agilent 7500a). Multiple in-ternal standard isotopes of many of the target elements wereanalyzed as part of the overall data quality assessment. Mixedmetal calibration standard solutions used for the analysis wereobtained from the National Center for Standard Materials (China),with a series of known concentrations (0.1e100 ng mL1). Non-filter/filter and reagent blanks were also used to ensure a goodanalytical precision.</p><p>Lead isotope ratios in both filter and soil sample digests weremeasured by quadruple ICP-MS (Agilent 7500a) operated in stan-dard mode, at a dwell time of 2.5 cms and quadruple settle time of1000 ms to minimize the plasma effects. Solution of the Pb wireisotope standard NIST-981 was used to determine correction fac-tors for the detector dead time and for mass bias (K-factors) duringanalysis. Only data measured in true pulse-counting mode wereused to determine isotopic abundances; data were rejected if thedetector tripped to an analog signal (Cheng and Hu, 2010).</p><p>OC and ECweremeasured by DRI-2001AOC/EC Analyzer (Model2001 A, Desert Research Institute) with a detection limit of0.2 mg cm2. A small piece of the filter (0.518 cm2) was analyzedusing the IMPROVE heating procedure (Cheng et al., 2010), with OCfractions released at the first stage under carrier gas He at tem-peratures 140e580 C and EC fractions at the second stage in a</p></li><li><p>Q. Liu et al. / Atmospheric Environment 91 (2014) 85e94 87helium and oxygen atmosphere at temperatures 580e840 C. Atthe end of each sample analysis, quantitative internal standard gas(CH4) was injected into the system to calibrate the FID signals, andthe final results. The OC/EC split was determined using a lasersignal based on that the amount of carbon pyrolyzed in the firststage equals to the amount of carbon burnt off the filter in thesecond stage, which brings the laser reflectance value to its initiallevel. A repeat sample run was carried out for every 9 samples tocheck the instrument precision, for which a 10% or less is accepted;otherwise all 9 samples must be re-run. External standards, sucroseand potassium hydrogenphthalate (KHP)were used to calibrate thesystem regularly. The response deviation of the standard solutionless than 5% indicates that the instrument is stable for analyzingsamples.Fig. 1. a) Trends in PM10 mass concentration at sites S1 and S2 in Beijing from March to Masampling period, the time interval is 24 h. Dust storm samples were highlighted by ellipse2.4. The PMF model</p><p>PMF assumes that concentrations at receptor sites are impactedby linear combinations of source emissions, which are derived asfactors in the model. In this study, PMF was applied to PM10 massconcentration that were collected at sampling sites to identifyemission sources and quantify the contributions of these sources.We used a PMF 3.0 for the current analysis, as shown in Eq. (1):</p><p>Xij Xp</p><p>k1gik,fkj eij (1)</p><p>The data set can be expressed as a matrix X of i by j dimensions,where i is the number of samples and j is the species measured.y 2012; b) The temporal variation of meteorological variables at the site S2 during thecircle.</p></li><li><p>Fig. 2. The variation of water soluble ions in PM10 at the sites S1 and S2 during sam-pling time in Beijing. Dust storm samples were highlighted by ellipse circle.</p><p>Q. Liu et al. / Atmospheric Environment 91 (2014) 85e9488Additionally, gik is the concentration of chemical species, fkj is themass fraction, eij is the residual of the jth species concentrationmeasured in the ith sample, and p is the total number of the in-dependent factors. The objective of a PMF analysis is to determinethe number of factors p, the chemical composition profile fk, thefactor contributions gk, and residuals e. Concentrations (Xij) andtheir associated uncertainties were prepared according to themethod suggested by Zhang et al. (2010a). The PMF 3.0 determinessignal-to-noise ratio (S/N) statistics for each input species and al-lows the user to downgrade the importance or remove species withsmall S/N values (Liu et al., 2014). Among the species calculated, allspecies were found to have S/N levels more than 3, and wereconsidered strong.</p><p>2.5. Air mass back trajectories and data analysis</p><p>Air mass back trajectories were calculated using the NationalOceanic and Atmospheric Administration (NOAA)s ARL HYSPLIT4.0 model (with meteorological data from the Global Data Assim-ilation System, GDAS). 24 h backward trajectories,...</p></li></ul>