A multidisciplinary approach to trace Asian dust storms from source to sink

Atmospheric Environment 105 (2015) 43e52

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A multidisciplinary approach to trace Asian dust storms from source to sink Yan Yan a, b, *, Youbin Sun a, c, *, Long Ma a, b, Xin Long a, b a

State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710075, China University of Chinese Academy of Sciences, Beijing 100049, China c Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an 710049, China b

h i g h l i g h t s
A multidisciplinary approach can efficiently trace the source of Asian dust storms.
Spring dust storms can be attributed to natural and anthropogenic origins.
The northern Chinese deserts are the main sources for the natural dust storms.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 September 2014 Received in revised form 14 January 2015 Accepted 18 January 2015 Available online 19 January 2015

Tracing the source of dust storm (DS) in mega-cities of northern China currently suffers ambiguities from different approaches including source-sink proxy comparison, air mass back trajectory modeling, and satellite image monitoring. By integrating advantages of all three methods, we present a multidisciplinary approach to trace the provenance of dust fall in Xi'an during the spring season (March to May) of 2012. We collected daily dust fall to calculate dust flux variation, and detected eight DS events with remarkable high flux values based on meteorological comparison and extreme detection algorithm. By combining MODIS images and accompanying real-time air mass back trajectories, we attribute four of them as natural DS events and the other four as anthropogenic DS events, suggesting the importance of natural and anthropogenic processes in supplying long-range transported dust. The primary sources of these DS events were constrained to three possible areas, including the northern Chinese deserts, Taklimakan desert, and Gurbantunggut desert. Proxy comparisons based upon the quartz crystallinity index and oxygen isotope further confirmed the source-to-sink linkage between the natural DS events in Xi'an and the dust emissions from the northern Chinese deserts. The integration of geochemical and meteorological tracing approaches favors the dominant contribution of short-distance transportation of modern dust fall on the Chinese Loess Plateau. Our study shows that the multidisciplinary approach could permit a better source identification of modern dust and should be applied properly for tracing the provenance fluctuations of geological dust deposits. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Dust storm Source tracing Satellite image Back trajectory analysis Proxy comparison

1. Introduction Dust storm (DS) is a disastrous natural phenomenon, which could bring serious damage to downwind human habitats, including causing soil degradation, breaking down industrial machineries, disrupting traffics, harming vegetation and crops, deteriorating air quality, and inducing acute respiratory diseases (Zhang

* Corresponding authors. 97 Yanxiang Road, Yanta zone, Xi'an 710061, China. E-mail addresses: [email protected] (Y. Yan), [email protected] (Y. Sun). http://dx.doi.org/10.1016/j.atmosenv.2015.01.039 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

et al., 2003a; Chen et al., 2004; Kan et al., 2007; Baddock et al., 2013). High dust loading in the air due to the DS events could also impact the solar radiation balance, cloud formation, secondary pollutant generation, and marine primary productivity, which have far more complex influence on the ecosystem (Zhuang et al., 1992; Zhang et al., 1994; Miller and Tegen, 1998; Jickells et al., 2005; Uno et al., 2009). Dust provenance studies in downwind mega-cities thus could provide critical information for government policymaking in DS damage control, and identify the parental material of the entrained dust for further assessment of its climatic, environmental and societal impacts. DS is also the primary dust

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transport mechanism in loess formation, which formed the Chinese Loess Plateau (CLP), the most valuable continental archive of past monsoonal climate (Liu, 1985; Zhang et al., 1997; An, 2000; Prins et al., 2007). Provenance studies of deposited “modern loess” (i.e., dust fall) are therefore critical to provide modern analogies on interpreting loess provenance. Modern observation and modeling results had identified the Badain Juran and Tengger deserts (referred to as the northern Chinese deserts hereafter), Taklimakan desert, and Mongolian Gobi desert as the prime sources of modern dust in East Asia (Sun et al., 2001; Zhang et al., 2003b). Other areas including the Qaidam basin, Gurbantunggut desert, Mu Us desert, Hobq desert, and Horqin and Onqin Daga sandy lands were disputably considered as minor dust sources (Sun et al., 2001; Prospero et al., 2002; Zhang et al., 2003a,b; Shao et al., 2003; Qian et al., 2004; Laurent et al., 2006). To determine the sources of the dust deposits on the land and in the ocean, previous studies mainly followed the methodology of source-sink proxy comparison, including elemental ratios (Zhang et al., 1996; Lee et al., 2010b), rare earth elements (Lee et al., 2010a), mineralogy (Li et al., 2007), NdeSr isotopes (Chen et al., 2007; Lee et al., 2010a), UePb age spectrum (Stevens et al., 2010; Pullen et al., 2011; Xiao et al., 2012), Electronic Spin Resonance (ESR) signal intensity of the E1' center and crystallinity index (CI) of quartz (Sun et al., 2007, 2013), and quartz oxygen isotope (d18O) (Yan et al., 2014). These studies provided reasonable constraints on the dominant sources of aeolian deposits on the Chinese Loess Plateau, consistent with the meteorological and modeling evidence. Proxy comparison, however, is often insufficient to make unambiguous identification of dust sources, since the potential sources may share similar geological and geochemical characteristics to a certain extent. For example, differentiating the dust of the Qaidam Basin from that of the northern Chinese deserts remains difficult by applying NdeSr isotopes (Chen et al., 2007) and quartz d18O (Yan et al., 2014). Similarly, mixing of dust from different sources may account for similar UePb age spectrums of zircon in Chinese loess deposits (Pullen et al., 2011; Che et al., 2013; Stevens et al., 2010). Alternatively, meteorological methods, including satellite images and air mass back trajectory modeling have been used to monitor the DS activities and dust transport routes, which could preclude irrelevant source candidates (e.g. Fang et al., 2004; Li et al., 2007; Yuan et al., 2008; Feng et al., 2008; Li et al., 2009; Lee et al., 2010a; Lee et al., 2010b; Tsai et al., 2014). To ascertain DS activities affecting certain sink areas, a robust source-to-sink linkage should be supported by satellite images that prove dust entrainments in the sources, air mass trajectories that pass through dust plumes in the sources and arrive at the sink, and similar physiochemical characteristics of the dust particles. Previously, only Lee et al. (2010a,b) tried to integrate these three methodologies to trace the provenance of local dust aerosols. The representativeness of daily air mass trajectories in the studies, however, fell short in constraining possible source candidates for the studied DS events. In this study, we conduct a multidisciplinary investigation on the provenance of DS events occurring in Xi'an, southern Chinese Loess Plateau, in the spring of 2012. Our investigation is based on the three methodologies described above with a better representativeness of air movements by hourly back trajectory modeling. We collected daily dust fall, determined the flux variation time series, and identified eight DS events based on meteorological comparison and extreme detection algorithm. We discussed the transport routes of the spring DS events through air mass back trajectory modeling. Combining satellite images and accompanying real-time hourly air mass back trajectories, we separated the DS events into four natural and four anthropogenic DS events, analyzed the influences of natural dust sources and anthropogenic activities on them, and further constrained the dust sources of the

four natural DS events to the Taklimakan desert, Gurbantunggut desert, and the northern Chinese deserts. Additional proxy comparisons, including quartz crystallinity index and oxygen isotope, confirmed the material linkage between the natural DS samples and the northern Chinese deserts, suggesting the northern Chinese deserts as the primary sources for natural DS events in Xi'an in the spring of 2012. 2. Sampling and methodology 2.1. Sampling daily dust fall in Xi'an In this work, we adopted the wet collection method (Qian and Dong, 2004) to collect daily dust fall in Xi'an, which lies on the southern CLP, downwind to the well-known natural dust sources in East Asia (Fig. 1). The sampling site is situated at the southwestern part of Xi'an, which is surrounded by commercial and residential districts, and some manufacturing factories. To avoid fugitive dust from the ground, four glass dust collectors were placed adjacently 1 m above the roof of a four-storied building with bottoms covered by sufficient water during the sampling intervals. 86 dust samples were collected at 14 O'clock everyday from March 8 till June 1, 2012. No obvious dust pollution had been observed from surrounding districts during the whole sampling campaign. Dust samples were sifted using a 100 mesh sieve to remove the fallen leaves and insects, dried at 40  C, and then weighed. The daily dust flux was calculated using the following formula:

Flux ¼ weight=ðarea*timeÞ where weight is in gram, area is in m2, and time is in day. To analyze the relationship of dust deposition and meteorological factors, daily maximum wind speed and rainfall were acquired from the global weather profile dataset provided by the China Meteorological Data Sharing Service System (http://cdc.cma. gov.cn). The CLIM-X-DETECT algorithm (Mudelsee, 2006), a robust time-dependent extreme detection method, was applied on the time series of the dust flux to discern the DS events. 2.2. Dust storm detection using MODIS images Satellite image is the best choice for large-scale and long-term dust storm detection (Akhlaq et al., 2012). There are four most commonly used satellite imaging devices, i.e. MODIS (MODerate resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer), GOES (Geostationary Operational Environmental Satellites), and SEVIRI (Spinning Enhanced Visible and Infrared Imager). Both GOES and SEVIRI provide 15-min temporal resolution with no less than 1000-m spatial resolution, while AVHRR provides 1-day and 1000-m resolutions. MODIS, however, provides a spatial resolution of 250e1000 m with the same 1-day temporal resolution. MODIS also provides global monitoring in 36 spectral bands, whose wavelengths range from 0.415 mm (visible) to 14.235 mm (infrared), while the others have no more than 12 spectral bands. Since the one-day temporal resolution is enough, we adopted MODIS because of its better spatial resolution and more information channels. All MODIS images were acquired from the Goddard Space Flight Center, NASA (http://ladsweb.nascom.nasa. gov), provided by the Earth Observing System Terra and Aqua satellites. Robust extraction of DS coverage from infrared and/or visible bands using a variety of algorithms remains challengeable. In our test runs, we found that BTD11e12 (bright temperature difference between 11 and 12 mm bands, Ackerman, 1997), and TIIDI (thermal infrared integrated dust index, Liu and Liu, 2011) can not accurately

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Fig. 1. Geographic setting of the studied area. The boundaries of inland drainage basins and distribution of loess, deserts, and Gobi deserts follow the same origins in Yan et al. (2014). MG-Mongolian Gobi desert, Gur-Gurbantunggut desert, TK-Taklimakan desert, QB-Qaidam Basin, BJ-Badain Juran desert, TG-Tengger desert, MUeMu Us desert, and CLPChinese Loess Plateau. The northern Chinese desert will be used instead of BJ and TG in the discussion.

detect dust storms over different deserts in East Asia, probably due to different properties of the underlying surfaces. Moreover, data were missing in stripes in 8.6 mm band (band 26) during the studied period, causing indices based on the band unusable. New proxies, such as artificial neural network based BTD (El-ossta et al., 2013), DAI (dust aerosol index, Ciren and Kondragunta, 2014), etc., are reported to be accurate for around 70 %e90 % of the cases, while more indices have not been validated in long-term studies (e.g. Han et al., 2013; Samadi et al., 2014; Park et al., 2014). On the other hand, DS activities are easily discerned by the naked eye from the truecolor image composed from visible bands. Visual detection still serves as a useful reference for the dust detection algorithms (Elossta et al., 2013; Han et al., 2013; Ciren and Kondragunta, 2014). Since the existence of a dust storm is our focus, we resorted to manual recognition to guarantee the DS detection accuracy. Two true-color images can be obtained every day. The acquisition times are both in the morning with one to two hours apart. During the DS events, the average maximum surface wind speed varies between 11 and 13 m/s in the northern Chinese deserts (Zhao et al., 2013) and between 6 and 17 m/s in different parts of Xinjiang province (Chen et al., 2003). Since the average distance between the source and sink is around 1000 km for the northern Chinese deserts, and more for other sources, by calculation, it would take around a day or more for dust storms to propagate to the southern CLP from the potential sources. The temporal resolution of MODIS images should be enough to detect major dust storm events. Notably, since the detritus material in the sources should mostly originate from the high terrace within their corresponding interior drainage basins (Bullard et al., 2011; Yan et al., 2014), we refer to the sources as confined by the boundaries of the interior drainage basins (Fig. 1). Dust storms inside different drainage basins are thus considered originating from corresponding sources.

2.3. Back trajectory analysis Air mass trajectory analysis describing the paths of air parcels dates back to 1940, when Petterssen (1940) first introduced a graphical trajectory calculation method. After decades, more than ten trajectory models had been developed, among which the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT,

Draxler and Hess, 1997, 1998; Draxler, 1999) and FLEXible TRAjectory (FLEXTRA, Stohl et al., 1995) models are the most widely employed (Fleming et al., 2012). Because the air movements link the airborne materials from source to sink, back trajectory analysis was applied to trace sources of pollutants in as early as 1970s (Fox and Ludwick, 1976; Fleming et al., 2012). In the last decade, this method has been employed intensively to trace the provenance of dust and pollutant in East Asia (e.g. Li et al., 2007; Yuan et al., 2008; Wehner et al., 2008; Li et al., 2009; Lee Y. et al., 2010b; Lee M. et al., 2010a; Tsai et al., 2014). Here we adopted the HYSPLIT model developed by the National Oceanic and Atmospheric Administration (NOAA) to calculate back trajectories. The 3-hourly archive data at 1 latitudeelongitude resolution, GDAS1, from the National Centers for Environmental Prediction (NCEP) was used to drive the model. Since Asian dust is mostly transported to the CLP by surface northwesterly winds (mostly the winter monsoon) (Zhang et al., 1999; Roe, 2009), we set the initial tracking elevation to 500 m. Test runs of the model suggest that the air paths do not vary much within hours, but could change dramatically within one day. We thus calculated hourly trajectories to simulate air mass trajectories during the whole sampling interval. According to the known average maximum surface wind speed of dust storms described above, it would generally take around four days for air masses to travel more than 3000 km from the farthest sources (e.g., the western Taklimakan desert) to Xi'an. Tracking duration of the trajectories was thus set to four days.

2.4. Measurements of quartz-based tracers Quartz crystallinity index (CI) and oxygen isotope (d18O) are both proxies reflecting quartz formation information. These proxies stay stable during diagenetic and weathering processes (Sridhar et al., 1975; Clayton et al., 1978; Sun et al., 2008). CI is determined by the formation temperature and speed of crystallization of quartz (Murata and Norman, 1976), while d18O is mainly controlled by the formation temperature (Chacko et al., 2001). Both proxies had been proven good source tracers of Asian dust and Chinese loess (Nagashima et al., 2007, 2011; Sun et al., 2007, 2008, 2013; Gu et al., 1987; Ishii et al., 1995; Yan et al., 2014), and were shown to be

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more efficient, if they are used in combination (Yan et al., 2014). These two tracers are used for source-sink proxy comparison in this study. Since fine and coarse dust particles are transported through different processes (Pye, 1987; Zhang et al., 1999), CI and d18O of fine and coarse quartz are measured and discussed separately. Consistent with previous studies (Sun et al., 2013; Yan et al., 2014), selected DS samples were first separated into two size fractions (i.e. <16 and 16e63 mm) using wet sieving and the pipette method based on Stokes’ law (Lerman et al., 1974). To measure quartz CI, samples were treated subsequently with hydrogen peroxide (H2O2), acetic acid (CH3COOH), and sodium dithionite (Na2S2O3)-sodium citrate (Na3C6H5O7$2H2O) buffered with sodium bicarbonate (NaHCO3) to remove organic matter, carbonate, and iron and manganese oxides, respectively (Rea and Janecek, 1981; Sun et al., 2007). Sample powder mixed quantitatively with standard Si powder (Wako, Co. Ltd, Japan) was grounded in an agate mill, then placed in a glass holder and irradiated with a Cu Ka source under 40 KV and 40 mA conditions to obtain its X-ray diffractogram. CI was calculated as following (Murata and Norman, 1976):

CI ¼ 10  F1  a=b where the scaling factor F1 is estimated to be 1.21 (Sun et al., 2013), a is the height of the (212) peak at 2q of 67.74 , b is its total height against the background. Quartz CI was measured using an X'pert Pro MPD X-ray diffractometer (XRD) at the Institute of Earth Environment, University of Chinese Academy of Sciences. The relative standard deviation of CI measurements was estimated below 1.5% by repeated measurements of a standard sample of the Lab (Wako quartz, CI ¼ 10). For d18O measurements, samples were first treated with H2O2 and hydrogen chloride (HCl) to remove organic matter, carbonate, and iron and manganese oxides. Sample powder was then fused with potassium pyrosulfate (K2S2O7) at 640  C for 45 min to remove clay minerals (Kiely and Jackson, 1965; Sun et al., 2000), soaked in fluosilicic acid (H2SiF6) for three days to dissolve feldspars and amorphous materials (Sridhar et al., 1975), and finally soaked in saturated boric acid for one night to remove fluorides (Jackson et al., 1974). Oxygen of the extracted quartz was liberated by reacting with bromine pentafluoride (BrF5) at 550  C, and then turned into CO2 by reacting with a graphite rod at 700  C. Oxygen isotope composition is then determined and calculated using the usual d notation as per mil deviations from Standard Mean Ocean Water (SMOW):

baseline-spike pattern (Fig. 2). Among the 86 days, dust flux remained under 0.5 g m2$day1 on 56 days, and above 1.0 g m2$day1 on 13 days only. The average flux is 0.7 g m2$day1, with the lowest flux being 0.1 g m2$day1 on May 3 and the highest flux being 8.2 g m2$day1 on April 2. Urban dust fall consists of dust input from local sources as well as regional sources out of local vicinity. It is possible that dust induced by human activities from local sources might have a strong influence on local dust flux fluctuation. Urban human activities such as traffic, manufacturing, and construction normally follow a consistent daily pattern. If certain activities could emit a large quantity of dust that impacted our sampling site, they would have resulted in a consecutive high dust flux baseline rather than sporadic narrow spikes. Abnormal activities such as short-term building demolition or explosion, which might result in the flux spikes, were not observed during the sampling campaign. It is thus reasonable to assume that the dust flux fluctuation mostly relies on the meteorological factors. Wind speed and rainfall are two key meteorological factors controlling dust deposition. High dust fluxes always happened on rainy and/or windy days (Fig. 2). High wind speed would entrain more dust into the air, and consequently increase the dust deposition, while rainfall works as a super detergent for the atmosphere, effectively rinsing off the dust and raising the dust flux. High wind speed or rainfall alone, however, does not guarantee flux extremes. During the wet deposition days, no significant correlation was identified between the rainfall and dust flux (correlation coefficient less than 0.1). Among the rainy days, there were 7 days with dust flux over 1.0 g m2$day1, including the highest flux (8.2 g m2$day1) on April 2 with limited rainfall (<1 mm). In contrast, the dust flux on the day with the heaviest rainfall (27.3 mm on May 29) was merely 0.2 g m2$day1. The amount of precipitation apparently shows a minor influence on the dust flux. The consistency between the rainfall initiations and the spiking dust fluxes, however, explicitly indicates that rainfall would always increase the dust fall at the beginning, but not necessarily result in high dust flux afterwards. For dry deposition, the correlation coefficient between the flux and wind speed on non-raining days is less than 0.3, suggesting no significant linear correlation. Among these days, dust flux exceeded 1.0 g m2$day1 on 6 days, with the highest flux (6.3 g m2$day1) on March 19 corresponding to strong wind speed (12.3 m/s). However, wind speeds were also very high on March 11 and 18 (10.1e11.1 m/s), corresponding to relatively low dust flux

   d18 OSASMOW ¼ d18 OSAR þ 103  d18 OSTSMOW    þ 103 = d18 OSTR þ 103  103 where d18OSA e SMOW is the d18O of the sample relative to SMOW, d18OSA e R is the d18O of the sample relative to the referential CO2, d18OST e SMOW is the d18O of the standard sample relative to SMOW, and d18OST e R is the d18O of the standard sample relative to the referential CO2. Quartz d18O was analyzed using a Finnigan Mat 251 Mass Spectrometer at the Institute of Mineral Resources, Chinese Academy of Geological Sciences. The analytical precision of the instrument is 0.2‰. 3. Results and discussion 3.1. Daily spring dust flux and its relationship with meteorological factors The variation of the spring dust flux shows a prominent

Fig. 2. Time series of dust flux, wind speed and rainfall. Solid gray line denotes the time-dependent threshold calculated by the CLIM-X-DETECT algorithm (Mudelsee, 2006). Gray bars highlight the detected dust storm (DS) events.

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(<1.0 g m2$day1). Furthermore, under similar meteorological conditions, including rainfall and comparable wind speed, dust flux reached 2.8 g m2$day1 on May 21, while it stayed around 0.3 g m2$day1 on March 15 and 0.5 g m2$day1 on March 28. The drastic flux contrasts in the cases above with comparable high wind speed, rainfall, or both, suggest that local dust input is very limited even under favorable synoptic conditions. The high dust fluxes thus suggest more dust input from regional sources rather than local ones, which can be presumably considered as DS events. 3.2. DS events and their transport routes There is not a predetermined dust flux threshold to distinguish the DS events from the daily flux variation. Applying the CLIM-XDETECT algorithm, 11 flux extremes had been detected during the spring of 2012 in Xi'an. Flux extremes on single days or two consecutive days can be viewed as one event. 8 DS events were thus identified as on March 19 (DS 1), March 23 (DS 2), April 2 and 3 (DS 3), April 9 (DS 4), April 12 (DS 5), April 24 and 25 (DS 6), May 11 (DS 7), and May 20 and 21 (DS 8), respectively. It is noteworthy that the dust flux is 1.2 g m2$day1 for DS 4, and 1.1 g m2$day1 for DS 7,

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lower than that on April 30 (1.4 g m2$day1). This might be attributed to the arbitrariness of the statistical detection method, which exists in all statistical extreme event detection methods. The net dust deposition flux for a DS event on the CLP was estimated between 1.6 and 4.3 g m2$day1 (Shao et al., 2003), suggesting that our detection is nonetheless sufficient to include all DS events. We established the connection between the DS events and the dust input from regional sources. Whether these DS events were attributed to the well known natural dust sources or regional anthropogenic sources, as suggested by previous studies (Fu et al., 2008; Lee et al., 2007; Wehner et al., 2008; Moreno et al., 2011), needs further proof. Hourly air mass back trajectories were calculated for every DS event. The results show that air masses of four DS events (DS 2, 3, 5, and 6) mainly follow the northern and northwestern routes, tracing back through the northern Chinese deserts to the Mongolia Gobi desert and Taklimakan desert, respectively (Fig. 3A). Cluster analysis demonstrates that only 8% of the trajectories of the four DS events come from nearby areas to the south, suggesting that the natural dust sources are their potential dust suppliers. Air masses of the other four DS events, including DS 1, 4, 7, and 8, either never went through those natural sources (e.g. DS 1

Fig. 3. Different transport routes of the air masses for the DS events. A shows the trajectories mainly coming from the north and northwest for DS 2, 3, 5, and 6. B shows those mainly from the south for DS 1, 4, 7, 8.

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and 4), or partially went through the Taklimakan desert, including 6 trajectories of DS 7 and 3 of DS 8 (Fig. 3B). Only 9% of the trajectories of these four DS events went through known natural dust sources, while most of them came from nearby places to the south and southeast of Xi'an. This suggests that the human habitats rather than natural dust sources likely played a key role in supplying dust for these four DS events. Among the 264 trajectories of all DS events, less than 10 trajectories went through the Qaidam Basin. There are more trajectories, still significantly fewer in comparison with other routes, going through other parts of the Tibetan Plateau. The high topographic setting is clearly a powerful constraint preventing air masses from the Tibetan Plateau interfering with the surface wind system on the CLP. Evidence further shows no dust storms happened in the Qaidam Basin and on the Tibetan Plateau during these DS events (see section 3.3). Therefore, the Qaidam Basin is a less important source of spring dust storms on the CLP in modern days, consistent with meteorological observation and modeling result (Sun et al., 2001; Zhang et al., 2003b).

3.3. Impacts of natural and anthropogenic sources on the DS events We composed the true-color satellite images, incorporated with air mass trajectories calculated to the acquisition time of the images, which could provide real-time glimpses into the history of dust entrainment and air mass movements from source to sink. If the air masses of a DS event are found in the dust plumes of certain source area on the satellite images, we identify the source as a dust supplier for the DS event. Note that a composed MODIS image of three or more original images was needed to cover all potential

sources. The acquisition time of the composed image was then assigned the same time of the original image covering more area of these potential sources. Air mass trajectories were calculated to the assigned times, which led to around one to two hour time discrepancies between the termination points of the trajectories and the composed MODIS images. Such discrepancies, however, do not affect our discussion. From the composed images, we found that there were no trajectories of DS 1 and 4 deriving from the natural dust sources, implying that the corresponding DS events are most likely of anthropogenic origin (Fig. 4A and B, for simplicity, only representative images are shown). During the four days prior to DS 7 and 8, there were no dust plumes transporting from the natural sources to the CLP, and the corresponding air masses were not in dust plumes (Fig. 4C and D), indicating the anthropogenic origin of these two DS events. The majority of the trajectories of these anthropogenic DS events, however, did pass some gray dusty clouds to the south and southeast of Xi'an (Fig. 4). Examination of multiple satellite images assured the anthropogenic origin for those dusty clouds, suggesting possible haze events and therefore the linkage between the anthropogenic DS events and haze events. For the other four DS events (DS 2, 3, 5, and 6), satellite images recorded that dust plumes in the sources progressively propagated to the east and southeast. More importantly, the corresponding air mass trajectories were obviously passing through the dust plumes in various sources. Air mass trajectories of DS 2 exclusively passed through the dust plumes in the northern Chinese deserts on March 22, which arrived at Xi'an on March 23 (Fig. 5A and B), even though there were also DS activities in the Taklimakan desert. For DS 3, only one trajectory was recorded passing through a thin dust plume

Fig. 4. MODIS images with accompanying real-time air mass back trajectories for anthropogenic DS events. Gray trajectories are four-day trajectories. The termination points of the red ones indicate the positions of the air masses at the acquisition time of the images. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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in the Taklimakan desert on March 31, while more passed through the dust plumes in the northern Chinese deserts on April 1 (Fig. 5C and D). There were DS activities detected in the Taklimakan desert

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and northern Chinese deserts for DS 5 as well. Its four day trajectories were, however, only traced back into the dust plumes in the northern Chinese deserts on April 10 and 11 (Fig. 5E and F). Air mass

Fig. 5. MODIS images with accompanying real-time air mass back trajectories for natural DS events. Dashed yellow lines confine the dust plume areas. Red trajectories mean the same as in Fig. 4. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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trajectories of DS 6 on April 24, on the other hand, passed through dust plumes in the Taklimakan desert and Gurbantunggut desert on April 22, and the northern Chinese deserts on April 23 (Fig. 5G and H). We thus assured that these DS events were originated from the natural dust sources. It is worth noting that the four natural DS events yielded 19.90 g/m2 of dust fall in the spring of 2012, contributing 31% of the total spring dust deposition. By contrast, the four anthropogenic DS events amounted to 12.4 g/m2, with a 20% contribution to the total spring dust deposition. The dust flux of the severest anthropogenic DS on March 19 (6.3 g m2$day1) is comparable to that of the severest natural DS on April 2 (8.2 g m2$day1). This suggests that human habitats have played an important role in providing longrange transported mineral dust to the atmosphere. In addition, lots of trajectories went through the Mongolian Gobi desert. There were dust entrainments in the Mongolian Gobi desert, while no trajectories in the dust plumes were detected. This suggests that the Mongolian Gobi desert is an unlikely source for dust fall in Xi'an in the spring of 2012.

3.4. Constraint on the primary sources of spring DS events During different natural DS events, air masses passed through dust plumes in one or more sources, including the Taklimakan desert, Gurbantunggut desert, and northern Chinese deserts. The primary sources of these natural DS events can be further constrained by applying provenance tracer comparison. Quartz CI and

d18O of three DS samples (DS 3, 5, and 6) were compared with those from the desert surface samples. DS 2 was excluded because of the sample insufficiency. For fine dust, quartz CI is 9.2, 9.5, and 8.9 for DS 3, 5, and 6, respectively, and the corresponding quartz d18O is 16.7, 16.1, and 16.6‰. We plotted quartz d18O against CI of three DS samples and the source inventory (Fig. 6a). The lowest quartz CI for DS 6 is still higher than the maximum quartz CI (8.8) for both the Taklimakan desert and Mongolian Gobi desert. All three DS samples thus fall roughly into the range of the northern Chinese deserts. For coarse dust, quartz d18O is 15.3‰ for DS 3, 15.4‰ for DS 5, and 15.6‰ for DS 6. Paired quartz d18O values of fine and coarse dust in three DS samples share the same characteristics with those of the northern Chinese deserts (Fig. 6b). Since they fall between the Taklimakan desert and Mongolian Gobi desert, this could also mean that the studied dust is the mixture of dust from both the Taklimakan desert and Mongolian Gobi desert. The Mongolian Gobi desert is, however, not a potential source for these DS events. The mixing interpretation thus does not apply here. For the DS 3, dust plumes were identified in the Taklimakan desert and northern Chinese deserts. Based on the quartz CI and d18O characteristics, the primary source of the coarse and fine dust can be confined to the northern Chinese deserts. Satellite images and back trajectory analysis indicated that DS 5 was only originated from the northern Chinese deserts, which is further confirmed by the tracer comparison. However, the situation is more complicated with DS 6. Beside the Taklimakan desert and northern Chinese deserts, the Gurbantunggut desert is also the possible primary source. Being deemed as a subordinate dust source, the Gurbantunggut desert was not considered in previous quartz d18O study, while the CI of fine quartz of the Gurbantunggut desert falls into the same value range of the northern Chinese deserts (Sun et al., 2007). The Gurbantunggut desert is therefore indistinguishable by quartz CI-d18O comparison. There is nonetheless no reason to think its role in DS 6 should be any different from that of the Taklimakan desert in DS 6 and DS 3. In addition, after the trajectories passed the Gurbantunggut desert, most of them passed the dust plumes in the Taklimakan desert and/or northern Chinese deserts again (Fig. 5G and H). It is reasonable to assume that they acquired more dust later. The slightly lower quartz CI of DS 6 might be resulted from the dust contribution from the Taklimakan desert and/or Gurbantunggut desert (Fig. 6a). Its prime source, however, should still be the northern Chinese deserts. We therefore conclude that the northern Chinese deserts are the prime sources for both fine and coarse DS dust on the southern CLP in the spring of 2012. This conclusion is consistent with previous provenance understanding of dust depositions on the CLP (Sun et al., 2001, 2008; Li et al., 2009), also in favor of the short-distance deposition theory of Chinese loess (Chen and Li, 2011). 4. Conclusions

Fig. 6. Bivariate plots of quartz d18O versus quartz CI of the fine fraction (a), and quartz d18O of both fine and coarse fractions (b).

We collected daily dust fall in Xi'an in the spring of 2012, and identified eight DS events using the CLIM-X-DETECT algorithm. By combining MODIS images and real-time air mass back trajectories, these DS events were divided into two types, originating from various natural or anthropogenic sources. Similar dust flux intensities between the natural and anthropogenic DS events suggest that more attention should be paid to the influence of anthropogenic activities on urban mineral aerosols and to environmental impacts of anthropogenic dust. By tracing air masses back into the dust plumes in diverse sources, the primary sources of the natural DS events were confined to three possible sources, including the northern Chinese deserts, Taklimakan desert, and Gurbantuggut desert. Source-sink comparisons of quartz CI and d18O further confirmed the northern Chinese deserts as their prime sources.

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Since the sampling campaign was limited to a single spring season, the conclusions based on the eight DS events need to be validated by long-term investigation of the urban mineral dust variations on seasonal to annual timescales in future. Nevertheless, our study indicates that a multidisciplinary approach by integrating satellite image monitoring, air mass trajectory modeling, and proxy comparison can successfully constrain the primary sources of modern dust storms. This integrated approach should be also properly employed to trace the provenance of Cenozoic aeolian depositions on the land and in the ocean, since the source range, source-to-sink distance, past atmospheric circulations, and topography likely changed significantly in the geological past. Acknowledgments We thank Dr. Singh and two anonymous reviewers for their constructive comments. We are also grateful for the help of Defang Wan with oxygen isotope measurements, and Susan Clemens with language polishing. This work was supported by the Natural Science Foundation of China (No. 41072272) and Key Innovation Project (KZCX-EW-114) from the Chinese Academy of Sciences. References Ackerman, S.A., 1997. Remote sensing aerosols using satellite infrared observations. J. Geophys. Res. 102, 17069e17080. Akhlaq, M., Sheltami, T.R., Mouftah, H.T., 2012. A review of techniques and technologies for sand and dust storm detection. Rev. Environ. Sci. Bio/Technology 11 (3), 305e322. An, Z., 2000. The history and variability of the East Asian paleomonsoon climate. Quat. Sci. Rev. 19 (1), 171e187. Baddock, M.C., Strong, C.L., Murray, P.S., McTainsh, G.H., 2013. Aeolian dust as a transport hazard. Atmos. Environ. 71, 7e14. Bullard, J.E., Harrison, S.P., Baddock, M.C., Drake, N., Gill, T.E., McTainsh, G., Sun, Y., 2011. Preferential dust sources: a geomorphological classification designed for use in global dust - cycle models. J. Geophys. Res. 116 (F4). Che, X.D., Li, G.J., 2013. Binary sources of loess on the Chinese Loess Plateau revealed by U-Pb ages of zircon. Quat. Res. 80, 545e551. Chen, H.W., Wang, X., Ma, Y., 2003. Effects of strong winds on sandstorms in Xinjiang. Acta Sci. Nat. Univ. Pekin. 39 (2), 187e193 (in Chinese). Chen, J., Li, G.J., Yang, J.D., Rao, W.B., Lu, H.Y., Balsam, W., Sun, Y.B., Ji, J.F., 2007. Nd and Sr isotopic characteristics of Chinese deserts: implications for the provenances of Asian dust. Geochim. Cosmochim. Acta 71, 3904e3914. Chen, J., Li, G.J., 2011. Geochemical studies on the source region of Asian dust. Sci. China Earth Sci. 54, 1279e1301. http://dx.doi.org/10.1007/s11430-011-4269-z. Chen, Y.S., Sheen, P.C., Chen, E.R., Liu, Y.K., Wu, T.N., Yang, C.Y., 2004. Effects of Asian dust storm events on daily mortality in Taipei, Taiwan. Environ. Res. 95 (2), 151e155. Chacko, T., Cole, D.R., Horita, J., 2001. Equilibrium oxygen, hydrogen and carbon isotope fractionation factors applicable to geologic systems. Rev. Mineral. Geochem. 43, 1e81. Ciren, P., Kondragunta, S., 2014. Dust aerosol index (DAI) algorithm for MODIS. J. Geophys. Res-Atmos. 119 (8), 4770e4792. Clayton, R.N., Jackson, M.L., Sridhar, K., 1978. Resistance of quartz silt to isotopic exchange under burial and intense weathering conditions. Geochim. Cosmochim. Acta 42, 1517e1522. Draxler, R.R., 1999. HYSPLIT4 User's Guide. NOAA Tech. Memo. ERL ARL-230. NOAA Air Resources Laboratory, Silver Spring, MD. Draxler, R.R., Hess, G.D., 1997. Description of the HYSPLIT_4 Modeling System. NOAA Tech. Memo. ERL ARL-224. NOAA Air Resources Laboratory, Silver Spring, MD, p. 24. Draxler, R.R., Hess, G.D., 1998. An overview of the HYSPLIT_4 modeling system of trajectories, dispersion, and deposition. Aust. Meteor. Mag. 47, 295e308. El-ossta, E., Qahwaji, R., Ipson, S.S., 2013. Detection of dust storms using MODIS reflective and emissive bands. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 6 (6), 2480e2485. Fang, X.M., Han, Y.X., Ma, J.H., Song, L.C., Yang, S.L., Zhang, X.Y., 2004. Dust storms and loess accumulation on the Tibetan Plateau: a case study of dust event on 4 March 2003 in Lhasa. Chin. Sci. Bull. 49 (9), 953e960. Feng, J.L., Zhu, L.P., Ju, J.T., Zhou, L.P., Zhen, X.L., Zhang, W., Gao, S.P., 2008. Heavy dust fall in Beijing, on April 16e17, 2006: geochemical properties and indications of the dust provenance. Geochem. J. 42 (2), 221e236. Fleming, Z.L., Monks, P.S., Manning, A.J., 2012. Review: untangling the influence of air-mass history in interpreting observed atmospheric composition. Atmos. Res. 104, 1e39. Fox, T.D., Ludwick, J.D., 1976. Lead (Pb) concentrations associated with 1000-MB geostrophic back trajectories at Quillayute, Washington. Atmos. Environ. 10,

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