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Characterization of Natural and Affected Environments
Dust Sources in the Salton Sea Basin: A Clear Case of an Anthropogenically Impacted Dust Budget Alexander L. Frie, Alexis C. Garrison, Michael V Schaefer, Steve Bates, Jon Botthoff, Mia Maltz, Samantha C. Ying, Timothy W. Lyons, Michael Allen, Emma Aronson, and Roya Bahreini Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b02137 • Publication Date (Web): 24 Jul 2019 Downloaded from pubs.acs.org on July 27, 2019
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Dust Sources in the Salton Sea Basin: A Clear Case of an
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Anthropogenically Impacted Dust Budget
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Alexander L. Frie 1, Alexis C. Garrison 1, Michael V. Schaefer 1, Steve M. Bates 2, Jon Botthoff 3, Mia Maltz 3,
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Samantha C. Ying, 1 Timothy Lyons 2, Michael F. Allen 3,4, Emma Aronson 3,4, Roya Bahreini* 1
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1Department
of Environmental Sciences, University of California, Riverside, CA 92521
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2Department
of Earth Sciences, University of California, Riverside, CA 92521
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3Center
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4Department
for Conservation Biology, University of California, Riverside, CA 92521 of Microbiology and Plant Pathology, University of California, Riverside, CA 92521
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*Correspondence to: Roya Bahreini (email:
[email protected], phone:(951) 827-4506), address: University of
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California Riverside, Department of Environmental Sciences, 900 University Ave, Riverside, CA, 92521)
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Abstract
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The Salton Sea Basin in California suffers from poor air quality, and an expanding dry lakebed
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(playa) presents a new potential dust source. In 2017-18, depositing dust was collected ~monthly
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at five sites in the Salton Sea Basin and analyzed for total elemental and soluble anion content.
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These data were analyzed with Positive Matrix Factorization (PMF). The PMF method resolved
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seven dust sources with distinct compositional markers: Playa (Mg, SO42-, Na, Ca, Sr), Colorado
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Alluvium (U, Ca), Local Alluvium (Al, Fe, Ti), Agricultural Burning (K, PO43-), Sea Spray (Na,
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Cl-, Se), Anthropogenic Trace Metals (Sb, As, Zn, Cd, Pb, Na), and Anthropogenic Copper (Cu).
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All sources except Local Alluvium are influenced or caused by current or historic anthropogenic
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activities. PMF attributed 55 to 80 % of the measured dust flux to these six sources. The dust fluxes
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at the site where the playa source was dominant (89 g m-2 yr-1) were less than, but approaching the
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scale of, those observed at Owens Lake playas in the late 20th century. Playa emissions in the
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Salton Sea region were most intense during the late spring to early summer and contain high
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concentrations of evaporite mineral tracers, particularly Mg, Ca, and SO42-.
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1.0 Introduction
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Atmospheric mineral dust particles, aerosols sourced from the wind-driven suspension of
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soils, play several important roles in the Earth system. Suspended mineral dust decreases air
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quality by increasing particulate matter (PM) concentrations and shifts the Earth’s radiation budget
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by absorbing and scattering radiation.1 When dust particles deposit, they can be sources of essential
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nutrients or deadly toxicants and also change the Earth’s surface albedo.2-5 Developing an
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understanding of dust is increasingly important, as emissions have been increasing in recent
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decades.6-8 Despite these concerns, knowledge of mineral dust composition and emission into the
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atmosphere is limited.9-11 To better constrain mineral dust influences on air quality, climate, and
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downwind environments, dust sources and their controls must be identified. Here, we characterize
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the sources of dust in the Salton Sea Basin in the American Southwest.
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The Salton Sea Basin in southeastern California is classified as ‘non-attainment’ for
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particulate matter less than 10 µm (PM10) by the US Environmental Protection Agency.12 Mineral
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dust emissions are thought to be the major driver of poor air quality in the American Southwest
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and are often attributed to natural sources.13-16 Compounding this issue is the Salton Sea, a large
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endorheic lake that is shrinking due to changes in water management. The shrinking of endorheic
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lakes is not a local phenomenon; similar systems and impacts are observed throughout the world.17
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The dry lakebeds (playa) are often significant dust sources and represent a clear anthropogenic
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contribution to the dust budget.18-20 Such impacts were observed at Owen’s Lake in California in
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the 20th century when the lakebed became the largest point-source of PM10 in North America.21
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The Salton Sea playa represents a dust source of potentially similar, but uncertain, scale.22
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Here, low-cost samplers, high accuracy compositional measurements, and modern source
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apportionment tools are combined to quantify the impact of specific dust sources on a regional 3 ACS Paragon Plus Environment
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scale. The compositional profiles and temporal variation of seven dust sources, including Salton
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Sea Playa, are identified.
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2.0 Methods
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2.1 Sample Collection and Treatment
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Dust deposition samplers based on the design of Reheis and Kihl were used to collect
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samples for analyses.23 Briefly, a Teflon coated pan (25.4 cm diameter) was inset with a coarse
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Kevlar mesh. Glass marbles (1.5 cm in diameter) were placed on top of the mesh to prevent dust
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resuspension. The pan was protected from avian disruption using two crossed Tanglefoot coated
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straps domed over the pan. All samplers were deployed at a height of ~ 2.5 m, as in Aciego et al.24
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The samplers do not target a specific particle size class, but do have collection efficiency biases
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towards smaller coarse particles (10-20 µm). 25
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Samplers were deployed in duplicates at five sites in the Salton Sea Basin of California:
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Sonny Bono (SB), Wister (WI), Dos Palmas (DP), Boyd Deep Canyon (BD), and Palm Desert
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(PD) (Table 1, Figure S1). Samples were collected approximately monthly from April 28th, 2017
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until May 2nd, 2018, yielding 11 sample sets from all sites except Dos Palmas (Table S1). Access
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to Dos Palmas was not possible on two occasions. Occasionally, samplers were unusable (e.g.,
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bird feces were present) and were excluded from the analysis. It is worth noting that ‘month’ used
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to describe the time of sampling does not correspond to an exact calendar month but to the month
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during which the sampler was deployed the most days. Upon collection, the samples were
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extracted with water, dried in a pre-weighed vial, and weighed (Mettler AE 260, 10-4 g). One
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sample, SB April 2018, was physically extracted using a nylon brush to maintain the integrity of
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soluble minerals and allow physical analysis (Section 2.3). Sample treatment is discussed further
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in the supplemental information (SI).
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Class
Dominate Mass Source
Flux (mg m2 yr-1)
Site
Lat.
Long.
Management
Sonny Bono (SB)
33.19
-115.6
USFW
On-Playa
Playa
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Wister (WI)
33.28
-115.60
CAFW
Managed Wetland
Colorado & Local Alluvium
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Dos Palmas (DP)
33.49
-115.84
BLM
Open Colorado Desert
Local Alluvium
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Palm Desert (PD)
33.77
-116.35
UC
Urban
Local Alluvium
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Boyd Deep (DP)
33.65
-116.37
UC
Protected Canyon
Local Alluvium
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92 93 94 95 96
Table 1. Sampling sites’ location, management, qualitative class, dominant mass source, and observed mass flux. Management abbreviations are as follows: University of California (UC), United States Bureau of Land Management (BLM), California Fish and wildlife (CAFW), and United States Fish and Wildlife (USFW).
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2.2 Flux Estimates Dust flux was calculated as described by Reheis and Kihl (Equation 1):23, 26
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𝐹𝑙𝑢𝑥 =
𝑀𝑎𝑠𝑠 ∗ (1 ― 𝑓𝑜𝑟𝑔)
(1)
𝐴𝑟𝑒𝑎 ∗ 𝑇𝑖𝑚𝑒
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where Mass is the dust mass collected in the pan, forg is the fraction of organic matter as measured
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via ashing (SI), Area is the planar area of the pan, and Time is the length of deployment. It should
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be noted that the fluxes are not a quantitative measurement of the net difference between emission
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and deposition but the term used to describe time and area normalized mass measurements from
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these samplers.23, 24, 27-29 5 ACS Paragon Plus Environment
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2.3 Chemical and Physical Analysis
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To measure elemental content, samples were digested then analyzed with inductively
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coupled plasma mass spectroscopy (ICP-MS, Aligent 7900) for total As, Al, Ba, Ca, Cd, Co, Cu,
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Cr, Fe, K, V, Mg, Mn, Mo, Na, Ni, Pb, Se, Sn, Sb, Sr, Ti, U, Th, and Zn. To measure soluble anion
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content, samples were extracted with ultrapure water, and the resulting solution was analyzed for
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sulfate (SO42-), phosphate (PO43-), nitrate (NO3-), nitrite (NO2-), and chloride (Cl-) using ion
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chromatography (IC) (Dionex Aquion, Thermo Scientific). All elemental concentrations are
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presented on a dry mass basis (e.g., mg kg-1).
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The physically extracted SB April 2018 sample, thought to be dominated by playa dust
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(Section 3.3.1), was analyzed with SEM (NovaNanoSEM 450) combined with Energy-Dispersive
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X-ray Spectroscopy (AztecSynergy, Oxford Instruments) (SEM-EDS). SEM imaging and
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associated EDS data are reported as a qualitative measure of the physical and chemical nature of
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playa emissions (Section 3.4). To investigate the mineralogy, the sample was also analyzed using
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X-ray diffraction (Siemens D500). Additional information concerning the digestion procedure and
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the analyses by the ICP-MS, IC, and XRD are included in the SI.
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2.4 Data Analysis Tools
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Positive matrix factorization (EPA PMF 5.0) (PMF) and crustal enrichment factors (EFs)
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were used to investigate the sources of, and chemical variation within, the sampled dusts. PMF is
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a source apportionment model that identifies source profiles and contributions by exploring
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sources of variation within a sample set.30 PMF is analogous to principle component analysis but
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unique and powerful for three main reasons. First, PMF does not allow significant negative
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contributions of sources and only identifies sources with a physical meaning in an environmental
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context. Second, PMF weighs data using an uncertainty matrix such that more certain
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measurements are more influential. Third, PMF does not require source profiles to be known, so
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it can arrive at a solution without assuming specific sources are present. Since PMF profiles are
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created mathematically from the dataset a priori, assigning source profiles to specific sources
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relies on the operator’s knowledge of potential source composition. Here, the total elemental and
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soluble anion mass (mg pan-1) of each sample was input into PMF. Mass, forg, Sn, NO3-, and NO2-
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were input as weak variables due to high signal to noise ratios. Construction of the uncertainty
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matrix is discussed in detail in the SI.
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PMF was tested with two to nine factors to find the best fit to the data. A seven-factor
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model was chosen as the best fit because factors were relatable to identifiable sources (Section
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3.3). Additionally, displacement uncertainty analysis indicated a stable solution and scaled
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residues were generally low and normally distributed. Lastly, the ratio of fit parameters, which
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measure how well the PMF model fits the dataset (Q/Qexp), only slightly decreased upon adding
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additional factors (Figure S2); small decreases in Q/Qexp indicate that adding additional factors
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would overfit the data and thus is not ideal.31 The uncertainties in source factor profiles and source
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contributions were estimated using the displacement error analysis method.31
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EFs were used to explore compositional variation among the samples. EFs are defined as
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the ratio of an element to a low-mobility element, such as Al or Ti, divided by the same ratio in a
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given geologic baseline. EFs explore differences between a sample and a given geologic baseline
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and track normalized changes in composition. Here, the average composition of the upper
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continental crust (UCC) as reported by Wedephol (1995) is used as the baseline, and Al is used as
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the reference element (Equation 2)32. Aluminum was chosen as a reference element because it has
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high concentrations in most soils, is relatively immobile, and is used commonly in EF analysis of
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atmospheric dust.33-35
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𝐸𝐹 =
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where Mmeasured is the concentration of an element in the sample, Almeasured is the concentration of
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Al in the sample, MUCC is the UCC concentration of the element, and AlUCC is the UCC
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concentration of Al.32 Although EFs are insightful, differences between local geology and the UCC
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can lead to false interpretations.36 Additionally, the significance of an EF is dependent on an
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element’s bulk concentration. For example, a twofold EF increase of a trace element is much less
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important than a twofold increase of a major element. This means EFs should be viewed in the
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context of the specific species and the sampling location.
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2.5 Meteorological Data
(𝑀𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑/𝐴𝑙𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑)
(2)
(𝑀𝑈𝐶𝐶/𝐴𝑙𝑈𝐶𝐶)
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To contextualize sampling conditions, we examined data from local long-term
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precipitation measurements (five stations, average of 81 years of data, 1902-2018) and wind
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measurements (three stations, average of 18 years of data, 1999-2018) (Tables S2 and S3). Station
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data were obtained from the National Oceanic and Atmospheric Association’s (NOAA) National
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Centers for Environmental Information (NCEI).37 Additionally, site specific wind speed and
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direction, as determined from nearby meteorological monitoring sites, are included in the SI
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(Figure S3-S6).
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3 Results and Discussion
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3.1 Meteorological Conditions
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In this region, winter precipitation and associated runoff are theorized to reduce the
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following season’s non-playa dust emissions (e.g., alluvial fans) and increase those from the
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playas.18, 38 Winter (September-March) precipitation was high (on average 77th percentile) prior to
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sampling in 2016-17 and low (on average at 31st percentile) during 2017-18 sampling (Table S2).
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Therefore, relative to precipitation, non-playa dust emissions would be expected to be low during
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2017 and high during 2018; the inverse would be expected for playa emissions.
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Generally, higher wind speeds correspond to higher dust emissions.38, 39 Among the wind
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stations examined, seasonal wind speeds are highest between calendar months March and April
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(Figure S7). During sampling, basin-wide high wind speeds were observed in calendar months
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August and September (>89th percentile of NCEI data at all sites), and very low windspeeds were
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observed in November (10) (Cu, As, Cd, Sb, Se) at all sites,
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indicating high background values or basin-wide sources. Among these, As displayed similar
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enrichments in local soils, which suggests that its high EF may reflect local soil characteristics.46
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Copper, Sb, and Cd are commonly associated with anthropogenic sources and are all most
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enriched at PD, the most urban site, suggesting that the enrichments are anthropogenic.48-51 The
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observation that all three elements are relatively enriched at all sites, suggests basin-wide
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anthropogenic influences.
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Selenium is also associated with anthropogenic emissions, but microbial Se volatilization
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could occur in Salton Sea sediments and playas and increase Se content of ambient PM after
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atmospheric oxidation.45, 46, 52 These processes would be most important during hot months and
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near the Salton Sea, as is observed in this dataset (Figure S11). Selenium EFs were highest at WI,
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which is closest to the sea. Furthermore, the DP, WI, and SB sites had clear peaks in Se EFs
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between August and September, when temperatures are the highest. These observations suggest
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that microbial Se processing and volatilization may be an important if not the dominant Se source.
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3.4 Source Identification and Distribution
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The seven-factor PMF output was related to specific dust sources by examining confident
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elemental attributions (Figure 2) and applying knowledge of known source compositions, regional
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soil composition, and previously identified source profiles. Seven dust sources were identified:
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Playa, Colorado Alluvium, Local Alluvium, Agricultural Burning, Sea Spray, Anthropogenic
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Trace Metals, and Anthropogenic Copper (Italicized text refers specifically to the PMF identified
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source). Note that since sampling sites only spanned the northern, eastern, and central portions of
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the Salton Sea Basin, source attributions may underestimate the importance of sources located in
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In the following discussion, confidence in attributions is defined by the relative negative
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uncertainty as given by the displacement method.31, 53 Intervals were defined as confident (
