Historical Analysis of Urban Dust Generated by the Great Salt Lake Playa

Historical Analysis of Urban Dust Generated by the Great Salt Lake Playa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Historical Analysis of Urban Dust Generated by the Great Salt Lake Playa Jaron Hansen, Callum Flowerday, Rebekah Stanley, Kaitlyn Brewster, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3994858/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Great Salt Lake (GSL) has undergone a reduction in size, from 1046.0 miles 2 on January 1, 2015, to 940.5 miles 2 on January 1, 2022, exposing 105.5 miles 2 of playa that was once covered. This emerging playa raises concerns regarding the toxicity of the ensuing dust. While considerable efforts have been made to understand aeolian dust in urban areas along the Wasatch Front, located just east of the GSL, there is still a need to consolidate existing research and conduct a compositional analysis of the dust found in these urban areas. In this study, we investigated the dust reaching urban monitoring sites around the GSL, managed by the Utah Division of Air Quality. By analyzing historical data dating back to 1988, we found no evidence to support the idea that the decrease in the GSL’s surface area has led to an increase in dust events in urban areas. Backwind trajectories align with prior research, indicating that heightened dust levels in urban areas coincide with winds originating from the south or west, passing over identified playas and deserts such as the Milford flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. Conducting a compositional analysis of urban dust revealed no concentrations of metals that raise health concerns, with the highest health quotient being four orders of magnitude lower than a level of concern. Earth and environmental sciences/Environmental sciences/Environmental chemistry/Atmospheric chemistry Earth and environmental sciences/Environmental sciences/Environmental impact dust PM10 Great Salt Lake playa dust urban dust analysis elemental dust analysis metal dust analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background The Great Salt Lake (GSL) has undergone a reduction in size, from 1046.0 miles 2 on January 1, 2015, to 940.5 miles 2 on January 1, 2022, exposing 105.5 miles 2 of playa that was once covered. As this playa dries out, it becomes a potential source of dust that may be carried by the wind into urban areas east of the GSL, causing visibility issues and potential health concerns. Additionally, research indicates that the deposition of aeolian (windblown) dust on mountainous snowpacks leads to premature melting, impacting the crucial water supply for Utah throughout the year 1–6 . This windblown dust is characterized as particulate matter with a diameter of 10 micrometers or smaller (PM 10 ), routinely measured in accordance with the National Ambient Air Quality Standards (NAAQS) established by the United States Environmental Protection Agency (EPA) 7,8 . The health effects of exposure to PM 10 is often assessed using an enrichment factor, representing the ratio of the measured analyte to background concentrations 9,10 . Within these particles lies the potential for carrying heavy metals, possibly originating from the playa surrounding the GSL 7 . The presence of these metals raises concerns about health risks associated with dust inhalation 11,12 . Various methods, such as gas chromatography-mass spectrometry (GC-MS), x-ray fluorescence, and inductively coupled plasma mass spectrometry (ICP-MS), have been employed to quantify the concentrations of heavy metals in PM 10 2,4,11–20 . Considerable research has been undertaken to trace the origins of dust along the Wasatch Front in Utah, USA 21 . Much of this dust has been identified as originating from playas or the Great Salt Lake Desert (GSLD). Lang et al., utilizing backwind trajectories, determined that 23% of the deposited dust in the snow originated from the GSL playa, while 45% originated from GSLD or the playas of Sevier Dry Lake and Tule Dry Lake in Southern Utah 1 . Carling et al. employed strontium isotope ratios in deposited dust, revealing that the GSL playa contributes 5% of dust along the Southern Wasatch Front and 30–34% of dust along the Northern Wasatch Front 2 . Putman et al. concluded, using strontium isotope ratios, that most of the dust, especially the coarsest dust, from these playas was deposited outside urban areas. They found that much of the dust measured in these areas originated from local soil material or activities such as industrial processes, mining, oil refining, and agriculture 18 . However, they also noted the presence of metals such as As, V, Pb, Tl, and Ni. Hahnenberger et al. studied soil and dust samples from Sevier Dry Lake, Utah, and discovered compositional differences between soil and dust samples for minor soil elements, although major soil elements showed similarities. They also found that dust from Sevier Dry Lake could be traced to the Salt Lake City metropolitan area 16 . In a separate study, Hahnenberger et al. determined that dust storms primarily occur in the spring during late afternoon, with westward winds 22 . They identified Tule Dry Lake, Sevier Dry Lake, GSLD, and Milford Flats as dust sources, noting that 60% of dust originated from playas and 75% of dust from vegetated land cover originated from Milford Flats 23 . Goodman et al., through mass balance calculations, estimated that up to 90% of dust along the Wasatch Front originates from playas, with source locations matching those listed above 20 . Steenburgh et al. observed a general decline in dust days from 1930–2012, with emission sources identified as Sevier Dry Lake, Milford Valley, West Desert of Utah, Escalante Desert, and the Great Basin and Mojave Deserts of Nevada 24 . Nicoll et al. identified Milford Valley, Sevier Dry Lake, Tule Dry Lake, GSLD, and the Dugway Proving Grounds as major dust sources 5 . This study leverages historical data to assess the correlation between the surface area of the GSL and the quantity of dust observed in urban areas. Additionally, it identifies dust sources through backwind trajectories, conducts a compositional analysis on dust collected in urban areas around the GSL, and evaluates potential health risks associated with the dust using the health quotient method. 2. Method 2.1 Surface Area versus Dust Analysis Surface area and lake depth data utilized in this study were sourced from the United States Geological Survey (USGS) Utah Water Science Center. The EPA provided quality assured PM 10 data for three urban sites, forming the basis of this investigation. These data were instrumental in tracking the PM 10 concentration trends in relation to changes in the surface area of the Great Salt Lake. Furthermore, they were employed to assess the correlation between the Great Salt Lake's surface area and PM 10 concentrations. The analysis focused on three urban sites: Bountiful (EPA AIRS Code: 490110001 before 2003, changed to EPA AIRS Code: 490110004 in 2003; address: 171 West 1370 North, Bountiful, UT), Hawthorne Elementary (EPA AIRS Code: 490353006, address: 1675 South 600 East, Salt Lake City, UT), and Weber (EPA AIRS Code: 490353006, address: 425 West 2550 North, Harrisville, UT). The locations of these three sites are shown in Fig. 1 . Backwind trajectories, generated using the National Oceanic and Atmospheric Administration’s (NOAA) HYSPLIT atmospheric transport and dispersion modeling system, aided in identifying potential sources of dust 25 . 2.2 Dust Compositional Analysis PM 10 filters, supplied by the Utah Division of Air Quality (UDAQ), spanning from years 2015–2022 from five distinct urban areas bordering the Great Salt Lake Region, underwent compositional dust analysis. Figure 1 illustrates the locations of the sampling sites. Sixteen filters, exhibiting varying PM 10 concentrations detailed in Table 1 , were chosen for analysis. Selection criteria included filters exceeding standards or approaching the National Ambient Air Quality Standards (NAAQS) exceedance limit of 150 µg/m³ in the past ten years. UDAQ's policy of withholding exceedance filters for analysis that are less than three years old led to the selection of three other filters that approach the exceedance standard. The set is comprised of eight high-concentration filters, defined as filters with > 140 µg/m 3 of PM 10 and eight low-concentration filters, defined as filters with < 16 µg/m 3 of PM 10 , serving as background references. The latter were collected within a few days of the high-concentration filters to maintain similar collection conditions. Analysis encompassed twelve elements of interest: As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, V, and Zn—many listed as Hazardous Air Pollutants by the EPA 27 . Table 1 PM 10 filters acquired from the Utah Division of Air Quality with corresponding concentrations, GSL surface area, and predominant wind direction for that day. Filter # Site Date Concentration (µg/m 3 ) Notes Lake SA (miles 2 ) 0634111 Herriman (H3) 04/23/2022 8.1 Baseline 940.4831 0634365 Herriman (H3) 05/31/2022 6.2 Baseline 940.4831 0634535 Herriman (H3) 06/17/2022 118.8 3rd from max 926.744 0634539 Herriman (H3) 06/20/2022 15.2 Baseline 926.744 0634559 Utah Tech Center (EQ) 06/17/2022 146.7 2nd from max 926.744 0634560 Utah Tech Center (EQ) 06/18/2022 145.7 3rd from max 926.744 0634563 Utah Tech Center (EQ) 06/20/2022 9.3 Baseline 926.744 9610827 Utah Tech Center (EQ) 09/07/2020 162.7 Exceedance 1001.206 9610829 Utah Tech Center (EQ) 09/09/2020 10.6 Baseline 1001.206 7672706 Herriman (H3) 12/20/2017 229.4 Exceedance 1064.408 7672719 Magna (MG) 12/20/2017 170.0 Exceedance 1064.408 7672720 Magna (MG) 12/21/2017 9.0 Baseline 1064.408 2917265 Herriman (He) 04/14/2015 256 Exceedance 1116.213 2917266 Herriman (He) 04/15/2015 6 Baseline 1116.213 2917234 Ogden (O2) 04/14/2015 332 Exceedance 1116.213 2917235 Ogden (O2) 04/15/2015 5 Baseline 1116.213 EPA AIRS code and addresses: H3–490353013 (14058 Mirabella Drive, Herriman), EQ – 490353015 (240 North 1950 West, Salt Lake City), MG – 490351001 (2935 S 8560 W, Magna), He – 490353008 (12885 5600 W, Herriman), O2–490570002 (228 32nd St, Ogden) All sample extraction, preparation, and analysis procedures adhered to EPA standard operating procedures for filter analysis 13–15 . After receiving the PM 10 filters from UDAQ, filters were weighed and cut in half to preserve some of the original sample. The dust sample was extracted from the filter via heated acid sonication according to EPA 68-D-00-264. This was then diluted to a 2% nitric acid matrix for running analysis on an inductively coupled plasma-mass spectrometer (ICP-MS, PerkinElmer, Nexion 300x). Seven method blanks and seven method spike samples were prepared over three days following the same extraction method as above (EPA 821-R-16-006). In addition, interference checks were prepared and run. Three internal standards (Y, Sc, Tb) were used to monitor samples and standards for matrix effects and instrument stability. All dust filters and interference checks were analyzed in a single day, while method blanks and spikes were analyzed on three separate days as directed. Each instrument run followed the same calibration and continuing quality control check procedures. In addition, instrument tuning procedures were conducted daily as directed by manufacturer guidelines. Daily calibrations were verified by high standard analysis and initial calibration verification with a quality control standard. Calibration blanks and quality control standards were also rerun every ten samples and at the end of the instrument run for the day. Passing criteria can be found in EPA-68-D-00-264. Method blank and spike samples were used to calculate method detection limits according to EPA 821-R-16-006 using these equations: $${MDL}_{s}={t}_{(n-1 , 1-\alpha =0.99)}{S}_{s}$$ 1 Where MDL s is the method detection limit based on spiked samples, t (n−1, 1−α=0.99) is Student’s t-value appropriate for the single-tailed 99th percentile, and S s is the sample standard deviation of the replicates of spike samples. $${MDL}_{b}=\stackrel{-}{X}+{t}_{(n-1 , 1-\alpha =0.99)}{S}_{b}$$ 2 Where MDL b is the method detection limit based on blank samples, X̅ is the mean of method blank results, t (n−1, 1−α=0.99) is Student’s t-value appropriate for the single-tailed 99th percentile, and S b is the sample standard deviation of the replicates of blank samples. After instrument analysis, metal concentration in the air was calculated from the dust filter sample data according to: $$C=\left[\left(:g\frac{metal}{L}\right)\left(Digestion Volume \left(\frac{L}{filter}\right)\right)\right]/{V}_{std}$$ 3 Where C is the concentration of the metal, : g metal/L is the metal concentration defined in section 14.2 of the procedure, the digestion volume is 0.050 L as defined by the procedure, and V std is the standard air volume pulled through the filter in m 3 which was provided for each filter by UDAQ. Uncertainties associated with the quantification of metals in the dust samples were combined in quadrature to produce an uncertainty associated with each element. These uncertainties originated from measurement in the ICP-MS (< 1% for each element as per EPA passing criteria), measurement of the standard volume of air passed through the filter (5%), and from sample preparation and dilution (2%). All waste produced was disposed of according to EPA guidelines. 2.3 Health Risk Assessment One method for determining whether the concentrations of metals in dust are carcinogenic involves calculating the health quotient (HQ) 12,28 . An HQ greater than 1 indicates the potential for adverse health effects, while a HQ less than 1 suggests minimal to no risk of such effects. The HQ is computed using Eq. 4 , where the average daily dose (ADD) is divided by the reference dose (RfD) for each specific metal. The reference doses for the tested metals are outlined in Table 2 . $$HQ= \raisebox{1ex}{$ADD$}\!\left/ \!\raisebox{-1ex}{$RfD$}\right.$$ 4 Table 2 Metal toxicity levels as defined by the Environmental Protection Agency (EPA), World Health Organization (WHO), Occupational Safety and Health Administration (OHSA) who report an 8-hour exposure standard, National Institute for Occupational Safety and Health (NIOSH) who report an 8-hour exposure standard, and the California Office of Environmental Health Hazard Assessment (OEHHA) who set an acute, an 8-hour, and chronic exposure standard. 29–42 . Element EPA Standard (µg/m 3 ) WHO Standard (µg/m 3 ) OSHA/NIOSH Standard (µg/m 3 ) OEHHA standard (µg/m 3 ) Acute 8-hour Chronic As - - 10 0.200 0.015 0.015 Ba - - 0.5 - - - Cd - 0.005 5/9 - - 0.02 Co 0.02 - - - - - Cr - - 100 - - - Cu - - - 100 - - Hg 0.3 20 - 0.6 0.06 0.03 Mn - - 5/1 - 0.17 0.09 Ni - - - 0.2 0.06 0.014 Pb 0.15 - - 15 - - V - 1 0.5 30 - - Zn - - 500 - - - 3. Results and Discussion 3.1 Surface Area versus Dust Analysis Figures 3 A- 5 A depict the surface area of the GSL plotted against PM 10 concentrations for the years 1988 to 2022. The PM 10 concentrations used were measured at three urban regions where UDAQ operates sampling sites, namely, Bountiful, Hawthorne and Weber. In Figs. 3 B- 5 B, a correlation plot illustrates the relationship between the GSL’s surface area and PM 10 concentrations. Notably, these figures reveal a consistent decline in the GSL’s surface area between the years 2015–2022. Contrastingly, the average concentration of PM 10 has maintained a steady trend during the same timeframe. The correlation plots underline that these two trends exhibit minimal correlation, suggesting that the reduction in the GSL’s surface area does not result in an increase in PM 10 (dust) levels in the populated urban areas surrounding the lake. 3.2 Backwind Trajectories Backwind trajectories computed using the National Oceanic and Atmospheric Administration’s (NOAA) HYSPLIT atmospheric transport and dispersion modeling system, revealed that during days with "low" PM 10 concentrations, defined as filters that have a concentration of < 16 µg/m 3 , the wind predominantly emanated from the NNW to E regions. On these occasions, winds associated with low concentration filters tended to circulate around urban areas where the filters were collected. Conversely, all winds linked to “high” concentration filters, defined as filters that have a concentration of > 140 µg/m 3 , exhibited a SSW component, with some also having a W component. This indicates that elevated dust concentrations occur when winds originate from the south or the west. This finding is consistent with numerous referenced papers, which assert that significant dust contributions arise from the southern playas (Sevier Dry Lake, Tule Dry Lake, Milford Valley, Escalante Desert) and the western deserts (Great Salt Lake Desert, Dugway Proving Grounds, West Desert of Utah, Great Basin, and Mojave Deserts of Nevada), with possible minor contributions from the GSL playa. These locations can be seen in Fig. 1 . Backwind trajectories are visually represented in Figs. 6 and 7 , as well as in S1-S9. 3.3 Dust Compositional Analysis and Health Risk Assessment Compositional PM 10 analysis reveals minimal metal concentrations in the dust collected on the filters. In certain instances, the measured metal concentrations fell below the limit of detection of the ICP-MS method used for the analysis. Although lead levels were slightly below the quality assurance standards set by the EPA's methods, all other quality assurance checks and interference checks passed successfully. The greatest amounts of Ba, Cd, Co, Cr, Mn, Ni, Pb, and V were detected on a filter collected at the Utah Tech Center (EPA AIRS code: 490353015) on June 17, 2022 (filter number: 0634559). Meanwhile, the highest concentrations of Cu and Zn were observed on a filter from Herriman (H3) (EPA AIRS code: 490353013) on June 20, 2022 (filter number: 0634539). The filter from Herriman (H3) (EPA AIRS code: 490353013) on May 31, 2022 (filter number: 0634365), exhibited the highest levels of As. Lastly, the filter from the Utah Tech Center (EPA AIRS code: 490353015) on September 9, 2020, exhibited the highest levels of Hg. Notably, the filter from June 17, 2022, recorded a high concentration of PM 10 (146.7 µg/m 3 ), while those from June 20, 2022, and May 31, 2022, showed lower concentrations (15.2, 6.2, and 10.6 µg/m 3 , respectively). The calculation of health quotients involved utilizing the highest concentrations of each metal to report a worst-case measured health quotient. A health quotient exceeding one suggests a potential health risk and raises concerns. Conversely, health quotients below one indicates that the measured metal concentrations in the dust are not a cause for concern. This analysis employed the maximum concentrations of metals from the collected filters to determine the most concerning health quotients. Table 3 presents the concentration of each metal, expressed in µg/m 3 , along with their respective health quotients. Table 3 The highest elemental concentrations found in analyzed filters and corresponding health quotients with corresponding highest recorded values. Element Highest concentration filters (µg/m 3 ) Health Quotient As 3 9.5× 10 − 7 ± 5 × 10 − 8 4.7 × 10 − 6 ± 3 × 10 − 7 Ba 1 5.2 × 10 − 5 ±3 × 10 − 6 1.04 × 10 − 4 ± 6 × 10 − 6 Cd 1 2.5 × 10 − 7 ± 1 × 10 − 8 5.0 × 10 − 5 ± 3 × 10 − 6 Co 1 1.0 × 10 − 6 ± 5 × 10 − 8 5.0 × 10 − 5 ± 3 × 10 − 6 Cr 1 4.8 × 10 − 6 ±3 × 10 − 7 4.8 × 10 − 8 ± 3 × 10 − 9 Cu 2 1.9 × 10 − 5 ± 1 × 10 − 6 1.9 × 10 − 7 ± 1 × 10 − 8 Hg 4 4.6 × 10 − 7 ± 2 × 10 − 8 7.7 × 10 − 7 ±4 × 10 − 8 Mn 1 7.4 × 10 − 5 ± 4 × 10 − 6 4.4 × 10 − 4 ±2 × 10 − 5 Ni 1 5.0 × 10 − 6 ± 3 × 10 − 7 2.5 × 10 − 5 ±1 × 10 − 6 Pb 1 1.75 × 10 − 6 ± 9 × 10 − 8 1.17 × 10 − 7 ±6 × 10 − 9 V 1 9.3 × 10 − 6 ± 5 × 10 − 7 3.1 × 10 − 7 ±2 × 10 − 8 Zn 2 6.0 × 10 − 5 ± 3 × 10 − 6 1.20 × 10 − 7 ±6 × 10 − 9 1 – Utah Tech Center (EQ) EPA site number: 490353015 filter number: 0634559 date: June 17, 2022 (a high concentration filter) 2 – Herriman (H3) EPA site number: 490353013 filter number: 0634539 date: June 20, 2022 (a low concentration filter) 3 – Herriman (H3) EPA site number: 490353013 filter number: 0634365 date: May 31, 2022 (a low concentration filter) 4 – Utah Tech Center (EQ) EPA site number: 490353015 filter number: 9610829 date: September 9, 2020 (a low concentration filter) As indicated in Table 3 , all health quotients are consistently below one. This implies that there is minimal reason for concern regarding the concentration of any measured metals in the dust collected by UDAQ on their filters. In other words, there is no toxic concentration of metals present in the dust reaching urban measurement sites. 4. Conclusions Recent concerns regarding the toxicity of dust from the exposed playa of the GSL have prompted a reevaluation of past research. In this study, we examined the dust that has reached three urban monitoring sites surrounding the GSL, managed by the Utah Division of Air Quality. Utilizing historical data dating back to 1988, no correlation was found to support the notion that a decrease in the surface area of the Great Salt Lake has led to an increase in dust events in urban areas. Backwind trajectories align with previous research, indicating that elevated dust levels in urban areas coincide with winds originating from the south or west, passing over playas and deserts such as the Milford flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. A compositional analysis of urban dust revealed no concentrations of metals that raise health concerns, with the highest health quotient being four orders of magnitude lower than a level of concern. Declarations Author Contributions: C.E.F: Conceptualization, writing of the original manuscript, sample preparation, experimental setup, data analysis. R.S.: sample preparation, experimental setup, data acquisition, data analysis. K.B.: sample preparation, experimental setup, data acquisition. W.F.P.: funding, writing review and editing J.C.H.: Conceptualization, writing review and editing, supervision, and funding acquisition. All Authors have read and agreed to the published version of the manuscript. Acknowledgements: Young Living – Use of their ICP-MS for analysis. Utah Division of Air Quality – providing sample filters for analysis. Funding: This study was funded by the National Science Foundation, grant #2114655. The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Data Availability: The datasets generated and/or analyzed during the current study are available in the BYU’s Scholar’s Archive repository, https://scholarsarchive.byu.edu/data/62 Declaration of competing interest(s): All authors declare no financial or non-financial competing interests. References Lang, O. I., Mallia, D. & Skiles, S. M. The shrinking Great Salt Lake contributes to record high dust-on-snow deposition in the Wasatch Mountains during the 2022 snowmelt season. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3994858","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276763670,"identity":"6be3edf0-e5d5-4a6c-b4a2-613914746abd","order_by":0,"name":"Jaron Hansen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxhkQWo5BAsRrAHMMiNJiTLwWsEogSGwgWgvz7OZnD39U3Envn918dAPjjsOJDezN2yTwOmzOMXNjnjPPcmfcOZZ2g/EMUAvPsTL8WmYkmEkzth3O3SCRY3YDyAC6MMeMgJb0b5I//x1ON5DI/wbRIv+GkBagmbwNhxMMJHLYoLbwENRSJs1z7LDhjBtpZjcSz6Qbt/GkFVvg02I4I32b5I+aw/L8M5Kf3fi4w1q2n/3wxht4tTQg8xIYmhnY8CkHAXk0fh0hDaNgFIyCUTACAQCdWU8XeAl1cQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2894-7987","institution":"Brigham Young University","correspondingAuthor":true,"prefix":"","firstName":"Jaron","middleName":"","lastName":"Hansen","suffix":""},{"id":276763671,"identity":"484e69b9-47e4-4651-98f5-7bcfb435ea39","order_by":1,"name":"Callum Flowerday","email":"","orcid":"https://orcid.org/0000-0003-4172-3910","institution":"Brigham Young University","correspondingAuthor":false,"prefix":"","firstName":"Callum","middleName":"","lastName":"Flowerday","suffix":""},{"id":276763672,"identity":"ccf24694-dbc4-4db4-ae35-bf4d437bb751","order_by":2,"name":"Rebekah Stanley","email":"","orcid":"","institution":"Brigham Young University","correspondingAuthor":false,"prefix":"","firstName":"Rebekah","middleName":"","lastName":"Stanley","suffix":""},{"id":276763673,"identity":"13bf2382-72fa-48f7-8e0c-c01caff1dd53","order_by":3,"name":"Kaitlyn Brewster","email":"","orcid":"","institution":"Brigham Young University","correspondingAuthor":false,"prefix":"","firstName":"Kaitlyn","middleName":"","lastName":"Brewster","suffix":""},{"id":276763674,"identity":"1d46f19e-0ede-4981-b19b-329d3100470d","order_by":4,"name":"Walter Paxton","email":"","orcid":"","institution":"Brigham Young University","correspondingAuthor":false,"prefix":"","firstName":"Walter","middleName":"","lastName":"Paxton","suffix":""}],"badges":[],"createdAt":"2024-02-27 21:25:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3994858/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3994858/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52434530,"identity":"7d733143-0008-42c0-803e-fc9aaee71a68","added_by":"auto","created_at":"2024-03-11 15:24:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3133901,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Utah, USA.\u0026nbsp;Blue pins – Locations of PM\u003csub\u003e10\u003c/sub\u003e monitoring sites from which PM\u003csub\u003e10\u003c/sub\u003e concentrations were collected to analyze for correlations between dust and the surface area of the GSL \u003csup\u003e26\u003c/sup\u003e. Red pins – Locations of PM\u003csub\u003e10\u003c/sub\u003e monitoring sites from which filters were collected and then analyzed.\u0026nbsp;Purple pins – partial region of the exposed GSL playa. Yellow pins – Identified regions of PM\u003csub\u003e10\u003c/sub\u003e contributions to dust measured in urban regions including the Great Salt Lake Desert (GSLD), Dugway Proving Grounds (DPG), Tule Dry Lake (TDL), Sevier Dry Lake (SDL), and Milford Valley (MV) \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/28eda75c09cadf99e087d398.png"},{"id":52434788,"identity":"d12c07a1-5114-4b63-ab97-95c46064da76","added_by":"auto","created_at":"2024-03-11 15:32:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e (A) Surface area of the GSL versus 24-hour averaged PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Bountiful, UT DAQ sampling site. (B) correlation plot of the surface area of the GSL versus PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Bountiful site using 24-hour averaged data.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/52babaabc74efd92438d03be.jpeg"},{"id":52434527,"identity":"3cb0c7dc-d13d-4053-8e64-a49c15bb67f4","added_by":"auto","created_at":"2024-03-11 15:24:01","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":291008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e (A) Surface area of the GSL versus 24-hour averaged PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Hawthorne Elementary School UDAQ sampling site. (B) correlation plot of surface area of the GSL versus PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Hawthorne site using 24-hour averaged data.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/67b7c9a6c24451c6f4e50249.jpeg"},{"id":52434525,"identity":"075fb574-9f5a-4c5c-bf2a-629ba8a87782","added_by":"auto","created_at":"2024-03-11 15:24:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":346766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e (A) Surface area of the GSL versus 24-hour averaged PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Weber UDAQ sampling site. (B) correlation plot of surface area of the GSL versus PM\u003csub\u003e10\u003c/sub\u003e concentrations measured at the Weber site using 24-hour averaged data.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/58559d53906eb17735a77d11.jpeg"},{"id":52434787,"identity":"4f9426e6-cfbc-4f76-9795-ee58b713162f","added_by":"auto","created_at":"2024-03-11 15:32:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":338434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. \u003c/strong\u003e12-hour backwind trajectories for each hour, each represented by its own line, of the 24-hour period that a filter was collected ending at midnight local time. This was for “low” filters collected on 9 September 2020. The green dot is the Salt Lake City International Airport.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/9d8de9b17814f1c2791f8908.png"},{"id":52434529,"identity":"99321166-6706-455e-a51d-e4d08c35576e","added_by":"auto","created_at":"2024-03-11 15:24:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":395072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7.\u003c/strong\u003e 12-hour backwind trajectories for each hour, each represented by its own line, of the 24-hour period that a filter was collecting ending at midnight local time. This was for “high” filters collected on 17 June 2022. The green dot is the Salt Lake City International Airport.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/624e2530125b4f2b03628fb6.png"},{"id":56455746,"identity":"538e809f-1dd4-4694-8ea3-63cc6f42f8db","added_by":"auto","created_at":"2024-05-14 12:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4268714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/27c4876d-2330-45eb-b6ab-f034e0673f3d.pdf"},{"id":52434531,"identity":"318c1ae4-fafa-4345-8308-79203d9caa59","added_by":"auto","created_at":"2024-03-11 15:24:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2594302,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Material\u003c/p\u003e","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3994858/v1/c1ee05bb4a6991f4f9ffb71e.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Historical Analysis of Urban Dust Generated by the Great Salt Lake Playa","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe Great Salt Lake (GSL) has undergone a reduction in size, from 1046.0 miles\u003csup\u003e2\u003c/sup\u003e on January 1, 2015, to 940.5 miles\u003csup\u003e2\u003c/sup\u003e on January 1, 2022, exposing 105.5 miles\u003csup\u003e2\u003c/sup\u003e of playa that was once covered. As this playa dries out, it becomes a potential source of dust that may be carried by the wind into urban areas east of the GSL, causing visibility issues and potential health concerns. Additionally, research indicates that the deposition of aeolian (windblown) dust on mountainous snowpacks leads to premature melting, impacting the crucial water supply for Utah throughout the year\u003csup\u003e1\u0026ndash;6\u003c/sup\u003e. This windblown dust is characterized as particulate matter with a diameter of 10 micrometers or smaller (PM\u003csub\u003e10\u003c/sub\u003e), routinely measured in accordance with the National Ambient Air Quality Standards (NAAQS) established by the United States Environmental Protection Agency (EPA) \u003csup\u003e7,8\u003c/sup\u003e. The health effects of exposure to PM\u003csub\u003e10\u003c/sub\u003e is often assessed using an enrichment factor, representing the ratio of the measured analyte to background concentrations \u003csup\u003e9,10\u003c/sup\u003e. Within these particles lies the potential for carrying heavy metals, possibly originating from the playa surrounding the GSL \u003csup\u003e7\u003c/sup\u003e. The presence of these metals raises concerns about health risks associated with dust inhalation \u003csup\u003e11,12\u003c/sup\u003e. Various methods, such as gas chromatography-mass spectrometry (GC-MS), x-ray fluorescence, and inductively coupled plasma mass spectrometry (ICP-MS), have been employed to quantify the concentrations of heavy metals in PM\u003csub\u003e10\u003c/sub\u003e \u003csup\u003e2,4,11\u0026ndash;20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsiderable research has been undertaken to trace the origins of dust along the Wasatch Front in Utah, USA \u003csup\u003e21\u003c/sup\u003e. Much of this dust has been identified as originating from playas or the Great Salt Lake Desert (GSLD). Lang et al., utilizing backwind trajectories, determined that 23% of the deposited dust in the snow originated from the GSL playa, while 45% originated from GSLD or the playas of Sevier Dry Lake and Tule Dry Lake in Southern Utah \u003csup\u003e1\u003c/sup\u003e. Carling et al. employed strontium isotope ratios in deposited dust, revealing that the GSL playa contributes 5% of dust along the Southern Wasatch Front and 30\u0026ndash;34% of dust along the Northern Wasatch Front \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePutman et al. concluded, using strontium isotope ratios, that most of the dust, especially the coarsest dust, from these playas was deposited outside urban areas. They found that much of the dust measured in these areas originated from local soil material or activities such as industrial processes, mining, oil refining, and agriculture \u003csup\u003e18\u003c/sup\u003e. However, they also noted the presence of metals such as As, V, Pb, Tl, and Ni. Hahnenberger et al. studied soil and dust samples from Sevier Dry Lake, Utah, and discovered compositional differences between soil and dust samples for minor soil elements, although major soil elements showed similarities. They also found that dust from Sevier Dry Lake could be traced to the Salt Lake City metropolitan area \u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a separate study, Hahnenberger et al. determined that dust storms primarily occur in the spring during late afternoon, with westward winds \u003csup\u003e22\u003c/sup\u003e. They identified Tule Dry Lake, Sevier Dry Lake, GSLD, and Milford Flats as dust sources, noting that 60% of dust originated from playas and 75% of dust from vegetated land cover originated from Milford Flats \u003csup\u003e23\u003c/sup\u003e. Goodman et al., through mass balance calculations, estimated that up to 90% of dust along the Wasatch Front originates from playas, with source locations matching those listed above \u003csup\u003e20\u003c/sup\u003e. Steenburgh et al. observed a general decline in dust days from 1930\u0026ndash;2012, with emission sources identified as Sevier Dry Lake, Milford Valley, West Desert of Utah, Escalante Desert, and the Great Basin and Mojave Deserts of Nevada \u003csup\u003e24\u003c/sup\u003e. Nicoll et al. identified Milford Valley, Sevier Dry Lake, Tule Dry Lake, GSLD, and the Dugway Proving Grounds as major dust sources \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study leverages historical data to assess the correlation between the surface area of the GSL and the quantity of dust observed in urban areas. Additionally, it identifies dust sources through backwind trajectories, conducts a compositional analysis on dust collected in urban areas around the GSL, and evaluates potential health risks associated with the dust using the health quotient method.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Surface Area versus Dust Analysis\u003c/h2\u003e\n\u003cp\u003eSurface area and lake depth data utilized in this study were sourced from the United States Geological Survey (USGS) Utah Water Science Center. The EPA provided quality assured PM\u003csub\u003e10\u003c/sub\u003e data for three urban sites, forming the basis of this investigation. These data were instrumental in tracking the PM\u003csub\u003e10\u003c/sub\u003e concentration trends in relation to changes in the surface area of the Great Salt Lake. Furthermore, they were employed to assess the correlation between the Great Salt Lake's surface area and PM\u003csub\u003e10\u003c/sub\u003e concentrations.\u003c/p\u003e\n\u003cp\u003eThe analysis focused on three urban sites: Bountiful (EPA AIRS Code: 490110001 before 2003, changed to EPA AIRS Code: 490110004 in 2003; address: 171 West 1370 North, Bountiful, UT), Hawthorne Elementary (EPA AIRS Code: 490353006, address: 1675 South 600 East, Salt Lake City, UT), and Weber (EPA AIRS Code: 490353006, address: 425 West 2550 North, Harrisville, UT). The locations of these three sites are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eBackwind trajectories, generated using the National Oceanic and Atmospheric Administration\u0026rsquo;s (NOAA) HYSPLIT atmospheric transport and dispersion modeling system, aided in identifying potential sources of dust \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Dust Compositional Analysis\u003c/h2\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e filters, supplied by the Utah Division of Air Quality (UDAQ), spanning from years 2015\u0026ndash;2022 from five distinct urban areas bordering the Great Salt Lake Region, underwent compositional dust analysis. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the locations of the sampling sites. Sixteen filters, exhibiting varying PM\u003csub\u003e10\u003c/sub\u003e concentrations detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, were chosen for analysis. Selection criteria included filters exceeding standards or approaching the National Ambient Air Quality Standards (NAAQS) exceedance limit of 150 \u0026micro;g/m\u0026sup3; in the past ten years. UDAQ's policy of withholding exceedance filters for analysis that are less than three years old led to the selection of three other filters that approach the exceedance standard. The set is comprised of eight high-concentration filters, defined as filters with \u0026gt;\u0026thinsp;140 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of PM\u003csub\u003e10\u003c/sub\u003e and eight low-concentration filters, defined as filters with \u0026lt;\u0026thinsp;16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of PM\u003csub\u003e10\u003c/sub\u003e, serving as background references. The latter were collected within a few days of the high-concentration filters to maintain similar collection conditions. Analysis encompassed twelve elements of interest: As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, V, and Zn\u0026mdash;many listed as Hazardous Air Pollutants by the EPA \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e filters acquired from the Utah Division of Air Quality with corresponding concentrations, GSL surface area, and predominant wind direction for that day.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFilter #\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSite\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConcentration (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNotes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLake SA (miles\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (H3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04/23/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e940.4831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (H3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e05/31/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e940.4831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (H3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e06/17/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3rd from max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (H3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e06/20/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtah Tech Center (EQ)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e06/17/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2nd from max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtah Tech Center (EQ)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e06/18/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3rd from max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0634563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtah Tech Center (EQ)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e06/20/2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e926.744\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9610827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtah Tech Center (EQ)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e09/07/2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExceedance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1001.206\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9610829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUtah Tech Center (EQ)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e09/09/2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1001.206\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7672706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (H3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/20/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExceedance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1064.408\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7672719\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMagna (MG)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/20/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExceedance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1064.408\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7672720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMagna (MG)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/21/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1064.408\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2917265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (He)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04/14/2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExceedance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1116.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2917266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHerriman (He)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04/15/2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1116.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2917234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOgden (O2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04/14/2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExceedance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1116.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2917235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOgden (O2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04/15/2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1116.213\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eEPA AIRS code and addresses: H3\u0026ndash;490353013 (14058 Mirabella Drive, Herriman), EQ \u0026ndash; 490353015 (240 North 1950 West, Salt Lake City), MG \u0026ndash; 490351001 (2935 S 8560 W, Magna), He \u0026ndash; 490353008 (12885 5600 W, Herriman), O2\u0026ndash;490570002 (228 32nd St, Ogden)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAll sample extraction, preparation, and analysis procedures adhered to EPA standard operating procedures for filter analysis \u003csup\u003e13\u0026ndash;15\u003c/sup\u003e. After receiving the PM\u003csub\u003e10\u003c/sub\u003e filters from UDAQ, filters were weighed and cut in half to preserve some of the original sample. The dust sample was extracted from the filter via heated acid sonication according to EPA 68-D-00-264. This was then diluted to a 2% nitric acid matrix for running analysis on an inductively coupled plasma-mass spectrometer (ICP-MS, PerkinElmer, Nexion 300x). Seven method blanks and seven method spike samples were prepared over three days following the same extraction method as above (EPA 821-R-16-006). In addition, interference checks were prepared and run. Three internal standards (Y, Sc, Tb) were used to monitor samples and standards for matrix effects and instrument stability.\u003c/p\u003e\n\u003cp\u003eAll dust filters and interference checks were analyzed in a single day, while method blanks and spikes were analyzed on three separate days as directed. Each instrument run followed the same calibration and continuing quality control check procedures. In addition, instrument tuning procedures were conducted daily as directed by manufacturer guidelines. Daily calibrations were verified by high standard analysis and initial calibration verification with a quality control standard. Calibration blanks and quality control standards were also rerun every ten samples and at the end of the instrument run for the day. Passing criteria can be found in EPA-68-D-00-264. Method blank and spike samples were used to calculate method detection limits according to EPA 821-R-16-006 using these equations:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${MDL}_{s}={t}_{(n-1 , 1-\\alpha =0.99)}{S}_{s}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere MDL\u003csub\u003es\u003c/sub\u003e is the method detection limit based on spiked samples, t\u003csub\u003e(n\u0026minus;1, 1\u0026minus;\u0026alpha;=0.99)\u003c/sub\u003e is Student\u0026rsquo;s t-value appropriate for the single-tailed 99th percentile, and S\u003csub\u003es\u003c/sub\u003e is the sample standard deviation of the replicates of spike samples.\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$${MDL}_{b}=\\stackrel{-}{X}+{t}_{(n-1 , 1-\\alpha =0.99)}{S}_{b}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere MDL\u003csub\u003eb\u003c/sub\u003e is the method detection limit based on blank samples, X̅ is the mean of method blank results, t\u003csub\u003e(n\u0026minus;1, 1\u0026minus;\u0026alpha;=0.99)\u003c/sub\u003e is Student\u0026rsquo;s t-value appropriate for the single-tailed 99th percentile, and S\u003csub\u003eb\u003c/sub\u003e is the sample standard deviation of the replicates of blank samples. After instrument analysis, metal concentration in the air was calculated from the dust filter sample data according to:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$C=\\left[\\left(:g\\frac{metal}{L}\\right)\\left(Digestion Volume \\left(\\frac{L}{filter}\\right)\\right)\\right]/{V}_{std}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere C is the concentration of the metal, : g metal/L is the metal concentration defined in section 14.2 of the procedure, the digestion volume is 0.050 L as defined by the procedure, and V\u003csub\u003estd\u003c/sub\u003e is the standard air volume pulled through the filter in m\u003csup\u003e3\u003c/sup\u003e which was provided for each filter by UDAQ. Uncertainties associated with the quantification of metals in the dust samples were combined in quadrature to produce an uncertainty associated with each element. These uncertainties originated from measurement in the ICP-MS (\u0026lt;\u0026thinsp;1% for each element as per EPA passing criteria), measurement of the standard volume of air passed through the filter (5%), and from sample preparation and dilution (2%). All waste produced was disposed of according to EPA guidelines.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Health Risk Assessment\u003c/h2\u003e\n\u003cp\u003eOne method for determining whether the concentrations of metals in dust are carcinogenic involves calculating the health quotient (HQ) \u003csup\u003e12,28\u003c/sup\u003e. An HQ greater than 1 indicates the potential for adverse health effects, while a HQ less than 1 suggests minimal to no risk of such effects. The HQ is computed using Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, where the average daily dose (ADD) is divided by the reference dose (RfD) for each specific metal. The reference doses for the tested metals are outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$HQ= \\raisebox{1ex}{$ADD$}\\!\\left/ \\!\\raisebox{-1ex}{$RfD$}\\right.$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMetal toxicity levels as defined by the Environmental Protection Agency (EPA), World Health Organization (WHO), Occupational Safety and Health Administration (OHSA) who report an 8-hour exposure standard, National Institute for Occupational Safety and Health (NIOSH) who report an 8-hour exposure standard, and the California Office of Environmental Health Hazard Assessment (OEHHA) who set an acute, an 8-hour, and chronic exposure standard. \u003csup\u003e29\u0026ndash;42\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eElement\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEPA Standard (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eWHO Standard (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOSHA/NIOSH Standard (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eOEHHA standard (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAcute\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e8-hour\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChronic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5/9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5/1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Surface Area versus Dust Analysis\u003c/h2\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA depict the surface area of the GSL plotted against PM\u003csub\u003e10\u003c/sub\u003e concentrations for the years 1988 to 2022. The PM\u003csub\u003e10\u003c/sub\u003e concentrations used were measured at three urban regions where UDAQ operates sampling sites, namely, Bountiful, Hawthorne and Weber. In Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, a correlation plot illustrates the relationship between the GSL\u0026rsquo;s surface area and PM\u003csub\u003e10\u003c/sub\u003e concentrations. Notably, these figures reveal a consistent decline in the GSL\u0026rsquo;s surface area between the years 2015\u0026ndash;2022.\u003c/p\u003e\n\u003cp\u003eContrastingly, the average concentration of PM\u003csub\u003e10\u003c/sub\u003e has maintained a steady trend during the same timeframe. The correlation plots underline that these two trends exhibit minimal correlation, suggesting that the reduction in the GSL\u0026rsquo;s surface area does not result in an increase in PM\u003csub\u003e10\u003c/sub\u003e (dust) levels in the populated urban areas surrounding the lake.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Backwind Trajectories\u003c/h2\u003e\n\u003cp\u003eBackwind trajectories computed using the National Oceanic and Atmospheric Administration\u0026rsquo;s (NOAA) HYSPLIT atmospheric transport and dispersion modeling system, revealed that during days with \"low\" PM\u003csub\u003e10\u003c/sub\u003e concentrations, defined as filters that have a concentration of \u0026lt;\u0026thinsp;16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, the wind predominantly emanated from the NNW to E regions. On these occasions, winds associated with low concentration filters tended to circulate around urban areas where the filters were collected.\u003c/p\u003e\n\u003cp\u003eConversely, all winds linked to \u0026ldquo;high\u0026rdquo; concentration filters, defined as filters that have a concentration of \u0026gt;\u0026thinsp;140 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, exhibited a SSW component, with some also having a W component. This indicates that elevated dust concentrations occur when winds originate from the south or the west. This finding is consistent with numerous referenced papers, which assert that significant dust contributions arise from the southern playas (Sevier Dry Lake, Tule Dry Lake, Milford Valley, Escalante Desert) and the western deserts (Great Salt Lake Desert, Dugway Proving Grounds, West Desert of Utah, Great Basin, and Mojave Deserts of Nevada), with possible minor contributions from the GSL playa. These locations can be seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Backwind trajectories are visually represented in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, as well as in S1-S9.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Dust Compositional Analysis and Health Risk Assessment\u003c/h2\u003e\n\u003cp\u003eCompositional PM\u003csub\u003e10\u003c/sub\u003e analysis reveals minimal metal concentrations in the dust collected on the filters. In certain instances, the measured metal concentrations fell below the limit of detection of the ICP-MS method used for the analysis. Although lead levels were slightly below the quality assurance standards set by the EPA's methods, all other quality assurance checks and interference checks passed successfully.\u003c/p\u003e\n\u003cp\u003eThe greatest amounts of Ba, Cd, Co, Cr, Mn, Ni, Pb, and V were detected on a filter collected at the Utah Tech Center (EPA AIRS code: 490353015) on June 17, 2022 (filter number: 0634559). Meanwhile, the highest concentrations of Cu and Zn were observed on a filter from Herriman (H3) (EPA AIRS code: 490353013) on June 20, 2022 (filter number: 0634539). The filter from Herriman (H3) (EPA AIRS code: 490353013) on May 31, 2022 (filter number: 0634365), exhibited the highest levels of As. Lastly, the filter from the Utah Tech Center (EPA AIRS code: 490353015) on September 9, 2020, exhibited the highest levels of Hg. Notably, the filter from June 17, 2022, recorded a high concentration of PM\u003csub\u003e10\u003c/sub\u003e (146.7 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), while those from June 20, 2022, and May 31, 2022, showed lower concentrations (15.2, 6.2, and 10.6 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively).\u003c/p\u003e\n\u003cp\u003eThe calculation of health quotients involved utilizing the highest concentrations of each metal to report a worst-case measured health quotient. A health quotient exceeding one suggests a potential health risk and raises concerns. Conversely, health quotients below one indicates that the measured metal concentrations in the dust are not a cause for concern. This analysis employed the maximum concentrations of metals from the collected filters to determine the most concerning health quotients. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the concentration of each metal, expressed in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, along with their respective health quotients.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe highest elemental concentrations found in analyzed filters and corresponding health quotients with corresponding highest recorded values.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eElement\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHighest concentration filters (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHealth Quotient\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAs\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e9.5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e4.7 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBa\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e5.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn;3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e \u0026plusmn; 6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCd\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e5.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCo\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e5.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e4.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn;3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e4.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCu\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHg\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e4.6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn; 2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e7.7 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn;4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMn\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e7.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn; 4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e4.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e \u0026plusmn;2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNi\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e5.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePb\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.75 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn; 9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.17 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn;6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eV\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e9.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e \u0026plusmn; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e3.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn;2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZn\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e6.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e \u0026plusmn; 3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e1.20 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e \u0026plusmn;6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e1 \u0026ndash; Utah Tech Center (EQ) EPA site number: 490353015 filter number: 0634559 date: June 17, 2022 (a high concentration filter)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e2 \u0026ndash; Herriman (H3) EPA site number: 490353013 filter number: 0634539 date: June 20, 2022 (a low concentration filter)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e3 \u0026ndash; Herriman (H3) EPA site number: 490353013 filter number: 0634365 date: May 31, 2022 (a low concentration filter)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e4 \u0026ndash; Utah Tech Center (EQ) EPA site number: 490353015 filter number: 9610829 date: September 9, 2020 (a low concentration filter)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, all health quotients are consistently below one. This implies that there is minimal reason for concern regarding the concentration of any measured metals in the dust collected by UDAQ on their filters. In other words, there is no toxic concentration of metals present in the dust reaching urban measurement sites.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eRecent concerns regarding the toxicity of dust from the exposed playa of the GSL have prompted a reevaluation of past research. In this study, we examined the dust that has reached three urban monitoring sites surrounding the GSL, managed by the Utah Division of Air Quality. Utilizing historical data dating back to 1988, no correlation was found to support the notion that a decrease in the surface area of the Great Salt Lake has led to an increase in dust events in urban areas.\u003c/p\u003e \u003cp\u003eBackwind trajectories align with previous research, indicating that elevated dust levels in urban areas coincide with winds originating from the south or west, passing over playas and deserts such as the Milford flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. A compositional analysis of urban dust revealed no concentrations of metals that raise health concerns, with the highest health quotient being four orders of magnitude lower than a level of concern.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e C.E.F: Conceptualization, writing of the original manuscript, sample preparation, experimental setup, data analysis. R.S.: sample preparation, experimental setup, data acquisition, data analysis. K.B.: sample preparation, experimental setup, data acquisition. W.F.P.: funding, writing review and editing J.C.H.: Conceptualization, writing review and editing, supervision, and funding acquisition. All Authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Young Living \u0026ndash; Use of their ICP-MS for analysis. Utah Division of Air Quality \u0026ndash; providing sample filters for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was funded by the National Science Foundation, grant #2114655. The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The datasets generated and/or analyzed during the current study are available in the BYU\u0026rsquo;s Scholar\u0026rsquo;s Archive repository, https://scholarsarchive.byu.edu/data/62\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest(s):\u003c/strong\u003e All authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLang, O. I., Mallia, D. \u0026amp; Skiles, S. M. The shrinking Great Salt Lake contributes to record high dust-on-snow deposition in the Wasatch Mountains during the 2022 snowmelt season. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e (2023). https://doi.org:10.1088/1748-9326/acd409\u003c/li\u003e\n\u003cli\u003eCarling, G. T.\u003cem\u003e et al.\u003c/em\u003e Using strontium isotopes to trace dust from a drying Great Salt Lake to adjacent urban areas and mountain snowpack. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2020). https://doi.org:10.1088/1748-9326/abbfc4\u003c/li\u003e\n\u003cli\u003eSkiles, S. M.\u003cem\u003e et al.\u003c/em\u003e Implications of a shrinking Great Salt Lake for dust on snow deposition in the Wasatch Mountains, UT, as informed by a source to sink case study from the 13-14 April 2017 dust event. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2018). https://doi.org:10.1088/1748-9326/aaefd8\u003c/li\u003e\n\u003cli\u003eCarling, G. T., Fernandez, D. P. \u0026amp; Johnson, W. P. Dust-mediated loading of trace and major elements to Wasatch Mountain snowpack. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e432\u003c/strong\u003e, 65-77 (2012). https://doi.org:10.1016/j.scitotenv.2012.05.077\u003c/li\u003e\n\u003cli\u003eNicoll, K., Hahnenberger, M. \u0026amp; Goldstein, H. L. \u0026apos;Dust in the wind\u0026apos; from source-to-sink: Analysis of the 14-15 April 2015 storm in Utah. \u003cem\u003eAeolian Research\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e (2020). https://doi.org:10.1016/j.aeolia.2019.06.002\u003c/li\u003e\n\u003cli\u003eHall, D. K., O\u0026apos;Leary, D. S., DiGirolamo, N. E., Miller, W. \u0026amp; Kang, D. H. The role of declining snow cover in the desiccation of the Great Salt Lake, Utah, using MODIS data. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e \u003cstrong\u003e252\u003c/strong\u003e (2021). https://doi.org:10.1016/j.rse.2020.112106\u003c/li\u003e\n\u003cli\u003eLawrence, C. R. \u0026amp; Neff, J. C. The contemporary physical and chemical flux of aeolian dust: A synthesis of direct measurements of dust deposition. \u003cem\u003eChemical Geology\u003c/em\u003e \u003cstrong\u003e267\u003c/strong\u003e, 46-63 (2009). https://doi.org:10.1016/j.chemgeo.2009.02.005\u003c/li\u003e\n\u003cli\u003eAgency, U. S. 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D. \u003cem\u003eArsenic Toxicity: Clinical Assessment\u003c/em\u003e, \u0026lt;https://www.atsdr.cdc.gov/csem/arsenic/clinical_assessment.html\u0026gt; (2023).\u003c/li\u003e\n\u003cli\u003eRegistry, A. f. T. S. a. D. \u003cem\u003eArsenic Toxicity: What Are the Standards and Regulation for Arsenic Exposure?\u003c/em\u003e, \u0026lt;https://www.atsdr.cdc.gov/csem/arsenic/standards.html\u0026gt; (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dust, PM10, Great Salt Lake, playa dust, urban dust analysis, elemental dust analysis, metal dust analysis","lastPublishedDoi":"10.21203/rs.3.rs-3994858/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3994858/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Great Salt Lake (GSL) has undergone a reduction in size, from 1046.0 miles\u003csup\u003e2\u003c/sup\u003e on January 1, 2015, to 940.5 miles\u003csup\u003e2\u003c/sup\u003e on January 1, 2022, exposing 105.5 miles\u003csup\u003e2\u003c/sup\u003e of playa that was once covered. This emerging playa raises concerns regarding the toxicity of the ensuing dust. While considerable efforts have been made to understand aeolian dust in urban areas along the Wasatch Front, located just east of the GSL, there is still a need to consolidate existing research and conduct a compositional analysis of the dust found in these urban areas. In this study, we investigated the dust reaching urban monitoring sites around the GSL, managed by the Utah Division of Air Quality. By analyzing historical data dating back to 1988, we found no evidence to support the idea that the decrease in the GSL\u0026rsquo;s surface area has led to an increase in dust events in urban areas. Backwind trajectories align with prior research, indicating that heightened dust levels in urban areas coincide with winds originating from the south or west, passing over identified playas and deserts such as the Milford flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. Conducting a compositional analysis of urban dust revealed no concentrations of metals that raise health concerns, with the highest health quotient being four orders of magnitude lower than a level of concern.\u003c/p\u003e","manuscriptTitle":"Historical Analysis of Urban Dust Generated by the Great Salt Lake Playa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 15:23:56","doi":"10.21203/rs.3.rs-3994858/v1","editorialEvents":[{"type":"communityComments","content":7}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"910c3dbc-3eb3-4757-99ad-e57767acba12","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29170602,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry/Atmospheric chemistry"},{"id":29170603,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2024-05-14T12:00:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 15:23:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3994858","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3994858","identity":"rs-3994858","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}