Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (10)

Search Parameters:
Keywords = Salt Lake aerosol

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
27 pages, 9077 KiB  
Article
Investigating the Spatial Patterns of Heavy Metals in Topsoil and Asthma in the Western Salt Lake Valley, Utah
by Long Yin Lee, Ruth Kerry, Ben Ingram, Connor S. Golden and Joshua J. LeMonte
Environments 2024, 11(10), 223; https://doi.org/10.3390/environments11100223 - 13 Oct 2024
Viewed by 867
Abstract
Mining activities, particularly in large excavations like the Bingham Canyon Copper Mine in Utah, have been increasingly linked to respiratory conditions due to heavy-metal-enriched waste and dust. Operating continuously since 1906, the Bingham Canyon Copper Mine contributes 4.4% of the Salt Lake Valley [...] Read more.
Mining activities, particularly in large excavations like the Bingham Canyon Copper Mine in Utah, have been increasingly linked to respiratory conditions due to heavy-metal-enriched waste and dust. Operating continuously since 1906, the Bingham Canyon Copper Mine contributes 4.4% of the Salt Lake Valley PM2.5 pollution. However, the extent of its contributions to larger-sized particulate matter (PM10) dust, soil and water contamination, and human health impacts is largely unknown. Aerosol optical depth data from Sentinel-2 imagery revealed discernible dust clouds downwind of the mine and smelter on non-prevailing-wind days, suggesting potential heavy metal dispersion from this fugitive dust and subsequent deposition to nearby surface soils. Our analysis of topsoils from across the western Salt Lake Valley found mean arsenic, copper, lead, and zinc concentrations to be well above global background concentrations. Also, the minimum values for arsenic and maximum values for lead were well above the US EPA regional screening levels for residential soils. Thus, arsenic is the metal of greatest concern for impacts on human health. Elevated concentrations of all metals were most notable near the mine, smelter, and tailings pond. Our study linked these elevated heavy metal levels to regional asthma outcomes through cluster analysis and distance-related comparison tests. Significant clusters of high asthma rates were observed in regions with elevated topsoil heavy metal concentrations, impacting both low- and high-income neighborhoods. The findings of this preliminary study suggest that the mine, smelter, and recent construction activities, especially on lands reclaimed from former tailings ponds, could be contributing to atmospheric dust containing high levels of heavy metals and exacerbating asthma outcomes for residents. However, the methods used in the study with aggregated health outcome data cannot determine causal links between the heavy metal contents of soil and health outcomes; they can only point to potential links and a need for further investigation. Such further investigation should involve individual-level data and control for potential confounding factors, such as socioeconomic status, access to healthcare, and lifestyle factors, to isolate the effect of metal exposures on asthma outcomes. This study focused on atmospheric deposition as a source of heavy metal enrichment of topsoil. However, future research is also essential to assess levels of heavy metals in subsoil parent materials and local surface and groundwaters to be able to assess the links between the sources or methods of soil contamination and health outcomes. Full article
(This article belongs to the Special Issue New Insights in Soil Quality and Management)
Show Figures

Figure 1

Figure 1
<p>A map showing the location of sampling points in relation to Utah Small Areas, the mine, the smelter, and the settling pond.</p>
Full article ">Figure 2
<p>Sentinel-2 AOD data from (<b>a</b>) 10 July, (<b>b</b>) 4 August, and (<b>c</b>) 19 August 2019. The pink polygon is Bingham Mine. The red arrows are pointing at small dust clouds near the mine. The black lines are Utah Small Area boundaries.</p>
Full article ">Figure 3
<p>Plots of the kriged distribution of topsoil (<b>a</b>) arsenic, (<b>b</b>) copper, (<b>c</b>) lead, (<b>d</b>) and zinc; (<b>e</b>) kriging variance for copper; and (<b>f</b>) the range of kriging errors for all metals in the western Salt Lake Valley. The green polygon is Bingham Mine, the blue polygon is the tailings pond, the black point is the copper smelter, the yellow dots are the sample points, and the black lines are Small Area boundaries. The blue lines in (<b>a</b>,<b>c</b>) are waterways on the western side of the valley, and the black lines in (<b>b</b>,<b>d</b>) are the road network.</p>
Full article ">Figure 3 Cont.
<p>Plots of the kriged distribution of topsoil (<b>a</b>) arsenic, (<b>b</b>) copper, (<b>c</b>) lead, (<b>d</b>) and zinc; (<b>e</b>) kriging variance for copper; and (<b>f</b>) the range of kriging errors for all metals in the western Salt Lake Valley. The green polygon is Bingham Mine, the blue polygon is the tailings pond, the black point is the copper smelter, the yellow dots are the sample points, and the black lines are Small Area boundaries. The blue lines in (<b>a</b>,<b>c</b>) are waterways on the western side of the valley, and the black lines in (<b>b</b>,<b>d</b>) are the road network.</p>
Full article ">Figure 4
<p>Local Moran’s I maps for kriged topsoil (<b>a</b>) arsenic, (<b>b</b>) copper, (<b>c</b>) lead, and (<b>d</b>) zinc concentrations in the western Salt Lake Valley. The blue polygon is the tailings pond, the green polygon is Bingham Mine, the black point is the copper smelter, and the black lines are Small Area boundaries.</p>
Full article ">Figure 5
<p>Box plots showing concentrations of heavy metals for locations &gt; 5 km or 10 km (0) and &lt;5 km or 10 km (1) from the mine or smelter: (<b>a</b>) copper concentration within 5 km or more of the smelter, (<b>b</b>) copper concentration within 10 km or more of the smelter, (<b>c</b>) copper concentration within 5 km or more of the mine, (<b>d</b>) copper concentration within 10 km or more of the mine, (<b>e</b>) arsenic concentration within 5 km or more of the smelter, (<b>f</b>) arsenic concentration within 10 km or more of the smelter, (<b>g</b>) arsenic concentration within 5 km or more of the mine, (<b>h</b>) arsenic concentration within 10 km or more of the mine.</p>
Full article ">Figure 6
<p>Asthma rate maps for Utah Small Areas (n = 99), expressed as percentages and per 10,000 population, for (<b>a</b>) asthma prevalence in Utah, (<b>b</b>) asthma prevalence in Salt Lake County, (<b>c</b>) emergency room (ER visits) in Utah, (<b>d</b>) ER visits in Salt Lake County, (<b>e</b>) hospitalizations in Utah, (<b>f</b>) hospitalizations in Salt Lake County. Note: In Figures (<b>a</b>,<b>c</b>,<b>e</b>), the black lines show Utah Small Areas (n = 99). In Figures (<b>b</b>,<b>d</b>,<b>f</b>), the road network is shown as thin black lines, the small black dots are sample points, the large black dot is the smelter, the blue polygon is the settling pond, and the green polygon is the mine. The State of Utah is approximately 250 miles wide.</p>
Full article ">Figure 7
<p>Maps showing the locations of significant clusters of asthma outcomes using univariate local Moran’s I analysis for Utah Small Area (n = 99) asthma data: (<b>a</b>) asthma prevalence in Utah, (<b>b</b>) asthma prevalence in Salt Lake County, (<b>c</b>) emergency room visits (ER visits) in Utah, (<b>d</b>) ER visits in Salt Lake County, (<b>e</b>) hospitalizations in Utah, (<b>f</b>) hospitalizations in Salt Lake County. Note: In Figures (<b>a</b>,<b>c</b>,<b>e</b>), the black lines show Utah Small Areas (n = 99). In Figures (<b>b</b>,<b>d</b>,<b>f</b>), the road network is shown as thin black lines, the small black dots are sample points, the large black dot is the smelter, the blue polygon is the settling pond, and the green polygon is the mine. The State of Utah is approximately 250 miles wide.</p>
Full article ">Figure 8
<p>Bivariate local Moran’s I for asthma hospitalizations and soil heavy metal levels (<b>a</b>) arsenic, (<b>b</b>) copper, (<b>c</b>) lead and (<b>d</b>) zinc in Salt Lake County. The road network and Small Area boundaries are shown as black lines, the small black points are sampling locations, the large black point is the smelter, the blue polygon is the settling pond, and the green polygon is the mine.</p>
Full article ">Figure 9
<p>Maps showing the Health Improvement Index (HII) for Utah Small Areas (background) with soil heavy metal levels at sampling locations (colored dots) for (<b>a</b>) arsenic, (<b>b</b>) copper, (<b>c</b>) lead, and (<b>d</b>) zinc. The road network is shown as black lines, the large black dot is the smelter, the blue shape is the settling pond, and the green shape is the mine. The yellow-circled area is Daybreak.</p>
Full article ">Figure 10
<p>(<b>a</b>) A location map of cities in the Salt Lake Valley and (<b>b</b>) population growth in the cities near the Bingham Canyon Copper Mine between 2000 and 2020. The color code for cities in the map (<b>a</b>) is the same as in the graph (<b>b</b>). The black lines show the road network, the red polygon is the mine, the blue polygon is the evaporation pond, and the red dot is the smelter. Source: U.S. Census Bureau, Quick Facts [<a href="#B81-environments-11-00223" class="html-bibr">81</a>].</p>
Full article ">
27 pages, 9447 KiB  
Review
Salt Lake Aerosol Overview: Emissions, Chemical Composition and Health Impacts under the Changing Climate
by Muhammad Subtain Abbas, Yajuan Yang, Quanxi Zhang, Donggang Guo, Ana Flavia Locateli Godoi, Ricardo Henrique Moreton Godoi and Hong Geng
Atmosphere 2024, 15(2), 212; https://doi.org/10.3390/atmos15020212 - 8 Feb 2024
Cited by 1 | Viewed by 1898
Abstract
Salt Lakes, having a salt concentration higher than that of seawater and hosting unique extremophiles, are predominantly located in drought-prone zones worldwide, accumulating diverse salts and continuously emitting salt dust or aerosols. However, knowledge on emission, chemical composition, and health impacts of Salt [...] Read more.
Salt Lakes, having a salt concentration higher than that of seawater and hosting unique extremophiles, are predominantly located in drought-prone zones worldwide, accumulating diverse salts and continuously emitting salt dust or aerosols. However, knowledge on emission, chemical composition, and health impacts of Salt Lake aerosols under climate change is scarce. This review delves into the intricate dynamics of Salt Lake aerosols in the context of climate change, pointing out that, as global warming develops and weather patterns shift, Salt Lakes undergo notable changes in water levels, salinity, and overall hydrological balance, leading to a significant alteration of Salt Lake aerosols in generation and emission patterns, physicochemical characteristics, and transportation. Linked to rising temperatures and intensified evaporation, a marked increase will occur in aerosol emissions from breaking waves on the Salt Lake surface and in saline dust emission from dry lakebeds. The hygroscopic nature of these aerosols, coupled with the emission of sulfate aerosols, will impart light-scattering properties and a cooling effect. The rising temperature and wind speed; increase in extreme weather in regard to the number of events; and blooms of aquatic microorganisms, phytoplankton, and artemia salina in and around Salt Lakes, will lead to the release of more organic substances or biogenic compounds, which contribute to the alteration of saline aerosols in regard to their quantitative and chemical composition. Although the inhalation of saline aerosols from Salt Lakes and fine salt particles suspended in the air due to salt dust storms raises potential health concerns, particularly causing respiratory and cardiovascular disease and leading to eye and skin discomfort, rock salt aerosol therapy is proved to be a good treatment and rehabilitation method for the prevention and treatment of pneumoconiosis and chronic obstructive pulmonary disease (COPD). It is implied that the Salt Lake aerosols, at a certain exposure concentration, likely can delay the pathogenesis of silicosis by regulating oxidative stress and reducing interstitial fibrosis of the lungs. It emphasizes the interconnectedness of climate changes, chemical composition, and health aspects, advocating for a comprehensive and practical approach to address the challenges faced by Salt Lake aerosols in an ever-changing global climate. Full article
Show Figures

Figure 1

Figure 1
<p>The relationship between lake salinity and inflow salinity.</p>
Full article ">Figure 2
<p>(<b>A</b>) Global distribution of Salt Lakes (shaded areas using black lines and dots). (<b>B</b>) Worldwide Salt Lake areas with the main hypersaline hotspots. (<b>a</b>) Great Salt Lake (Utah, USA), (<b>b</b>) Dead Sea (Israel), (<b>c</b>) Crimean Salt Lake (Crimea), (<b>d</b>) Dangxiong Co Salt Lake (Tibet, China), (<b>e</b>) Laguna Puilar, Salar de Atacama (Chile), (<b>f</b>) Gaet’ale Pond (Ethiopia), (<b>g</b>) Kati Thanda-Lake Eyre (Australia), and (<b>h</b>) Deep Lake (Antarctica). Oceania is illustrated in black within the rectangle at the bottom left corner of the map (adapted from Mattia Saccò 2021 [<a href="#B33-atmosphere-15-00212" class="html-bibr">33</a>]).</p>
Full article ">Figure 3
<p>The ethereal beauty of the Yuncheng Salt Lake (located in Shanxi Province, China), combined with its ecological and economic value, establishes it as a site of both natural wonder and cultural significance. (<b>a</b>) Location of Yuncheng Salt Lake. (<b>b</b>) Color pools due to different salinity and algal growth. (<b>c</b>) Salt Lake biodiversity. (<b>d</b>) Salt crystallization and accumulation under low temperature.</p>
Full article ">Figure 4
<p>Mechanism of Salt Lake aerosol generation: (<b>a</b>) aerosols generated from film and jet droplets; (<b>b</b>) possible organic and inorganic components of Salt Lake aerosols.</p>
Full article ">Figure 5
<p>A few examples of morphology and chemical composition of Salt Lake aerosols (SLAs). (<b>A</b>) The secondary electron images (SEIs) of Salt Lake aerosols collected over the Yuncheng Salt Lake, Shanxi Province, China, in September 2022. (<b>B</b>) The typical SEIs by SEM-EDX and elemental atomic concentrations of (<b>a</b>) a NaCl-containing particle; (<b>b</b>) a Na<sub>2</sub>SO<sub>4</sub>-containing particle, in Yuncheng Salt Lake aerosols collected on Al foil in September 2022 by the authors. The high aluminum peak in the EDX spectrum is attributed to the Al foil, used to collect the atmospheric aerosols. (<b>C</b>) The SEIs and EDX spectra of atmospheric aerosols collected during a dust storm episode: (<b>a</b>) a Na-, S-, and Cl-rich particle, likely from dried salt-lakes and saline soils; and (<b>b</b>) a common dust particle (Zhang et al., 2009 [<a href="#B7-atmosphere-15-00212" class="html-bibr">7</a>]).</p>
Full article ">Figure 5 Cont.
<p>A few examples of morphology and chemical composition of Salt Lake aerosols (SLAs). (<b>A</b>) The secondary electron images (SEIs) of Salt Lake aerosols collected over the Yuncheng Salt Lake, Shanxi Province, China, in September 2022. (<b>B</b>) The typical SEIs by SEM-EDX and elemental atomic concentrations of (<b>a</b>) a NaCl-containing particle; (<b>b</b>) a Na<sub>2</sub>SO<sub>4</sub>-containing particle, in Yuncheng Salt Lake aerosols collected on Al foil in September 2022 by the authors. The high aluminum peak in the EDX spectrum is attributed to the Al foil, used to collect the atmospheric aerosols. (<b>C</b>) The SEIs and EDX spectra of atmospheric aerosols collected during a dust storm episode: (<b>a</b>) a Na-, S-, and Cl-rich particle, likely from dried salt-lakes and saline soils; and (<b>b</b>) a common dust particle (Zhang et al., 2009 [<a href="#B7-atmosphere-15-00212" class="html-bibr">7</a>]).</p>
Full article ">Figure 6
<p>Emission pathways of Salt Lake aerosols.</p>
Full article ">Figure 7
<p>Aerosol generation under temperature ((<b>A</b>), adapted from [<a href="#B59-atmosphere-15-00212" class="html-bibr">59</a>]). (<b>B</b>) Lake spray aerosol emission flux under wind speed (<b>a</b>–<b>c</b>) (adapted from [<a href="#B92-atmosphere-15-00212" class="html-bibr">92</a>]). (<b>C</b>) Aerosol types and wind velocity in March 2016 (<b>a</b>,<b>b</b>) and June 2016 (<b>c</b>,<b>d</b>) (adapted from [<a href="#B93-atmosphere-15-00212" class="html-bibr">93</a>]).</p>
Full article ">Figure 7 Cont.
<p>Aerosol generation under temperature ((<b>A</b>), adapted from [<a href="#B59-atmosphere-15-00212" class="html-bibr">59</a>]). (<b>B</b>) Lake spray aerosol emission flux under wind speed (<b>a</b>–<b>c</b>) (adapted from [<a href="#B92-atmosphere-15-00212" class="html-bibr">92</a>]). (<b>C</b>) Aerosol types and wind velocity in March 2016 (<b>a</b>,<b>b</b>) and June 2016 (<b>c</b>,<b>d</b>) (adapted from [<a href="#B93-atmosphere-15-00212" class="html-bibr">93</a>]).</p>
Full article ">Figure 8
<p>Aerosolized toxins emission and health implications (adapted from study of Lim et al., 2023 [<a href="#B111-atmosphere-15-00212" class="html-bibr">111</a>]).</p>
Full article ">Figure 9
<p>Some possible health benefits of salt aerosols.</p>
Full article ">Figure 10
<p>(<b>a</b>,<b>b</b>) Dimethylsulfide emission and sulfate enhancement. (<b>c</b>) Salt Lake aerosols as cloud albedo agent (Modified from Sarwar et al., 2023 [<a href="#B124-atmosphere-15-00212" class="html-bibr">124</a>]).</p>
Full article ">
17 pages, 6108 KiB  
Article
A Long-Term Comparison between the AethLabs MA350 and Aerosol Magee Scientific AE33 Black Carbon Monitors in the Greater Salt Lake City Metropolitan Area
by Daniel L. Mendoza, L. Drew Hill, Jeffrey Blair and Erik T. Crosman
Sensors 2024, 24(3), 965; https://doi.org/10.3390/s24030965 - 1 Feb 2024
Cited by 3 | Viewed by 2504
Abstract
Black carbon (BC) or soot contains ultrafine combustion particles that are associated with a wide range of health impacts, leading to respiratory and cardiovascular diseases. Both long-term and short-term health impacts of BC have been documented, with even low-level exposures to BC resulting [...] Read more.
Black carbon (BC) or soot contains ultrafine combustion particles that are associated with a wide range of health impacts, leading to respiratory and cardiovascular diseases. Both long-term and short-term health impacts of BC have been documented, with even low-level exposures to BC resulting in negative health outcomes for vulnerable groups. Two aethalometers—AethLabs MA350 and Aerosol Magee Scientific AE33—were co-located at a Utah Division of Air Quality site in Bountiful, Utah for just under a year. The aethalometer comparison showed a close relationship between instruments for IR BC, Blue BC, and fossil fuel source-specific BC estimates. The biomass source-specific BC estimates were markedly different between instruments at the minute and hour scale but became more similar and perhaps less-affected by high-leverage outliers at the daily time scale. The greater inter-device difference for biomass BC may have been confounded by very low biomass-specific BC concentrations during the study period. These findings at a mountainous, high-elevation, Greater Salt Lake City Area site support previous study results and broaden the body of evidence validating the performance of the MA350. Full article
Show Figures

Figure 1

Figure 1
<p>Diagrammatic representation of the path that sample air takes through the MA350, showing high-level components.</p>
Full article ">Figure 2
<p>Flow diagram of the data cleaning approach with number of datapoints removed from each device dataset at each step.</p>
Full article ">Figure 3
<p>Raw and DEMA Infrared BC Concentrations.</p>
Full article ">Figure 4
<p>Diurnal cycles from 60 s timebase data for MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 5
<p>Monthly trends from 60 s timebase data for MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 5 Cont.
<p>Monthly trends from 60 s timebase data for MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 6
<p>Seasonal trends from 60 s timebase data for MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 7
<p>Minute resolved comparison between MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 8
<p>Hourly averaged data comparison between MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">Figure 9
<p>Daily averaged data comparison between MA350 and AE33: (<b>a</b>) Blue wavelength, (<b>b</b>) Infrared wavelength, (<b>c</b>) Calculated biomass BC concentrations, and (<b>d</b>) Calculated fossil fuel BC concentrations.</p>
Full article ">
15 pages, 5191 KiB  
Article
Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence
by Mohammad Kazemi Garajeh
Pollutants 2024, 4(1), 1-15; https://doi.org/10.3390/pollutants4010001 - 8 Jan 2024
Cited by 1 | Viewed by 1362
Abstract
Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has [...] Read more.
Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin. Full article
Show Figures

Figure 1

Figure 1
<p>Location of study area in: (<b>a</b>) the world, (<b>b</b>) Iran. (<b>c</b>,<b>d</b>) are observed salt lands using Landsats 7 ETM+ and 8 OLI images for the years 2010 and 2013.</p>
Full article ">Figure 2
<p>A summary of applied methodology for monitoring the distribution of soil salinity to detect the potential areas for salt storm occurrence.</p>
Full article ">Figure 3
<p>Spatio-temporal probability of saline storm occurrences, generated using machine learning algorithms in the GEE for the years 2000, 2010, 2015, and 2022.</p>
Full article ">
11 pages, 7911 KiB  
Article
Establishment of a Halophilic Bloom in a Sterile and Isolated Hypersaline Mesocosm
by Matthew E. Rhodes, Allyson D. Pace, Menny M. Benjamin, Heather Ghent and Katherine S. Dawson
Microorganisms 2023, 11(12), 2886; https://doi.org/10.3390/microorganisms11122886 - 29 Nov 2023
Viewed by 1878
Abstract
Extreme environments, including hypersaline pools, often serve as biogeographical islands. Putative colonizers would need to survive transport across potentially vast distances of inhospitable terrain. Hyperhalophiles, in particular, are often highly sensitive to osmotic pressure. Here, we assessed whether hyperhalophiles are capable of rapidly [...] Read more.
Extreme environments, including hypersaline pools, often serve as biogeographical islands. Putative colonizers would need to survive transport across potentially vast distances of inhospitable terrain. Hyperhalophiles, in particular, are often highly sensitive to osmotic pressure. Here, we assessed whether hyperhalophiles are capable of rapidly colonizing an isolated and sterile hypersaline pool and the order of succession of the ensuing colonizers. A sterile and isolated 1 m3 hypersaline mesocosm pool was constructed on a rooftop in Charleston, SC. Within months, numerous halophilic lineages successfully navigated the 20 m elevation and the greater than 1 km distance from the ocean shore, and a vibrant halophilic community was established. All told, in a nine-month period, greater than a dozen halophilic genera colonized the pool. The first to arrive were members of the Haloarchaeal genus Haloarcula. Like a weed, the Haloarcula rapidly colonized and dominated the mesocosm community but were later supplanted by other hyperhalophilic genera. As a possible source of long-distance inoculum, both aerosol and water column samples were obtained from the Great Salt Lake and its immediate vicinity. Members of the same genus, Haloarcula, were preferentially enriched in the aerosol sample relative to the water column samples. Therefore, it appears that a diverse array of hyperhalophiles are capable of surviving aeolian long-distance transport and that some lineages, in particular, have possibly adapted to that strategy. Full article
(This article belongs to the Section Environmental Microbiology)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Aerial view of the Saint Philip’s Street Parking Garage in Charleston, SC. The mesocosm pool is in the northwest corner; (<b>b</b>) Location of the mesocosm pool on the Charleston, SC peninsula showing distance from the Ashley and Cooper Rivers; (<b>c</b>) Regional scale location of the mesocosm pool in the Southeastern United States; (<b>d</b>) Location of the mesocosm pool and the Great Salt Lake on the North American continent.</p>
Full article ">Figure 2
<p>(<b>a</b>) Hypersaline mesocosm pool from first experimental run. The brown color of the water was imparted by the 2 kg of yeast extract that was added as an organic source. (<b>b</b>) Hypersaline mesocosm pool from the second experimental run. With the addition of the uncolored organic sources glycerol and glucose, the water obtained the characteristic red color of a haloarchaeal bloom.</p>
Full article ">Figure 3
<p>Relative abundance over time of archaeal and bacterial taxa in the mesocosm pool. (<b>A</b>) Taxa that achieved relatively large populations (&gt;10%) at any point during the experiment. (<b>B</b>) Halophilic bacterial and archaeal taxa that displayed measurable but smaller populations during the experiment.</p>
Full article ">
17 pages, 2005 KiB  
Article
Implications in Halotherapy of Aerosols from the Salt Mine Targu Ocna—Structural-Functional Characteristics
by Mihaela Orlanda Antonovici (Munteanu), Ioan Gabriel Sandu, Viorica Vasilache, Andrei Victor Sandu, Stefanita Arcana, Raluca Ioana Arcana and Ion Sandu
Healthcare 2023, 11(14), 2104; https://doi.org/10.3390/healthcare11142104 - 24 Jul 2023
Cited by 2 | Viewed by 1219
Abstract
The paper presents the evolution of the concentration level for four particle size groups of microaerosols (1.0, 2.5, 4.0 and 10.0 µm) in correlation with the microclimatic characteristics (temperature, humidity, lighting, pressure and concentration in CO2 and O2) in three [...] Read more.
The paper presents the evolution of the concentration level for four particle size groups of microaerosols (1.0, 2.5, 4.0 and 10.0 µm) in correlation with the microclimatic characteristics (temperature, humidity, lighting, pressure and concentration in CO2 and O2) in three active areas of the Targu Ocna Saltworks, currently used in treatments with solions (hydrated aerosols): in the vicinity of the walls of the old mining salt room, where there is a semi-wet static regime (SSR); in the transition area between the old rooms of exploitation with the semi-wet dynamic regime (DSR); and in the area of the waterfall and the marshy lake with the dynamic wet regime (DWR). The first and last halochamber are the ones recommended for cardio–respiratory, immuno–thyroid and osteo–muscular conditions, as well as in psycho–motor disorders. Based on questionnaires carried out over the course of a year, between 1 September 2021–31 August 2022, in two periods of stationing/treatment: a cold one (15 September 2021–15 December 2021) and a warm one (1 May 2022–30 July 2022), correlated with the data from the Salina medical office, achieved the profile of the improvement rate of the patients’ ailments depending on the type of treatment (working regime in halochambers). These studies have allowed the optimization of the treatment conditions in the artificial surface halochambers in order to reduce the stationary period and optimize the treatment cycles. Full article
Show Figures

Figure 1

Figure 1
<p>Old exploitation room. Halochamber with semi-wet static mode. Area with training and fitness equipment (acquisition of microclimate data at the level of the salt wall).</p>
Full article ">Figure 2
<p>Corridor between the old exploitation rooms. Halochamber with semi-wet static regime (retrieving microclimate data from the center of the transition corridor).</p>
Full article ">Figure 3
<p>Saltwater lake and waterfall: (<b>a</b>) halochamber with wet dynamic mode; (<b>b</b>) detail with the waterfall area.</p>
Full article ">Figure 4
<p>The model of the questionnaire registered for the two analyzed periods (15 September 2021–15 December 2021 and 1 May 2022–30 July 2022) the patients, athletes and tourists present in Salina Tg. Ocna.</p>
Full article ">
18 pages, 5718 KiB  
Article
Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China
by Mei Zhang, Jia Jia, Bo Wang, Weihong Zhang, Chenming Gu, Xiaochen Zhang and Yuanhao Zhao
Int. J. Environ. Res. Public Health 2022, 19(12), 7091; https://doi.org/10.3390/ijerph19127091 - 9 Jun 2022
Cited by 8 | Viewed by 2304
Abstract
Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+ [...] Read more.
Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl, NO3, SO42−), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China. Full article
(This article belongs to the Special Issue Healthy Lifestyle: Health Promotion and Prevention)
Show Figures

Figure 1

Figure 1
<p>Location of the sampling site.</p>
Full article ">Figure 2
<p>Time series of PM<sub>2.5</sub> mass concentrations and meteorological data (WS-D: wind speed- day, WS-N: wind speed-night; WD-D: wind direction-day, WD-N: wind direction-night; T-D: temperature-day, T-N: temperature-night; RH-D: relative humidity-day, RH-N: relative humidity-night; PM<sub>2.5</sub>-D: PM<sub>2.5</sub>-day, PM<sub>2.5</sub>-N: PM<sub>2.5</sub>-night).</p>
Full article ">Figure 3
<p>Average mass concentration and the proportion of water-soluble ions in Lanzhou City in winter and summer (WINTER-D: winter daytime, WINTER-N: winter nighttime, SUMMER-D: summer daytime, SUMMER-N: summer nighttime).</p>
Full article ">Figure 4
<p>Average concentration of SOC and POC during daytime and nighttime in winter and summer (WINTER-D: winter daytime, WINTER-N: winter nighttime, SUMMER-D: summer daytime, SUMMER-N: summer nighttime).</p>
Full article ">Figure 5
<p>Enrichment factors (EFs) of elements in Lanzhou (WINTER-D: winter daytime, WINTER-N: winter nighttime, SUMMER-D: summer daytime, SUMMER-N: summer nighttime).</p>
Full article ">Figure 6
<p>PMF factor profiles and chemical compounds in winter ((<b>a</b>) factor profiles for daytime, (<b>b</b>) factor profiles for nighttime). The columns are the concentrations of each species within a given source, and the dots represent the percentage contribution of that species to each factor.</p>
Full article ">Figure 7
<p>PMF factor profiles and chemical compounds in summer ((<b>a</b>) factor profiles for daytime, (<b>b</b>) factor profiles for nighttime). The columns are the concentrations of each species within a given source, and the dots represent the percentage contribution of the species to each factor.</p>
Full article ">Figure 8
<p>Source contribution percentages of PM2.5 in Lanzhou (WIN-D: winter-day, WIN-N: winter-night, SUM-D: summer-day, SUM-N: summer-night).</p>
Full article ">
15 pages, 3908 KiB  
Article
Hygroscopicity of Fresh and Aged Salt Mixtures from Saline Lakes
by Jun Li, Wanyu Liu, Linjie Li, Wenjun Gu, Xiying Zhang, Mattias Hallquist, Mingjin Tang, Sen Wang and Xiangrui Kong
Atmosphere 2021, 12(9), 1203; https://doi.org/10.3390/atmos12091203 - 16 Sep 2021
Viewed by 2277
Abstract
The high hygroscopicity of salt aerosol particles makes the particles active in aerosol and cloud formations. Inland saline lakes are an important and dynamic source of salt aerosol. The salt particles can be mixed with mineral dust and transported over long distances. During [...] Read more.
The high hygroscopicity of salt aerosol particles makes the particles active in aerosol and cloud formations. Inland saline lakes are an important and dynamic source of salt aerosol. The salt particles can be mixed with mineral dust and transported over long distances. During transportation, these particles participate in atmospheric heterogeneous chemistry and further impact the climate and air quality on a global scale. Despite their importance and potential, relatively little research has been done on saline lake salt mixtures from atmospheric perspectives. In this study, we use experimental and model methods to evaluate the hygroscopic properties of saline lake brines, fresh salt aerosol particles, and aged salt aerosol particles. Both original samples and literature data are investigated. The original brine samples are collected from six salt lakes in Shanxi and Qinghai provinces in China. The ionic compositions of the brines are determined and the hygroscopicity measurements are performed on crystallized brines. The experimental results agree well with theoretical deliquescence relative humidity (DRH) values estimated by a thermodynamic model. The correlations between DRHs of different salt components and the correlations between DRHs and ionic concentrations are presented and discussed. Positive matrix factorization (PMF) analysis is performed on the ionic concentrations data and the hygroscopicity results, and the solutions are interpreted and discussed. The fresh and aged salt aerosol particles are analyzed in the same way as the brines, and the comparison shows that the aged salt aerosol particles completely alter their hygroscopic property, i.e., transferring from MgCl2 governed to NH4NO3 governed. Full article
Show Figures

Figure 1

Figure 1
<p>Sampling sites in Shanxi and Qinghai provinces, China.</p>
Full article ">Figure 2
<p>IAP as a function of RH, including four salt components in two brine samples.</p>
Full article ">Figure 3
<p>(<b>a</b>) Ionic concentrations of brine samples. (<b>b</b>) Na-normalized ionic concentrations of brine samples. The letters after the numbers indicate the literature source, and the detailed literature information can be found in <a href="#app1-atmosphere-12-01203" class="html-app">Table S1</a>. c from [<a href="#B43-atmosphere-12-01203" class="html-bibr">43</a>,<a href="#B44-atmosphere-12-01203" class="html-bibr">44</a>], d from [<a href="#B45-atmosphere-12-01203" class="html-bibr">45</a>], e from [<a href="#B46-atmosphere-12-01203" class="html-bibr">46</a>], g from [<a href="#B47-atmosphere-12-01203" class="html-bibr">47</a>,<a href="#B48-atmosphere-12-01203" class="html-bibr">48</a>].</p>
Full article ">Figure 4
<p>(<b>a</b>) Factor profiles of PMF 3-factor results on ionic concentrations. (<b>b-1</b>,<b>b-2</b>) PMF factor distributions of all brine samples. (<b>c-1</b>–<b>c-3</b>) Three factor-representing samples: (<b>c-1</b>) 41c for Factor 1, (<b>c-2</b>) QH for Factor 2, (<b>c-3</b>) KK for Factor 3. b from [<a href="#B42-atmosphere-12-01203" class="html-bibr">42</a>], c from [<a href="#B43-atmosphere-12-01203" class="html-bibr">43</a>,<a href="#B44-atmosphere-12-01203" class="html-bibr">44</a>], d from [<a href="#B45-atmosphere-12-01203" class="html-bibr">45</a>], e from [<a href="#B46-atmosphere-12-01203" class="html-bibr">46</a>], g from [<a href="#B47-atmosphere-12-01203" class="html-bibr">47</a>,<a href="#B48-atmosphere-12-01203" class="html-bibr">48</a>].</p>
Full article ">Figure 5
<p>AIOMFAC modeled DRHs of four salt components in brine samples. b from [<a href="#B42-atmosphere-12-01203" class="html-bibr">42</a>], c from [<a href="#B43-atmosphere-12-01203" class="html-bibr">43</a>,<a href="#B44-atmosphere-12-01203" class="html-bibr">44</a>], d from [<a href="#B45-atmosphere-12-01203" class="html-bibr">45</a>], e from [<a href="#B46-atmosphere-12-01203" class="html-bibr">46</a>], g from [<a href="#B47-atmosphere-12-01203" class="html-bibr">47</a>,<a href="#B48-atmosphere-12-01203" class="html-bibr">48</a>].</p>
Full article ">Figure 6
<p>(<b>a</b>–<b>c</b>) Mass growth factor as a function of RH in different y-axis scales. (<b>d</b>) Calculated DRHs of four key salt components for the six samples. The red squares are the experimentally observed initial water uptakes and the blue squares are the experimentally observed major water uptakes.</p>
Full article ">Figure 7
<p>(<b>a</b>–<b>c</b>) Correlations of the DRHs and ionic concentration (moL/L) of four ions. (<b>d</b>–<b>f</b>) Correlations of the DRH value of three main salt components in brine samples.</p>
Full article ">Figure 8
<p>(<b>a</b>) PMF factor profiles of DRHs of brine samples. (<b>b</b>) PMF factor distributions of DRHs of brine samples. b from [<a href="#B42-atmosphere-12-01203" class="html-bibr">42</a>], c from [<a href="#B43-atmosphere-12-01203" class="html-bibr">43</a>,<a href="#B44-atmosphere-12-01203" class="html-bibr">44</a>], d from [<a href="#B45-atmosphere-12-01203" class="html-bibr">45</a>], e from [<a href="#B46-atmosphere-12-01203" class="html-bibr">46</a>], g from [<a href="#B47-atmosphere-12-01203" class="html-bibr">47</a>,<a href="#B48-atmosphere-12-01203" class="html-bibr">48</a>].</p>
Full article ">Figure 9
<p>(<b>a</b>) Normalized concentration of ions. (<b>b</b>) Modeled DRHs of size salts in five aerosol samples. The 1a–3a are aged salt aerosol particles [<a href="#B16-atmosphere-12-01203" class="html-bibr">16</a>]; 74f, 84f, 87f are fresh salt aerosol particles [<a href="#B6-atmosphere-12-01203" class="html-bibr">6</a>].</p>
Full article ">Figure 10
<p>(<b>a-1</b>,<b>a-2</b>) PMF solution of ionic concentrations. (<b>b-1</b>,<b>b-2</b>) PMF solution of calculated DRHs. (<b>c-1</b>,<b>c-2</b>) Ionic compositions of typical cases for the two factors.</p>
Full article ">
20 pages, 4925 KiB  
Article
Assessment of Sulfate Sources under Cold Conditions as a Geochemical Proxy for the Origin of Sulfates in the Circumpolar Dunes on Mars
by Anna Szynkiewicz and Janice L. Bishop
Minerals 2021, 11(5), 507; https://doi.org/10.3390/min11050507 - 11 May 2021
Cited by 6 | Viewed by 2890
Abstract
Determining aqueous sulfate sources in terrestrial cold environments can provide an insight into the surface hydrological conditions and sulfur cycle on Mars. In this study, we analyzed sulfur and oxygen isotope compositions of secondary sulfate salts (e.g., gypsum, thenardite) in the surficial sediments [...] Read more.
Determining aqueous sulfate sources in terrestrial cold environments can provide an insight into the surface hydrological conditions and sulfur cycle on Mars. In this study, we analyzed sulfur and oxygen isotope compositions of secondary sulfate salts (e.g., gypsum, thenardite) in the surficial sediments and soils of the McMurdo Dry Valleys (MDV), Antarctica to determine contributions of sulfate from bedrock chemical weathering and atmospheric deposition under persistent dry polar conditions. The sulfate showed wider variation of δ34S (+15.8‰ to +32.5‰) compared to smaller ranges of δ18O (−8.9‰ to −4.1‰). In contrast, the δ34S of bedrock sulfide showed significantly lower and consistent values across the studied area (−0.6‰ to +3.3‰). Based on the δ34S trends, sulfide weathering may contribute up to 20–50% of secondary sulfate salts in the MDV. While the remaining 50–80% of sulfate inputs may originate from atmospheric deposition (e.g., sea aerosols, dimethulsulfide oxidation), the subglacial brines derived by relicts of seawater and/or lake/pond water influenced by microbial sulfate reduction could also be important sulfate endmembers particularly in the Antarctic lowland thaw zones. Additional field observations of frost, ponding water, and thin gypsum crusts on the terrestrial gypsum dunes at White Sands supports reactivity of gypsum on the surface of these dunes during cold winter conditions. Combined with our improved geochemical model of the sulfur cycle for cold Antarctic settings, we propose that transient liquid water or frost was available in near-surface environments at the time of gypsum formation in the north polar region on Mars. Ice and/or water interaction with basaltic sand of the basal unit (paleo-erg) would have enhanced leaching of sulfate from both sulfide oxidation and atmospheric deposition and resulted in formation of secondary gypsum salts. Full article
Show Figures

Figure 1

Figure 1
<p>Location maps of sampling sites in Antarctica. (<b>A</b>) Location of the South Fork catchment (blue) in the McMurdo Dry Valleys compared to other sampling sites for the sediments (red) studied by Bao and Marchant [<a href="#B7-minerals-11-00507" class="html-bibr">7</a>]. Image credit: Landsat 7. (<b>B</b>) Locations of seven sampling sites in the South Fork catchment analyzed in our study. Image credit: Google Earth. (<b>C</b>) Regional map of Antarctica showing approximate locations of the McMurdo Dry Valleys (MDV), Lewis Cliff (LC), Shackleton Glacier (SG), and Ace Lake (AL).</p>
Full article ">Figure 2
<p>Comparison of δ<sup>34</sup>S and δ<sup>18</sup>O in secondary sulfate salts between the South Fork of Wright Valley (this study, black circles) and other areas of Antarctica (light gray symbols) studied by previous investigators [<a href="#B7-minerals-11-00507" class="html-bibr">7</a>,<a href="#B8-minerals-11-00507" class="html-bibr">8</a>,<a href="#B9-minerals-11-00507" class="html-bibr">9</a>,<a href="#B31-minerals-11-00507" class="html-bibr">31</a>]. Because of small isotopic variations with depth, average values are presented for sites no. 33 and 42. The hypothetical mixing line is presented for the sulfide- and atmospheric (sea aerosols)-derived sulfate inputs for the South Fork bedrock comprised of the Ferrar Group and Precambrian basement rocks. The δ<sup>34</sup>S of sulfide-derived sulfate is based on sulfur sequential extraction results (this study; <a href="#minerals-11-00507-t001" class="html-table">Table 1</a>) and δ<sup>18</sup>O is based on previously reported isotope compositions of surface water and groundwater [<a href="#B8-minerals-11-00507" class="html-bibr">8</a>,<a href="#B30-minerals-11-00507" class="html-bibr">30</a>,<a href="#B31-minerals-11-00507" class="html-bibr">31</a>]. Note that larger variation of δ<sup>18</sup>O in the Antarctic hydrological system precludes from more precise determination of the δ<sup>18</sup>O of sulfide-derived sulfate. Therefore, only δ<sup>34</sup>S of sulfides is used for quantitative estimates of sulfide-derived sulfate in the studied sediments from the South Fork catchment (see <a href="#sec5dot1dot2-minerals-11-00507" class="html-sec">Section 5.1.2</a> for more details).</p>
Full article ">Figure 3
<p>Surface features associated with groundwater rise in the White Sands interdunes during the winter of 2006/2007 (Photographs taken by A. Szynkiewicz). (<b>A</b>) ponding water in the center of White Sands dune field (January 2007). (<b>B</b>,<b>C</b>) newly formed thin gypsum crusts after groundwater rise and water ponding in the interdunes (May and October 2007). (<b>D</b>) subsequent wind erosion of thin gypsum crusts (April 2008) exposing older cross-bedding strata preserved by previous episodes of groundwater rise.</p>
Full article ">Figure 4
<p>White Sands dunes in January of 2010 (Photographs taken by A. Szynkiewicz). (<b>A</b>–<b>C</b>) lighter- toned frost on dune crests and slip faces during freezing and dry conditions on 1 and 30 January. (<b>D</b>,<b>E</b>) snow accumulations and wetting of gypsum sand on dune crests during warmer and humid conditions on 17 January.</p>
Full article ">Figure 5
<p>(<b>A</b>,<b>B</b>) Portions of HiRISE image PSP_009898_2615 showing sections of the Olympia Undae dunes and bright albedo interdunes with the cross-strata left by migrating dunes (<b>A</b>) and polygons (<b>B</b>). (<b>C</b>) Portion of HiRISE image PSP_010049_2795 showing examples of bright albedo ripples in the interdunes of Olympia Undae. (<b>D</b>) Portion of HiRISE image PSP_010071_2615 showing examples of bright albedo surfaces with polygons in the interdunes of Olympia Undae. (<b>E</b>) Examples of the cross-bedding strata and interdune gypsum crusts formed after groundwater rise in the interdunes of the White Sands Dune Field, New Mexico (Oct 2007). Image credit: A. Szynkiewicz. (<b>F</b>) Portion of HiRISE image PSP-010097_2650 showing the top section of the upper basal unit covered by the Ice Cap.</p>
Full article ">
16 pages, 5812 KiB  
Article
Temporal and Spatial Variation of PM2.5 in Xining, Northeast of the Qinghai–Xizang (Tibet) Plateau
by Xiaofeng Hu, Yongzheng Yin, Lian Duan, Hong Wang, Weijun Song and Guangli Xiu
Atmosphere 2020, 11(9), 953; https://doi.org/10.3390/atmos11090953 - 7 Sep 2020
Cited by 9 | Viewed by 2984
Abstract
PM2.5 was sampled from January 2017 to May 2018 at an urban, suburban, industrial, and rural sites in Xining. The annual mean of PM2.5 was highest at the urban site and lowest at the rural site, with an average of 51.5 [...] Read more.
PM2.5 was sampled from January 2017 to May 2018 at an urban, suburban, industrial, and rural sites in Xining. The annual mean of PM2.5 was highest at the urban site and lowest at the rural site, with an average of 51.5 ± 48.9 and 26.4 ± 17.8 μg·m−3, respectively. The average PM2.5 concentration of the industrial and suburban sites was 42.8 ± 27.4 and 37.2 ± 23.7 μg·m−3, respectively. All sites except for the rural had concentrations above the ambient air quality standards of China (GB3095-2012). The highest concentration of PM2.5 at all sites was observed in winter, followed by spring, autumn, and summer. The concentration of major constituents showed statistically significant seasonal and spatial variation. The highest concentrations of organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), and water-soluble inorganic ions (WSIIs) were found at the urban site in winter. The average concentration of F was higher than that in many studies, especially at the industrial site where the annual average concentration of F was 1.5 ± 1.7 μg·m−3. The range of sulfur oxidation ratio (SOR) was 0.1–0.18 and nitrogen oxidation ratio (NOR) was 0.02–0.1 in Xining. The higher SO42−/NO3 indicates that coal combustion has greater impact than vehicle emissions. The results of the potential source contribution function (PSCF) suggest that air mass from middle- and large-scale transport from the western areas of Xining have contributed to the higher level of PM2.5. On the basis of the positive matrix factorization (PMF) model, it was found that aerosols from salt lakes and dust were the main sources of PM2.5 in Xining, accounting for 26.3% of aerosol total mass. During the sandstorms, the concentration of PM2.5 increased sharply, and the concentrations of Na+, Ca2+ and Mg2+ were 1.13–2.70, 1.68–4.41, and 1.15–5.12 times higher, respectively, than annual average concentration, implying that aerosols were mainly from dust and the largest saltwater lake, Qinghai Lake, and many other salt lakes in the province of Qinghai. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was utilized to study the surface components of PM2.5 and F was found to be increasingly distributed from the surface to inside the particles. We determined that the extremely high PM2.5 concentration appears to be due to an episode of heavy pollution resulting from the combination of sandstorms and the burning of fireworks. Full article
(This article belongs to the Special Issue Air Quality Assessment and Management)
Show Figures

Figure 1

Figure 1
<p>Sampling-site locations.</p>
Full article ">Figure 2
<p>Regressions between water-soluble organic carbon (WSOC) and PM<sub>2.5</sub> at the four sampling sites. Note: (<b>A</b>) = Spring, (<b>B</b>) = Summer, (<b>C</b>) = Autumn, <b>(D</b>) = Winter.</p>
Full article ">Figure 3
<p>Distribution of water-soluble ions at four sampling sites in Xining, China (μg·m<sup>−3</sup>). Note: (<b>A</b>) = FPH, (<b>B</b>) = GID, (<b>C</b>) = QHU, (<b>D</b>) = YLV</p>
Full article ">Figure 4
<p>Images of PM<sub>2.5</sub> under positive mode. (<b>A1</b>,<b>A2</b>) were low-concentration samples (35.6 and 41.7 μg × m<sup>−3</sup>), (<b>A3</b>,<b>A4</b>) were high-concentration samples (132.6 and 98.4 μg × m<sup>−3</sup>); relative humidity (RH) on sampling day was 32%, 38%, 35%, and 32%, respectively. Green, fuchsia, and blue represent Ca<sup>+</sup>, K<sup>+</sup>, and Mg<sup>+</sup>, respectively.</p>
Full article ">Figure 5
<p>Depth profiles for PM<sub>2.5</sub> samples from FPH in winter. Note: (<b>A</b>,<b>B</b>) refer to Positive and Negative ions in samples A3, (<b>C</b>,<b>D</b>) refer to Positive and Negative ions in samples A4.</p>
Full article ">Figure 6
<p>Spatial contribution of PM<sub>2.5</sub> simulated by potential source contribution function (PSCF) model.</p>
Full article ">Figure 7
<p>Source apportionment of PM<sub>2.5</sub> by positive matrix factorization (PMF).</p>
Full article ">Figure 8
<p>Concentration comparison of PM<sub>2.5</sub> and PM<sub>10</sub> between sandstorms and average.</p>
Full article ">
Back to TopTop