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24 pages, 22425 KiB  
Article
Atmospheric Black Carbon Evaluation in Two Sites of San Luis Potosí City During the Years 2018–2020
by Valter Barrera, Cristian Guerrero, Guadalupe Galindo, Dara Salcedo, Andrés Ruiz and Carlos Contreras
Atmosphere 2025, 16(1), 65; https://doi.org/10.3390/atmos16010065 - 9 Jan 2025
Viewed by 456
Abstract
Nevertheless, there is a lot to know about air pollutants in Mexico’s largest cities, like San Luis Potosi City, which is one of the 12 most crowded cities and is expected to grow in the next years; however, there is little information about [...] Read more.
Nevertheless, there is a lot to know about air pollutants in Mexico’s largest cities, like San Luis Potosi City, which is one of the 12 most crowded cities and is expected to grow in the next years; however, there is little information about air pollutant levels mainly particulate matter in their regulated size fractions (PM10 or PM2.5), and its main component of the Organic fraction: Black Carbon (BC), which is especially important because of its chemical properties and their effects on human health, air pollution, and climate change. This work presents a one-year BC monitoring in the northern part of the city (2018–2019) and another one-year BC monitoring in the southern area (2019–2020) during the health contingency situation due to the SARX-CoV-2 virus to obtain direct equivalent black carbon (eBC) concentrations and their main fractions related to fossil fuel and biomass burning using aethalometer AE-33, as well as other air pollutants concentrations measured at the same periods by the governmental local monitoring network (SEGAM). At the North, BC mass annual average concentration was (1.11 µg m−3), divided into seasonal stations, the cold season was the highest with (1.44 µg m−3), followed by the dry season (1.23 µg m−3), rainy season (0.94 µg m−3) and finally warm dry season (0.83 µg m−3). In the south, BC annual average concentration was (1.96 µg m−3); divided into seasons, the highest was the dry season with (2.73 µg m−3), followed by the cold season (2.37 µg m−3), dry warm season (1.61 µg m−3) and the rainy season (1.28 µg m−3). One of the main findings was the dominance of annual mean concentrations of BC originating from fossil fuels (BCff) on the north site in the city was 0.97 and on the south site (BCff) was 0.91 due to some forest fires during the monitoring period. This study presented information from two zones of a growing city in Mexico to generate new air pollutant indicators to have a better understanding of pollutant interactions in the city, to decrease the emission precursor sources, and reduce the health risks in the population. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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Figure 1

Figure 1
<p>San Luis Potosi monitoring sites at North (red star) and South (black star) during years 2018–2020 with Industrial sources according to DENUE, 2021 (Image: Google Earth<sup>@</sup>).</p>
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<p>BC, BCff, and BCbb concentrations (µg m<sup>−3</sup>), and CO concentrations (ppm) at the north monitoring site.</p>
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<p>Wind Roses sorted by season (<b>a</b>) cold 2018 (<b>b</b>) dry 2019 (<b>c</b>) warm dry 2019 (<b>d</b>) rainy 2019. The data were obtained through the SEGAM network at the Biblioteca site (SEGAM, 2019).</p>
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<p>(<b>a</b>) Sierra de San Miguelito fire emissions during May 2019. Sentinel-2, LA2. (<b>b</b>) HYSPLIT backwards average trajectories towards San Luis Potosí downtown during the forest fire period. (<b>c</b>) Polar Plot of BCbb concentration (µg m<sup>−3</sup>) over the North Site in SLPMA in 2019.</p>
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<p>Average weekly BC concentration in the North Site (2018–2019). BC in the green line and BCff in the red line. The shading highlights the standard deviation.</p>
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<p>BC, BCff, BCbb, PM<sub>10</sub>, and PM<sub>2.5</sub> concentrations (µg m<sup>−3</sup>), at the southeast monitoring site.</p>
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<p>Average weekly BC concentration (μg m<sup>−3</sup>) in the South Site (2019–2020). BC in the blue line and BCff in the green line. The shading highlights the standard deviation.</p>
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<p>HYSPLIT average back trajectories at South site “FCHYS” by season: (<b>a</b>) cold 2019 (<b>b</b>) dry 2020 (<b>c</b>) warm dry 2020 (<b>d</b>) rainy 2020.</p>
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<p>(<b>a</b>) BC and (<b>b</b>) PM<sub>10</sub> average daily concentrations during the COVID period.</p>
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<p>Correlation matrix plot between air pollutants and meteorological parameters for each period: North Site (<b>left</b>), South Site (<b>right</b>).</p>
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23 pages, 4830 KiB  
Article
Vertical Profiles of Aerosol Optical Properties (VIS/NIR) over Wetland Environment: POLIMOS-2018 Field Campaign
by Michal T. Chilinski, Krzysztof M. Markowicz, Patryk Poczta, Bogdan H. Chojnicki, Kamila M. Harenda, Przemysław Makuch, Dongxiang Wang and Iwona S. Stachlewska
Remote Sens. 2024, 16(23), 4580; https://doi.org/10.3390/rs16234580 - 6 Dec 2024
Viewed by 636
Abstract
This study aims to present the benefits of unmanned aircraft systems (UAS) in atmospheric aerosol research, specifically to obtain information on the vertical variability of aerosol single-scattering properties in the lower troposphere. The results discussed in this paper were obtained during the Polish [...] Read more.
This study aims to present the benefits of unmanned aircraft systems (UAS) in atmospheric aerosol research, specifically to obtain information on the vertical variability of aerosol single-scattering properties in the lower troposphere. The results discussed in this paper were obtained during the Polish Radar and Lidar Mobile Observation System (POLIMOS) field campaign in 2018 at a wetland and rural site located in the Rzecin (Poland). UAS was equipped with miniaturised devices (low-cost aerosol optical counter, aethalometer AE-51, RS41 radiosonde) to measure aerosol properties (scattering and absorption coefficient) and air thermodynamic parameters. Typical UAS vertical profiles were conducted up to approximately 1000 m agl. During nighttime, UAS measurements show a very shallow inversion surface layer up to about 100–200 m agl, with significant enhancement of aerosol scattering and absorption coefficient. In this case, the Pearson correlation coefficient between aerosol single-scattering properties measured by ground-based equipment and UAS devices significantly decreases with altitude. In such conditions, aerosol properties at 200 m agl are independent of the ground-based observation. On the contrary, the ground observations are better correlated with UAS measurements at higher altitudes during daytime and under well-mixed conditions. During long-range transport of biomass burning from fire in North America, the aerosol absorption coefficient increases with altitude, probably due to entrainment of such particles into the PBL. Full article
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<p>Versa X6-Sci hexacopter with equipment mounted on the bottom side of the UAS.</p>
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<p>Time variability of AOD at 500 nm (Level 2.0) between May and September 2018, obtained from CIMEL observations at the Rzecin AERONET site. The navy blue, red, green, and black circles correspond to all data, 22–27 May, 28–30 August, and 8–9 September, respectively. The blue line indicates the run’s mean AOD with a time window of 10 days.</p>
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<p>Temporal variability of anthropogenic (blue), mineral dust (orange), smoke (orange), and sea salt (navy blue) AOD at 550 nm obtained from NAAPS reanalysis for Rzecin region between (<b>a</b>) 22 and 27 May and (<b>b</b>) 29 August and 9 September 2023.</p>
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<p>Time variability of (<b>a</b>) aerosol scattering coefficient at 550 nm, (<b>b</b>) eBC concentration [ng/m<sup>3</sup>], (<b>c</b>) surface SSA at 550 nm (blue line) and columnar SSA from AERONET at 441 nm (red circles), and (<b>d</b>) ratio of AOD to surface aerosol extinction coefficient [Mm].</p>
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<p>Vertical profile of the (<b>a</b>) aerosol scattering [Mm<sup>−1</sup>], (<b>b</b>) absorption coefficient [Mm<sup>−1</sup>], (<b>c</b>) single-scattering albedo (all at 525 nm), (<b>d</b>) air temperature [°C], and (<b>e</b>) relative humidity [%] obtained on 22 May 2018 at 23:13 UTC. The error bars show the uncertainty for optical properties and standard deviation for thermodynamic parameters. The black dots show in situ ground-based observations averaged during UAS measurements.</p>
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<p>Vertical profile of the (<b>a</b>) aerosol scattering [Mm<sup>−1</sup>], (<b>b</b>) absorption coefficient [Mm<sup>−1</sup>], (<b>c</b>) single-scattering albedo (all at 525 nm), (<b>d</b>) air temperature [°C], and (<b>e</b>) relative humidity [%] obtained on 23 May 2018 at 21:02 UTC. The error bars show the uncertainty for optical properties and standard deviation for thermodynamic parameters. The black dots show in situ ground-based observations averaged during profiling measurements.</p>
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<p>Vertical profile of the (<b>a</b>) aerosol scattering [Mm<sup>−1</sup>], (<b>b</b>) absorption coefficient [Mm<sup>−1</sup>], (<b>c</b>) single-scattering albedo (all at 525 nm), (<b>d</b>) air temperature [°C], and (<b>e</b>) relative humidity [%] obtained on 23 May 2018 at 08:44 UTC. The error bars show the uncertainty for optical properties and standard deviation for thermodynamic parameters. The black dots show in situ ground-based observations averaged during profiling measurements.</p>
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<p>Vertical profile of the (<b>a</b>) aerosol scattering [Mm<sup>−1</sup>], (<b>b</b>) absorption coefficient [Mm<sup>−1</sup>], (<b>c</b>) single-scattering albedo (all at 525 nm), (<b>d</b>) air temperature [°C], and (<b>e</b>) relative humidity [%] obtained on 27 May 2018 at 08:34 UTC. The error bars show the uncertainty for optical properties and standard deviation for thermodynamic parameters. The black dots show in situ ground-based observations averaged during profiling measurements.</p>
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<p>Scatter plot of the aerosol scattering (<b>a</b>) and absorption (<b>b</b>) coefficients measured by miniaturised equipment on the UAS at 525 nm just above the surface and by ground-based PAX devices at 532 nm. The dotted line corresponds to perfect agreement. Data were taken from all flights where the corresponding sensors were mounted.</p>
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<p>Pearson correlation coefficient of aerosol single-scattering properties measured at the ground station and by miniaturised devices onboard of UAS as a function of altitude. The dotted red and solid blue lines correspond to aerosol scattering and absorption coefficient, respectively, while the solid black and orange lines respectively correspond to the aerosol absorption coefficient during inversion and the convection conditions.</p>
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<p>AOD at 525 nm during UAS flights in May 2023 obtained from the CIMEL sun-photometer (blue), Aurora 4000 (ASC), and PAX (AAC) at the ground station (red) and from SPS7003 (ASC) and AE-51 (AAE) at the UAS (orange). In the last two cases, the AOD was estimated in the layer between the surface and 1 km agl.</p>
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<p>Mean vertical variability of (<b>a</b>) eBC concentration [ng/m<sup>3</sup>], (<b>b</b>) air temperature [°C], and (<b>c</b>) relative humidity [%] during day (blue) and night (orange).</p>
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<p>Mean hourly surface variability of aerosol (<b>a</b>) scattering coefficient [Mm<sup>−1</sup>], (<b>b</b>) absorption coefficient [Mm<sup>−1</sup>], (<b>c</b>) single-scattering albedo at 532 nm, (<b>d</b>) scattering Angstrom exponent (870/532 nm), (<b>e</b>) absorbing Angstrom exponent (950/370 nm), and (<b>f</b>) PBL top height [m]. Optical properties were measured by the PAX and AE-31 (AAE only) devices, while the PBL was obtained from the HYSPLIT model. The means were calculated for the period of the whole field campaign.</p>
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<p>The lidar range-corrected signal at 532 nm (arbitrary units) between 28 and 30 August 2018.</p>
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<p>Vertical profiles of (<b>a</b>) aerosol absorption coefficient at 525 nm [Mm<sup>−1</sup>], (<b>b</b>) air temperature, and (<b>c</b>) relative humidity during surface inversion conditions on 29 August at 07:37 UTC (blue), 30 August at 04:40 UTC (red), and 30 August at 05:40 UTC (orange).</p>
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<p>Vertical profiles of (<b>a</b>) aerosol absorption coefficient at 532 nm [Mm<sup>−1</sup>], (<b>b</b>) air temperature, and (<b>c</b>) relative humidity in the well-mixed lower troposphere on 28 August at 13:52 UTC (blue), 28 August at 15:27 UTC (red), 29 August at 10:35 UTC (orange), and 29 August at 12:22 UTC (navy blue).</p>
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<p>Histogram of the maximum UAS flight level in [m].</p>
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<p>Mean AOD at 500 nm as a function of the distance of the 48 h back-trajectory ending in Rzecin at 1.5 km. agl. The error bars correspond to the standard deviation, while yellow values correspond to the percentage of cases.</p>
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<p>Time variability of the organic carbon mixing ratio as a function of pressure obtained from MERRA-II reanalysis during the second half of August 2018 over the Rzecin site.</p>
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<p>The 96 h back-trajectories ending on 27 May 2018 at 12 UTC over Rzecin at 0.5, 1.5, and 3.0 km agl.</p>
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<p>The 200 h back-trajectories ending on 29 August 2018 at 00 UTC over Rzecin at 7, 8.5, and 10 km agl.</p>
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<p>Smoke from burning biomass over Northern California and British Columbia observed by the MODIS detector on 22 August 2018 (marked with black ovals).</p>
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14 pages, 4382 KiB  
Article
Investigations on Stubble-Burning Aerosols over a Rural Location Using Ground-Based, Model, and Spaceborne Data
by Katta Vijayakumar, Panuganti China Sattilingam Devara and Saurabh Yadav
Atmosphere 2024, 15(11), 1383; https://doi.org/10.3390/atmos15111383 - 17 Nov 2024
Viewed by 683
Abstract
Agriculture crop residue burning has become a major environmental problem facing the Indo-Gangetic plain, as well as contributing to global warming. This paper reports the results of a comprehensive study, examining the variations in aerosol optical, microphysical, and radiative properties that occur during [...] Read more.
Agriculture crop residue burning has become a major environmental problem facing the Indo-Gangetic plain, as well as contributing to global warming. This paper reports the results of a comprehensive study, examining the variations in aerosol optical, microphysical, and radiative properties that occur during biomass-burning events at Amity University Haryana (AUH), at a rural station in Gurugram (Latitude: 28.31° N, Longitude: 76.90° E, 285 m AMSL), employing ground-based observations of AERONET and Aethalometer, as well as satellite and model simulations during 7–16 November 2021. The smoke emissions during the burning events enhanced the aerosol optical depth (AOD) and increased the Angstrom exponent (AE), suggesting the dominance of fine-mode aerosols. A smoke event that affected the study region on 11 November 2021 is simulated using the regional NAAPS model to assess the role of smoke in regional aerosol loading that caused an atmospheric forcing of 230.4 W/m2. The higher values of BC (black carbon) and BB (biomass burning), and lower values of AAE (absorption Angstrom exponent) are also observed during the peak intensity of the smoke-event period. A notable layer of smoke has been observed, extending from the surface up to an altitude of approximately 3 km. In addition, the observations gathered from CALIPSO regarding the vertical profiles of aerosols show a qualitative agreement with the values obtained from AERONET observations. Further, the smoke plumes that arose due to transport of a wide-spread agricultural crop residue burning are observed nationwide, as shown by MODIS imagery, and HYSPLIT back trajectories. Thus, the present study highlights that the smoke aerosol emissions during crop residue burning occasions play a critical role in the local/regional aerosol microphysical and radiation properties, and hence in the climate variability. Full article
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<p>The MODIS-Aqua satellite true color image of fires over Punjab state on 11 November 2021. Here, the red color indicates fire detection using VIIRS satellite and the blue-colored star mark indicates the observational site Gurgaon (Amity University).</p>
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<p>Day-to-day variation in AOD at different wavelengths (nm).</p>
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<p>Day-to-day variation in the AERONET (<b>a</b>) fine-mode and coarse-mode AOD at 500 nm; (<b>b</b>) Ångström exponent a in the spectral band 440–870 nm; and (<b>c</b>) fine-mode fraction at 500 nm over the experimental site. The vertical bars show one standard deviation from the mean area-averaged value.</p>
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<p>Time evolution of aerosol volume size distribution for different aerosol radius (in um) over observation site from 7 to 16 November 2021.</p>
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<p>Day-to-day variation in SSA at all wavelengths. The vertical bars show one standard deviation from the mean area-averaged value.</p>
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<p>Day-to-day variation in Aerosol Radiative Forcing (ARF).</p>
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<p>Daily mean variation in (<b>a</b>) BC mass concentration and absorption Ångström exponent (AAE); and (<b>b</b>) biomass burning (BB%) during smoke event period.</p>
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<p>The concentration of smoke at the surface on 11 November 2021 during 00:00, 06:00, 12:00, and 18:00 h UTC.</p>
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<p>(<b>a</b>) CALIPSO retrieved the aerosol classification (sub-type profile), and (<b>b</b>) vertical feature mask on 11 November 2021 over the studied region.</p>
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<p>(<b>a</b>) Active true color image on 11 November 2021. (<b>b</b>) NOAA HYSPLIT hourly backward trajectories ending at Amity University Haryana (AUH), India on 11 November 2021.</p>
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18 pages, 6145 KiB  
Article
Black Carbon in the Air of the Baikal Region, (Russia): Sources and Spatiotemporal Variations
by Tamara V. Khodzher, Elena P. Yausheva, Maxim Yu. Shikhovtsev, Galina S. Zhamsueva, Alexander S. Zayakhanov and Liudmila P. Golobokova
Appl. Sci. 2024, 14(16), 6996; https://doi.org/10.3390/app14166996 - 9 Aug 2024
Cited by 2 | Viewed by 956
Abstract
In recent years, the role of the atmosphere in the formation of the chemical composition of water in Lake Baikal and its tributaries has been increasing. In this regard, the study of equivalent black carbon (eBC) in the air above the lake and [...] Read more.
In recent years, the role of the atmosphere in the formation of the chemical composition of water in Lake Baikal and its tributaries has been increasing. In this regard, the study of equivalent black carbon (eBC) in the air above the lake and its coast has an important practical application. This paper presents the results of the mass concentration of eBC and submicron aerosol in the air above the water area of Lake Baikal, which were obtained during expeditions onboard research vessels in the summer of 2019 and 2023. We analyzed the data from the coastal monitoring station Listvyanka. To measure eBC, an MDA-02 aethalometer was used in the water area of the lake, and a BAC-10 aethalometer at the Listvyanka station. The background level of the eBC concentration in the air at different areas of the lake ranged between 0.15 and 0.3 µg m−3. The results of the two expeditions revealed the influence of the coastal settlements and the air mass transport along the valleys of the lake’s large tributaries on the five- to twentyfold growth of the eBC concentration in the near-water atmosphere. In the diurnal dynamics of eBC near settlements, we recorded high values in the evening and at night. In background areas, the diurnal dynamics were poorly manifested. In the summer of 2019, there were smoke plumes in the water area of Lake Baikal from distant wildfires and a local fire site on the east coast of the lake. The eBC concentration increased to 5–6 µg m−3, which was 10 to 40 times higher than the background. The long-range transport of plumes from coal-fired thermal power plants in large cities of the region made the major contribution to the eBC concentration at «Listvyanka» in winter, which data on aerosol, gas impurities, and meteorological parameters confirmed. Full article
(This article belongs to the Special Issue Air Pollution and Its Impact on the Atmospheric Environment)
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<p>Routes of comprehensive scientific expeditions in the water area of Lake Baikal, 2019 and 2023 (<b>a</b>); RV “Akademik V.A. Koptyug” and location of measuring equipment on the upper deck, 2019 (<b>b</b>).</p>
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<p>Location and equipment of the atmospheric monitoring station “Listvyanka”: (1) layout of the Listvyanka reference station with the largest air pollution sources at Lake Baikal; (2) station location on the hilltop; (3) the main module of the reference station; (4) MTP-5 temperature profiler; (5) Sokol-M1 meteorological complex; (6) RA-915AM spectrometer; gas analyzer; (7) Hg; gas analyzer; (8) K-100 (CO); (9) R-310A gas analyzer (NO<sub>2</sub> and NO); (10) CV-320 gas analyzer (SO<sub>2</sub>); (11) BAC-10 aethalometer analyzer eBC; (12) DUSTTRAK 8533 dust analyzer (PM<sub>10</sub>, PM<sub>2,5</sub>, and PM<sub>1.0</sub>).</p>
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<p>Spatiotemporal variability of the eBC mass concentration in the coastal zone of Lake Baikal during the 2019.</p>
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<p>Satellite image of the territory near Lake Baikal: (<b>a</b>) with wildfire sites in the north (28–29 July 2019); (<b>b</b>,<b>c</b>) backward trajectories calculated using the HYSPLIT models for 29 July 2019 (GMT) (<a href="http://fires-dv.kosmosnimki.ru" target="_blank">http://fires-dv.kosmosnimki.ru</a>, accessed on 1 June 2024).</p>
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<p>Satellite image of the central basin of Lake Baikal: (<b>a</b>) with smoke plume from a wildfire near Sosnovka Bay (red circle); (<b>b</b>) backward trajectories calculated using the HYSPLIT model for 26 July 2019 (GMT).</p>
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<p>Spatiotemporal variability of the eBC mass concentration in the coastal water area of Lake Baikal during the expedition in the summer of 2023.</p>
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<p>Diurnal variation of the eBC mass concentration under background conditions (<b>a</b>) and near populated areas. (<b>b</b>) RMSD areas are highlighted in color.</p>
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<p>Dynamics of mean monthly temperature and humidity at the Listvyanka station in 2023–2024.</p>
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<p>Mean hourly concentrations of eBC and SO<sub>2</sub> at the Listvyanka station, January 2024.</p>
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<p>Mean hourly concentrations of BC, nitrogen oxide, and sulfur dioxide (<b>a</b>) and meteorological parameters at the Listvyanka station (<b>b</b>), September 2023.</p>
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17 pages, 4248 KiB  
Article
Understanding the Dynamics of Source-Apportioned Black Carbon in an Urban Background Environment
by Daria Pashneva, Agnė Minderytė, Lina Davulienė, Vadimas Dudoitis and Steigvilė Byčenkienė
Atmosphere 2024, 15(7), 832; https://doi.org/10.3390/atmos15070832 - 11 Jul 2024
Cited by 2 | Viewed by 1065
Abstract
This study aims to delineate the characteristics of black carbon (BC) in the atmosphere over the urban background environment in Vilnius (Lithuania) from 1 June 2021 to 31 May 2022 using aethalometer (Magee Scientific) measurements. The annual mean concentrations of BC originating from [...] Read more.
This study aims to delineate the characteristics of black carbon (BC) in the atmosphere over the urban background environment in Vilnius (Lithuania) from 1 June 2021 to 31 May 2022 using aethalometer (Magee Scientific) measurements. The annual mean concentrations of BC originating from fossil fuels (BCff) and from biomass burning (BCbb) were found to be 0.63 μg m−3 with a standard deviation (SD) of 0.67 μg m−3 and 0.27 µg m−3 (0.35 μg m−3). The further findings highlight the dominance of fossil-fuel-related BC throughout the study period (71%) and the seasonal variability of BC pollution, with biomass-burning-related BC making the largest contribution during the summer season (41%) and the smallest contribution during autumn (23%). This information provides valuable insights into the sources and dynamics of BC pollution in the region. The sources and composition of BC on the days with the highest pollution levels were influenced by a combination of local and regional factors in every season. Additionally, this study employs an advanced approach to understanding urban BC pollution by focusing on high-pollution days (18), identified based on a daily mean BC mass concentration exceeding the 95th percentile, alongside an analysis of overall seasonal and diurnal variations. This methodology surpasses many those of previous urban BC studies, offering a comprehensive examination of the sources and composition of BC pollution. Full article
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<p>Maps of Lithuania, Vilnius and the sampling location.</p>
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<p>The time series of BC mass concentration and temperature (the black and blue lines represent the 72-h moving averages) (<b>b</b>) with box plots during the measurement period (<b>a</b>) (the heating season started on 20 September 2021 and ended on 5 May 2022). The colours of the lines and boxes represent the seasons, with summer represented by orange, autumn by red, winter by blue, and spring by green.</p>
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<p>Seasonal mass concentrations of BC, BC<sub>ff</sub> and BC<sub>bb</sub> in µg m<sup>−3</sup> in Vilnius during the study period. The range of the box depicts the bounds of the 25th and 75th percentiles of the data, while the whiskers extending from the box represent the bounds of the 5th and 95th percentiles, the colour of the box is white for BC, grey for BC<sub>ff</sub> and green for BC<sub>bb</sub>.</p>
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<p>BC mass concentration during the four seasons grouped by day of the week (summer—orange, autumn—red, winter—blue, spring—green).</p>
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<p>BC<sub>ff</sub> and BC<sub>bb</sub> mass concentrations during the four seasons: (<b>a</b>) diurnal variation and (<b>b</b>) histograms of relative frequency.</p>
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<p>Bivariate polar plots for hourly BC mass concentration as a function of WS and WD (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and CPF 75th percentile (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) in the four seasons: summer, autumn, winter and spring.</p>
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<p>Correlation matrix plot between various air pollutants (BC, PM<sub>10</sub>, NO<sub>X</sub>) and meteorological parameters (WS, WD, RH, T, Pr) for summer, autumn, winter and spring. The intensity of colour, with red indicating a positive correlation and blue indicating a negative correlation, represents the strength of the relationship between each pair of parameters.</p>
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<p>The time series of BC, PM<sub>10</sub> and air temperature during the four seasons with pronounced days of high air pollution (blue rectangles—periods with hourly BC mass concentration exceeding the 95th percentile of the BC concentration for the respective season).</p>
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<p>BC, PM<sub>10</sub> and NOx concentrations on non-pollution and high-pollution days.</p>
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<p>Case of spring grass burning observed on 22 March, 2022: (<b>a</b>) active fires (red dots) detected during March 2022 by the MODIS Rapid Response System (each red dot represents a single 1 km MODIS active fire pixel); (<b>b</b>) HYSPLIT back-trajectories of air masses arriving at Vilnius on 22 March 2022 at 500 m (red), 1000 m (blue) and 1500 m (green); (<b>c</b>) smoke surface concentration (µg m<sup>−3</sup>).</p>
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12 pages, 2505 KiB  
Article
Wintertime Diurnal Variation in Absorption Coefficient of Brown Carbon Associated with the Molecular Marker of Levoglucosan
by Geun-Hye Yu, Myoungki Song, Sea-Ho Oh, Seoyeong Choe, Hajeong Jeon, Dong-Hoon Ko and Min-Suk Bae
Appl. Sci. 2024, 14(10), 4117; https://doi.org/10.3390/app14104117 - 13 May 2024
Cited by 3 | Viewed by 1052
Abstract
This study investigated the aerosol particle properties and light absorption properties of brown carbon (BrC) by utilizing a seven-wavelength aethalometer, and analyzed NH4+, NO3?, SO42?, K+, K, organic carbon, elemental carbon, levoglucosan, [...] Read more.
This study investigated the aerosol particle properties and light absorption properties of brown carbon (BrC) by utilizing a seven-wavelength aethalometer, and analyzed NH4+, NO3?, SO42?, K+, K, organic carbon, elemental carbon, levoglucosan, and mannosan in PM2.5. The research was conducted in a rural area of Jeonnam, South Korea, during the winter season. In addition, the dithiothreitol assay-oxidative potential normalized to 9,10-phenanthrenequinone (QDTT-OP) was investigated throughout the study period. The absorption coefficient was found to be 2.6 to 5.6 times higher at 370 nm compared to 880 nm, suggesting the presence of light-absorbing substances in addition to black carbon (BC) particles. The estimated absorption coefficient of BrC370 was 29.9% of the total light absorption coefficient at 370 nm. Furthermore, BrC370 exhibited a strong affinity with levoglucosan while showing a weak correlation with K+, confirming the suitability of levoglucosan as a tracer for biomass burning. The QDTT-OP was 5.3 nM m?3, and highly correlated with the carbonaceous components levoglucosan and mannosan, suggesting a relatively high contribution of biomass combustion emissions to oxidative potential. Further research should be conducted to assess the health risks associated with future PM2.5 exposure related to biomass burning in the atmosphere. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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<p>Sampling site on the rooftop of a 4-story building at the Mokpo University, Republic of Korea.</p>
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<p>DTT-OP reduction rate results by quinone concentration.</p>
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<p>Time series concentrations of (<b>a</b>) wind speed, temperature, and relative humidity; (<b>b</b>) OC, NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>−</sup>, and SO<sub>4</sub><sup>2−</sup>; (<b>c</b>) K<sup>+</sup> and K; (<b>d</b>) levoglucosan and mannosan; (<b>e</b>) BC<sub>880</sub> and EC; and (<b>f</b>) BC<sub>370</sub> and BrC<sub>370</sub> in the sampling periods.</p>
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<p>(<b>a</b>) Diurnal patterns of 3-h integrated elemental carbon (EC) and brown carbon at 880 nm (BrC<sub>880</sub>), and (<b>b</b>) pairwise correlation scatterplots between EC and black carbon at 880 nm (BC<sub>880</sub>) during the measurement period.</p>
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<p>(<b>a</b>) Diurnal patterns of 3-h integrated brown carbon at 370 nm (BrC<sub>370</sub>), levoglucosan, and mannosan; (<b>b</b>) correlation of levoglucosan and BrC<sub>370</sub>; (<b>c</b>) diurnal patterns of mean hourly potassium ions (K<sup>+</sup>) and elemental potassium (K); (<b>d</b>) correlation of K<sup>+</sup> and K during the measurement period.</p>
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<p>Correlation of DTT-OP with (<b>a</b>) organic carbon (OC), (<b>b</b>) levoglucosan, (<b>c</b>) black carbon at 370 nm (BC<sub>370</sub>), and (<b>d</b>) brown carbon at 370 nm (BrC<sub>370</sub>) during the measurement period.</p>
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16 pages, 6464 KiB  
Article
Pollution Characteristics and Source Apportionment of Black Carbon Aerosols during Spring in Beijing
by Wenkai Lei, Xingru Li, Zhongyi Yin, Lan Zhang and Wenji Zhao
Toxics 2024, 12(3), 202; https://doi.org/10.3390/toxics12030202 - 5 Mar 2024
Cited by 1 | Viewed by 1698
Abstract
Black carbon (BC) aerosols are important for absorbing aerosols, affecting global climate change and regional air quality, and potentially harming human health. From March to May 2023, we investigated black carbon aerosol levels and air pollution in Beijing. Employing methods such as linear [...] Read more.
Black carbon (BC) aerosols are important for absorbing aerosols, affecting global climate change and regional air quality, and potentially harming human health. From March to May 2023, we investigated black carbon aerosol levels and air pollution in Beijing. Employing methods such as linear regression, Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT), we analyzed the characteristics and sources of black carbon aerosols in the region. Results indicate that the light absorption coefficients of BC and BrC decrease with increasing wavelength, with BrC accounting for less than 40% at 370 nm. Daily variations in BC and PM2.5 concentrations exhibit similar trends, peaking in March, and BC displays a distinct bimodal hourly concentration structure during this period. Aethalometer model results suggest that liquid fuel combustion contributes significantly to black carbon (1.08 ± 0.71 μg·m−3), surpassing the contribution from solid fuel combustion (0.31 ± 0.2 μg·m−3). Furthermore, the significant positive correlation between BC and CO suggests that BC emissions in Beijing predominantly result from liquid fuel combustion. Potential source area analysis indicates that air masses of spring in Beijing mainly originate from the northwest (40.93%), while potential source areas for BC are predominantly distributed in the Beijing–Tianjin–Hebei region, as well as parts of the Shandong, Shanxi and Henan provinces. Moreover, this study reveals that dust processes during spring in Beijing have a limited impact on black carbon concentrations. This study’s findings support controlling pollution in Beijing and improving regional air quality. Full article
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<p>Location of the sampling site in this study.</p>
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<p>(<b>a</b>) Box-and-whisker plot of absorption coefficients at seven wavelengths as measured with AE33. (<b>b</b>) Average values of Abs<sub>BC</sub> and Abs<sub>BrC</sub> at different wavelengths.</p>
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<p>The absorption contribution of Abs<sub>BC</sub> and Abs<sub>BrC</sub> at 370 nm.</p>
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<p>(<b>a</b>) Variation of BC and PM<sub>2.5</sub> daily average concentration (<b>b</b>) the linear regression in Beijing urban area in spring.</p>
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<p>(<b>a</b>) Monthly average concentrations of BC and PM<sub>2.5</sub>; (<b>b</b>) diurnal variation of BC concentration.</p>
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<p>The characteristics of BC concentration by solid fuel and liquid fuel produced, as well as the trend of BC<sub>solid</sub>/BC ratio in spring in Beijing urban area.</p>
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<p>Linear fitting of BC with atmospheric pollutants: (<b>a</b>) NO<sub>2</sub> (Red), (<b>b</b>) O<sub>3</sub> (Orange) and (<b>c</b>) CO (Blue).</p>
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<p>(<b>a</b>) Cluster analysis of 48 h backward air mass trajectories arriving at Beijing; (<b>b</b>) CWT analysis of the BC; (<b>c</b>) wind rose of Beijing for the spring, 2023.</p>
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<p>The PSCF analysis during three dust events in spring.</p>
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17 pages, 4467 KiB  
Article
Black Carbon along a Highway and in a Residential Neighborhood during Rush-Hour Traffic in a Cold Climate
by Hrund Ólöf Andradóttir, Bergljót Hjartardóttir and Throstur Thorsteinsson
Atmosphere 2024, 15(3), 312; https://doi.org/10.3390/atmos15030312 - 1 Mar 2024
Cited by 1 | Viewed by 2206
Abstract
Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold [...] Read more.
Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold climate along an urban highway and 300 m into an adjacent residential neighborhood. BC was measured with an aethalometer (MA350, Aethlabs) along the main traffic artery in geothermally heated Reykjavík, the capital of Iceland (64.135° N–21.895° W, 230,000 inhabitants). Stationary monitoring confirmed that traffic was the dominant source of roadside BC in winter, averaging 1.0 ± 1.1 µg/m3 (0.6 and 1.1 µg/m3 median and interquartile range; 28,000 vehicles/day). Inter-day variations in BC were primarily correlated to the atmospheric lapse rate and wind speed, both during stationary and mobile campaigns. During winter stills, BC levels surpassed 10 µg/m3 at intersections and built up to 5 µg/m3 during the afternoon in the residential neighborhood (adjacent to the highway with 43,000 vehicles/day). The BC penetrated deeply into the neighborhood, where the lowest concentration was 1.8 µg/m3 within 300 m. BC concentration was highly correlated to nitrogen dioxide (r > 0.8) monitored at the local Urban Traffic Monitoring site. Full article
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<p>Aerial photo of Reykjavík peninsula with a City Center–HRI stationary monitoring site (white star; insert) and closeup of the mobile walking routes along the MIK highway and inside residential Hlíðar neighborhood (green dots). Annual Average Daily Traffic [AADT], distance to centerline of highway and land activities are indicated on the map: Schools (yellow), parks and sporting fields (green), commercial areas (gray), pedestrian crossing lights (blue line), official weather and air quality monitoring sites (triangles). Frequency of hot spots (&gt;10 µg/m<sup>3</sup> BC) are noted in orange circles.</p>
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<p>Highway transects facing west: (<b>a</b>) Stationary site (canyon width 50 ± 10 m); (<b>b</b>) Residential Hlíðar neighborhood with public park to the right (canyon width 55 ± 10 m); (<b>c</b>) Near Kringlan Shopping Center (canyon width ranges 60–155 m).</p>
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<p>Noontime atmospheric conditions during the study period from 27 October 2017 to 9 April 2018 (line) in relation to the historical range (2010–2018, shade): (<b>a</b>) global radiation; (<b>b</b>) air temperature; (<b>c</b>) wind speed; (<b>d</b>) atmospheric lapse rate; (<b>e</b>) black carbon at HRI, during walk highway campaigns and during diurnal precipitation; (<b>f</b>) nitrogen dioxide, and (<b>g</b>) particulate matter (PM<sub>10</sub>) at the GRE urban traffic station.</p>
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<p>Diurnal variations at the HRI stationary site: (<b>a</b>) black carbon; (<b>b</b>) traffic volumes. The central mark indicates the median value within the hour and the edges of the box represent the interquartile range. The whiskers extend to the most extreme data points not considered outliers. The ‘+’ marker symbol represent outliers. M/ARH = Morning/Afternoon Rush Hour.</p>
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<p>Distribution of time-adjusted median BC during the drive highway (DH), walk highway (WH) and walk residential (WR) mobile campaigns in Reykjavík City during (<b>a</b>) winter (<b>b</b>) spring. The central mark indicates the median value and the edges of the box represent the interquartile range. The whiskers extend to the most extreme data points not considered outliers (marked as ‘+’).</p>
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<p>Relationship between BC along highway and ambient air quality: (<b>a</b>) NO<sub>2</sub>; (<b>b</b>) PM<sub>10</sub>; (<b>c</b>) PM<sub>2.5</sub>. Stationary BC represents hourly measurements from 27 October to 20 November (<span class="html-italic">N</span> = 563); Mobile BC are medians while walking along the highway (WH). Linear regression (LR) lines are forced through zero.</p>
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<p>Spatial distribution of BC when walking along MIK highway (top) and in the Hlíðar residential neighborhood (bottom) during (<b>a</b>) spring dust and (<b>b</b>) winter BC episodes. Dashed gray lines delineate the urban background area used for calculating concentration profiles.</p>
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<p>Median BC concentration along 120 m long sections of roads in residential neighborhoods, classified according to transport regimes: (<b>a</b>) wind directed from BUS highway into neighborhood, <span class="html-italic">W<sub>in</sub></span> &lt; −0.5 m/s; (<b>b</b>) wind from MIK highway into neighborhood, <span class="html-italic">W<sub>in</sub></span> &gt; 0.5 m/s; (<b>c</b>) parallel or no wind. Dot sizes represent wind speed, <span class="html-italic">W</span><sub>10</sub>.</p>
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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 2812
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
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<p>Diagrammatic representation of the path that sample air takes through the MA350, showing high-level components.</p>
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<p>Flow diagram of the data cleaning approach with number of datapoints removed from each device dataset at each step.</p>
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<p>Raw and DEMA Infrared BC Concentrations.</p>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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16 pages, 5786 KiB  
Technical Note
Inversion of Near-Surface Aerosol Equivalent Complex Refractive Index Based on Aethalometer, Micro-Pulse Lidar and Portable Optical Particle Profiler
by Xuebin Ma, Tao Luo, Xuebin Li, Changyu Liu, Nana Liu, Qiang Liu, Kun Zhang, Jie Chen and Liming Zhu
Remote Sens. 2024, 16(2), 279; https://doi.org/10.3390/rs16020279 - 10 Jan 2024
Viewed by 953
Abstract
In order to investigate the equivalent complex refractive index of atmospheric aerosols near the Earth’s surface, we conducted measurements in the Hefei region from March to April 2022. These measurements utilized a micro-pulse lidar, an Aethalometer, and a Portable Optical Particle Profiler. These [...] Read more.
In order to investigate the equivalent complex refractive index of atmospheric aerosols near the Earth’s surface, we conducted measurements in the Hefei region from March to April 2022. These measurements utilized a micro-pulse lidar, an Aethalometer, and a Portable Optical Particle Profiler. These measurements encompassed aerosol particle size distribution as well as standard meteorological parameters including temperature, humidity, atmospheric pressure, and wind speed. Subsequently, this dataset was employed to develop an optimization algorithm for retrieving the equivalent complex refractive indices of near-surface aerosols. The methodology relies on lookup tables containing data for extinction efficiency and absorption efficiency factors. It operates on the premise of aerosol property stability within a defined time frame, utilizing measured extinction and absorption coefficients as simultaneous constraints during this period to inversely derive both the real and imaginary parts of the aerosol complex refractive index. Results from the simulation analysis reveal that the newly optimized retrieval algorithm, which relies on lookup tables, exhibits reduced sensitivity to instrument errors when compared to single-point constraint algorithms. This enhancement results in a more efficient and dependable approach for retrieving the aerosol complex refractive index. Empirical inversion and simulation studies were carried out to determine the aerosol equivalent complex refractive index in the Hefei region, utilizing measured data. This inversion process yielded an average complex refractive index of 1.48-i0.017 for aerosols in the Hefei region throughout the experimental period. Correlation analysis unveiled a positive association between the real part of the aerosol complex refractive index and the single-scattering albedo (SSA), whereas the imaginary part displayed a linear negative correlation with the SSA. The mathematical relationship between the real part and the SSA is y=0.19x+0.62, and the corresponding relationship between the imaginary part and the SSA is y=5.3x+0.99. This research offers a novel method for the retrieval of the aerosol equivalent complex refractive index. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>The red pentagram in the picture represents the experimental site.</p>
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<p>Flow chart of the inverse equivalent complex refractive index algorithm.</p>
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<p>(<b>left</b>) The trend of extinction coefficient with complex refractive index; (<b>right</b>) the trend of absorption coefficient with complex refractive index.</p>
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<p>Flowchart of the simulation analysis.</p>
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<p>POPS with random errors of 1% to 10% added to obtain box plots using single-point constraint and multi-point constraint inversion methods, respectively. (<b>a</b>) The range of variation in the real part of the complex refractive index obtained by inversion; (<b>b</b>) the range of variation in the imaginary part of the complex refractive index obtained by the inversion.</p>
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<p>(<b>top</b>) The range of variation in the real part obtained by inversion for different combinations of instrumental errors (the upper and lower sides of the blue line represent the 75 percentile and 25 percentile, respectively, and the red line represents the median). (<b>bottom</b>) The range of variation in the imaginary part obtained by inversion for different combinations of instrumental errors.</p>
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<p>Changes in temperature, humidity (<b>a</b>), and wind speed and direction (<b>b</b>) over time during the test period.</p>
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<p>Comparison of extinction coefficients obtained from MPL measurements and inversion algorithms (the black scattered points (Measured) in the figure represent the change curve of the extinction coefficient measured by the MPL during the measurement period, and the red solid line (Sim) is the change curve of the extinction coefficient obtained from the lookup table by employing the methodology proposed in this study).</p>
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<p>Fitting of measured and simulated values of extinction coefficients using Mie scattering theory.</p>
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<p>Comparison of absorption coefficients obtained using the Aethalometer and using the algorithm (the black scatter points (Measured) in the figure represent the variation curve of the absorption coefficient measured by the Aethalometer during the observation period, and the red solid line (Sim) represents the change curve of the absorption coefficient obtained using the algorithm proposed in this study).</p>
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<p>Fitting of measured and simulated values of absorption coefficients using Mie scattering theory.</p>
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<p>Complex refractive index of the aerosol during the measurement period obtained based on a look-up table optimization algorithm. (<b>a</b>) Evolution of the real part over time; (<b>b</b>) evolution of the imaginary part over time.</p>
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<p>(<b>a</b>) Correlation analysis of the real part and SSA obtained by inversion; (<b>b</b>) Correlation analysis of the imaginary part and the SSA obtained by inversion.</p>
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14 pages, 2813 KiB  
Article
Effects of Bio-Coal Briquette for Residential Combustion on Brown Carbon Emission Reduction
by Juan Qi and Jianjun Wu
Processes 2023, 11(6), 1834; https://doi.org/10.3390/pr11061834 - 16 Jun 2023
Viewed by 2049
Abstract
Biomass burning is an important source of brown carbon (BrC) which poses high-risk threats to human health and the environment. In this study, bio-coal briquette (coal mixed with biomass), a promising solid fuel for residential combustion, is proven to be a clean fuel [...] Read more.
Biomass burning is an important source of brown carbon (BrC) which poses high-risk threats to human health and the environment. In this study, bio-coal briquette (coal mixed with biomass), a promising solid fuel for residential combustion, is proven to be a clean fuel which can effectively reduce BrC emission. First of all, an orthogonal experiment with three factors and three levels on the physical property of bio-briquette was carried out to identify the optimal preparation conditions including the ratio of biomass to anthracite, particle size and molding pressure. Then a combustion experiment of the bio-coal briquetted was implemented in a simulated residential combustion system. BrC emission factors (EFs) were calculated based on the detected black carbon (BC) concentration by an aethalometer, and other optical characteristics for organic components of extract samplers, such as mass absorption efficiency (MAE) and absorption angstrom index (AAE), were also explored. Lastly, composition analysis of BrC by a gas chromatography (GC) tandem mass spectrometer (MS) and direct visible images by scanning electron microscopy (SEM) were investigated to provide more detail information on BrC EFs and property change. It was shown that bio-coal briquette had such low BrC EFs that 70–81% BrC was reduced in comparison with an interpolation value of 100% biomass and 100% coal. Furthermore, the composition of BrC from bio-coal briquette burning was different, which consisted of more substances with strong wavelength dependence. Consequently, although MAE declined by 60% at a 540 nm wavelength, the AAE value of bio-coal briquette only decreased slightly compared with interpolation values. To be more specific, tar balls, the main existing form of BrC, were distributed much more sparsely in the SEM image of bio-coal briquette. To sum up, a positive reduction effect on BrC was discovered in bio-coal briquette. It is evident that bio-coal briquette can serve as an alternative solid fuel for residential combustion, which is beneficial for both human health and the atmosphere. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>Experimental scheme for this study.</p>
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<p>Simulated residential combustion system for the experiment test.</p>
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<p>BrC EFs of combustion smoke from different fuel in PM<sub>2.5</sub> compared with calculated mass-weighted average ones.</p>
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<p>MAE reduction ratio of BrC from bio-coal briquette burning compared with calculated mass-weighted average ones.</p>
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<p>The AAE value from bio-coal briquette burning compared with calculated mass-weighted average ones.</p>
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<p>EFs of organic chemical species in PM<sub>2.5</sub> emitted from different fuel combustion.</p>
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<p>Scanning electron microscopy image of typical tar balls emitted from different fuel: (<b>a</b>) corn straw briquette, (<b>b</b>) corn straw-coal briquette, and (<b>c</b>) anthracite chunk.</p>
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19 pages, 18785 KiB  
Article
Highly Time-Resolved Apportionment of Carbonaceous Aerosols from Wildfire Using the TC–BC Method: Camp Fire 2018 Case Study
by Matic Ivančič, Martin Rigler, Bálint Alföldy, Gašper Lavrič, Irena Ježek Brecelj and Asta Gregorič
Toxics 2023, 11(6), 497; https://doi.org/10.3390/toxics11060497 - 31 May 2023
Cited by 3 | Viewed by 2181
Abstract
The Camp Fire was one of California’s deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant [...] Read more.
The Camp Fire was one of California’s deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant site in Berkeley, highly time-resolved total carbon (TC), black carbon (BC), and organic carbon (OC) were measured using the Carbonaceous Aerosol Speciation System (CASS, Aerosol Magee Scientific), comprising two instruments, a Total Carbon Analyzer TCA08 in tandem with an Aethalometer AE33. During the period when the air quality was affected by wildfire smoke, the BC concentrations increased four times above the typical air pollution level presented in Berkeley before and after the event, and the OC increased approximately ten times. High-time-resolution measurements allow us to study the aging of OC and investigate how the characteristics of carbonaceous aerosols evolve over the course of the fire event. A higher fraction of secondary carbonaceous aerosols was observed in the later phase of the fire. At the same time, the amount of light-absorbing organic aerosol (brown carbon) declined with time. Full article
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<p>Location of measurement site in Berkeley and the approximate location where Camp Fire started in Northern California, USA (<bold>a</bold>). The site is located near Aquatic Park (<bold>b</bold>), and it is located near the exit of the 10-lane highway (<bold>c</bold>). (<bold>a</bold>,<bold>b</bold>) were plotted with python library basemap using ArcGis WorldImagery, and (<bold>c</bold>) was taken from google maps street view.</p>
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<p>Time series of TC–BC measurements (<bold>a</bold>,<bold>b</bold>), CA apportionment (<bold>c</bold>,<bold>d</bold>), and absorption coefficient b<sub>abs</sub> at 370 nm (<bold>e</bold>,<bold>f</bold>) in November 2018. Subplots (<bold>a</bold>,<bold>c</bold>,<bold>e</bold>) contain absolute values, and subplots (<bold>b</bold>,<bold>d</bold>,<bold>f</bold>) are presented in fractional contribution. White vertical lines are missing data, and green lines represent the split into phases: before the event, Fire Phase I, Fire Phase II, and after the event.</p>
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<p>The upper row compares median BC and OC concentrations between different phases in November 2018 (<bold>a</bold>), where the bar height represents the median concentration and the numbers above the graph indicate the relative change. The middle row contains typical fingerprints for phases before and after Camp Fire event (<bold>b</bold>), during Fire Phase I (<bold>c</bold>), and during Fire Phase II (<bold>d</bold>). The median wavelength-dependent optical absorption is presented at the bottom for phases before and after Camp Fire event (<bold>e</bold>), during Fire Phase I (<bold>f</bold>), and during Fire Phase II (<bold>g</bold>). The pie charts in Figures (<bold>e</bold>–<bold>g</bold>) contain the average split of optical absorption between BC and BrC at 370 nm for the respective phases.</p>
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<p>The probability distribution of AAE<sub>7λ</sub> in blue (<bold>a</bold>–<bold>c</bold>) and AAE<sub>BrC</sub> in brown (<bold>d</bold>–<bold>f</bold>). Subplots (<bold>a</bold>,<bold>d</bold>) contain the distributions before/after the Camp Fire event, (<bold>b</bold>,<bold>e</bold>) for Fire Phase I, and (<bold>c</bold>,<bold>f</bold>) for Fire Phase II. The gray, shadowed distributions in (<bold>b</bold>,<bold>c</bold>,<bold>e</bold>,<bold>f</bold>), are added for orientation and represent the typical distributions before/after the Camp Fire event from (<bold>a</bold>,<bold>d</bold>). The vertical black lines represent mean values; the value of the standard deviation for each distribution is also added to the plots.</p>
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<p>BC tracer model before/after Camp Fire event (<bold>a</bold>) and when the air was affected by Camp Fire smoke (<bold>b</bold>). OC/BC probability distribution is presented with blue and cumulative OC/BC distribution with a green line. The orange curve shows the R<sup>2</sup> score between the hypothetical SOC and BC for different hypothetical (OC/BC)<sub>prim</sub> ratios. The vertical dashed orange line marks the minimum of the R<sup>2</sup> function (optimal value). The optimal value is also written in the title with the calculated percentile in brackets.</p>
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<p>Diurnal profiles of BC (<bold>a</bold>), SOC (<bold>b</bold>), POC (<bold>c</bold>), and <inline-formula><mml:math id="mm10"><mml:semantics><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">b</mml:mi><mml:mrow><mml:mi>abs</mml:mi></mml:mrow><mml:mrow><mml:mi>BrC</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:semantics></mml:math></inline-formula> (<bold>d</bold>) for typical November conditions in Berkeley before and after the Camp Fire event. The line represents the median value and the shaded area concentrations between the first and third quartiles.</p>
Full article ">Figure A3
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 7 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A4
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 8 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A5
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 9 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A6
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 10 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A7
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 11 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A8
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 12 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A9
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 13 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A10
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 14 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A11
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 15 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A12
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 16 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A13
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 17 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A14
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 18 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A15
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 19 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A16
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 20 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
Full article ">Figure A17
<p>The spatial distribution of the smoke from the Camp Fire in Northern California on 21 November 2018. The satellite images by NASA Worldview [<xref ref-type="bibr" rid="B32-toxics-11-00497">32</xref>] and back trajectories by the HYSPLIT model [<xref ref-type="bibr" rid="B33-toxics-11-00497">33</xref>] are on the left, and Smoke maps by NOAA Hazard Mapping System [<xref ref-type="bibr" rid="B34-toxics-11-00497">34</xref>] and daily averaged PM<sub>2.5</sub> concentrations measured by Purple Air sensors [<xref ref-type="bibr" rid="B35-toxics-11-00497">35</xref>] are on the right.</p>
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27 pages, 17254 KiB  
Article
Black Carbon Emissions, Transport and Effect on Radiation Forcing Modelling during the Summer 2019–2020 Wildfires in Southeast Australia
by Hiep Nguyen Duc, Merched Azzi, Yang Zhang, John Kirkwood, Stephen White, Toan Trieu, Matthew Riley, David Salter, Lisa Tzu-Chi Chang, Jordan Capnerhurst, Joseph Ho, Gunaratnam Gunashanhar and Khalia Monk
Atmosphere 2023, 14(4), 699; https://doi.org/10.3390/atmos14040699 - 10 Apr 2023
Cited by 2 | Viewed by 2654
Abstract
The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019–2020 in south-eastern Australia was unprecedented in [...] Read more.
The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019–2020 in south-eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast–Chemistry (WRF–Chem) model aims to determine the extent of BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data using the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and remote sensing data. The BC concentration as predicted from the WRF–Chem model, is in general, less than the observed data as measured using the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019–2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and clouds. Full article
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Figure 1
<p>BC hourly emissions as estimated from FINN during the period from 1 November 2019 to 7 January 2020 of wildfires in southeast Australia. Date and time are in GMT.</p>
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<p>(<b>a</b>) Aethalometer network in NSW operated by DPIE. (<b>b</b>) WRF–Chem domain configuration with domain d02 covering most of eastern NSW and domain d03 covering the greater metropolitan region (GMR) of Sydney.</p>
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<p>Observed BC<sub>wb</sub> and BC<sub>ff</sub> hourly concentration at Armidale, Richmond, Newcastle, Liverpool, Wollongong and Wagga Wagga North during the wildfire period 1 November 2019 to 15 January 2020.</p>
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<p>MODIS Terra/Aqua satellite image and hot spots over southeast Australia on 7 November 2019 (<b>a</b>), 21 November 2019 (<b>b</b>), 5 December 2019 (<b>c</b>), 11 December 2019 (<b>d</b>), 21 December 2019 (<b>e</b>), 30 December 2019 (<b>f</b>), and 4 January 2020 (<b>g</b>). Average BC extinction AOT at 550 nm for March 2020 as predicted by MERRA-2 (<b>h</b>) (Source: NASA Worldview and NASA <a href="https://giovanni.gsfc.nasa.gov/" target="_blank">https://giovanni.gsfc.nasa.gov/</a>, accessed on 11 April 2022).</p>
Full article ">Figure 4 Cont.
<p>MODIS Terra/Aqua satellite image and hot spots over southeast Australia on 7 November 2019 (<b>a</b>), 21 November 2019 (<b>b</b>), 5 December 2019 (<b>c</b>), 11 December 2019 (<b>d</b>), 21 December 2019 (<b>e</b>), 30 December 2019 (<b>f</b>), and 4 January 2020 (<b>g</b>). Average BC extinction AOT at 550 nm for March 2020 as predicted by MERRA-2 (<b>h</b>) (Source: NASA Worldview and NASA <a href="https://giovanni.gsfc.nasa.gov/" target="_blank">https://giovanni.gsfc.nasa.gov/</a>, accessed on 11 April 2022).</p>
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<p>Predicted daily total BC and observed BC in micrograms/m<sup>3</sup>, as measured using aethalometers at Armidale, Richmond, Newcastle, Liverpool, Wollongong and Wagga Wagga North during December 2019 period. Correlation coefficients of predicted and observed data at these sites are 0.41, 0.61, 0.58, 0.60, 0.24, 0.78, respectively.</p>
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<p>WRF–Chem and MERRA-2 prediction of daily average BC concentration for December 2019 as compared to BC measured at monitoring sites (Armidale, Richmond, Newcastle, Liverpool, Wollongong and Wagga Wagga North).</p>
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<p>MERRA-2 reanalysis model prediction of surface BC on 5 December 2019 13 AEST (<b>a</b>) and on 21 December 2019 13 AEST (<b>b</b>). WRF–Chem prediction of BC on 5 December 2019 13 AEST (<b>c</b>) and 21 December 2019 13 AEST (<b>d</b>).</p>
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<p>Spatial pattern of monthly average BC column mass density and BC surface mass concentration in March 2020 as predicted by MERRA-2.</p>
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<p>BC concentration and wind field at 850mb (~1450 m) (<b>a</b>) and 700 mb (~3000 m) (<b>b</b>) on 5 December 2019 12:00 UTC (5 December 2019 22:00 AEST).</p>
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<p>MODIS observation on 1 December 2019 with red spots as fires (<b>a</b>) upwelling SW radiation at the TOA, (<b>b</b>) downward SW minus upwelling SW at TOA (<b>c</b>), and downward SW minus upwelling SW at TOA in clear sky situation (<b>d</b>), as simulated without fires on 1 December 2019 at 04 UTC.</p>
Full article ">Figure 10 Cont.
<p>MODIS observation on 1 December 2019 with red spots as fires (<b>a</b>) upwelling SW radiation at the TOA, (<b>b</b>) downward SW minus upwelling SW at TOA (<b>c</b>), and downward SW minus upwelling SW at TOA in clear sky situation (<b>d</b>), as simulated without fires on 1 December 2019 at 04 UTC.</p>
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<p>Predicted difference in upwelling SW radiation between fire and no fire simulation with vapour (<b>a</b>) and without vapour (<b>b</b>) and predicted surface BC concentration with surface wind field (<b>c</b>) on 1 December 2019 04 UTC. Off the coast of NSW and eastern Australia, there were high BC concentrations from wildfires. The histogram of negative forcing values at TOA over the domain (<b>d</b>).</p>
Full article ">Figure 11 Cont.
<p>Predicted difference in upwelling SW radiation between fire and no fire simulation with vapour (<b>a</b>) and without vapour (<b>b</b>) and predicted surface BC concentration with surface wind field (<b>c</b>) on 1 December 2019 04 UTC. Off the coast of NSW and eastern Australia, there were high BC concentrations from wildfires. The histogram of negative forcing values at TOA over the domain (<b>d</b>).</p>
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<p>Net short-wave (<b>a</b>) and long-wave radiation flux (<b>b</b>) on 1 December 2019 daily 0.25 deg. (GLDAS Model GLDAS_CLSM025_DA1_D v2.2). Unit measurement is in W/m<sup>2</sup>.</p>
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<p>Histograms of hourly AAE for the period of 1 November to 31 December 2019 at Richmond, Liverpool, Wollongong, Newcastle (urban sites), Wagga Wagga and Armidale (regional sites).</p>
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<p>Histograms of hourly AAE for the period of 1 November to 31 December 2019 at Richmond, Liverpool, Wollongong, Newcastle (urban sites), Wagga Wagga and Armidale (regional sites).</p>
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<p>Predicted temperature, wind speed and direction and observation at Bringelly and Newcastle.</p>
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<p>Predicted temperature, wind speed and direction and observation at Bringelly and Newcastle.</p>
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<p>Upwelling SW radiation at the TOA (<b>a</b>) downward minus upwelling SW at TOA (<b>b</b>) and downward minus upwelling SW at TOA in clear sky situation (<b>c</b>) as simulated with fires on 1 December 2019 at 04 UTC.</p>
Full article ">Figure A4
<p>Predicted difference in upwelling SW radiation and downwelling with no fire simulation at the surface (<b>a</b>), with no fire simulation and under clear sky conditions (<b>b</b>), with wildfire simulation (<b>c</b>) and negative forcing at the surface (<b>d</b>) (difference between (<b>a</b>,<b>c</b>)).</p>
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19 pages, 10571 KiB  
Article
Estimation of Carbonaceous Aerosol Sources under Extremely Cold Weather Conditions in an Urban Environment
by Steigvilė Byčenkienė, Touqeer Gill, Abdullah Khan, Audrė Kalinauskaitė, Vidmantas Ulevicius and Kristina Plauškaitė
Atmosphere 2023, 14(2), 310; https://doi.org/10.3390/atmos14020310 - 4 Feb 2023
Cited by 3 | Viewed by 2230
Abstract
The present study investigated the characteristics of carbonaceous species in an urban background site. Real-time measurements of inorganic (sulfate, nitrate, ammonium, chloride, and black carbon [BC]) and organic submicron aerosols (OA) were carried out at the urban background site of Vilnius, Lithuania, during [...] Read more.
The present study investigated the characteristics of carbonaceous species in an urban background site. Real-time measurements of inorganic (sulfate, nitrate, ammonium, chloride, and black carbon [BC]) and organic submicron aerosols (OA) were carried out at the urban background site of Vilnius, Lithuania, during January–February 2014. An aerosol chemical speciation monitor (ACSM, Aerodyne Research Inc., Billerica, MA, USA) and co-located 7-λ aethalometer (AE-31, Magee Scientific, Berkeley, CA, USA) were used to analyze the chemical compositions, sources, and extinction characteristics of the PM1. Extremely contrasting meteorological conditions were observed during the studied period due to the transition from moderately cold (~2 °C) conditions to extremely cold conditions with a lowest temperature of −25 °C; therefore, three investigation episodes were considered. The identified periods corresponded to the transition time from the moderately cold to the extremely cold winter period, which was traced by the change in the average temperature for the study days of 1–13 January, with T = −5 °C and RH = 92%, in contrast to the period of 14–31 January, with T = −14 °C and RH = 74%, and the very short third period of 1–3 February, with T = −8 °C and RH = 35%. On average, organics accounted for the major part (53%) of the non-refractory submicron aerosols (NR-PM1), followed by nitrate (18%) and sulfate (9%). The source apportionment results showed the five most common OA components, such as traffic and heating, to be related to hydrocarbon-like organic aerosols (HOAtraffic and HOAheating, respectively), biomass-burning organic aerosols (BBOA), local organic aerosol (LOA), and secondary organic aerosol (SOA). Traffic emissions contributed 53% and biomass burning 47% to the BC concentration level. The highest BC and OA concentrations were, on average, associated with air masses originating from the southwest and east–southeast. Furthermore, the results of the PSCF and CWT methods indicated the main source regions that contributed the most to the BC concentration in Vilnius to be the following: central–southwestern and northeastern Poland, northwestern–southwestern and eastern Belarus, northwestern Ukraine, and western Russia. However, the potential sources of OA were widely distributed. Full article
(This article belongs to the Special Issue Carbon Emission and Transport: Measurement and Simulation)
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<p>Map of the observation site in Vilnius (indicated by the red marker) (source of maps: free within <a href="http://www.maps.lt" target="_blank">www.maps.lt</a> (accessed on 10 December 2022)).</p>
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<p>Temporal variation of species (organics, sulfate, nitrate, ammonium, and chloride) and aerosol black carbon. The pie charts show the fractional abundances of individual BC and ACSM species averaged over the episodes.</p>
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<p>Time series of SO<sub>2</sub>, PM<sub>10</sub>, NO<sub>2</sub>, NO<sub>3</sub>, NO<sub>x</sub>, ambient temperature, wind direction (dots), wind speed, and humidity.</p>
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<p>Time series and contributions of the hourly average BC<sub>ff</sub> and BC<sub>bb</sub> to the total BC (<b>a</b>), as well as <span class="html-italic">b<sub>abs</sub></span> BrC and <span class="html-italic">b<sub>abs</sub></span> BC to the total <span class="html-italic">b<sub>abs</sub></span> (Mm<sup>−1</sup>) (<b>b</b>).</p>
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<p>Mass spectra of the five factors from the PMF calculation. The time series (<b>left</b>), mass spectra (<b>middle</b>), and diurnal patterns (<b>right</b>) of the five OA factors determined on the basis of the PMF analysis.</p>
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<p>Diurnal patterns of the five OA factors (HOA<sub>heating</sub>, HOA<sub>traffic</sub>, LOA, BBOA, and SOA) determined based on the PMF analysis.</p>
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<p>Air mass backward trajectories and clusters with associated BC mass concentration in January and February 2014.</p>
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<p>Air mass backward trajectories and clusters with associated OA concentration in January and February 2014.</p>
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<p>Weighted potential source contribution function of BC in Vilnius in January and February 2014.</p>
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<p>Weighted concentration weighted trajectory of BC in Vilnius in January and February 2014.</p>
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<p>Weighted potential source contribution function (WPSCF) of OA in Vilnius in January and February 2014.</p>
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<p>Weighted concentration weighted trajectory (WCWT) of OA in Vilnius in January and February 2014.</p>
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<p>The 96 h backward trajectories of air masses arriving at Vilnius when the pollutant mass concentrations were the lowest. Trajectories were evaluated at each 3 h intervals.</p>
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20 pages, 13602 KiB  
Article
Spatiotemporal Analysis of Black Carbon Sources: Case of Santiago, Chile, under SARS-CoV-2 Lockdowns
by Carla Adasme, Ana María Villalobos and Héctor Jorquera
Int. J. Environ. Res. Public Health 2022, 19(24), 17064; https://doi.org/10.3390/ijerph192417064 - 19 Dec 2022
Cited by 3 | Viewed by 1653
Abstract
Background: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BCff) and wood-burning sources (BCwb). However, this apportionment is aggregated: local and [...] Read more.
Background: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BCff) and wood-burning sources (BCwb). However, this apportionment is aggregated: local and non-local BC sources are lumped together in the aethalometer results. Methods: We propose a spatiotemporal analysis of BC results along with meteorological data, using a fuzzy clustering approach, to resolve local and non-local BC contributions. We apply this methodology to BC measurements taken at an urban site in Santiago, Chile, from March through December 2020, including lockdown periods of different intensities. Results: BCff accounts for 85% of total BC; there was up to an 80% reduction in total BC during the most restrictive lockdowns (April–June); the reduction was 40–50% in periods with less restrictive lockdowns. The new methodology can apportion BCff and BCwb into local and non-local contributions; local traffic (wood burning) sources account for 66% (86%) of BCff (BCwb). Conclusions: The intensive lockdowns brought down ambient BC across the city. The proposed fuzzy clustering methodology can resolve local and non-local contributions to BC in urban zones. Full article
(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)
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<p>Workflow of the methodology.</p>
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<p>Diel profiles of AAE for (austral) summer and winter months.</p>
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<p>Diel profiles of AAE for workdays and weekends.</p>
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<p>Time variability plot for BC<sub>ff</sub> and BC<sub>wb</sub> contributions.</p>
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<p>Time variability plot for the five fuzzy clusters’ contributions to BC<sub>ff</sub>.</p>
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<p>Bivariate plots for the five fuzzy clusters’ contributions to BC<sub>ff</sub>.</p>
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<p>Time variability plot for the four fuzzy clusters’ contributions to BC<sub>wb</sub>.</p>
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<p>Bivariate plots for the four fuzzy clusters’ contributions to BC<sub>wb</sub>.</p>
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<p>Time plot of daily source contributions to BC<sub>ff</sub>.</p>
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<p>Time plot of daily source contributions to BC<sub>wb</sub>.</p>
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<p>Histogram of AAE values computed using Equation (1).</p>
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<p>Time plot of hourly BC<sub>ff</sub> and BC<sub>wb</sub> concentrations, 2020 campaign.</p>
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<p>Time variability of BC<sub>wb</sub> concentrations, 2020 campaign.</p>
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<p>Bivariate plot of hourly BC<sub>ff</sub> contributions, 2020 campaign, using temperature instead of wind speed.</p>
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<p>Bivariate plot of hourly BC<sub>ff</sub> contributions, 2020 campaign, using pressure instead of wind speed.</p>
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<p>Bivariate plot of hourly BC<sub>wb</sub> contributions, 2020 campaign, using temperature instead of wind speed.</p>
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<p>Bivariate plot of hourly BC<sub>wb</sub> contributions, 2020 campaign, using pressure instead of wind speed.</p>
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<p>Time series plot of hourly BC<sub>ff</sub> source contributions, 2020 campaign.</p>
Full article ">Figure A9
<p>Time series plot of hourly BC<sub>wf</sub> source contributions, 2020 campaign.</p>
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