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Volume 5
Issue 2
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Atmosphere, Volume 5, Issue 2 (June 2014) – 16 articles , Pages 156-472

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965 KiB  
Article
Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution
by Siddi Tengeleng and Nzeukou Armand
Atmosphere 2014, 5(2), 454-472; https://doi.org/10.3390/atmos5020454 - 18 Jun 2014
Cited by 25 | Viewed by 8573
Abstract
The aim of our study is to estimate the parameters M (water content), R (rain rate) and Z (radar reflectivity) with raindrop size distribution by using the neural network method. Our investigations have been conducted in five African localities: Abidjan (Côte d’Ivoire), Boyele [...] Read more.
The aim of our study is to estimate the parameters M (water content), R (rain rate) and Z (radar reflectivity) with raindrop size distribution by using the neural network method. Our investigations have been conducted in five African localities: Abidjan (Côte d’Ivoire), Boyele (Congo-Brazzaville), Debuncha (Cameroon), Dakar (Senegal) and Niamey (Niger). For the first time, we have predicted the values of the various parameters in each locality after using neural models (LANN) which have been developed with locally obtained disdrometer data. We have shown that each LANN can be used under other latitudes to get satisfactory results. Secondly, we have also constructed a model, using as train-data, a combination of data issued from all five localities. With this last model called PANN, we could obtain satisfactory estimates forall localities. Lastly, we have distinguished between stratiform and convective rain while building the neural networks. In fact, using simulation data from stratiform rain situations, we have obtained smaller root mean square errors (RMSE) between neural values and disdrometer values than using data issued from convective situations. Full article
(This article belongs to the Special Issue Cloud and Precipitation)
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Figure 1
<p>Radar reflectivity factor <span class="html-italic">Z vs.</span> rain rate <span class="html-italic">R</span> as deduced from the 1-min rain RDSD (Rain Drop Size Distribution) observed with the JWD (Joss-Waldvogel Disdrometer) in Dakar (Senegal), during 1997–2002, and fitted curve.</p> Full article ">Figure 2
<p>Representation of a neuron: (<b>a</b>) model; (<b>b</b>) matrix representation for S = 1.</p> Full article ">Figure 3
<p>An example of synoptic representation of a 3 layer-neural network in a feed forward back propagation form.</p> Full article ">Figure 4
<p>Location of the data collection sites.</p> Full article ">Figure 5
<p>Model of Cascade Forward Back Propagation (CFBP).</p> Full article ">Figure 6
<p>Diagram of the learning process of the neural network.</p> Full article ">Figure 7
<p>ANN models: (<b>a</b>) description of the model; (<b>b</b>) the faster CFBP model, which was used for our simulations.</p> Full article ">Figure 8
<p>Neural prediction in Abidjan (Ivory Coast) of the liquid water content M (<b>a</b>), the rain rate R (<b>b</b>) and the radar reflectivity Z (<b>c</b>) by a LANN constructed with data issued only from Debuncha (Cameroon).</p> Full article ">
1632 KiB  
Article
Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy
by Daniele Contini, Daniela Cesari, Antonio Donateo, Daniela Chirizzi and Franco Belosi
Atmosphere 2014, 5(2), 435-453; https://doi.org/10.3390/atmos5020435 - 16 Jun 2014
Cited by 59 | Viewed by 8421
Abstract
Samples of PM10 and PM2.5 were collected discontinuously between 2003 and 2010 at fifteen monitoring sites (urban, background, industrial) in the south-eastern part of Italy using a mobile laboratory. In total, 483 PM10 and 154 PM2.5 samples were collected [...] Read more.
Samples of PM10 and PM2.5 were collected discontinuously between 2003 and 2010 at fifteen monitoring sites (urban, background, industrial) in the south-eastern part of Italy using a mobile laboratory. In total, 483 PM10 and 154 PM2.5 samples were collected and chemically analyzed for the determination of metal content. Data were used to investigate concentration differences among the typologies of sites, the seasonal patterns, and the influence of advection of Saharan dust (SD). PM10 and PM2.5 average concentrations increase from background to industrial and urban sites but the ratio PM2.5/PM10 is significantly lower (0.61 ± 0.10) in background sites. The average metals concentrations in PM10 and in PM2.5 do not show a clear dependence on site typology apart an increase in crustal elements in background sites and an increase in the enrichment factors of Ni and of Cr in PM10 in industrial sites. Urban sites show a statistically significant increase of PM10 average concentration during the cold seasons (autumn and winter), likely associated with the anthropogenic urban emissions, instead, the background sites show a decrease in concentrations during the cold seasons. This could be due to more frequent cases of SD observed in spring and summer periods that mainly influence background sites. The seasonal difference on the average concentration for industrial sites is not statistically significant. The SD cases influence both PM10 and PM2.5 concentrations but their effect is significantly larger on PM10. Over the studied area, the effect is relatively limited on long-term average PM10 (estimated increase of 3.2%) and PM2.5 (estimated increase of 1.5%) concentrations but it is significant on daily concentrations. It is estimated an increase of 22% of the probability to overcome the air quality standard daily threshold for PM10. Full article
(This article belongs to the Special Issue Air Quality and Climate)
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<p>Map showing the positions of the measurement sites. The colors indicate the typology of the site (green = background; yellow = industrial; red = urban). The small inset in green reports the orography of the area.</p> Full article ">Figure 2
<p>Crustal enrichment factors calculated for PM<sub>10</sub> and PM<sub>2.5</sub> in the different typologies of sites including the average for the studied area.</p> Full article ">
376 KiB  
Article
Atmospheric Abundances, Trends and Emissions of CFC-216ba, CFC-216ca and HCFC-225ca
by Corinna Kloss, Mike J. Newland, David E. Oram, Paul J. Fraser, Carl A. M. Brenninkmeijer, Thomas Röckmann and Johannes C. Laube
Atmosphere 2014, 5(2), 420-434; https://doi.org/10.3390/atmos5020420 - 4 Jun 2014
Cited by 8 | Viewed by 15638
Abstract
The first observations of the feedstocks, CFC-216ba (1,2-dichlorohexafluoropropane) and CFC-216ca (1,3-dichlorohexafluoropropane), as well as the CFC substitute HCFC-225ca (3,3-dichloro-1,1,1,2,2-pentafluoropropane), are reported in air samples collected between 1978 and 2012 at Cape Grim, Tasmania. Present day (2012) mixing ratios are 37.8 ± 0.08 ppq [...] Read more.
The first observations of the feedstocks, CFC-216ba (1,2-dichlorohexafluoropropane) and CFC-216ca (1,3-dichlorohexafluoropropane), as well as the CFC substitute HCFC-225ca (3,3-dichloro-1,1,1,2,2-pentafluoropropane), are reported in air samples collected between 1978 and 2012 at Cape Grim, Tasmania. Present day (2012) mixing ratios are 37.8 ± 0.08 ppq (parts per quadrillion; 1015) and 20.2 ± 0.3 ppq for CFC-216ba and CFC-216ca, respectively. The abundance of CFC-216ba has been approximately constant for the past 20 years, whilst that of CFC-216ca is increasing, at a current rate of 0.2 ppq/year. Upper tropospheric air samples collected in 2013 suggest a further continuation of this trend. Inferred annual emissions peaked 421 at 0.18 Gg/year (CFC-216ba) and 0.05 Gg/year (CFC-216ca) in the mid-1980s and then decreased sharply as expected from the Montreal Protocol phase-out schedule for CFCs. The atmospheric trend of CFC-216ca and CFC-216ba translates into continuing emissions of around 0.01 Gg/year in 2011, indicating that significant banks still exist or that they are still being used. HCFC-225ca was not detected in air samples collected before 1992. The highest mixing ratio of 52 ± 1 ppq was observed in 2001. Increasing annual emissions were found in the 1990s (i.e., when HCFC-225ca was being introduced as a replacement for CFCs). Emissions peaked around 1999 at about 1.51 Gg/year. In accordance with the Montreal Protocol, restrictions on HCFC consumption and the short lifetime of HCFC-225ca, mixing ratios declined after 2001 to 23.3 ± 0.7 ppq by 2012. Full article
(This article belongs to the Special Issue Ozone Depletion and Climate Change)
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Graphical abstract

Graphical abstract
Full article ">
<p>Correlation of fractional release factors of CFC-216ba with mean ages of air (as derived from measurements of SF<sub>6</sub>) and their respective 1 <span class="html-italic">σ</span> uncertainties. The data originate from stratospheric aircraft campaigns at mid-latitudes in late 2009 (red) and high latitudes in early 2010 (blue).</p> Full article ">
<p>Correlation of fractional release factors (FRFs) with stratospheric lifetimes at mid-latitudes at a mean age of three years (red) and at high latitudes at a mean age of 5.5 years (blue) for nine ozone-relevant source gases, as derived in [<a href="#b24-atmosphere-05-00420" class="html-bibr">24</a>]. These correlations were used to estimate a stratospheric lifetime of 135 years and an uncertainty range of 85 to 472 years for CFC-216ba (green) from the FRFs inferred in <a href="#f1-atmosphere-05-00420" class="html-fig">Figure 1</a>.</p> Full article ">
<p>Mixing ratio time series for the Cape Grim samples for HCFC-225ca (red), CFC-216ba (blue) and CFC-216ca (green). Dots represent measured abundances with their respective 1 <span class="html-italic">σ</span> uncertainty estimates. Solid lines show the model fits used to determine the best fit emissions scenarios.</p> Full article ">
<p>Global annual emission estimates of CFC-216ba, CFC-216ca and HCFC-225ca based on 2D atmospheric modelling. Uncertainty ranges for the CFCs were calculated based on uncertainties in the atmospheric lifetime estimates, calibration, measurement and modelling uncertainties. Uncertainty ranges for HCFC-225ca were calculated based on uncertainties in OH reaction rates [<a href="#b28-atmosphere-05-00420" class="html-bibr">28</a>] and measurement uncertainties.</p> Full article ">
2697 KiB  
Article
Seasonal and Diurnal Variations of Total Gaseous Mercury in Urban Houston, TX, USA
by Xin Lan, Robert Talbot, Patrick Laine, Barry Lefer, James Flynn and Azucena Torres
Atmosphere 2014, 5(2), 399-419; https://doi.org/10.3390/atmos5020399 - 30 May 2014
Cited by 15 | Viewed by 8385
Abstract
Total gaseous mercury (THg) observations in urban Houston, over the period from August 2011 to October 2012, were analyzed for their seasonal and diurnal characteristics. Our continuous measurements found that the median level of THg was 172 parts per quadrillion by volume (ppqv), [...] Read more.
Total gaseous mercury (THg) observations in urban Houston, over the period from August 2011 to October 2012, were analyzed for their seasonal and diurnal characteristics. Our continuous measurements found that the median level of THg was 172 parts per quadrillion by volume (ppqv), consistent with the current global background level. The seasonal variation showed that the highest median THg mixing ratios occurred in summer and the lowest ones in winter. This seasonal pattern was closely related to the frequency of THg episodes, energy production/consumption and precipitation in the area. The diurnal variations of THg exhibited a pattern where THg accumulated overnight and reached its maximum level right before sunrise, followed by a rapid decrease after sunrise. This pattern was clearly influenced by planetary boundary layer (PBL) height and horizontal winds, including the complex sea breeze system in the Houston area. A predominant feature of THg in the Houston area was the frequent occurrence of large THg spikes. Highly concentrated pollution plumes revealed that mixing ratios of THg were related to not only the combustion tracers CO, CO2, and NO, but also CH4 which is presumably released from oil and natural gas operations, landfills and waste treatment. Many THg episodes occurred simultaneously with peaks in CO, CO2, CH4, NOx, and/or SO2, suggesting possible contributions from similar sources with multi-source types. Our measurements revealed that the mixing ratios and variability of THg were primarily controlled by nearby mercury sources. Full article
(This article belongs to the Special Issue Atmospheric Mercury)
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<p>Facility emission sources around MT site from 2008 NEI facility data. A satellite image of the red box area is provided in the supporting material (Figure S1) for details of Houston Ship Channel area.</p> Full article ">Figure 2
<p>Complete time series of THg from MT measurements. (<b>a</b>) and (<b>b</b>) show the same data with different ranges in y axis.</p> Full article ">Figure 3
<p>Monthly medians of THg, energy production and precipitation.</p> Full article ">Figure 4
<p>Seasonally diurnal variations of THg (<b>a</b>), PBL height (<b>b</b>) and horizontal wind speed (<b>c</b>). Data shows the median values with 5 min. time interval. The top x axis shows time in LST and the bottom x axis shows time in UTC. LST = UTC − 6:00.</p> Full article ">Figure 5
<p>Complete THg <span class="html-italic">versus</span> wind direction (<b>a</b>), THg <span class="html-italic">versus</span> wind direction in HMP (<b>b</b>), THg <span class="html-italic">versus</span> wind direction in LMP (<b>c</b>). The color scale shows the ranges of THg mixing ratios. The percentage values on the R axis shows the frequency of THg coming from a certain range (22.5°) of wind directions.</p> Full article ">Figure 6
<p>Time series of THg, CO, NO/NO<sub>x</sub>, wind speed and wind direction. (<b>a</b>,<b>b</b>,<b>c</b>) show the influences of three common wind patterns on THg levels.</p> Full article ">Figure 7
<p>Time series of THg, CO, CO<sub>2</sub>, NO, SO<sub>2</sub>, and CH<sub>4</sub>. The two boxes are comparisons of two episodes with different CH<sub>4</sub> mixing ratios.</p> Full article ">Figure 8
<p>Time series of THg, CO, CO<sub>2</sub>, NO, O<sub>3</sub> and CH<sub>4</sub> in the largest THg plume.</p> Full article ">Figure 9
<p>Diurnal variations of THg, CO, CO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub> and CH<sub>4</sub> in summer 2012. Data shows the median values within a 5 min. time interval.</p> Full article ">Figure 10
<p>Enhancement ratios <span class="html-italic">versus</span> wind direction in high THg episodes.(<b>a</b>,<b>b</b>,<b>c</b>) show ∆THg/∆CO, ∆THg/∆CO<sub>2</sub> and ∆THg/∆CH<sub>4</sub> <span class="html-italic">versus</span> wind direction, respectively. The yellow circles point out two ERs potentially from the same source at ~40° direction and the blue circles represent another two episodes from a source at ~30° direction.</p> Full article ">
11099 KiB  
Article
Patterns of Precipitation and Convection Occurrence over the Mediterranean Basin Derived from a Decade of Microwave Satellite Observations
by Bahjat Alhammoud, Chantal Claud, Beatriz M. Funatsu, Karine Béranger and Jean-Pierre Chaboureau
Atmosphere 2014, 5(2), 370-398; https://doi.org/10.3390/atmos5020370 - 30 May 2014
Cited by 13 | Viewed by 9626
Abstract
The Mediterranean region is characterized by its vulnerability to changes in the water cycle, with the impact of global warming on the water resources being one of the major concerns in social, economical and scientific ambits. Even if precipitation is the best-known term [...] Read more.
The Mediterranean region is characterized by its vulnerability to changes in the water cycle, with the impact of global warming on the water resources being one of the major concerns in social, economical and scientific ambits. Even if precipitation is the best-known term of the Mediterranean water budget, large uncertainties remain due to the lack of suitable offshore observational data. In this study, we use the data provided by the Advanced Microwave Sounding Unit-B (AMSU-B) on board NOAA satellites to detect and analyze precipitating and convective events over the last decade at spatial resolution of 0.2° latitude × 0.2° longitude. AMSU-B observation shows that rain occurrence is widespread over the Mediterranean in wintertime while reduced in the eastern part of the basin in summer. Both precipitation and convection occurrences display a weak diurnal cycle over sea. In addition, convection occurrences, which are essentially located over land during summertime, shift to mostly over the sea during autumn with maxima in the Ionian sub-basin and the Adriatic Sea. Precipitation occurrence is also inferred over the sea from two other widely used climatological datasets, HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis interim (ERA-Interim). There is generally a rather fair agreement between these climatologies for describing the large-scale patterns such as the strong latitudinal gradient of rain and eastward rain signal propagation. Furthermore, the higher spatial resolution of AMSU-B measurements (16 km at nadir) gives access to mesoscale details in the region (e.g., coastal areas). AMSU-B measurements show less rain occurrences than HOAPS during wintertime, thereby suggesting that some of the thresholds used in our method might be too stringent during this season. We also observed that convection occurrences in ERA-Interim are systematically lower than those derived from AMSU-B. These results are potentially valuable to evaluate the rainfall parameterization in weather and climate models and to constrain ocean models. Full article
(This article belongs to the Special Issue Cloud and Precipitation)
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<p>Orography of the studied area (in meters). wMed: The western Mediterranean basin consists of the Alboran (ALB), the Algerian (ALG), the Balearic (BAL), the Ligurian (LIG) and the Tyrrhenian (TYR) sub-basins; cMed: the central Mediterranean basin consists of the Adriatic Sea (ADR) and the Ionian sub-basin (ION); and eMed: the eastern Mediterranean basin consists of the Aegean Sea (AEG) and the Levantine sub-basin (LEV); BLK: Black Sea; ALP: Alps; ATL: Atlas; BAL: Balkan; PYR: Pyrenees; ANT: Anatolian and TRS: Taurus Mountains. Blue-solid lines indicate limits between western, central and eastern Mediterranean basins.</p> Full article ">Figure 2
<p>Descending (solid) and ascending (dashed) annual cycle of (<b>left</b> column) moderate rain (MR) and (<b>right</b> column) deep convection (DC) frequency for the Mediterranean Sea (<b>upper</b> panel) and for land-only of the Mediterranean region (<b>lower</b> panel) based on NOAA-15 (black), NOAA-16 (blue) and NOAA-17 (red) observations over 2002–2007.</p> Full article ">Figure 3
<p>Monthly mean of (<b>a</b>) MR and (<b>b</b>) DC frequency over the Mediterranean Sea based on NOAA-15 (black), NOAA-16 (blue) and NOAA-17 (red) observations.</p> Full article ">Figure 4
<p>NOAA-15 climatologies: (left) mean state and (right) interannual variability computed as standard deviation of the monthly means over the period 2000–2007. (<b>a</b>,<b>b</b>) MR frequencies (‰), and (<b>c</b>,<b>d</b>) DC frequencies (‰).</p> Full article ">Figure 5
<p>Spatial distribution of monthly climatology of MR frequency over the Mediterranean region from January 2000 throughDecember 2007 from NOAA-15 satellite.</p> Full article ">Figure 6
<p>Spatial distribution of monthly climatology of DC frequency over the Mediterranean region from January 2000 through December 2007 from NOAA-15 satellite.</p> Full article ">Figure 7
<p>Latitude time sections (<b>a</b>,<b>b</b>) and time longitude (<b>c</b>,<b>d</b>) of MR (left column) and DC (right column) frequencies over the Mediterranean region based on NOAA-15 observations, 2000–2007.</p> Full article ">Figure 8
<p>(<b>a</b>) HOAPS-3.2 rain-frequency climatology (‰) and (<b>b</b>) interannual variability computed as standard deviation (‰) of the monthly means over the period 1999–2005 for a threshold of 30 mm/day.</p> Full article ">Figure 9
<p>Monthly mean annual cycle averaged over the period 1999–2005 of NOAA-15 MR frequency for different thresholds of UTH (&lt;70%, &lt;50%, and &lt;30%; thick-solid, dashed and dotted black, respectively), and HOAPS-3.2 rain frequency (thin-solid) for different thresholds (30, 40 and 50 mm/day; blue, purple, and green, respectively) in (<b>a</b>) Mediterranean Sea (Med), (<b>b</b>) wMed, (<b>c</b>) cMed and (<b>d</b>) eMed (see <a href="#atmosphere-05-00370-f008" class="html-fig">Figure 8</a> for the regions location).</p> Full article ">Figure 10
<p>ERA-Interim climatologies (left) mean state and (right) interannual variability computed as standard deviation of the monthly means over the period 2000–2007 for a threshold of 30 mm/day. (<b>a</b>,<b>b</b>) rain frequency (‰) and (<b>c</b>,<b>d</b>) convective rain frequency (‰).</p> Full article ">Figure 11
<p>Monthly mean annual cycle averaged over the period 2000–2007 in (<b>a</b>) Mediterranean sea (Med), (<b>b</b>) wMed, (<b>c</b>) cMed and (<b>d</b>) eMed of NOAA-15 MR frequency (thick-solid black), and ERA-Interim reanalysis rain frequency (thin-solid) for different thresholds (30, 40 and 50 mm/day; blue, purple, and green respectively) and in (<b>e</b>) Mediterranean sea (Med), (<b>f</b>) wMed, (<b>g</b>) cMed and (<b>h</b>) eMed of NOAA-15 DC frequency (thick-solid black), and ERA-Interim reanalysis DC frequency (thin-solid) for different thresholds (30, 40 and 50 mm/day; blue, purple, and green respectively). See <a href="#atmosphere-05-00370-f008" class="html-fig">Figure 8</a> for the regions location.</p> Full article ">
4712 KiB  
Article
Mercury Plumes in the Global Upper Troposphere Observed during Flights with the CARIBIC Observatory from May 2005 until June 2013
by Franz Slemr, Andreas Weigelt, Ralf Ebinghaus, Carl Brenninkmeijer, Angela Baker, Tanja Schuck, Armin Rauthe-Schöch, Hella Riede, Emma Leedham, Markus Hermann, Peter Van Velthoven, David Oram, Debbie O'Sullivan, Christoph Dyroff, Andreas Zahn and Helmut Ziereis
Atmosphere 2014, 5(2), 342-369; https://doi.org/10.3390/atmos5020342 - 28 May 2014
Cited by 22 | Viewed by 12039
Abstract
Tropospheric sections of flights with the CARIBIC (Civil Aircraft for Regular Investigation of the Atmosphere Based on an Instrumented Container) observatory from May 2005 until June 2013, are investigated for the occurrence of plumes with elevated Hg concentrations. Additional information on CO, CO [...] Read more.
Tropospheric sections of flights with the CARIBIC (Civil Aircraft for Regular Investigation of the Atmosphere Based on an Instrumented Container) observatory from May 2005 until June 2013, are investigated for the occurrence of plumes with elevated Hg concentrations. Additional information on CO, CO2, CH4, NOy, O3, hydrocarbons, halocarbons, acetone and acetonitrile enable us to attribute the plumes to biomass burning, urban/industrial sources or a mixture of both. Altogether, 98 pollution plumes with elevated Hg concentrations and CO mixing ratios were encountered, and the Hg/CO emission ratios for 49 of them could be calculated. Most of the plumes were found over East Asia, in the African equatorial region, over South America and over Pakistan and India. The plumes encountered over equatorial Africa and over South America originate predominantly from biomass burning, as evidenced by the low Hg/CO emission ratios and elevated mixing ratios of acetonitrile, CH3Cl and particle concentrations. The backward trajectories point to the regions around the Rift Valley and the Amazon Basin, with its outskirts, as the source areas. The plumes encountered over East Asia and over Pakistan and India are predominantly of urban/industrial origin, sometimes mixed with products of biomass/biofuel burning. Backward trajectories point mostly to source areas in China and northern India. The Hg/CO2 and Hg/CH4 emission ratios for several plumes are also presented and discussed. Full article
(This article belongs to the Special Issue Atmospheric Mercury)
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<p>The tracks of 328 CARIBIC (Civil Aircraft for Regular Investigation of the Atmosphere Based on an Instrumented Container) flights from May 2005 until June 2013. The colours denote the classification of destination airports used in this paper: green, East Asia; yellow, South Asia; light blue, Africa; dark blue, South America; red, North America.</p> Full article ">Figure 2
<p>Overview of the data from Flight 158 from Frankfurt to Guangzhou on 31 July 2006. (<b>Top</b>) Flight track and the locations of whole air samples. Time series plots are below: (<b>uppermost panel</b>): flight altitude (magenta) and latitude (black), potential vorticity (blue), sampling intervals (grey bars); (<b>second panel from top</b>): mixing ratios of CO (black), O<sub>3</sub> (green) and Hg concentrations (red); (<b>middle panel</b>): mixing ratios of NO (black), NOy (red) and total water content (blue); (<b>second panel from bottom</b>): mixing ratios of CH<sub>4</sub> (blue), CH<sub>3</sub>Cl (olive green) and CFC12 (CCl<sub>2</sub>F<sub>2</sub>, magenta)) in whole air samples; (<b>bottom panel</b>): mixing ratios of acetone (green) and CO<sub>2</sub> (blue). The three identified plumes are marked with A, B and C in the second panel from top. Another event, due to a crossing of a filament of tropospheric air within the lower stratosphere, is marked with D. Although similar to Events A, B and C, this event has no relation to surface emissions (see the text).</p> Full article ">Figure 3
<p>Geographic distribution and the extension of the plumes with statistically significant Hg <span class="html-italic">vs.</span> CO correlations. The magnitude of Hg/CO emission ratios in pg∙m<sup>−3</sup>∙ppb<sup>−1</sup> is colour coded.</p> Full article ">Figure 4
<p>Eight-day backward trajectories for whole air Samples 9 (<b>a</b>), 11 (<b>b</b>), 12 (<b>c</b>) and 14 (<b>d</b>) taken during Flight 158.</p> Full article ">Figure 5
<p>The overview of the data from Flight 334 from Cape Town to Frankfurt on 21/22 March 2011. The parameters displayed here are similar to those in <a href="#atmosphere-05-00342-f002" class="html-fig">Figure 2</a>. (<b>Middle</b>) The time series plots additionally show the particle surface area concentrations (green); (<b>second panel from the bottom</b>) mixing ratios of SF<sub>6</sub> (magenta) and N<sub>2</sub>O (green) in whole air samples, as well as continuously measured mixing ratios of CH<sub>4</sub> (dark blue) and CO<sub>2</sub> (light blue); (<b>bottom</b>) cloud water content (light blue) and concentrations of particles within the 4–12 nm size range (red) and larger than 12 nm (black).</p> Full article ">Figure 6
<p>Five-day backward trajectories (every 3 min) for Flight 334 from Cape Town to Frankfurt on 21 and 22 March 2011: (<b>a</b>) 21–22 UTC; (<b>b</b>) 22–23 UTC; (<b>c</b>) 23–24 UTC; and (<b>d</b>) 0–1 UTC.</p> Full article ">Figure 7
<p>The map of fire counts for the period from 12 to 21 March 2011 (<a href="http://rapidfire.sci.gsfc.nasa.gov/firemaps/" target="_blank">http://rapidfire.sci.gsfc.nasa.gov/firemaps/</a>, accessed on 10 October 2013).</p> Full article ">Figure 8
<p>An overview of the data from Flight 348 from Bogotá to Frankfurt on 17 June 2011. The same parameters are displayed as in <a href="#atmosphere-05-00342-f005" class="html-fig">Figure 5</a>. Additionally, total water content (dark blue) is shown in the bottom panel.</p> Full article ">Figure 9
<p>(<b>a</b>) eight-day backward trajectories for whole air Sample 6 from the CO peak encountered around 17:20 UTC during Flight 347 from Frankfurt to Bogota on 16 June 2011; and (<b>b</b>) for whole air Sample 20 from the CO peak encountered around 8:05 UTC during Flight 348 from Bogota to Frankfurt on 17 June 2011. (<b>c</b>) The map of the fire counts for 10–19 June 2011 (<a href="http://rapidfire.sci.gsfc.nasa.gov/firemaps/" target="_blank">http://rapidfire.sci.gsfc.nasa.gov/firemaps/</a>, accessed on 10 October 2013).</p> Full article ">
1316 KiB  
Article
Comparison of Atmospheric Travel Distances of Several PAHs Calculated by Two Fate and Transport Models (The Tool and ELPOS) with Experimental Values Derived from a Peat Bog Transect
by Sabine Thuens, Christian Blodau, Frank Wania and Michael Radke
Atmosphere 2014, 5(2), 324-341; https://doi.org/10.3390/atmos5020324 - 23 May 2014
Cited by 15 | Viewed by 7898
Abstract
Multimedia fate and transport models are used to evaluate the long range transport potential (LRTP) of organic pollutants, often by calculating their characteristic travel distance (CTD). We calculated the CTD of several polycyclic aromatic hydrocarbons (PAHs) and metals using two models: the OECD [...] Read more.
Multimedia fate and transport models are used to evaluate the long range transport potential (LRTP) of organic pollutants, often by calculating their characteristic travel distance (CTD). We calculated the CTD of several polycyclic aromatic hydrocarbons (PAHs) and metals using two models: the OECD POV& LRTP Screening Tool (The Tool), and ELPOS. The absolute CTDs of PAHs estimated with the two models agree reasonably well for predominantly particle-bound congeners, while discrepancies are observed for more volatile congeners. We test the performance of the models by comparing the relative ranking of CTDs with the one of experimentally determined travel distances (ETDs). ETDs were estimated from historical deposition rates of pollutants to peat bogs in Eastern Canada. CTDs and ETDs of PAHs indicate a low LRTP. To eliminate the high influence on specific model assumptions and to emphasize the difference between the travel distances of single PAHs, ETDs and CTDs were analyzed relative to the travel distances of particle-bound compounds. The ETDs determined for PAHs, Cu, and Zn ranged from 173 to 321 km with relative uncertainties between 26% and 46%. The ETDs of two metals were shorter than those of the PAHs. For particle-bound PAHs the relative ETDs and CTDs were similar, while they differed for Chrysene. Full article
(This article belongs to the Special Issue Persistent Organic Pollutants (POPs) in the Atmosphere: From Nowadays Background Sites to Hot Spots)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Locations of sampled peat bogs in Ontario, Canada.</p> Full article ">Figure 2
<p>Average maximum deposition rates of (<b>A</b>) PAHs and (<b>B</b>) metals as a function of distance from the Greater Sudbury source area. The error bars represent the standard deviation of the three replicate peat cores.</p> Full article ">Figure 3
<p>Characteristic Travel Distance (CTD) of selected PAHs normalized to the CTD of particles estimated with (<b>A</b>) The Tool; and (<b>C</b>) ELPOS for the three temperature scenarios, and sensitivity of the CTD at 4 °C to the model input parameters estimated with (<b>B</b>) The Tool; and (<b>D</b>) ELPOS (note: data not presented here available in Supporting Material, Tables S11 and S12; the lines in sub-figures B and D are shown for visual support in identifying the individual parameters). The assignment of PAHs to groups according to their atmospheric partition behavior is indicated in brackets. depovelo = dry particle deposition velocity.</p> Full article ">Figure 4
<p>Comparison of characteristic travel distances predicted by The Tool and ELPOS for the average temperature scenario (4 °C). Results from ELPOS were obtained both with the built-in temperature correction of partition coefficients and reaction rates (ELPOS auto T-adj) and with the parameters that were temperature-adjusted manually as in The Tool (ELPOS man. T-adj). (<b>A</b>) Absolute CTDs; (<b>B</b>) CTDs relative to the particle-CTD; (<b>C</b>) CTDs relative to Ind. The 1:1-line is shown as dotted line.</p> Full article ">Figure 5
<p>Comparison of relative ETDs and CTDs of PAHs at 4° C ((<b>left</b>): relative to metals (ETD) and particles (CTD); (<b>right</b>): relative to Ind).</p> Full article ">
683 KiB  
Article
Designation of Environmental Impacts and Damages of Turbojet Engine: A Case Study with GE-J85
by Onder Altuntas
Atmosphere 2014, 5(2), 307-323; https://doi.org/10.3390/atmos5020307 - 23 May 2014
Cited by 8 | Viewed by 11111
Abstract
Between the troposphere and stratosphere layers of the atmosphere is a critical zone for collecting emissions and negative effects on the Earth (ecological, humanity, and resources). Aircrafts are the main causes of the impacts in this layer. In this study, environmental effects (Damages, [...] Read more.
Between the troposphere and stratosphere layers of the atmosphere is a critical zone for collecting emissions and negative effects on the Earth (ecological, humanity, and resources). Aircrafts are the main causes of the impacts in this layer. In this study, environmental effects (Damages, Specific Fuel Consumption Impact-SFCI and Thrust Environmental Impact-TEI) of different fueled (Jet-A and Liquid Hydrogen-H2) jet engines (a case study with GE-J85) are investigated. This comparison was made between 7000–10,000 m altitude and 0.7–1.0 Mach. The maximum damages were found to be 82.44 PDF∙m2∙yr (Potentially Disappeared Fraction from one m2 area during one year), 1.75 × 10−3 DALY (disability-adjusted life years), and 8100 MJ Surplus for Ecosystem Quality, Human Health and Resources, respectively, at Jet-A fueled aircraft, 1 Mach, and 7000 m altitude. Additionally, the maximum SFCI was calculated as 344.03 mPts/kg at H2-fueled, 0.7 Mach, and 10,000 m; the minimum TEI was calculated as 13.78 mPts/N at H2-fueled aircraft, 0.7 Mach, and 9000 m. The best environmental (low specific fuel consumption and thrust impacts) flight situations were found in this study at a high altitude and a low Mach number. Full article
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<p>Schematic diagram of a basic turbojet engine.</p> Full article ">Figure 2
<p>(<b>a</b>) Ecosystem Quality Damages (EQD) variation with Altitude and Mach number; (<b>b</b>) Human Health Damages (HHD) variation with Altitude and Mach number; (<b>c</b>) Resources Damages (RD) variation with Altitude and Mach number.</p> Full article ">Figure 3
<p>Total fuel consumption variation with Altitude and Mach number.</p> Full article ">Figure 4
<p>Variation of Mach number and Altitude with (<b>a</b>) Specific Fuel Consumption Impact of EQ; (<b>b</b>) Specific Fuel Consumption Impact of HH; (<b>c</b>) Specific Fuel Consumption Impact of R.</p> Full article ">Figure 5
<p>Variation of Mach number and Altitude with total Specific Fuel Consumption Impact.</p> Full article ">Figure 6
<p>Variation of Mach number and Altitude with Thrust Environmental Impact (TEI).</p> Full article ">
21154 KiB  
Article
Atmospheric Black Carbon along a Cruise Path through the Arctic Ocean during the Fifth Chinese Arctic Research Expedition
by Jie Xing, Lingen Bian, Qihou Hu, Juan Yu, Chen Sun and Zhouqing Xie
Atmosphere 2014, 5(2), 292-306; https://doi.org/10.3390/atmos5020292 - 5 May 2014
Cited by 16 | Viewed by 6308
Abstract
From July to September 2012, during the fifth Chinese National Arctic Research Expedition (CHINARE), the concentrations of black carbon (BC) aerosols inside the marine boundary layer were measured by an in situ aethalometer. BC concentrations ranged from 0.20 ng∙m−3 to 1063.20 ng∙m [...] Read more.
From July to September 2012, during the fifth Chinese National Arctic Research Expedition (CHINARE), the concentrations of black carbon (BC) aerosols inside the marine boundary layer were measured by an in situ aethalometer. BC concentrations ranged from 0.20 ng∙m−3 to 1063.20 ng∙m−3, with an average of 75.74 ng∙m−3. The BC concentrations were significantly higher over the mid-latitude and coastal areas than those over the remote ocean and high latitude areas. The highest average concentration was found over offshore China (643.44 ng∙m−3) during the cruise, while the lowest average was found over the Arctic Ocean (5.96 ng∙m−3). BC aerosol was found mainly affected by the terrestrial input and displayed seasonal and spatial variations. Compared with the results from the third and fourth CHINARE of summer 2008, and summer 2010, the inter-annual variation of BC over the Arctic Ocean was negligible. Full article
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<p>The route of the 5th Chinese Arctic Research Expedition (CHINARE).</p> Full article ">Figure 2
<p>Hourly average concentrations of black carbon aerosols along the fifth CHINARE. (<b>a</b>) Departing route; (<b>b</b>) return route.</p> Full article ">Figure 3
<p>Black carbon (BC) concentrations of the same sea areas during the third, fourth and fifth CHINARE-Arctic.</p> Full article ">Figure 4
<p>Seven-day air mass backward trajectories of some special locations belonging to offshore China and the Sea of Japan. (<b>a</b>,<b>b</b>,<b>c</b>) Departing route; (<b>d</b>,<b>e</b>,<b>f</b>) return route. HYSPLIT, hybrid single-particle Lagrangian integrated trajectory.</p> Full article ">Figure 5
<p>Seven-day air mass backward trajectories of some special locations around the Bering Strait and the Kamchatka Peninsula. (<b>a</b>,<b>b</b>,<b>c</b>) Departing route, (<b>d</b>,<b>e</b>,<b>f</b>) Return route.</p> Full article ">
1509 KiB  
Article
Tracing Sources of Total Gaseous Mercury to Yongheung Island off the Coast of Korea
by Gang S. Lee, Pyung R. Kim, Young J. Han, Thomas M. Holsen and Seung H. Lee
Atmosphere 2014, 5(2), 273-291; https://doi.org/10.3390/atmos5020273 - 30 Apr 2014
Cited by 15 | Viewed by 7297
Abstract
In this study, total gaseous mercury (TGM) concentrations were measured on Yongheung Island off the coast of Korea between mainland Korea and Eastern China in 2013. The purpose of this study was to qualitatively evaluate the impact of local mainland Korean sources and [...] Read more.
In this study, total gaseous mercury (TGM) concentrations were measured on Yongheung Island off the coast of Korea between mainland Korea and Eastern China in 2013. The purpose of this study was to qualitatively evaluate the impact of local mainland Korean sources and regional Chinese sources on local TGM concentrations using multiple tools including the relationship with other pollutants, meteorological data, conditional probability function, backward trajectories, and potential source contribution function (PSCF) receptor modeling. Among the five sampling campaigns, two sampling periods were affected by both mainland Korean and regional sources, one was caused by mainland vehicle emissions, another one was significantly impacted by regional sources, and, in the remaining period, Hg volatilization from oceans was determined to be a significant source and responsible for the increase in TGM concentration. PSCF identified potential source areas located in metropolitan areas, western coal-fired power plant locations, and the southeastern industrial area of Korea as well as the Liaoning province, the largest Hg emitting province in China. In general, TGM concentrations generally showed morning peaks (07:00~12:00) and was significantly correlated with solar radiation during all sampling periods. Full article
(This article belongs to the Special Issue Atmospheric Mercury)
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<p>The Hg sampling site (with a cross mark) in this study and the national air quality monitoring site (with a star mark) for other atmospheric pollutants. The upper right panel indicates the anthropogenic TGM emission sources in Korea.</p> Full article ">Figure 2
<p>Measured TGM concentrations (left y-axis) with wind direction (right y-axis) for five sampling periods.</p> Full article ">Figure 3
<p>Wind rose (<b>a</b>) and pollution rose (<b>b</b>) for the entire sampling period.</p> Full article ">Figure 4
<p>Diel pattern of mean TGM concentration for each sampling period. The error bar indicates one standard deviation.</p> Full article ">Figure 5
<p>CPF (Conditional Probability Function) plot for each sampling period.</p> Full article ">Figure 6
<p>Back-trajectories during each sampling period. Red and blue points indicate top 10% and bottom 10% of TGM concentrations, respectively.</p> Full article ">Figure 7
<p>PSCF result for tracing regional sources. A grid cell size of 0.5° by 0.5° was used.</p> Full article ">
2749 KiB  
Article
Assessing the Influence of Seasonal and Spatial Variations on the Estimation of Secondary Organic Carbon in Urban Particulate Matter by Applying the EC-Tracer Method
by Sandra Wagener, Marcel Langner, Ute Hansen, Heinz-Jörn Moriske and Wilfried R. Endlicher
Atmosphere 2014, 5(2), 252-272; https://doi.org/10.3390/atmos5020252 - 29 Apr 2014
Cited by 4 | Viewed by 6778
Abstract
The elemental carbon (EC)-tracer method was applied to PM10 and PM1 data of three sampling sites in the City of Berlin from February to October 2010. The sites were characterized by differing exposure to traffic and vegetation. The aim was to [...] Read more.
The elemental carbon (EC)-tracer method was applied to PM10 and PM1 data of three sampling sites in the City of Berlin from February to October 2010. The sites were characterized by differing exposure to traffic and vegetation. The aim was to determine the secondary organic carbon (SOC) concentration and to describe the parameters influencing the application of the EC-tracer method. The evaluation was based on comparisons with results obtained from positive matrix factorization (PMF) applied to the same samples. To obtain site- and seasonal representative primary OC/EC-ratios ([OC/EC]p), the EC-tracer method was performed separately for each station, and additionally discrete for samples with high and low contribution of biomass burning. Estimated SOC-concentrations for all stations were between 11% and 33% of total OC. SOC-concentrations obtained with PMF exceeded EC-tracer results more than 100% at the park in the period with low biomass burning emissions in PM10. The deviations were besides others attributed to the high ratio of biogenic to combustion emissions and to direct exposure to vegetation. The occurrences of biomass burning emissions in contrast lead to increased SOC-concentrations compared to PMF in PM10. The obtained results distinguish that the EC-tracer-method provides well comparable results with PMF if sites are strongly influenced by one characteristic primary combustion source, but was found to be adversely influenced by direct and relatively high biogenic emissions. Full article
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<p>PM<sub>10</sub>-SOA-concentrations from compound analysis. Secondary organic aerosol (SOA) is given by the sum of the compounds pinic acid, pinonic acid, 2-Methylthreitol and 2-Methylerythritol [<a href="#B22-atmosphere-05-00252" class="html-bibr">22</a>].</p> Full article ">Figure 2
<p>Concentration tendencies for OC, EC, OC/EC-ratio, levoglucosan and NO<sub>X</sub> between the BB- and non-BB-period.</p> Full article ">Figure 3
<p>Deviation between EC-tracer and PMF results.</p> Full article ">Figure 4
<p>PMF- and EC-tracer-SOC and correlations between SOC derived with both methods (after Pearson). Time series are represented for the BB- and nonBB-period.</p> Full article ">Figure 5
<p>Primary biogenic carbon (POC<sub>bio</sub>) for the HV and regV sites, separated by BB- and nonBB-period (obtained from PMF-analysis [<a href="#B14-atmosphere-05-00252" class="html-bibr">14</a>]).</p> Full article ">Figure 6
<p>Evaluation of [OC/EC]<span class="html-italic"><sub>p</sub></span> at site HV, PM<sub>10</sub>.</p> Full article ">Figure 7
<p>Evaluation of [OC/EC]<span class="html-italic"><sub>p</sub></span> at site HV, PM<sub>1</sub>.</p> Full article ">Figure 8
<p>Evaluation of [OC/EC]<span class="html-italic"><sub>p</sub></span> at site LV, PM<sub>10</sub>.</p> Full article ">Figure 9
<p>Evaluation of [OC/EC]<span class="html-italic"><sub>p</sub></span> at site LV, PM<sub>1</sub>.</p> Full article ">Figure 10
<p>Evaluation of [OC/EC]<span class="html-italic"><sub>p</sub></span> at site regV, PM<sub>10</sub>.</p> Full article ">
2118 KiB  
Article
Mercury Speciation at a Coastal Site in the Northern Gulf of Mexico: Results from the Grand Bay Intensive Studies in Summer 2010 and Spring 2011
by Xinrong Ren, Winston T. Luke, Paul Kelley, Mark Cohen, Fong Ngan, Richard Artz, Jake Walker, Steve Brooks, Christopher Moore, Phil Swartzendruber, Dieter Bauer, James Remeika, Anthony Hynes, Jack Dibb, John Rolison, Nishanth Krishnamurthy, William M. Landing, Arsineh Hecobian, Jeffery Shook and L. Greg Huey
Atmosphere 2014, 5(2), 230-251; https://doi.org/10.3390/atmos5020230 - 29 Apr 2014
Cited by 18 | Viewed by 10442
Abstract
During two intensive studies in summer 2010 and spring 2011, measurements of mercury species including gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM), trace chemical species including O3, SO2, CO, NO, NOY, and [...] Read more.
During two intensive studies in summer 2010 and spring 2011, measurements of mercury species including gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM), trace chemical species including O3, SO2, CO, NO, NOY, and black carbon, and meteorological parameters were made at an Atmospheric Mercury Network (AMNet) site at the Grand Bay National Estuarine Research Reserve (NERR) in Moss Point, Mississippi. Surface measurements indicate that the mean mercury concentrations were 1.42 ± 0.12 ng∙m−3 for GEM, 5.4 ± 10.2 pg∙m−3 for GOM, and 3.1 ± 1.9 pg∙m−3 for PBM during the summer 2010 intensive and 1.53 ± 0.11 ng∙m−3 for GEM, 5.3 ± 10.2 pg∙m−3 for GOM, and 5.7 ± 6.2 pg∙m−3 for PBM during the spring 2011 intensive. Elevated daytime GOM levels (>20 pg∙m−3) were observed on a few days in each study and were usually associated with either elevated O3 (>50 ppbv), BrO, and solar radiation or elevated SO2 (>a few ppbv) but lower O3 (~20–40 ppbv). This behavior suggests two potential sources of GOM: photochemical oxidation of GEM and direct emissions of GOM from nearby local sources. Lack of correlation between GOM and Beryllium-7 (7Be) suggests little influence on surface GOM from downward mixing of GOM from the upper troposphere. These data were analyzed using the HYSPLIT back trajectory model and principal component analysis in order to develop source-receptor relationships for mercury species in this coastal environment. Trajectory frequency analysis shows that high GOM events were generally associated with high frequencies of the trajectories passing through the areas with high mercury emissions, while low GOM levels were largely associated the trajectories passing through relatively clean areas. Principal component analysis also reveals two main factors: direct emission and photochemical processes that were clustered with high GOM and PBM. This study indicates that the receptor site, which is located in a coastal environment of the Gulf of Mexico, experienced impacts from mercury sources that are both local and regional in nature. Full article
(This article belongs to the Special Issue Atmospheric Mercury)
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<p>(<b>Left</b>) location of the Grand Bay NERR monitoring station, along with large point sources of gaseous oxidized mercury (GOM) in the region, based on the US EPA’s 2005 National Emissions Inventory (NEI). (<b>Right top</b>) site view from the Grand Bay NERR atmospheric mercury measurement tower. (<b>Right bottom</b>) the measurement tower at Grand Bay NERR, and two sets of Tekran mercury speciation units in a climate-controlled shelter adjacent to the tower.</p> Full article ">Figure 2
<p>Measurements of meteorological parameters, trace chemical pollutants, and mercury species during the Grand Bay Intensive in summer 2010.</p> Full article ">Figure 3
<p>Measurements of meteorological parameters, trace chemical pollutants, and mercury species during the Grand Bay Intensive in spring 2011.</p> Full article ">Figure 4
<p>(<b>Left</b>) Time series of GEM and altitude during the flight on 6 August 2010. (<b>Right</b>) vertical profiles of aircraft GEM concentration and ozonesonde data, including ozone, relative humidity, and temperature. The ozonesonde was launched at 10:55 CST on 6 August 2010.</p> Full article ">Figure 5
<p>Hourly averaged ozone <span class="html-italic">versus</span> GOM color-coded with log<sub>10</sub>([SO<sub>2</sub>]) during daytime in the spring 2011 intensive.</p> Full article ">Figure 6
<p>Diurnal variations of ozone, SO<sub>2</sub>, and GOM observed on 5 August 2010 (<b>Left</b>) and 5 May 2011 (<b>Right</b>).</p> Full article ">Figure 7
<p>Wind rose plots for GOM with [GOM] &gt; 2 pg∙m<sup>−3</sup> (<b>Left</b>) and SO<sub>2</sub> with [SO<sub>2</sub>] &gt; 1 ppbv (<b>Right</b>) during the summer 2010 campaign.</p> Full article ">Figure 8
<p>Correlation between CO and GOM (<b>Left</b>) and between CO and GEM (<b>Right</b>) during the summer 2010 intensive (blue circles) and the spring 2011 intensive (red dots).</p> Full article ">Figure 9
<p>(<b>Left</b>) hourly averaged water volume mixing ratio <span class="html-italic">versus</span> GOM concentration during the 2010 intensive (blue circles) and the 2011 intensive (red dots). (<b>Right</b>) hourly averaged water volume mixing ratio <span class="html-italic">versus</span> PBM concentration during the 2010 intensive (blue circles) and the 2011 intensive (red dots). Data collected during the day with solar radiation greater than 10 W∙m<sup>−2</sup> are plotted.</p> Full article ">Figure 10
<p>Correlation between <sup>7</sup>Be and GOM for the entire study (<b>Left</b>) and between <sup>7</sup>Be and ozone for nighttime periods when the solar radiation was less than 200 W∙m<sup>−2</sup> (<b>Right</b>) during the spring 2011 intensive. Hourly GOM and ozone measurements were averaged based on the time periods when <sup>7</sup>Be samples were collected.</p> Full article ">Figure 11
<p>Back trajectory frequencies of high GOM (&gt;20 pg∙m<sup>−3</sup>, (<b>Left)</b>) and low GOM (&lt;2 pg∙m<sup>−3</sup>, (<b>Right)</b>) events during the spring 2011 intensive study. The color-coded trajectory frequency at each grid represents the percentage of trajectories passing through the gridded area. Symbols represent the major mercury emission sources with source types and strengths as the same as in <a href="#atmosphere-05-00230-f001" class="html-fig">Figure 1</a>.</p> Full article ">
2528 KiB  
Article
Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image
by Yu Liu, Du-Gang Xi, Zhao-Liang Li and Chun-Xiang Shi
Atmosphere 2014, 5(2), 211-229; https://doi.org/10.3390/atmos5020211 - 14 Apr 2014
Cited by 14 | Viewed by 6372
Abstract
Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both [...] Read more.
Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both at the pixel and cloud patch scale, and then to apply it in precipitation estimation in the Huaihe River Basin using China’s first operational geostationary meteorological satellite, FengYun-2C (FY-2C), and the hourly precipitation data of 286 gauges from 2007. First, 31 Cb parameters (14 parameters of three pixel features and 17 parameters of four cloud patch features) were extracted based on a Cb tracking method using an artificial neural network (ANN) cloud classification as a pre-processing procedure to identify homogeneous Cb patches. Then, the relationship between Cb cloud properties and precipitation was analyzed and applied in a look-up table algorithm to estimate precipitation. The results were as follows: (1) Precipitation increases first and then declines with increasing values for cold cloud and time evolution parameters, and heavy precipitation may occur not only near the convective center, but also on the front of the Cb clouds on the pixel scale. (2) As for the cloud patch scale, precipitation is typically associated with cold cloud and rough cloud surfaces, whereas the coldest and roughest cloud surfaces do not correspond to the strongest rain. Moreover, rainfall has no obvious relationship with the cloud motion features and varies significantly over different life stages. The involvement of mergers and splits of minor Cb patches is crucial for precipitation processes. (3) The correlation coefficients of the estimated rain rate and gauge rain can reach 0.62 in the cross-validation period and 0.51 in the testing period, which indicates the feasibility of the further application of the relationship in precipitation estimation. Full article
(This article belongs to the Special Issue Cloud and Precipitation)
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<p>The relationship between cloud pixel characteristics and precipitation in the Huaihe River Basin (HRB). (<b>a</b>) Precipitation characteristic of top brightness temperature (TB) for infrared channel 1 (IR<sub>1</sub>) (10.3–11.3 μm); (<b>b</b>) and (<b>c</b>) are the same as (<b>a</b>) but for channel IR<sub>2</sub> (11.5–12.5 μm) and channel WV (6.3–7.6 μm); (<b>d</b>)–(<b>f</b>): the same as (<b>a</b>)–(<b>c</b>) but for the gradient of pixel TB (GT) for IR<sub>1</sub>, IR<sub>2</sub> and WV channels (GT<sub>1</sub>, GT<sub>2</sub>, GT<sub>3</sub>), respectively; (<b>g</b>)–(<b>i</b>): the same as (<b>a</b>)–(<b>c</b>) but for the difference of pixel TB (DT) for three IR channels (DT<sub>21</sub>, DT<sub>31</sub>, DT<sub>32</sub>), respectively. DT<sub>21</sub> is the difference of TB of IR<sub>2</sub> and IR<sub>1</sub>. DT<sub>31</sub> is the difference of WV and IR<sub>1,</sub> and DT<sub>32</sub> is the difference of WV and IR<sub>2</sub>; (<b>j</b>)–(<b>l</b>): the same as (<b>a</b>)–(<b>c</b>) but for the changing ratio of pixel TB (CT) for three IR channels (CT<sub>1</sub>, CT<sub>2</sub>, CT<sub>3</sub>), respectively; (<b>m</b>)–(<b>n</b>): the same as (<b>a</b>)–(<b>c</b>) but for deviation to the convective cloud center (DCC). DCC<sub>1</sub> is the deviation to the geometric center, and DCC<sub>2</sub> is the deviation to the gravity center.</p> Full article ">Figure 2
<p>The relationship between the cloud patch characteristics and precipitation in the HRB. (<b>a</b>) Distribution of rain variables and cloud patch area (Area); (<b>b</b>)–(<b>l</b>) are the same as (<b>a</b>) but for perimeter (PERI), shape index of the geometric inertia momentum (SIGM), shape index of the perimeter (SIP), eccentricity (ECCT), mean TB of cloud patch (T<sub>mean</sub>P), minimum TB of cloud patch (T<sub>min</sub>P), difference of Cb patch TB for the split window (DSWT), difference of Cb patch TB for IR and WV channel (DIWT), standard deviations of the cloud patch (STD), TB gradient of cloud patch(TGOP), and boundary steepness (BS), respectively.</p> Full article ">Figure 3
<p>Scatter plot of the estimated rain rate <span class="html-italic">vs.</span> the observed rain rate of the gauge. (<b>a</b>) Cross-validation results with data from June to October 2007; (<b>b</b>) testing results with data of May 2007 and July 2008.</p> Full article ">
304 KiB  
Article
Sensitivity of a Remote Alpine System to the Stockholm and LRTAP Regulations in POP Emissions
by Barend L. Van Drooge, Guillem Garriga, Karin Koinig, Roland Psenner, Paul Pechan and Joan O. Grimalt
Atmosphere 2014, 5(2), 198-210; https://doi.org/10.3390/atmos5020198 - 10 Apr 2014
Cited by 8 | Viewed by 6475
Abstract
Persistent Organic Pollutants (POPs) have been restricted and prohibited at national level for several decades now and since the 21st century at international level under the Stockholm Convention and the Convention of Long-Range Transboundary Air Pollution (LRTAP). A high mountain lake sediment core [...] Read more.
Persistent Organic Pollutants (POPs) have been restricted and prohibited at national level for several decades now and since the 21st century at international level under the Stockholm Convention and the Convention of Long-Range Transboundary Air Pollution (LRTAP). A high mountain lake sediment core was sampled in the Alps (Gossenköllesee) in summer 2010 and analyzed on POPs to examine whether the expected decreasing trends due to the implementation of the international Conventions could be observed. Higher POPs concentrations were observed in the sections corresponding to the period of large scale production and usage. p,p’-DDE and p,p’-DDD showed maximum concentrations in the core sections corresponding to the 1970s. These concentrations decreased to more or less constant levels in the top sediments, which is in agreement with the timing of past usage and banning of this pesticide. On the other hand, PCBs and HCB peaked in 1980s and the concentrations fluctuated afterwards. These observed profiles suggest that the studied site is still under influence of primary or secondary emissions and that the regulations of the international Conventions have still not been noticed in this site. Full article
(This article belongs to the Special Issue Persistent Organic Pollutants (POPs) in the Atmosphere: From Nowadays Background Sites to Hot Spots)
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<p>Concentrations (ng/g dw) of (<b>a</b>) hexachlorobenzene (HCB) (triangles); ∑polychlorobiphenyls (PCBs) (sum of #52, #101, #118, #153, #138, #180; black squares) and ∑DDTs (<span class="html-italic">p,p’</span>-DDE+<span class="html-italic">p,p’</span>-DDD; grey dots) in the sediment samples (#1 = top sediment).</p> Full article ">Figure 2
<p>Contribution (%) of <span class="html-italic">p,p’</span>-DDD to the ∑DDTs (<span class="html-italic">p,p’</span>-DDE + <span class="html-italic">p,p’</span>-DDD) in the sediment samples (#1 = top sediment). The linear regression was applied in the first 16 samples that had levels above limit of quantification (LOQ).</p> Full article ">Figure 3
<p>Decrease of <span class="html-italic">p,p’</span>-DDE concentrations (ng/g dw) as a relation to “time”. The decrease rate, λ = −0.034.</p> Full article ">Figure 4
<p>Relative average composition of PCB congeners to ∑PCBs in the sample#1 to #13.</p> Full article ">Figure 5
<p>Relative contributions OC%, HCB, ∑PCBs, and ∑DDT (OC% and POP concentrations divided by maximum OC% or concentrations in the sediment samples).</p> Full article ">
1592 KiB  
Article
A Multi-Year Aerosol Characterization for the Greater Tehran Area Using Satellite, Surface, and Modeling Data
by Ewan Crosbie, Armin Sorooshian, Negar Abolhassani Monfared, Taylor Shingler and Omid Esmaili
Atmosphere 2014, 5(2), 178-197; https://doi.org/10.3390/atmos5020178 - 4 Apr 2014
Cited by 84 | Viewed by 9454
Abstract
This study reports a multi-year (2000–2009) aerosol characterization for metropolitan Tehran and surrounding areas using multiple datasets (Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Total Ozone Mapping Spectrometer (TOMS), Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART), and surface and upper [...] Read more.
This study reports a multi-year (2000–2009) aerosol characterization for metropolitan Tehran and surrounding areas using multiple datasets (Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Total Ozone Mapping Spectrometer (TOMS), Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART), and surface and upper air data from local stations). Monthly trends in aerosol characteristics are examined in the context of the local meteorology, regional and local emission sources, and air mass back-trajectory data. Dust strongly affects the region during the late spring and summer months (May–August) when aerosol optical depth (AOD) is at its peak and precipitation accumulation is at a minimum. In addition, the peak AOD that occurs in July is further enhanced by a substantial number of seasonal wildfires in upwind regions. Conversely, AOD is at a minimum during winter; however, reduced mixing heights and a stagnant lower atmosphere trap local aerosol emissions near the surface and lead to significant reductions in visibility within Tehran. The unique meteorology and topographic setting makes wintertime visibility and surface aerosol concentrations particularly sensitive to local anthropogenic sources and is evident in the noteworthy improvement in visibility observed on weekends. Scavenging of aerosol due to precipitation is evident during the winter when a consistent increase in surface visibility and concurrent decrease in AOD is observed in the days after rain compared with the days immediately before rain. Full article
(This article belongs to the Special Issue Air Quality and Climate)
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Figure 1

Figure 1
<p>Geographic locations of the ground-based meteorological monitoring stations.</p> Full article ">Figure 2
<p>Monthly summary of surface meteorological data at three sites near Tehran (see <a href="#atmosphere-05-00178-f001" class="html-fig">Figure 1</a> for locations) between 2000 and 2009 for (<b>a</b>) dry bulb temperature (T), (<b>b</b>) relative humidity (RH), (<b>c</b>) wind speed, (<b>d</b>) accumulated precipitation, and (<b>e</b>) visibility. Monthly summary of upper air data for the same sites: (<b>f</b>) mixed layer height derived from Mehrabad radiosonde data (1980–2012; 00Z and 12Z soundings shown as triangle markers) and MERRA reanalysis data (2000–2009; square markers represent daily mean and whiskers represent average daily range) at grid points near Tehran (35.50°N, 51.33°E), Semnan (35.50°N, 53.33°E) and Gharakhil (36.50°N, 52.67°E); (<b>g</b>) Same as (f) except for average total column water vapor (CWV).</p> Full article ">Figure 3
<p>Monthly pattern in air mass source region as determined by analysis of daily Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) data between 2000 and 2009. Back-trajectories are classified by time spent in each region. The region totals are shown, by month, for end points at 500 m, 1000 m and 3000 m above ground level (AGL) (<b>Right</b>).</p> Full article ">Figure 4
<p>Decadal (2000–2009) summary of seasonal HYSPLIT five-day back-trajectory frequencies, ending at 500m above Tehran (35.70°N, 51.42°E) for (<b>a</b>) winter (DJF), (<b>b</b>) spring (MAM), (<b>c</b>) summer (JJA), and (<b>d</b>) fall (SON). Frequency is defined as the number of trajectory-hours spent in each 0.5° × 0.5° grid box divided by the total number of trajectories analyzed. Source regions, as illustrated in <a href="#atmosphere-05-00178-f003" class="html-fig">Figure 3</a>, are overlaid.</p> Full article ">Figure 5
<p>Seasonal patterns in Fire Radiative Power (FRP) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Fire Information for Resource Management System (FIRMS) data from 2000 to 2009. FRP is shown as integrated seasonal average power (megawatts per 0.5° × 0.5° grid box) for (<b>a</b>) winter (DJF), (<b>b</b>) spring (MAM), (<b>c</b>) summer (JJA), and (<b>d</b>) fall (SON).</p> Full article ">Figure 6
<p>Monthly summary of remotely-sensed and model data for the greater Tehran area (see <a href="#atmosphere-05-00178-t001" class="html-table">Table 1</a>) for different satellite products (a and b): (<b>a</b>) aerosol optical depth (AOD) from MODIS Deep Blue (Terra and Aqua) and Multi-angle Imaging Spectroradiometer (MISR); (<b>b</b>) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) ultraviolet aerosol index; (<b>c</b>) monthly summary of fractional AOD from Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART).</p> Full article ">Figure 7
<p>Change in (<b>a</b>) visibility and (<b>b</b>) satellite-derived AOD immediately before and after rain days. The plots show the composite average difference between the mean visibility/AOD during the two days after rain and the two days before rain. The composite is taken for rain events, which exceed the given threshold daily rainfall rate and is presented as a percentage with respect to the mean visibility/AOD before the rain.</p> Full article ">Figure 8
<p>Air mass source origin for all days, high AOD days (&gt;90th percentile) and low AOD days (&lt;10th percentile) for (<b>a</b>) spring upper air trajectories (ending altitude of 3000 m AGL) and (<b>b</b>) winter low level (500 m AGL) trajectories. High and low AOD days were identified using the consolidated MODIS Deep Blue (Terra and Aqua) and MISR AOD data.</p> Full article ">Figure 9
<p>Average visibility anomaly in Tehran (Mehrabad) filtered by day-of-week for each season of the year. The visibility anomaly is calculated as the average deviation for each day-of-week from the climatological mean for each season. Note that the weekend in the study region is Friday, although some industries also observe Thursday as a reduced working day.</p> Full article ">
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Article
Air-Sea Exchange of Legacy POPs in the North Sea Based on Results of Fate and Transport, and Shelf-Sea Hydrodynamic Ocean Models
by Kieran O'Driscoll
Atmosphere 2014, 5(2), 156-177; https://doi.org/10.3390/atmos5020156 - 4 Apr 2014
Cited by 4 | Viewed by 7072
Abstract
The air-sea exchange of two legacy persistent organic pollutants (POPs), γ-HCH and PCB 153, in the North Sea, is presented and discussed using results of regional fate and transport and shelf-sea hydrodynamic ocean models for the period 1996–2005. Air-sea exchange occurs through gas [...] Read more.
The air-sea exchange of two legacy persistent organic pollutants (POPs), γ-HCH and PCB 153, in the North Sea, is presented and discussed using results of regional fate and transport and shelf-sea hydrodynamic ocean models for the period 1996–2005. Air-sea exchange occurs through gas exchange (deposition and volatilization), wet deposition and dry deposition. Atmospheric concentrations are interpolated into the model domain from results of the EMEP MSC-East multi-compartmental model (Gusev et al, 2009). The North Sea is net depositional for γ-HCH, and is dominated by gas deposition with notable seasonal variability and a downward trend over the 10 year period. Volatilization rates of γ-HCH are generally a factor of 2–3 less than gas deposition in winter, spring and summer but greater in autumn when the North Sea is net volatilizational. A downward trend in fugacity ratios is found, since gas deposition is decreasing faster than volatilization. The North Sea is net volatilizational for PCB 153, with highest rates of volatilization to deposition found in the areas surrounding polluted British and continental river sources. Large quantities of PCB 153 entering through rivers lead to very high local rates of volatilization. Full article
(This article belongs to the Special Issue Persistent Organic Pollutants (POPs) in the Atmosphere: From Nowadays Background Sites to Hot Spots)
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Figure 1

Figure 1
<p>Model domain (both models) with topography (meters) and river sources of freshwater and persistent organic pollutants (POPs) (magenta dots) with names of major rivers. International Council for the Sea (ICES) boxes are superimposed in black.</p> Full article ">Figure 2
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for γ-HCH in 1996. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 3
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for γ-HCH in 2000. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 4
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for γ-HCH in 2004. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 5
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of γ-HCH in 1996: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 6
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of γ-HCH in 2000: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 7
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of γ-HCH in 2004: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 8
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for PCB 153 in 1996. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 9
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for PCB 153 in 2000. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 10
<p>Distributions of quarterly (3 month) averaged rates of gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>) for PCB 153 in 2004. Rates are in (ng∙m<sup>−2</sup>∙s<sup>−1</sup>). Note: colorbar scales shown on 3rd column panels are used in connection with the left 3 panels. Note the change in scales for autumn (right figures), which are reduced relative to winter, spring and summer. Scales vary from row to row.</p> Full article ">Figure 11
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of PCB 153 in 1996: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 12
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of PCB 153 in 2000: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 13
<p>Bar charts of integrated quarterly (3 month) fluxes (kg) by ICES boxes (<a href="#atmosphere-05-00156-f001" class="html-fig">Figure 1</a>) of PCB 153 in 2004: gas deposition (<b>top row</b>), dry deposition (<b>2nd row</b>), wet deposition (<b>3rd row</b>), volatilization (<b>4th row</b>), and net air-sea exchange (<b>bottom row</b>).</p> Full article ">Figure 14
<p>Distributions of quarterly (3 month) averaged fugacity ratios for γ-HCH in 1996, 2000 and 2004, calculated as minus the ratio of gas deposition to volatilization.</p> Full article ">Figure 15
<p>Bar charts of quarterly (3 month) POP river input into ICES boxes (kg) of γ-HCH (left) and PCB 153 (right) for 1996, 2000 and 2004. All rivers entering any particular box are included.</p> Full article ">Figure 16
<p>Distributions of quarterly (3 month) averaged fugacity ratios for PCB 153 in 1996, 2000 and 2004, calculated as the ratio of volatilization to gas deposition.</p> Full article ">
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