[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 7, December
Previous Issue
Volume 7, October
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 

Atmosphere, Volume 7, Issue 11 (November 2016) – 13 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
4934 KiB  
Article
Satellite-Observed Transport of Dust to the East China Sea and the North Pacific Subtropical Gyre: Contribution of Dust to the Increase in Chlorophyll during Spring 2010
by Saichun Tan, Jiawei Li, Huiwang Gao, Hong Wang, Huizheng Che and Bin Chen
Atmosphere 2016, 7(11), 152; https://doi.org/10.3390/atmos7110152 - 23 Nov 2016
Cited by 21 | Viewed by 6309
Abstract
Multiple data sets were combined to investigate five dust storm events over East Asia in spring 2010 and their impacts on chlorophyll in the East China Sea (ECS) and the North Pacific Subtropical Gyre (NPSG). Satellite-observed column aerosol images were able to show [...] Read more.
Multiple data sets were combined to investigate five dust storm events over East Asia in spring 2010 and their impacts on chlorophyll in the East China Sea (ECS) and the North Pacific Subtropical Gyre (NPSG). Satellite-observed column aerosol images were able to show the spatial distribution of the transport of dust from the source regions to the two seas for some of the dust storm events. The CALIPSO satellite showed the vertical structure of dust aerosol for a greater number of dust storm events, including some weak events. This was confirmed by simulations of dust deposition and backward trajectories traced to dust source regions. The simulated dust deposition flux for five dust storms ranged from 13.0 to 145.6 mg·m−2·d−1 in the ECS and from 0.6 to 5.5 mg·m−2·d−1 in the NPSG, suggesting that the highest deposition was about one order of magnitude higher than the lowest. The estimated nutrients from dust showed that dust containing iron had the greatest effect on phytoplankton growth in both seas; the iron deposited by one dust storm event accounted for at least 5% of growth and satisfied the increase in demand required for chlorophyll a concentration. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>,<b>b</b>) Satellite-derived climatological annual chlorophyll <span class="html-italic">a</span> concentration from SeaWiFS (1997–2010) and MODIS (2002–2010) data, respectively. The contours labels are the annual mean surface concentrations of nitrate (<b>a</b>) and phosphate (<b>b</b>) from the World Ocean Atlas (2009). The rectangles labeled ECS (123°–129°E and 25°–32°N) and NPSG (160°E to 160°W and 13°–29°N) represent the two study regions.</p>
Full article ">Figure 2
<p>(<b>A</b>) CALIPSO orbit tracks. Each track shown in different colors is labeled a1, a2, etc., and the date of each track is also shown. (<b>B</b>) Altitude-orbit cross-section of aerosol types for each satellite track in part (<b>A</b>); the bright yellow color represents dust aerosols. (<b>C</b>) Vertical profiles of averaged extinction coefficient at 532 nm (km<sup>−1</sup>) for dust aerosols. The colors of the profiles correspond to those in part (<b>A</b>). The altitude in parts (<b>B</b>) and (<b>C</b>) is above ground level.</p>
Full article ">Figure 3
<p>(<b>a</b>,<b>b</b>) Daily aerosol index, aerosol optical depth (AOD) and coarse mode AOD over the East China Sea from 1 March to 31 August 2010; (<b>c</b>,<b>d</b>) daily aerosol index, AOD and coarse mode AOD over the North Pacific Subtropical Gyre; and (<b>e</b>,<b>f</b>) spatial distribution of aerosol index and AOD averaged during dust days. Black arrows represent mean NCEP/NCAR wind velocities during the period. Labels A and B in (<b>e</b>,<b>f</b>) show the Taklimakan Desert and the Gobi Desert, respectively. Label H shows high pressure center. The small rectangle shows the East China Sea (123°–129°E and 25°–32°N) and the big one shows the North Pacific Subtropical Gyre (160°E to 160°W and 13°–29°N).</p>
Full article ">Figure 4
<p>Backward trajectories from the altitudes showing the locations of the dust aerosol observed by the CALIOP instrument; the range of altitudes is also shown. The colors of the tracks are the same as in <a href="#atmosphere-07-00152-f002" class="html-fig">Figure 2</a>A. ECS: East China Sea; NPSG: North Pacific Subtropical Gyre.</p>
Full article ">Figure 5
<p>(<b>a</b>) Daily chlorophyll <span class="html-italic">a</span> concentration ((Chl), mg·m<sup>−3</sup>) and area of (Chl) higher than the spring climatological mean in the East China Sea. The three peaks of (Chl) are shown as peaks (1), (2), and (3). (<b>b</b>) Daily (Chl) and area of (Chl) higher than the background value in the North Pacific Subtropical Gyre. Green triangles show the start and end date of the bloom. The eight-day averaged sea surface temperature (SST) and photosynthetic available radiation (PAR) are also shown.</p>
Full article ">Figure 6
<p>Daily or several-day averaged chlorophyll <span class="html-italic">a</span> concentrations (mg·m<sup>−3</sup>) in the East China Sea before, during and after the spring 2010 phytoplankton bloom.</p>
Full article ">Figure 7
<p>Daily or several-day averaged chlorophyll <span class="html-italic">a</span> concentration (mg·m<sup>−3</sup>) in the North Pacific Subtropical Gyre before, during and after summer 2010 phytoplankton bloom.</p>
Full article ">Figure 8
<p>Simulated total dust deposition (mg·m<sup>−2</sup>) in the East China Sea for 20–23 March 2010 (<b>a</b>); and the North Pacific Subtropical Gyre for 13–18 April 2010 (<b>b</b>).</p>
Full article ">
1521 KiB  
Article
On the Momentum Transported by the Radiation Field of a Long Transient Dipole and Time Energy Uncertainty Principle
by Vernon Cooray and Gerald Cooray
Atmosphere 2016, 7(11), 151; https://doi.org/10.3390/atmos7110151 - 23 Nov 2016
Cited by 5 | Viewed by 5247
Abstract
The paper describes the net momentum transported by the transient electromagnetic radiation field of a long transient dipole in free space. In the dipole a current is initiated at one end and propagates towards the other end where it is absorbed. The results [...] Read more.
The paper describes the net momentum transported by the transient electromagnetic radiation field of a long transient dipole in free space. In the dipole a current is initiated at one end and propagates towards the other end where it is absorbed. The results show that the net momentum transported by the radiation is directed along the axis of the dipole where the currents are propagating. In general, the net momentum P transported by the electromagnetic radiation of the dipole is less than the quantity U / c , where U is the total energy radiated by the dipole and c is the speed of light in free space. In the case of a Hertzian dipole, the net momentum transported by the radiation field is zero because of the spatial symmetry of the radiation field. As the effective wavelength of the current decreases with respect to the length of the dipole (or the duration of the current decreases with respect to the travel time of the current along the dipole), the net momentum transported by the radiation field becomes closer and closer to U / c , and for effective wavelengths which are much shorter than the length of the dipole, P U / c . The results show that when the condition P U / c is satisfied, the radiated fields satisfy the condition Δ t Δ U h / 4 π where Δ t is the duration of the radiation, Δ U is the uncertainty in the dissipated energy and h is the Plank constant. Full article
Show Figures

Figure 1

Figure 1
<p>Geometry relevant to the derivation of equations presented in this paper. (<b>a</b>) Hertzian dipole; (<b>b</b>) long dipole. Observe that in defining the unit vectors <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">r</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">θ</mi> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">φ</mi> </msub> </mrow> </semantics> </math> we are using a spherical coordinate system with the centre of the dipole located at the origin of coordinate system. In this coordinate system <math display="inline"> <semantics> <mi>r</mi> </semantics> </math> is the radial distance, <math display="inline"> <semantics> <mi>θ</mi> </semantics> </math> is the polar angle, and <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> is the azimuthal angle. <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">r</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">θ</mi> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mi mathvariant="bold-italic">φ</mi> </msub> </mrow> </semantics> </math> are unit vectors in the direction of increasing radial distance, polar angle and azimuthal angle, respectively.</p>
Full article ">Figure 2
<p>The normalized Gaussian current pulse with standard deviation <math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> = 10 ns.</p>
Full article ">Figure 3
<p>Normalized radiation field generated along the direction <math display="inline"> <semantics> <mrow> <mi>θ</mi> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>20</mn> <mo>°</mo> </mrow> </semantics> </math> at a distant point by a long dipole. The dipole is excited by a Gaussian current pulse. In the calculation, <math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> = 10 ns and <math display="inline"> <semantics> <mrow> <mi>τ</mi> <mo>/</mo> <mo stretchy="false">(</mo> <mi>L</mi> <mo>/</mo> <mi>c</mi> <mo stretchy="false">)</mo> <mo> </mo> <mo>=</mo> <mo> </mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p>
Full article ">Figure 4
<p>The variation of the ratio of <span class="html-italic">z</span>-momentum to <math display="inline"> <semantics> <mrow> <mi>U</mi> <mo>/</mo> <mi>c</mi> </mrow> </semantics> </math> as a function of <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mi>β</mi> </mrow> </semantics> </math>. The solid line is calculated using Equation (24) and the dashed line by using Equation (29).</p>
Full article ">
1427 KiB  
Article
Patterns of Dekadal Rainfall Variation Over a Selected Region in Lake Victoria Basin, Uganda
by Isaac Mugume, Michel D. S. Mesquita, Charles Basalirwa, Yazidhi Bamutaze, Joachim Reuder, Alex Nimusiima, Daniel Waiswa, Godfrey Mujuni, Sulin Tao and Triphonia Jacob Ngailo
Atmosphere 2016, 7(11), 150; https://doi.org/10.3390/atmos7110150 - 22 Nov 2016
Cited by 16 | Viewed by 9372
Abstract
Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March–May season using the Mann–Kendall ( [...] Read more.
Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March–May season using the Mann–Kendall ( M K ) trend test and simple linear regression ( S L R ) over the period 2000–2015. Results showed an increasing trend of both dekadal rainfall amount and rain days (third and seventh dekads). The light rain days ( S L R = 0.181; M K = 0.350) and wet days ( S L R = 0.092; M K = 0.118) also depict an increasing trend. The rate of increase of light rain days and wet days during the third dekad (light rain days: S L R = 0.020; M K = 0.279 and wet days: S L R = 0.146; M K = 0.376) was slightly greater than during the seventh dekad (light rain days: S L R = 0.014; M K = 0.018 and wet days: S L R = 0.061; M K = 0.315) dekad. Seventy-four percent accounted for 2–4 consecutive dry days, but no significant trend was detected. The extreme rainfall was increasing over the third ( M K = 0.363) and seventh ( M K = 0.429) dekads. The rainfall amount and rain days were highly correlated (r: 0.43–0.72). Full article
(This article belongs to the Special Issue Global Precipitation with Climate Change)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The map of Uganda and the study region; (<b>b</b>) average Lake Victoria Basin (LVB) MAM seasonal rainfall; and (<b>c</b>) the average LVB SON seasonal rainfall.</p>
Full article ">Figure 2
<p>Double-mass curves for the study areas in reference to Entebbe station.</p>
Full article ">Figure 3
<p>The figure is for MAM light rain days and MAM wet days for each of the study locations with (<b>i</b>) indicating the average over the study region. “<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>L</mi> <mi>R</mi> </mrow> </semantics> </math>_lrd” is the regression rate for light rain days; “<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>L</mi> <mi>R</mi> </mrow> </semantics> </math>_wd” is the regression rate for wet days; “<math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>K</mi> </mrow> </semantics> </math>_lrd” is the Mann–Kendall trend score for light rain days; “<math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>K</mi> </mrow> </semantics> </math>_wd” is the Mann–Kendall trend score for wet days; “p_lrd” is the significance level for light rain days; “p_wd” is the significance level for wet days; “lrd” means light rain days; and “wd” is wet days. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) average.</p>
Full article ">Figure 3 Cont.
<p>The figure is for MAM light rain days and MAM wet days for each of the study locations with (<b>i</b>) indicating the average over the study region. “<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>L</mi> <mi>R</mi> </mrow> </semantics> </math>_lrd” is the regression rate for light rain days; “<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>L</mi> <mi>R</mi> </mrow> </semantics> </math>_wd” is the regression rate for wet days; “<math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>K</mi> </mrow> </semantics> </math>_lrd” is the Mann–Kendall trend score for light rain days; “<math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>K</mi> </mrow> </semantics> </math>_wd” is the Mann–Kendall trend score for wet days; “p_lrd” is the significance level for light rain days; “p_wd” is the significance level for wet days; “lrd” means light rain days; and “wd” is wet days. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) average.</p>
Full article ">Figure 4
<p>The figure represents the analysis of the light rain days and wet days over the third dekad. The vertical axis is the number of rain days, and the horizontal axis is the period. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) Average over the entire region representing the stations.</p>
Full article ">Figure 4 Cont.
<p>The figure represents the analysis of the light rain days and wet days over the third dekad. The vertical axis is the number of rain days, and the horizontal axis is the period. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) Average over the entire region representing the stations.</p>
Full article ">Figure 5
<p>The figure represents the analysis of the light rain days and wet days over the seventh dekad. The vertical axis is the number of rain days, and the horizontal axis is the period. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) Average over the entire region representing the stations.</p>
Full article ">Figure 5 Cont.
<p>The figure represents the analysis of the light rain days and wet days over the seventh dekad. The vertical axis is the number of rain days, and the horizontal axis is the period. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) Average over the entire region representing the stations.</p>
Full article ">Figure 6
<p>The figure is for accumulated dekadal rainfall averaged over the study locations. The vertical axis represents average dekadal rainfall for a given dekad, and we have used same scale for the ease of comparison. The rate of decrease/increase of the average dekadal rainfall; the Mann–Kendall trend along with its significant level is presented along with each subfigure (<b>a</b>–<b>i</b>). “dk” means dekad. (<b>a</b>) First dekad rainfall; (<b>b</b>) second dkrainfall; (<b>c</b>) third dk rainfall; (<b>d</b>) fourth dk rainfall; (<b>e</b>) fifth dk rainfall; (<b>f</b>) sixth dk rainfall; (<b>g</b>) seventh dk rainfall; (<b>h</b>) eighth dk rainfall; (<b>i</b>) ninth dk rainfall.</p>
Full article ">Figure 6 Cont.
<p>The figure is for accumulated dekadal rainfall averaged over the study locations. The vertical axis represents average dekadal rainfall for a given dekad, and we have used same scale for the ease of comparison. The rate of decrease/increase of the average dekadal rainfall; the Mann–Kendall trend along with its significant level is presented along with each subfigure (<b>a</b>–<b>i</b>). “dk” means dekad. (<b>a</b>) First dekad rainfall; (<b>b</b>) second dkrainfall; (<b>c</b>) third dk rainfall; (<b>d</b>) fourth dk rainfall; (<b>e</b>) fifth dk rainfall; (<b>f</b>) sixth dk rainfall; (<b>g</b>) seventh dk rainfall; (<b>h</b>) eighth dk rainfall; (<b>i</b>) ninth dk rainfall.</p>
Full article ">Figure 7
<p>The figure is for consecutive dry days. For each station, an average of consecutive dry days (days with no rainfall) is computed to give the trend for different stations. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) An average over the number of consecutive dry days over the study area.</p>
Full article ">Figure 7 Cont.
<p>The figure is for consecutive dry days. For each station, an average of consecutive dry days (days with no rainfall) is computed to give the trend for different stations. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) An average over the number of consecutive dry days over the study area.</p>
Full article ">Figure 7 Cont.
<p>The figure is for consecutive dry days. For each station, an average of consecutive dry days (days with no rainfall) is computed to give the trend for different stations. (<b>a</b>) Entebbe; (<b>b</b>) Jinja; (<b>c</b>) Kamenyamigo; (<b>d</b>) Kituza; (<b>e</b>) Makerere; (<b>f</b>) Namulonge; (<b>g</b>) Ntusi; (<b>h</b>) Tororo; (<b>i</b>) An average over the number of consecutive dry days over the study area.</p>
Full article ">Figure 8
<p>Figure representing the percentage of dry consecutive days over the period 2000–2015.</p>
Full article ">
4565 KiB  
Article
Impact of Stratospheric Volcanic Aerosols on Age-of-Air and Transport of Long-Lived Species
by Giovanni Pitari, Irene Cionni, Glauco Di Genova, Daniele Visioni, Ilaria Gandolfi and Eva Mancini
Atmosphere 2016, 7(11), 149; https://doi.org/10.3390/atmos7110149 - 22 Nov 2016
Cited by 21 | Viewed by 5912
Abstract
The radiative perturbation associated to stratospheric aerosols from major explosive volcanic eruptions may induce significant changes in stratospheric dynamics. The aerosol heating rates warm up the lower stratosphere and cause a westerly wind anomaly, with additional tropical upwelling. Large scale transport of stratospheric [...] Read more.
The radiative perturbation associated to stratospheric aerosols from major explosive volcanic eruptions may induce significant changes in stratospheric dynamics. The aerosol heating rates warm up the lower stratosphere and cause a westerly wind anomaly, with additional tropical upwelling. Large scale transport of stratospheric trace species may be perturbed as a consequence of this intensified Brewer–Dobson circulation. The radiatively forced changes of the stratospheric circulation during the first two years after the eruption of Mt. Pinatubo (June 1991) may help explain the observed trend decline of long-lived greenhouse gases at surface stations (approximately −8 and −0.4 ppbv/year for CH4 and N2O, respectively). This decline is partly driven by the increased mid- to high-latitude downward flux at the tropopause and also by an increased isolation of the tropical pipe in the vertical layer near the tropopause, with reduced horizontal eddy mixing. Results from a climate-chemistry coupled model are shown for both long-lived trace species and the stratospheric age-of-air. The latter results to be younger by approximately 0.5 year at 30 hPa for 3–4 years after the June 1991 Pinatubo eruption, as a result of the volcanic aerosols radiative perturbation and is consistent with independent estimates based on long time series of in situ profile measurements of SF6 and CO2. Younger age of air is also calculated after Agung, El Chichón and Ruiz eruptions, as well as negative anomalies of the N2O growth rate at the extratropical tropopause layer. This type of analysis is made comparing the results of two ensembles of model simulations (1960–2005), one including stratospheric volcanic aerosols and their radiative interactions and a reference case where the volcanic aerosols do not interact with solar and planetary radiation. Full article
(This article belongs to the Special Issue Atmospheric Aerosols and Their Radiative Effects)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Time series of annually averaged values of residual vertical velocity anomalies (VE-REF) in the tropics (mm/s) at 30 hPa (~24.5 km altitude) (1960–2005); (<b>b</b>) as in (<b>a</b>), but for monthly averages of w* (1988–1994), to better highlight the Pinatubo perturbation; and (<b>c</b>) time series of annually and globally averaged temperature changes (K) at 50 hPa (~21 km altitude) (1960–2005). The solid curve is for the model calculated VE-REF anomalies; the dashed curve with diamonds is for the ERA-40 and radiosondes detrended anomalies, i.e., the difference between the actual annually averaged global temperature and the corresponding value obtained from a linear fit of the temperatures between 1960 and 2005 (ERA-interim is used for years 2001–2005). The 1960–2005 temperature trends are: −0.38 K/decade and −0.45 K/decade for model and ERA-40 and radiosondes time series, respectively. The red triangles indicate the timing of the four tropical eruptions considered in this study (see <a href="#atmosphere-07-00149-t001" class="html-table">Table 1</a>); that is, Agung, El Chichón, Ruiz and Pinatubo.</p>
Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) Residual vertical velocity profiles (VE, REF) in: the tropics (<b>a</b>); and mid-latitudes (<b>b</b>), averaged over June 1991–June 1992 (mm/s); and (<b>c</b>) residual vertical velocity percent changes (VE-REF) averaged in the 50–30 hPa (~21–24.5 km altitude) layer as a function of latitude (June 1991–June 1992).</p>
Full article ">Figure 3
<p>Evaluation of the ULAQ model residual vertical velocity (w*, mm/s) in the tropical stratosphere (20° S–20° N) (VE simulation), using MERRA reanalysis data [<a href="#B49-atmosphere-07-00149" class="html-bibr">49</a>]: (<b>a</b>) Time series of annually averaged 30 hPa w* values (ULAQ in red and MERRA with black dots) (1979–2005). Dashed lines show a linear fit of the data: the slope is 0.009 mm·s<sup>−1</sup>·decade<sup>−1</sup> for ULAQ and 0.013 mm·s<sup>−1</sup>·decade<sup>−1</sup> for MERRA (dashed black line); (<b>b</b>) As in (<b>a</b>), but for 50 hPa monthly averaged w* values (1990–1999); (<b>c</b>) The 30 hPa mean annual cycle of w*, averaged over 1979–2005; (<b>d</b>) The w* vertical profile (1979–2005). The dashed lines show a ±30% range of variability of the vertical velocity [<a href="#B17-atmosphere-07-00149" class="html-bibr">17</a>], related to seasonal and interannual oscillations.</p>
Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) Time series of the equatorial age of air (10°S–10°N) (years) at: 10 hPa (~32 km altitude) (<b>a</b>); and 30 hPa (~24.5 km altitude) (<b>b</b>), for VE and REF experiments, in red and black, respectively; (<b>c</b>,<b>d</b>) As in (<b>a</b>,<b>b</b>), but for the latitudinal age gradient, calculated as the mean age difference between 45° N and the equator. Dashed lines show a linear fit of the calculated age or age gradient. The linear fit is calculated between 1960 and 2005, except in (<b>a</b>), where two different slopes of mid stratospheric age air are highlighted with a linear fit between 1960 and 1986 and a second one between 1986 and 2005.</p>
Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Vertical profiles of mean stratospheric age of air (years) at: the equator (10°S–10°N) (<b>a</b>); and northern mid-latitudes (35°N–55°N) (<b>b</b>), for VE and REF experiments, in red and black, respectively (1991–1992 average, i.e., Pinatubo case); (<b>c</b>): As in (<b>a</b>,<b>b</b>), but for the latitudinal age gradient (45° N–EQT); (<b>d</b>) Mean age of air (years) at 30 hPa as a function of latitude, for VE and REF experiments, in red and black, respectively.</p>
Full article ">Figure 6
<p>(<b>a</b>) Model calculated zonally and annually averaged age of air (years), for the VE case; contour line increment 0.5 years. (<b>b</b>–<b>d</b>) E valuation of the ULAQ-CCM age of air, using observations from Andrews et al. [<a href="#B53-atmosphere-07-00149" class="html-bibr">53</a>] and Engel et al. [<a href="#B30-atmosphere-07-00149" class="html-bibr">30</a>]: 50 hPa (~21 km altitude) and 10 hPa (~32 km altitude) latitudinal sections (<b>b</b>); and vertical profiles (<b>c</b>,<b>d</b>) for the equator and 35°N–50°N latitude, respectively. Dots show mean values, with 1σ variability in the yellow-filled areas [<a href="#B28-atmosphere-07-00149" class="html-bibr">28</a>]. Whiskers show the uncertainty in the measurement-derived mean age of air at 10 hPa; (<b>e</b>) As in panels (<b>c</b>,<b>d</b>), but for the latitudinal gradient (45°N-EQT) of age of air (AoA); (<b>f</b>,<b>g</b>) Bias of the: ULAQ-CCM AoA (<b>f</b>); and latitudinal AoA gradient (<b>g</b>), both with respect to the measurements mean. The yellow-filled areas show the bias interval, considering the range of observations in (<b>c</b>,<b>d</b>).</p>
Full article ">Figure 7
<p>Evaluation of ULAQ-CCM upwelling and horizontal mixing in the tropical lower stratosphere (10°S–10°N), using N<sub>2</sub>O and age of air observations: (<b>a</b>) Vertical profile of the ULAQ-CCM N<sub>2</sub>O mixing ratio bias (ppbv) with respect to Odin/SMR observations [<a href="#B55-atmosphere-07-00149" class="html-bibr">55</a>,<a href="#B56-atmosphere-07-00149" class="html-bibr">56</a>] (2001–2005 time average; VE case, solid black line). The yellow-filled area shows the bias interval, considering the range of variability of SMR measurements (i.e., ±1σ calculated over all months between 2001 and 2005). The red dashed line shows the ULAQ-CCM VE-REF anomaly for the Pinatubo case (June 1991–June 1992). (<b>b</b>) Scatter plot of AoA (years) versus the N<sub>2</sub>O mixing ratio (ppbv), for the ULAQ-CCM (VE case, red line with open circles) and the age observations median of <a href="#atmosphere-07-00149-f006" class="html-fig">Figure 6</a>c with N<sub>2</sub>O SMR observations (black line with asterisks). (<b>c</b>,<b>d</b>) The 50–70 hPa latitudinal section of the: N<sub>2</sub>O mixing ratio (ppbv) (<b>c</b>); and model-measurements bias (<b>d</b>). The uncertainty bars show the bias interval, considering the range of variability of SMR measurements (i.e., ±1σ, as above).</p>
Full article ">Figure 8
<p>(<b>a</b>) Latitude dependent CH<sub>4</sub> (green) and N<sub>2</sub>O (blue) horizontal eddy mass flux anomalies VE-REF from the ULAQ-CCM calculations (mass weighted average 50–150 hPa; time average July 1991–June 1992). Units are kg·m<sup>−2</sup>·year<sup>−1</sup>; (<b>b</b>,<b>c</b>) The latitude integrated flux anomalies: Southern Hemisphere (SH) from 90° S to 20° S; and Northern Hemisphere (NH) from 20°N to 90°N. The horizontal eddy mass flux anomalies ΔΦ<sub>H</sub> are defined as Δ[v’ρ’<sub>CH4</sub>] and Δ[v’ρ’<sub>N2O</sub>], where v is the meridional wind component, ρ<sub>CH4</sub> and ρ<sub>N2O</sub> are the mass concentrations of CH<sub>4</sub> and N<sub>2</sub>O, respectively, and Δ refers to the difference VE-REF. The square brackets [] denote a zonal average and the prime a deviation from the zonal average.</p>
Full article ">Figure 9
<p>As in <a href="#atmosphere-07-00149-f007" class="html-fig">Figure 7</a>, but for tropical CH<sub>4</sub> (10°S–10°N) and age of air observations: (<b>a</b>) Vertical profile of the ULAQ-CCM CH<sub>4</sub> mixing ratio bias (ppmv) with respect to HALOE observations [<a href="#B57-atmosphere-07-00149" class="html-bibr">57</a>] (1991–2005 time average; VE case, solid black line). The yellow-filled area shows the bias interval, considering the range of variability of HALOE measurements (i.e., ±1σ calculated over all months between 1991 and 2005). The red dashed line shows the ULAQ-CCM VE-REF anomaly for the Pinatubo case (June 1991–June 1992); (<b>b</b>) Scatter plot of AoA (years) versus the CH<sub>4</sub> mixing ratio (ppmv), for the ULAQ-CCM (VE case, red line with open circles) and the age observations median of <a href="#atmosphere-07-00149-f006" class="html-fig">Figure 6</a>c with CH<sub>4</sub> HALOE observations (black line with asterisks); (<b>c</b>,<b>d</b>) The 50–70 hPa latitudinal section of the: CH<sub>4</sub> mixing ratio (ppbv) (<b>c</b>); and model-measurements bias (<b>d</b>). The uncertainty bars show the bias interval, considering the range of variability of HALOE measurements (i.e., ±1σ, as above).</p>
Full article ">Figure 10
<p>Vertical profiles of VE-REF anomalies of: CH<sub>4</sub> (<b>a</b>,<b>b</b>); and N<sub>2</sub>O (<b>c</b>,<b>d</b>), averaged over July 1991–June 1992 (ppbv). (<b>a</b>,<b>c</b>) Tropical anomalies (average 25°S–25°N); and (<b>c</b>,<b>d</b>) extra-tropical anomalies (average 90°S–25°S and 25°N–90°N).</p>
Full article ">Figure 11
<p>(<b>a</b>) Time series of the model calculated tropical CH<sub>4</sub> mixing ratios (25°S–25°N) at 30 hPa (ppbv), for REF and VE cases (black and red lines, respectively); and (<b>b</b>) time series of the globally averaged surface stations observed CH<sub>4</sub> growth rate (blue line; ppbv/year) [<a href="#B63-atmosphere-07-00149" class="html-bibr">63</a>]; superimposed is the time series of the CH<sub>4</sub> growth rate at the extratropical tropopause (average 90°S–25°S and 25°N–90°N; 150–250 hPa), for REF and VE cases (black and red lines, respectively). (<b>c</b>,<b>d</b>) As in panels (<b>a</b>,<b>b</b>), but for N<sub>2</sub>O. Volume mixing ratios for both CH<sub>4</sub> and N<sub>2</sub>O are smoothed with respect to seasonal variations in order to derive long term trends. The long term trends are then time-differentiated to compute growth rates.</p>
Full article ">Figure 12
<p>(<b>a</b>) Volcanic aerosol induced perturbation on O<sub>2</sub> photolysis during October 1991 (percent) (20°S–20°N); and (<b>b</b>): O<sub>3</sub> concentration changes at the equator (VE, REF, OBS), during October–November 1991 with respect to April–May–June 1991. Observations are from ozone sounding at Brazzaville, Congo [<a href="#B64-atmosphere-07-00149" class="html-bibr">64</a>]. Calculated column ozone changes are −20 DU from the observed profiles and −23 DU in the aerosol radiative-interactive case VE, to be compared with −10 DU in the REF case without the aerosol radiative perturbations.</p>
Full article ">
3909 KiB  
Short Note
Short-Term Effects of Drying and Rewetting on CO2 and CH4 Emissions from High-Altitude Peatlands on the Tibetan Plateau
by Xiaoyang Zeng and Yongheng Gao
Atmosphere 2016, 7(11), 148; https://doi.org/10.3390/atmos7110148 - 20 Nov 2016
Cited by 15 | Viewed by 4364
Abstract
This study used mesocosms to examine the effects of alternate drying and rewetting on CO2 and CH4 emissions from high-altitude peatlands on the Tibetan Plateau. The drying and rewetting experiment conducted in this study included three phases: a 10-day predrying phase, [...] Read more.
This study used mesocosms to examine the effects of alternate drying and rewetting on CO2 and CH4 emissions from high-altitude peatlands on the Tibetan Plateau. The drying and rewetting experiment conducted in this study included three phases: a 10-day predrying phase, a 32-day drying phase, and an 18-day rewetting phase. During the experiment, the water table varied between 0 and 50 cm with respect to the reference peat column where the water table stayed constant at 0 cm. The study found that drying and rewetting had no significant effect on CO2 emissions from the peatland, while CH4 emissions decreased. The cumulative CH4 emissions in the control group was 2.1 times higher than in the drying and rewetting treatment over the study period. Moreover, CO2 and CH4 emissions were positively correlated with soil temperature, and the drying process increased the goodness of fit of the regression models predicting the relationships between CO2 and CH4 emissions and temperature. These results indicate that small-scale water table variation has a limited effect on CO2 emissions, but might reduce CH4 emissions in high-altitude peatlands on the Tibetan Plateau. Full article
Show Figures

Figure 1

Figure 1
<p>Water table, air temperature, and soil temperature at 10 cm depth. CW and DW represent constantly high water table and a drying and rewetting process, respectively.</p>
Full article ">Figure 2
<p>CO<sub>2</sub> and CH<sub>4</sub> emissions during drying and rewetting in high-altitude peat core. Vertical bars indicate standard errors of three replications.</p>
Full article ">Figure 3
<p>Cumulative CO<sub>2</sub> and CH<sub>4</sub> emissions during drying and rewetting in high-altitude peat core. Vertical bars indicate standard errors of three replications. Asterisks represent significant difference between the treatments at the 0.05 level.</p>
Full article ">Figure 4
<p>Relationships between CO<sub>2</sub> and CH<sub>4</sub> fluxes and water table, air temperature, and 10 cm soil temperature in high-altitude peat core.</p>
Full article ">
1826 KiB  
Article
A Methodology to Reduce the Computational Effort in the Evaluation of the Lightning Performance of Distribution Networks
by Ilaria Bendato, Massimo Brignone, Federico Delfino, Renato Procopio and Farhad Rachidi
Atmosphere 2016, 7(11), 147; https://doi.org/10.3390/atmos7110147 - 20 Nov 2016
Cited by 7 | Viewed by 4148
Abstract
The estimation of the lightning performance of a power distribution network is of great importance to design its protection system against lightning. An accurate evaluation of the number of lightning events that can create dangerous overvoltages requires a huge computational effort, as it [...] Read more.
The estimation of the lightning performance of a power distribution network is of great importance to design its protection system against lightning. An accurate evaluation of the number of lightning events that can create dangerous overvoltages requires a huge computational effort, as it implies the adoption of a Monte Carlo procedure. Such a procedure consists of generating many different random lightning events and calculating the corresponding overvoltages. The paper proposes a methodology to deal with the problem in two computationally efficient ways: (i) finding out the minimum number of Monte Carlo runs that lead to reliable results; and (ii) setting up a procedure that bypasses the lightning field-to-line coupling problem for each Monte Carlo run. The proposed approach is shown to provide results consistent with existing approaches while exhibiting superior Central Processing Unit (CPU) time performances. Full article
Show Figures

Figure 1

Figure 1
<p>Geometry of the Multiconductor Transmission Line (MTL) system.</p>
Full article ">Figure 2
<p>Distribution network topology for the three considered tests. Panels (<b>a</b>–<b>c</b>) show the networks for Test1, Test2, and Test3, respectively.</p>
Full article ">Figure 3
<p>Graphical evolution of <math display="inline"> <semantics> <mrow> <msub> <mi>E</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>a</b>) and (<b>b</b>)), <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>c</b>) and (<b>d</b>)), confidence strip and <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>p</mi> <mo>¯</mo> </mover> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>e</b>) and (<b>f</b>)) as a function of <span class="html-italic">N</span> for CFO = 150 kV.</p>
Full article ">Figure 3 Cont.
<p>Graphical evolution of <math display="inline"> <semantics> <mrow> <msub> <mi>E</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>a</b>) and (<b>b</b>)), <math display="inline"> <semantics> <mrow> <msub> <mi>δ</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>c</b>) and (<b>d</b>)), confidence strip and <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>p</mi> <mo>¯</mo> </mover> <mi>N</mi> </msub> </mrow> </semantics> </math> (Panel (<b>e</b>) and (<b>f</b>)) as a function of <span class="html-italic">N</span> for CFO = 150 kV.</p>
Full article ">Figure 4
<p>Comparison of the proposed approach with the ones in [<a href="#B3-atmosphere-07-00147" class="html-bibr">3</a>,<a href="#B4-atmosphere-07-00147" class="html-bibr">4</a>] in the evaluation of the lightning performance of a complex distribution network. Panels (<b>a</b>–<b>c</b>) show the results for Test1, Test2, and Test3, respectively.</p>
Full article ">Figure 5
<p>Graphical representation of the domain <span class="html-italic">D</span> defined in Equations (15)–(17).</p>
Full article ">Figure 6
<p>Comparison of the proposed approach performed on the original domain (blue) and on the reduced one (red). Panels (<b>a</b>–<b>c</b>) show the results for Test1, Test2, and Test3, respectively.</p>
Full article ">Figure 6 Cont.
<p>Comparison of the proposed approach performed on the original domain (blue) and on the reduced one (red). Panels (<b>a</b>–<b>c</b>) show the results for Test1, Test2, and Test3, respectively.</p>
Full article ">
1927 KiB  
Article
Characteristics of PM10 Chemical Source Profiles for Geological Dust from the South-West Region of China
by Yayong Liu, Wenjie Zhang, Zhipeng Bai, Wen Yang, Xueyan Zhao, Bin Han and Xinhua Wang
Atmosphere 2016, 7(11), 146; https://doi.org/10.3390/atmos7110146 - 19 Nov 2016
Cited by 12 | Viewed by 5122
Abstract
Ninety-six particulate matter (PM10) chemical source profiles for geological sources in typical cities of southwest China were acquired from Source Profile Shared Service in China. Twenty-six elements (Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, [...] Read more.
Ninety-six particulate matter (PM10) chemical source profiles for geological sources in typical cities of southwest China were acquired from Source Profile Shared Service in China. Twenty-six elements (Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Sr, Cd, Sn, Sb, Ba, Be, Tl and Pb), nine ions (F, Cl, SO42−, NO3, Na+, NH4+, K+, Mg2+ and Ca2+), and carbon-containing species (organic carbon and elemental carbon) were determined to construct these profiles. Individual source profiles were averaged and compared to quantify similarities and differences in chemical abundances using the profile-compositing method. Overall, the major components of PM10 in geological sources were crustal minerals and undefined fraction. Different chemical species could be used as tracers for various types of geological dust in the region that resulted from different anthropogenic influence. For example, elemental carbon, V and Zn could be used as tracers for urban paved road dust; Al, Si, K+ and NH4+ for agricultural soil; Al and Si for natural soil; and SO42− for urban resuspended dust. The enrichment factor analysis showed that Cu, Se, Sr and Ba were highly enriched by human activities in geological dust samples from south-west China. Elemental ratios were taken to highlight the features of geological dust from south-west China by comparing with northern urban fugitive dust, loess and desert samples. Low Si/Al and Fe/Al ratios can be used as markers to trace geological sources from southwestern China. High Pb/Al and Zn/Al ratios observed in urban areas demonstrated that urban geological dust was influenced seriously by non-crustal sources. Full article
Show Figures

Figure 1

Figure 1
<p>Source profile-compositing scheme for testing within-source and between-source variability. Source profile abbreviations for each level are given in parentheses.</p>
Full article ">Figure 2
<p>Geological source profiles of paved road dust, urban resuspended dust, natural soil and agricultural soil from southwestern regions of China. The height of each bar indicates the percentage of the corresponding chemical species to PM<sub>10</sub>. The position of each short line shows the variability in percentage composition, which includes measurement errors and source variabilities.</p>
Full article ">Figure 3
<p>Major components for geological sources of urban paved road dust (PVRD), urban resuspended dust (URD), agricultural soil (AS) and natural soil (NS) from southwestern regions of China. Error bars represent the total propagated error calculated from the standard deviation of samples within each source type for each chemical component; the definition of each component can be found under the heading “<span class="html-italic">Contributions of the major components</span>”.</p>
Full article ">Figure 4
<p>Elemental enrichment factors for geological sources of urban paved road dust (PVRD), urban resuspended dust (URD), agricultural soil (AS) and natural soil (NS) from southwestern regions of China. Al is used as reference.</p>
Full article ">
34485 KiB  
Article
Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season
by Yuan Li, Guihua Lu, Zhiyong Wu, Hai He, Jun Shi, Yuexiong Ma and Shichuang Weng
Atmosphere 2016, 7(11), 145; https://doi.org/10.3390/atmos7110145 - 17 Nov 2016
Cited by 20 | Viewed by 6422
Abstract
In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic [...] Read more.
In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI), but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS) over this region. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
Show Figures

Figure 1

Figure 1
<p>Regional map showing the location of three ground-based meteorological stations (red dots), the Xijiang River System (blue), and the local topography (elevation in metre) of the Xijiang Basin. The geographical location of the study region within China is shown in the small inset map (bottom left).</p>
Full article ">Figure 2
<p>Mean annual precipitation (mm/year) for the 1980–2010 period from the National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) unified gauge-based analysis.</p>
Full article ">Figure 3
<p>Map of domain sizes of the d01–d03 simulations.</p>
Full article ">Figure 4
<p>Mean critical success index (CSI), bias (B) false alarm rate (FAR), and probability of detection (POD) scores for seven case studies at different horizontal resolutions (5 km, 10 km, 15 km, 20 km, 30 km, 45 km).</p>
Full article ">Figure 5
<p>Study region maps showing the observed and simulated precipitation (mm) distribution at different horizontal resolutions for Case 5.</p>
Full article ">Figure 6
<p>Mean CSI, B, FAR, and POD scores for seven case studies at different physical parameterization schemes (threshold ≥ 100 mm/day). (KF: Kain–Fritsch; BMJ: Betts–Miller–Janjic; GD: Grell–Devenyi; WSM: WRF–Single–Moment.)</p>
Full article ">Figure 7
<p>Study region maps showing the spatial distribution of correlation coefficient (R) in the Xijiang Basin. (<b>a</b>) 24 h lead time; (<b>b</b>) 48 h lead time; (<b>c</b>) 72 h lead time; (<b>d</b>) 96 h lead time.</p>
Full article ">Figure 8
<p>Study region maps showing the spatial distribution of CSI score (≥0.1 mm/day) in the Xijiang Basin. (<b>a</b>) 24 h lead time; (<b>b</b>) 48 h lead time; (<b>c</b>) 72 h lead time; (<b>d</b>) 96 h lead time.</p>
Full article ">Figure 9
<p>Study region maps showing the spatial distribution of Bias (≥0.1 mm/day) in the Xijiang Basin. (<b>a</b>) 24 h lead time; (<b>b</b>) 48 h lead time; (<b>c</b>) 72 h lead time; (<b>d</b>) 96 h lead time.</p>
Full article ">Figure 10
<p>Study region maps showing the spatial distribution of CSI score (≥20 mm/day) in the Xijiang Basin. (<b>a</b>) 24 h lead time; (<b>b</b>) 48 h lead time; (<b>c</b>) 72 h lead time; (<b>d</b>) 96 h lead time.</p>
Full article ">Figure 11
<p>Study region maps showing the spatial distribution of Bias (≥20 mm/day) in the Xijiang Basin. (<b>a</b>) 24 h lead time; (<b>b</b>) 48 h lead time; (<b>c</b>) 72 h lead time; (<b>d</b>) 96 h lead time.</p>
Full article ">Figure 12
<p>Mean relative error (MRE) of areal average forecast (yellow bars = 0–24 h; blue bars = 24–48 h; green bars = 48–72 h; red bars = 72–96 h) precipitation ((<b>a</b>) 0–10 mm; (<b>b</b>) 10–20 mm; (<b>c</b>) &gt; 20 mm) for each sub-basin (BPJ = Beipanjiang; NPJ = Nanpanjiang; QLJ = Qianjiang–Liujiang; YJ = Yujiang; GJ = Guijiang; XUN = Xunjiang; XIJ = Xijiang).</p>
Full article ">Figure 13
<p>Mean precipitation from 1st June to 31st August for the 1980–2010 period.</p>
Full article ">
1605 KiB  
Article
Removal of Low-Molecular Weight Aldehydes by Selected Houseplants under Different Light Intensities and CO2 Concentrations
by Jian Li, Chun-Juan Xie, Jing Cai, Liu-Shui Yan and Ming-Ming Lu
Atmosphere 2016, 7(11), 144; https://doi.org/10.3390/atmos7110144 - 11 Nov 2016
Cited by 3 | Viewed by 6258
Abstract
The removal of five low-molecular weight aldehydes by two houseplants (Schefflera octophylla (Lour.) Harms and Chamaedorea elegans) were investigated in a laboratory simulation environment with short-term exposure to different low light intensities and CO2 concentrations. Under normal circumstances, the C [...] Read more.
The removal of five low-molecular weight aldehydes by two houseplants (Schefflera octophylla (Lour.) Harms and Chamaedorea elegans) were investigated in a laboratory simulation environment with short-term exposure to different low light intensities and CO2 concentrations. Under normal circumstances, the C1–C5 aldehyde removal rates of Schefflera octophylla (Lour.) Harms and Chamaedorea elegans (Lour.) Harms ranged from 0.311 μmol/m2/h for valeraldehyde to 0.677 μmol/m2/h for formaldehyde, and 0.526 μmol/m2/h for propionaldehyde to 1.440 μmol/m2/h for formaldehyde, respectively. However, when the light intensities varied from 0 to 600 lx, a significant correlation between the aldehyde removal rate and the light intensity was found. Moreover, the CO2 experiments showed that the total aldehyde removal rates of Schefflera octophylla (Lour.) Harms and Chamaedorea elegans (Lour.) Harms decreased 32.0% and 43.2%, respectively, with increasing CO2 concentrations from 350 ppmv to 1400 ppmv. This might be explained by the fact that the excessive CO2 concentration decreased the stomatal conductance which limited the carbonyl uptake from the stomata. Full article
Show Figures

Figure 1

Figure 1
<p>Scheme (<b>a</b>) and pictures (<b>b</b>,<b>c</b>) of the experimental apparatus for measuring the removal of aldehyde by plants. (A: pump; B: valve; C: flowmeter; D: Dinitrophenylhydrazine (DNPH)-Acid aqueous solution; E: activated charcoal; F: CO<sub>2</sub> gas; G: aldehydes solution injection port; H: gas mixing bag; I: buffer vessel; J: gas sampling port; K: lamp; L: water tank; M: fan; N: potting-plant; O: exposure chamber. All apparatus were connected by Teflon tubes.)</p>
Full article ">Figure 2
<p>SIM Ion: selective ion monitoring ion Gas Chromatograms of standard mixture of Pentafluorophenyl hydrazine (PFPH) derivatives. Peaks 0: PFPH; 1: formaldehyde; 2 and 2′: acetaldehyde; 3 and 3′: propionaldehyde; 4 and 4′: n-butyraldehyde; 5 and 5′: valeraldehyde; 6 and 6′: 4-fluorobenzaldehyde (IS). The numbers from 2′ to 6′ are the isomers of the corresponding aldehydes.</p>
Full article ">Figure 3
<p>The mass spectrum of the PFPH-hydrazone derivatives of formaldehyde, acetaldehyde, propionaldehyde, n-butyraldehyde, and valeraldehyde. SIM Ion: selective ion monitoring ion; Qion: quantification ion.</p>
Full article ">Figure 4
<p>The ratios of peak area derived from GC of C<sub>1</sub>–C<sub>5</sub> aldehydes (Ai) to internal standard (As) from the inlet gas (white bars) and the outlet gas (grey bars) of the <span class="html-italic">Schefflera octophylla (Lour.) Harms</span> (<b>a</b>) and <span class="html-italic">Chamaedorea elegans (Lour.) Harms</span> (<b>b</b>), species enclosed in the chamber at light intensity of about 600 lx respectively. Mean ± S.E. are shown.</p>
Full article ">Figure 5
<p>Relationship between the removal rate of C<sub>1</sub>–C<sub>5</sub> aldehydes of the two houseplant species and the C<sub>1</sub>–C<sub>5</sub> aldehydes concentration. (<b>a</b>) <span class="html-italic">Schefflera octophylla (Lour.) Harms</span> (R<sup>2</sup> = 0.778~0.979); (<b>b</b>) <span class="html-italic">Chamaedorea elegans</span>. (R<sup>2</sup> = 0.890~0.928) C<sub>1</sub>: Formaldehyde; C<sub>2</sub>: Acetaldehyde; C<sub>3</sub>: Propionaldehyde; C<sub>4</sub>: N-butyraldehyde; C<sub>5</sub>: Valeraldehyde. Significant differences for <span class="html-italic">p</span> &lt; 0.05 are shown with least significant (LSD) test.</p>
Full article ">Figure 6
<p>The removal rate of C<sub>1</sub>–C<sub>5</sub> aldehydes of <span class="html-italic">Schefflera octophylla (Lour.) Harms</span> (<b>a</b>) and <span class="html-italic">Chamaedorea elegans (Lour.) Harms</span> (<b>b</b>). C<sub>1</sub>: formaldehyde; C<sub>2</sub>: acetaldehyde; C<sub>3</sub>: propionaldehyde; C<sub>4</sub>: N-butyraldehyde; C<sub>5</sub>: valeraldehyde. Significant differences for <span class="html-italic">p</span> &lt; 0.05 are shown with least significant (LSD) test.</p>
Full article ">Figure 7
<p>Relationship between the removal rates of C<sub>1</sub>–C<sub>5</sub> aldehydes and the transpiration rate for the two houseplant species. (<b>a</b>) <span class="html-italic">Schefflera octophylla (Lour.) Harms</span> (R<sup>2</sup> = 0.720–0.837); (<b>b</b>) <span class="html-italic">Chamaedorea elegans</span> (R<sup>2</sup> = 0.607–0.932). C<sub>1</sub>: formaldehyde; C<sub>2</sub>: acetaldehyde; C<sub>3</sub>: propionaldehyde; C<sub>4</sub>: N-butyraldehyde; C<sub>5</sub>: valeraldehyde; transpiration rate was measured by weighing the whole plant before and after exposing to a specific light intensity according to previous research [<a href="#B49-atmosphere-07-00144" class="html-bibr">49</a>]. Significant differences for <span class="html-italic">p</span> &lt; 0.05 are shown with least significant (LSD) test.</p>
Full article ">Figure 8
<p>The removel capacity on formaldehyde, actealdehyde, propionaldehyde, n-butyraldehyde, valeraldehyde by (<b>a</b>) <span class="html-italic">Schefflera octophylla (Lour.) Harms</span>; and (<b>b</b>) <span class="html-italic">Chamaedorea elegans (Lour.) Harms</span> at three CO<sub>2</sub> concentrations (350, 700, 1400 ppmv). C<sub>1</sub>: formaldehyde; C<sub>2</sub>: acetaldehyde; C<sub>3</sub>: propionaldehyde; C<sub>4</sub>: N-butyraldehyde; C<sub>5</sub>: valeraldehyde Means (<span class="html-italic">n</span> = 3) ± S.E. are shown. Significant differences for <span class="html-italic">p</span> &lt; 0.05 are shown with least significant (LSD) test.</p>
Full article ">
3165 KiB  
Article
The Effect of Freezing Drizzle, Sleet and Snow on Microphysical Characteristics of Supercooled Fog during the Icing Process in a Mountainous Area
by Yue Zhou, Shengjie Niu, Jingjing Lü and Yuehua Zhou
Atmosphere 2016, 7(11), 143; https://doi.org/10.3390/atmos7110143 - 11 Nov 2016
Cited by 7 | Viewed by 4573
Abstract
Both the similar and different effects of freezing drizzle, sleet and snow on microphysical properties of supercooled fog were analyzed for fourteen events during a comprehensive wire icing, fog, and precipitation observation experiment conducted at Enshi radar station (30°17′N, 109°16′E; 1722 m a.s.l.) [...] Read more.
Both the similar and different effects of freezing drizzle, sleet and snow on microphysical properties of supercooled fog were analyzed for fourteen events during a comprehensive wire icing, fog, and precipitation observation experiment conducted at Enshi radar station (30°17′N, 109°16′E; 1722 m a.s.l.) on a hilltop in Shibanling, Hubei, China. Liquid precipitation is in a relatively stable form in mountainous areas. Short-term precipitation (1–3 h) is dominant with temperature below 0 °C. The wet scavenging effect of freezing drizzle on small fog droplets with a size range less than 6–12 μm is weak but is stronger for fog droplets with a larger size, which is opposite to the effects of solid precipitation, broadening the fog droplet spectra significantly. As the fog droplet diameter increases, the distributions of droplet spectra change from leptokurtosis to platykurtosis and from positive skewness to negative skewness. Occurrence of freezing drizzle would improve the positive correlation of N-r in dissipation and oscillation periods, resulting in the N-r relationship having a weak negative correlation in the maturity period, and resulting in the transition of the N-L and N-r relationships into positive correlations in the development period. Meanwhile, the emergence of solid precipitation particles would result in negative values for the correlation coefficients of N-L and N-r. The change in relationships among the microphysical properties was caused by the occurrence of different phase precipitation, showing the influence on the main microphysical mechanisms of supercooled fog. Full article
Show Figures

Figure 1

Figure 1
<p>Map of the observation site.</p>
Full article ">Figure 2
<p>Temporal evolution of visibility, temperature, and occurrence time of rain, sleet, and snow in two winter observations in (<b>a</b>) 2009; and (<b>b</b>) 2010.</p>
Full article ">Figure 3
<p>Temporal evolutions and correlation coefficients (in parentheses) of average number concentrations of fog droplets in three categories with similar variation trends. (<b>a</b>) Case1; (<b>b</b>) case2; (<b>c</b>) case3; (<b>d</b>) case4; (<b>e</b>) case5; (<b>f</b>) case6; (<b>g</b>) case7; (<b>h</b>) case8; (<b>i</b>) case9; (<b>j</b>) case10; (<b>k</b>) case11; (<b>l</b>) case12; (<b>m</b>) case13; (<b>n</b>) case14.</p>
Full article ">Figure 4
<p>Temporal evolution of fog spectra based on one-hour-average data in cases 1–14 and one hour before/after. (<b>a</b>) Case1; (<b>b</b>) case2; (<b>c</b>) case3; (<b>d</b>) case4; (<b>e</b>) case5; (<b>f</b>) case6; (<b>g</b>) case7; (<b>h</b>) case8; (<b>i</b>) case9; (<b>j</b>) case10; (<b>k</b>) case11; (<b>l</b>) case12; (<b>m</b>) case13; (<b>n</b>) case14.</p>
Full article ">Figure 5
<p>The scatterplot of <span class="html-italic">K</span> and <span class="html-italic">S</span> calculated from the 1-min average data and the corresponding fitting curves during the no-precipitation (one hour before and after precipitation), freezing drizzle, and sleet/snow periods. (<b>a</b>) Kurtosis (<span class="html-italic">K</span>) as a function of diameter (<span class="html-italic">D</span>); (<b>b</b>) skewness (<span class="html-italic">S</span>) as a function of <span class="html-italic">D</span>.</p>
Full article ">Figure 6
<p>Relationships among <span class="html-italic">N</span>, <span class="html-italic">r</span>, and <span class="html-italic">L</span> under the effects of freezing drizzle, sleet, and snow during the development, maturity, dissipation, and oscillation periods of fog. (<b>a</b>) Liquid water content (<span class="html-italic">L</span>) as a function of number concentration (<span class="html-italic">N</span>); (<b>b</b>) radius (<span class="html-italic">r</span>) as a function of <span class="html-italic">N</span>; (<b>c</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the freezing drizzle event in development period of fog; (<b>d</b>) <span class="html-italic">L</span> as a function of <span class="html-italic">N</span>; (<b>e</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">N</span>; (<b>f</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the sleet event in oscillation period of fog; (<b>g</b>) <span class="html-italic">L</span> as a function of <span class="html-italic">N</span>; (<b>h</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">N</span>; (<b>i</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the snow event in dissipation period of fog; (<b>j</b>) <span class="html-italic">L</span> as a function of <span class="html-italic">N</span>; (<b>k</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">N</span>; (<b>l</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the freezing drizzle event in dissipation period of fog; (<b>m</b>) <span class="html-italic">L</span> as a function of <span class="html-italic">N</span>; (<b>n</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">N</span>; (<b>o</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the freezing drizzle event in oscillation period of fog; (<b>p</b>) <span class="html-italic">L</span> as a function of <span class="html-italic">N</span>; (<b>q</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">N</span>; (<b>r</b>) <span class="html-italic">r</span> as a function of <span class="html-italic">L</span> during the freezing drizzle event in maturity period of fog.</p>
Full article ">
1739 KiB  
Article
Bacterial and Fungal Aerosols in Rural Nursery Schools in Southern Poland
by Ewa Brągoszewska, Anna Mainka and Jozef S. Pastuszka
Atmosphere 2016, 7(11), 142; https://doi.org/10.3390/atmos7110142 - 9 Nov 2016
Cited by 33 | Viewed by 5612
Abstract
This study aimed to characterize airborne bacteria and fungi populations present in rural nursery schools in the Upper Silesia region of Poland during winter and spring seasons through quantification and identification procedures. Bacterial and fungal concentration levels and size distributions were obtained by [...] Read more.
This study aimed to characterize airborne bacteria and fungi populations present in rural nursery schools in the Upper Silesia region of Poland during winter and spring seasons through quantification and identification procedures. Bacterial and fungal concentration levels and size distributions were obtained by the use of a six-stage Andersen cascade impactor. Results showed a wide range of indoor bioaerosols levels. The maximum level of viable bacterial aerosols indoors was about 2600 CFU·m−3, two to three times higher than the outdoor level. Fungi levels were lower, from 82 to 1549 CFU·m−3, with indoor concentrations comparable to or lower than outdoor concentrations. The most prevalent bacteria found indoors were Gram-positive cocci (>65%). Using the obtained data, the nursery school exposure dose (NSED) of bioaerosols was estimated for both the children and personnel of nursery schools. The highest dose for younger children was estimated to range: 327–706 CFU·kg−1 for bacterial aerosols and 31–225 CFU·kg−1 for fungal aerosols. These results suggest an elevated risk of adverse health effects on younger children. These findings may contribute to the promotion and implementation of preventative public health programs and the formulation of recommendations aimed at providing healthier school environments. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the investigated nursery schools (Map data: 2015© Google, ORION-ME).</p>
Full article ">Figure 2
<p>Comparison of bacterial aerosol levels between rural sites (P) and (S) during winter and spring seasons.</p>
Full article ">Figure 3
<p>Respirable fraction of bacterial and fungal aerosols.</p>
Full article ">Figure 4
<p>The average percentage of main groups of bacteria isolated from the outdoor and indoor air during winter and spring seasons.</p>
Full article ">
301 KiB  
Review
Particulate Matter Emission Factors for Biomass Combustion
by Simone Simões Amaral, João Andrade de Carvalho, Maria Angélica Martins Costa and Cleverson Pinheiro
Atmosphere 2016, 7(11), 141; https://doi.org/10.3390/atmos7110141 - 31 Oct 2016
Cited by 51 | Viewed by 12655
Abstract
Emission factor is a relative measure and can be used to estimate emissions from multiple sources of air pollution. For this reason, data from literature on particulate matter emission factors from different types of biomass were evaluated in this paper. Initially, the main [...] Read more.
Emission factor is a relative measure and can be used to estimate emissions from multiple sources of air pollution. For this reason, data from literature on particulate matter emission factors from different types of biomass were evaluated in this paper. Initially, the main sources of particles were described, as well as relevant concepts associated with particle measurements. In addition, articles about particle emissions were classified and described in relation to the sampling environment (open or closed) and type of burned biomass (agricultural, garden, forest, and dung). Based on this analysis, a set of emission factors was presented and discussed. Important observations were made about the main emission sources of particulate matter. Combustion of compacted biomass resulted in lower particulate emission factors. PM2.5 emissions were predominant in the burning of forest biomass. Emission factors were more elevated in laboratory burning, followed by burns in the field, residences and combustors. Full article
(This article belongs to the Special Issue Biomass Burning)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Particulate matter emission factors found in the literature: (<b>A</b>) size range; (<b>B</b>) sampling environment; (<b>C</b>) biomass type; and (<b>D</b>) biomass densification.</p>
Full article ">
2782 KiB  
Article
Characteristics of Particulate Carbon in Precipitation during the Rainy Season in Xiamen Island, China
by Shuhui Zhao, Liqi Chen, Jinpei Yan, Peng Shi, Yun Li and Wei Li
Atmosphere 2016, 7(11), 140; https://doi.org/10.3390/atmos7110140 - 28 Oct 2016
Cited by 10 | Viewed by 4603
Abstract
Measuring wet deposition of organic carbon (OC) and elemental carbon (EC) aerosol is crucial for the understanding of their circulation and climate effect. To further understand the wet deposition of particulate carbon (OC and EC), precipitation samples were collected from April to August [...] Read more.
Measuring wet deposition of organic carbon (OC) and elemental carbon (EC) aerosol is crucial for the understanding of their circulation and climate effect. To further understand the wet deposition of particulate carbon (OC and EC), precipitation samples were collected from April to August 2014 on Xiamen Island in China. EC and water insoluble organic carbon (WIOC) concentrations were analyzed using a thermal optical method to investigate temporal variations and wet deposition fluxes. The average EC and WIOC concentrations were 7.3 μgC·L−1 and 495.3 μgC·L−1, respectively, which are both comparable to the results reported in European areas. EC and WIOC concentrations were higher in spring than in summer. Higher EC concentrations were found in April, which were probably associated with the transport of air masses from northern continental areas. Higher WIOC concentrations were found in May and were mainly attributed to air masses from the South China Sea. Lower concentrations of EC and WIOC in the summer were primarily due to the clean air masses transported from the ocean. The wet deposition flux was calculated as the product of concentration and precipitation amount. Average wet deposition fluxes of EC and WIOC were estimated to be 0.6 mgC·m−2·month−1 and 36.7 mgC·m−2·month−1, respectively. Wet deposition fluxes of EC and WIOC exhibited similar concentration trends. The largest flux in EC wet deposition occurred in April (1.8 mgC·m−2·month−1), while the largest flux in WIOC wet deposition occurred in May (63.1 mgC·m−2·month−1). Full article
Show Figures

Figure 1

Figure 1
<p>Map of Xiamen Island and the sampling site.</p>
Full article ">Figure 2
<p>Time series of the daily precipitation in Xiamen Island.</p>
Full article ">Figure 3
<p>Cluster analyses of 96-h backward trajectories ending at the sampling site: (<b>a</b>) cluster mean trajectories from April to August 2014; (<b>b</b>) monthly distributions of the four mean trajectories during the sampling period; and (<b>c</b>) monthly distributions of the four mean trajectories during the sampled precipitation events.</p>
Full article ">Figure 4
<p>WIOC (<b>a</b>) and EC (<b>b</b>) concentrations in precipitation in Xiamen Island during the study period.</p>
Full article ">Figure 5
<p>Relationships between the carbonaceous particle concentration in the rainwater and the precipitation amount of: (<b>a</b>) WIOC; and (<b>b</b>) EC.</p>
Full article ">Figure 6
<p>EC and WIOC daily wet deposition flux at Xiamen during the sampling period.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop