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Volume 8
Issue 6
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Atmosphere, Volume 8, Issue 6 (June 2017) – 18 articles

Cover Story (view full-size image): Concentrated livestock feeding operations have become a source of odorous gas emissions that impact air quality. Comprehensive and practical technologies are needed for a sustainable mitigation of the emissions. In this study, we advance the concept of using a catalyst for barn walls and ceilings for odor mitigation. Significant removal of several key odorants was achieved in lab-scale using treatment times consistent with typical barn ventilation rates. Of particular interest is the removal of p-cresol, a 'signature' gas thought to be one of the characteristic compounds responsible for livestock odor far downwind from livestock operations. View this paper.
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1465 KiB  
Article
Parameterization of Evapotranspiration Estimation for Two Typical East Asian Crops
by Peng Zhao and Johannes Lüers
Atmosphere 2017, 8(6), 111; https://doi.org/10.3390/atmos8060111 - 20 Jun 2017
Cited by 5 | Viewed by 5181
Abstract
Estimation of evapotranspiration plays an important role in understanding the water cycle on the earth, especially the water budget in agricultural ecosystems. The parameterization approach of the Penman-Monteith-Katerji-Perrier (PM-KP) model, accounting for the influence of meteorological variables and aerodynamic resistance on surface resistance, [...] Read more.
Estimation of evapotranspiration plays an important role in understanding the water cycle on the earth, especially the water budget in agricultural ecosystems. The parameterization approach of the Penman-Monteith-Katerji-Perrier (PM-KP) model, accounting for the influence of meteorological variables and aerodynamic resistance on surface resistance, was proposed in the literature, but it has not been applied to Asian croplands, and its error and sensitivity have not been reported yet. In this study, the estimation of evapotranspiration on half-hourly scale was carried out for two typical East Asian cropland research sites, and evaluated by using eddy-covariance measurements corrected with the energy-balance-closure concept. Sensitivity coefficients as well as systematic bias and random errors of the PM-KP approach were used to evaluate the model performance. Different distributions of the calibration coefficients between different crops were reported for the first time, indicating that the calibration of this model was more stable for the rice field than for the potato field. The commonly-used parameterization approach suggested by the Food and Agriculture Organization (FAO) was used as reference and was site-specifically optimized. The results suggest that the PM-KP approach would be a better alternative than the PM-FAO approach for estimating evapotranspiration for the flooded rice field, and an acceptable alternative for rain-fed croplands when the soil is well watered and the air is humid during the summer monsoon. Full article
(This article belongs to the Special Issue Biosphere-Atmosphere Interactions: Measurements, Models, and Model-Data Fusion)
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Figure 1

Figure 1
<p>Meteorological conditions and vegetation development at the research sites, including daily mean air temperature (<span class="html-italic">T</span>, solid line in <b>A</b>), daily mean relative humidity (RH, dashed line in <b>A</b>), daily sum precipitation (<span class="html-italic">P</span>, solid line in <b>B</b>), daily mean solar radiation (<span class="html-italic">R<sub>g</sub></span>, dashed line in <b>B</b>), and leaf area index (LAI, dashed line representing potato and solid line representing rice in <b>C</b> with standard deviations as error bars), and plant height (dashed line representing potato and solid line representing rice in <b>D</b>).</p> Full article ">Figure 2
<p>Diurnal (<b>A</b>,<b>B</b>) and seasonal (<b>C</b>,<b>D</b>) patterns of Penman-Monteith model sensitivity coefficients for available energy (closed circle), vapor pressure deficit (VPD, open circle), aerodynamic resistance (cross), and stomatal resistance (closed square) in the rice field (<b>A</b>,<b>C</b>) and in the potato field (<b>B</b>,<b>D</b>).</p> Full article ">Figure 3
<p>Statistical distribution of the regression coefficients <b><span class="html-italic">a</span></b> and <b><span class="html-italic">b</span></b> of the Penman-Monteith-Katerji-Perrier (PM-KP) approach for the potato site (<b>upper</b>) and the rice site (<b>lower</b>).</p> Full article ">Figure 4
<p>Sensitivity of PM-KP modelled evapotranspiration to systematic errors (<b>A</b>,<b>C</b>) and random errors (<b>B</b>,<b>D</b>) in PM-KP coefficients <span class="html-italic">a</span> (solid line with closed circle) and <span class="html-italic">b</span> (dotted line with open circle) for the potato site (<b>A</b>,<b>B</b>) and the rice site (<b>C</b>,<b>D</b>).</p> Full article ">Figure 5
<p>Nash-Sutcliffe model efficiency coefficient (NSeff) of PM-FAO (Penman-Monteith-Food and Agriculture Organization) modelled <span class="html-italic">Q</span><sub>E</sub> to modifications in <span class="html-italic">r</span><sub>si</sub> for the potato field (solid line) and rice field (dotted line).</p> Full article ">Figure 6
<p>Nash-Sutcliffe model efficiency coefficient (NSeff) of simulated evapotranspiration by PM-FAO approach (solid line with closed circle) and PM-KP approach (dash line with open circle) against air temperature (<span class="html-italic">T</span>), wind speed (<span class="html-italic">u</span>), relative humidity (RH), leaf area index (LAI), plant height (<span class="html-italic">h</span>), and day of the year (DOY), for the potato field.</p> Full article ">Figure 7
<p>Nash-Sutcliffe model efficiency coefficient (NSeff) of simulated evapotranspiration by the PM-FAO approach (solid line with closed circle) and the PM-KP approach (dash line with open circle) against air temperature (<span class="html-italic">T</span>), wind speed (<span class="html-italic">u</span>), relative humidity (RH), leaf area index (LAI), plant height (<span class="html-italic">h</span>), and day of the year (DOY), for the rice field.</p> Full article ">
9415 KiB  
Article
The Spatiotemporal Distribution of Air Pollutants and Their Relationship with Land-Use Patterns in Hangzhou City, China
by Sheng Zheng, Xueyuan Zhou, Ramesh P. Singh, Yuzhe Wu, Yanmei Ye and Cifang Wu
Atmosphere 2017, 8(6), 110; https://doi.org/10.3390/atmos8060110 - 20 Jun 2017
Cited by 43 | Viewed by 7490
Abstract
Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM10 and [...] Read more.
Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM10 and PM2.5) and trace gases (O3, SO2, NO2, and CO) pollutants in Hangzhou City (China) for the years 2014–2016. Our detailed analysis shows a relationship between air pollutants and land-use/land-cover change. Air quality parameters (PM2.5 and PM10) and trace gases (SO2, NO2, and CO) show strong monthly variations in the months of January (higher values) and July (lower values). During monsoon and summer seasons, air quality and trace gases show low values, whereas ozone (O3) is higher in the summer and lower in the winter. The spatial distribution of air pollutants is retrieved using the kriging method at the monitoring sites in Hangzhou City. We have considered normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Landsat 8 data. The correlation between air pollutants and land use at the street-town unit suggests that areas with low NDVI, high road density, large built-up density, and LST are consistent with high concentrations of particulate matter and SO2, NO2, and CO. Among the trace gases, NO2 is found to be the most sensitive element affected by land use patterns, and O3 shows weak correlation with land use. SO2 shows a strong positive correlation with road density and LST, whereas CO shows positive correlation with the built-up density, LST, and population density. Full article
(This article belongs to the Special Issue Urban Air Pollution)
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<p>(<b>a</b>) Location of Hangzhou in China; (<b>b</b>) the spatial distribution of the 10 state-controlled monitoring sites in the eight districts of Hangzhou; (<b>c</b>) the street-town boundary, West Lake, and the urban core of the study area.</p> Full article ">Figure 2
<p>Population density for the street-town unit.</p> Full article ">Figure 3
<p>Normalized difference vegetation index (NDVI) retrieved from the Landsat 8 image: (<b>a</b>) inversion result with 30 m spatial resolution, and (<b>b</b>) average NDVI for street-town unit.</p> Full article ">Figure 4
<p>Land surface temperature retrieved from the Landsat 8 image: (<b>a</b>) inversion result with 30 m spatial resolution, and (<b>b</b>) average surface temperature for street-town unit.</p> Full article ">Figure 5
<p>Built-up density for street-town unit.</p> Full article ">Figure 6
<p>Road density for the street-town unit.</p> Full article ">Figure 7
<p>Monthly variations of PM<sub>2.5</sub>, and PM<sub>10</sub> in Hangzhou City for the years 2014–2016.</p> Full article ">Figure 8
<p>Monthly variations of O<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, and CO in Hangzhou City for the years 2014–2016.</p> Full article ">Figure 9
<p>Seasonal variations of PM<sub>2.5</sub> and PM<sub>10</sub> in Hangzhou City for 2014–2016.</p> Full article ">Figure 10
<p>Seasonal variations of SO<sub>2</sub>, NO<sub>2</sub>, O<sub>3</sub>, and CO in Hangzhou City for 2014–2016.</p> Full article ">Figure 11
<p>Spatial distribution of seasonal average concentrations of particulate matter and trace gases in Hangzhou City for 2014–2016: (<b>a</b>) PM<sub>2.5</sub> (μg/m<sup>3</sup>); (<b>b</b>) PM<sub>10</sub> (μg/m<sup>3</sup>); (<b>c</b>) SO<sub>2</sub> (μg/m<sup>3</sup>); (<b>d</b>) O<sub>3</sub> (μg/m<sup>3</sup>); (<b>e</b>) NO<sub>2</sub> (μg/m<sup>3</sup>); (<b>f</b>) CO (mg/m<sup>3</sup>).</p> Full article ">Figure 11 Cont.
<p>Spatial distribution of seasonal average concentrations of particulate matter and trace gases in Hangzhou City for 2014–2016: (<b>a</b>) PM<sub>2.5</sub> (μg/m<sup>3</sup>); (<b>b</b>) PM<sub>10</sub> (μg/m<sup>3</sup>); (<b>c</b>) SO<sub>2</sub> (μg/m<sup>3</sup>); (<b>d</b>) O<sub>3</sub> (μg/m<sup>3</sup>); (<b>e</b>) NO<sub>2</sub> (μg/m<sup>3</sup>); (<b>f</b>) CO (mg/m<sup>3</sup>).</p> Full article ">Figure 12
<p>Spatial distribution of annual average concentrations of particulate matter and trace gases in Hangzhou City for 2015: (<b>a</b>) PM<sub>2.5</sub> (μg/m<sup>3</sup>); (<b>b</b>) PM<sub>10</sub> (μg/m<sup>3</sup>); (<b>c</b>) SO<sub>2</sub> (μg/m<sup>3</sup>); (<b>d</b>) O<sub>3</sub> (μg/m<sup>3</sup>); (<b>e</b>) NO<sub>2</sub> (μg/m<sup>3</sup>); (<b>f</b>) CO (mg/m<sup>3</sup>).</p> Full article ">Figure 12 Cont.
<p>Spatial distribution of annual average concentrations of particulate matter and trace gases in Hangzhou City for 2015: (<b>a</b>) PM<sub>2.5</sub> (μg/m<sup>3</sup>); (<b>b</b>) PM<sub>10</sub> (μg/m<sup>3</sup>); (<b>c</b>) SO<sub>2</sub> (μg/m<sup>3</sup>); (<b>d</b>) O<sub>3</sub> (μg/m<sup>3</sup>); (<b>e</b>) NO<sub>2</sub> (μg/m<sup>3</sup>); (<b>f</b>) CO (mg/m<sup>3</sup>).</p> Full article ">
2810 KiB  
Article
Estimating Cloud and Aerosol UV Modification Factors Based on Spectral Measurement from the Brewer Spectrophotometer
by Sang Seo Park, Migyoung Kim, Hanlim Lee, Hana Lee, Sang-Min Kim and Yun Gon Lee
Atmosphere 2017, 8(6), 109; https://doi.org/10.3390/atmos8060109 - 19 Jun 2017
Cited by 7 | Viewed by 4148
Abstract
Cloud and aerosol modification factors are investigated in the spectral range of ultraviolet (UV) to correct for cloud and aerosol extinction effects from clear sky irradiance. The cloud modification factor (CMF) and aerosol modification factor (AMF) are estimated using radiative transfer model (RTM) [...] Read more.
Cloud and aerosol modification factors are investigated in the spectral range of ultraviolet (UV) to correct for cloud and aerosol extinction effects from clear sky irradiance. The cloud modification factor (CMF) and aerosol modification factor (AMF) are estimated using radiative transfer model (RTM) simulations and ground-based observations in Seoul, Korea. The modification factors show a spectral dependence within the spectral range of 300 to 360 nm, which is the range used to estimate erythemal UV. The CMF and AMF values are estimated with high spectral resolution with considerations of solar zenith angle (SZA), cloud/aerosol amount, and total ozone variation. From the simulation studies, the variation in the CMFs within the spectral range of 300–360 nm is estimated to be 0.031–0.055, which is twice as large as the decrease in CMFs resulting from a SZA increase of 10°. In addition, the CMFs estimated from observational data show significant spectral dependence, varying from 2.5% to 10.0%. Because of the small aerosol optical depth (AOD) value, however, the variation in the AMF calculated from simulations is estimated to be between 0.007 and 0.045, indicating lower spectral dependence than the CMF. Furthermore, the spectral difference in the AMF calculated from observational data is insignificant relative to the daily-averaged total ozone error and uncertainties related to the reference irradiance spectrum under aerosol-free conditions. Full article
(This article belongs to the Section Aerosols)
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Figure 1

Figure 1
<p>Histogram of observation data number with respect to AOD for solar zenith angle (SZA) of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) and 70°, in C1 cases.</p> Full article ">Figure 1 Cont.
<p>Histogram of observation data number with respect to AOD for solar zenith angle (SZA) of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) and 70°, in C1 cases.</p> Full article ">Figure 2
<p>Averaged spectral ozone radiation amplification factor (O3RAF) including cases for all surface albedo of 0.0 to 0.5 from the RTM calculation on clear sky condition.</p> Full article ">Figure 3
<p>Spectral O3RAF from the observation for: (<b>a</b>) C1; (<b>b</b>) C2; (<b>c</b>) C3; (<b>d</b>) C4; and (<b>e</b>) C5 cases.</p> Full article ">Figure 4
<p>Maximum (Bold), minimum (dotted), and mean (dashed) value of spectral irradiance in ultraviolet (UV) after correcting the ozone absorption based on the total ozone of 300 Dobson Unit (DU) for: (<b>a</b>) C1; and (<b>b</b>) A1 cases.</p> Full article ">Figure 5
<p>Simulated spectral CMF for overcast sky with cloud optical depth (COD) of 5, 10, 15, and 20 at SZA of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) 70°.</p> Full article ">Figure 6
<p>Spectral dependence of CMF of each cloud groups using calculated O3RAF at SZA of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) 70°.</p> Full article ">Figure 7
<p>Simulated spectral AMF for A2, A3, and A4 at SZA of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) 70°.</p> Full article ">Figure 8
<p>Spectral AMF of each AOD categories at SZA of: (<b>a</b>) 40°; (<b>b</b>) 50°; (<b>c</b>) 60°; and (<b>d</b>) 70°.</p> Full article ">
1785 KiB  
Article
CCN Activity, Variability and Influence on Droplet Formation during the HygrA-Cd Campaign in Athens
by Aikaterini Bougiatioti, Athina Argyrouli, Stavros Solomos, Stergios Vratolis, Konstantinos Eleftheriadis, Alexandros Papayannis and Athanasios Nenes
Atmosphere 2017, 8(6), 108; https://doi.org/10.3390/atmos8060108 - 19 Jun 2017
Cited by 10 | Viewed by 5999
Abstract
Measurements of cloud condensation nuclei (CCN) concentrations (cm−3) at five levels of supersaturation between 0.2–1%, together with remote sensing profiling and aerosol size distributions, were performed at an urban background site of Athens during the Hygroscopic Aerosols to Cloud Droplets (HygrA-CD) [...] Read more.
Measurements of cloud condensation nuclei (CCN) concentrations (cm−3) at five levels of supersaturation between 0.2–1%, together with remote sensing profiling and aerosol size distributions, were performed at an urban background site of Athens during the Hygroscopic Aerosols to Cloud Droplets (HygrA-CD) campaign. The site is affected by local emissions and long-range transport, as portrayed by the aerosol size, hygroscopicity and mixing state. Application of a state-of-the-art droplet parameterization is used to link the observed size distribution measurements, bulk composition, and modeled boundary layer dynamics with potential supersaturation, droplet number, and sensitivity of these parameters for clouds forming above the site. The sensitivity is then used to understand the source of potential droplet number variability. We find that the importance of aerosol particle concentration levels associated with the background increases as vertical velocities increase. The updraft velocity variability was found to contribute 58–90% (68.6% on average) to the variance of the cloud droplet number, followed by the variance in aerosol number (6–32%, average 23.2%). Therefore, although local sources may strongly modulate CCN concentrations, their impact on droplet number is limited by the atmospheric dynamics expressed by the updraft velocity regime. Full article
(This article belongs to the Special Issue Atmospheric Aerosol Composition and its Impact on Clouds)
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Figure 1

Figure 1
<p>Cloud condensation nuclei (CCN) concentration levels at the measured supersaturations during the measurement period. As a reference for the influence of local sources, the NO<sub>2</sub> concentration levels are also shown. The two shaded areas represent the different periods of high and low CCN levels, respectively.</p> Full article ">Figure 2
<p>Diurnal variability of the activation fractions (CCN/CN) during the measurement period for: (<b>a</b>) 0.4% supersaturation; (<b>b</b>) 0.8% supersaturation. Activation fractions between 0.6–1% supersaturation did not differ considerably.</p> Full article ">Figure 3
<p>Calculated maximum supersaturation and droplet number with regard to the updraft velocity, during the measurement period.</p> Full article ">Figure 4
<p>Timeseries of the different estimated parameters that contribute to the droplet formation: (<b>a</b>) variance of updraft, hygroscopicity and aerosol number, (<b>b</b>) attribution of N<sub>d</sub> variability to N<sub>a</sub>, κ, and w, and (<b>c</b>) sensitivity of N<sub>d</sub> to N<sub>a</sub>, κ and w from the droplet parameterization.</p> Full article ">Figure 5
<p>Collocated EOLE Raman lidar measurements (1064 nm) for June 18 (<b>a</b>) and June 20 (<b>b</b>) denoting the PBL structure and the presence/absence of aerosols and clouds.</p> Full article ">Figure 6
<p>Correlation of droplet number with updraft velocity (<b>a</b>) and total aerosol number (<b>b</b>) during Period 1, when updraft velocities were low and aerosol numbers were high.</p> Full article ">
4290 KiB  
Article
Characterization and Sources of Aromatic Hydrocarbons (BTEX) in the Atmosphere of Two Urban Sites Located in Yucatan Peninsula in Mexico
by Julia Griselda Cerón Bretón, Rosa María Cerón Bretón, Francisco Vivas Ucan, Cynthia Barceló Baeza, María de la Luz Espinosa Fuentes, Evangelina Ramírez Lara, Marcela Rangel Marrón, Jorge Alfredo Montero Pacheco, Abril Rodríguez Guzmán and Martha Patricia Uc Chi
Atmosphere 2017, 8(6), 107; https://doi.org/10.3390/atmos8060107 - 17 Jun 2017
Cited by 31 | Viewed by 6753
Abstract
Benzene, toluene, ethylbenzene, p-xylene, O3, NOx, CO, PM2.5 and meteorological parameters were measured in urban air of two sites in Merida, Yucatan, Mexico during 2016–2017. Samples were collected using 1.5 h time intervals at three different sampling periods [...] Read more.
Benzene, toluene, ethylbenzene, p-xylene, O3, NOx, CO, PM2.5 and meteorological parameters were measured in urban air of two sites in Merida, Yucatan, Mexico during 2016–2017. Samples were collected using 1.5 h time intervals at three different sampling periods before being analyzed by gas chromatography with flame ionization detection. The highest concentrations of BTEX occurred during midday and afternoon in spring and summer seasons. Mean concentrations of, BTEX for the Cholul and SEDUMA sites, respectively, were 40.91 μg/m3 and 32.86 μg/m3 for benzene; 6.87 μg/m3 and 3.29 μg/m3 for toluene; 13.87 μg/m3 and 8.29 μg/m3 for p-xylene; and 6.23 μg/m3 and 4.48 μg/m3 for ethylbenzene. The toluene/benzene and xylene/ethylbenzene concentration ratios indicated that BTEX levels at both sites were influenced by local and fresh emissions (vehicular traffic). Bivariate and multivariate analyses were performed in order to correlate BTEX concentrations with criteria air pollutants to infer their possible sources. Health risk assessment revealed that exposure to benzene exceeded the recommended value for the integrated lifetime cancer risk. These results suggest that Merida’s population is exposed to cancer risk, and changes in the existing environmental policies should therefore be applied to improve air quality. Full article
(This article belongs to the Special Issue Tropospheric Ozone and Its Precursors)
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Figure 1
<p>Sampling sites’ location.</p> Full article ">Figure 2
<p>Seasonal variation and descriptive statistics for BTEX concentrations at the Cholul site. Note: SP: Spring season; SU: Summer season; AU: Autumn season; WI: Winter season. The central horizontal bars are the medians. The lower and upper limits of the box are the first and third quartiles. Where, <span class="html-fig-inline" id="atmosphere-08-00107-i001"> <img alt="Atmosphere 08 00107 i001" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i001.png"/></span> is the mean value; <span class="html-fig-inline" id="atmosphere-08-00107-i002"> <img alt="Atmosphere 08 00107 i002" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i002.png"/></span> represents maximum and minimum values; the horizontal width of the box has no statistical significance, and is only for better visualization. (<b>a</b>) box plots for benzene and toluene concentrations at the Cholul site (<b>b</b>) box plots for ethylbenzene and <span class="html-italic">p</span>-xylene concentrations at the Cholul site.</p> Full article ">Figure 2 Cont.
<p>Seasonal variation and descriptive statistics for BTEX concentrations at the Cholul site. Note: SP: Spring season; SU: Summer season; AU: Autumn season; WI: Winter season. The central horizontal bars are the medians. The lower and upper limits of the box are the first and third quartiles. Where, <span class="html-fig-inline" id="atmosphere-08-00107-i001"> <img alt="Atmosphere 08 00107 i001" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i001.png"/></span> is the mean value; <span class="html-fig-inline" id="atmosphere-08-00107-i002"> <img alt="Atmosphere 08 00107 i002" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i002.png"/></span> represents maximum and minimum values; the horizontal width of the box has no statistical significance, and is only for better visualization. (<b>a</b>) box plots for benzene and toluene concentrations at the Cholul site (<b>b</b>) box plots for ethylbenzene and <span class="html-italic">p</span>-xylene concentrations at the Cholul site.</p> Full article ">Figure 3
<p>Seasonal variation and descriptive statistics for BTEX concentrations at the SEDUMA site. Note: SP: Spring season; SU: Summer season; AU: Autumn season; WI: Winter season. The central horizontal bars are the medians. The lower and upper limits of the box are the first and third quartiles. Where <span class="html-fig-inline" id="atmosphere-08-00107-i003"> <img alt="Atmosphere 08 00107 i003" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i003.png"/></span> is the mean value; <span class="html-fig-inline" id="atmosphere-08-00107-i004"> <img alt="Atmosphere 08 00107 i004" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i004.png"/></span> represents maximum and minimum values; the horizontal width of the box has no statistical significance and is only for better visualization. (<b>a</b>) box plots for benzene and toluene concentrations at the SEDUMA site (<b>b</b>) box plots for ethylbenzene and <span class="html-italic">p</span>-xylene concentrations at the SEDUMA site.</p> Full article ">Figure 3 Cont.
<p>Seasonal variation and descriptive statistics for BTEX concentrations at the SEDUMA site. Note: SP: Spring season; SU: Summer season; AU: Autumn season; WI: Winter season. The central horizontal bars are the medians. The lower and upper limits of the box are the first and third quartiles. Where <span class="html-fig-inline" id="atmosphere-08-00107-i003"> <img alt="Atmosphere 08 00107 i003" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i003.png"/></span> is the mean value; <span class="html-fig-inline" id="atmosphere-08-00107-i004"> <img alt="Atmosphere 08 00107 i004" src="/atmosphere/atmosphere-08-00107/article_deploy/html/images/atmosphere-08-00107-i004.png"/></span> represents maximum and minimum values; the horizontal width of the box has no statistical significance and is only for better visualization. (<b>a</b>) box plots for benzene and toluene concentrations at the SEDUMA site (<b>b</b>) box plots for ethylbenzene and <span class="html-italic">p</span>-xylene concentrations at the SEDUMA site.</p> Full article ">Figure 4
<p>Seasonal wind roses for the study area. (<b>a</b>) wind rose for spring season (<b>b</b>) wind rose for summer season (<b>c</b>) wind rose for autumn season (<b>d</b>) wind rose for winter season.</p> Full article ">Figure 4 Cont.
<p>Seasonal wind roses for the study area. (<b>a</b>) wind rose for spring season (<b>b</b>) wind rose for summer season (<b>c</b>) wind rose for autumn season (<b>d</b>) wind rose for winter season.</p> Full article ">Figure 5
<p>Median concentrations (µg m<sup>−3</sup>) for BTEX and wind direction diagrams for both sampling sites. (<b>a</b>) results for spring season at the Cholul site (<b>b</b>) results for summer season at the Cholul site (<b>c</b>) results for autumn season at the Cholul site (<b>d</b>) results for winter season at the Cholul site (<b>e</b>) results for spring season at the SEDUMA site (<b>f</b>) results for summer season at the SEDUMA site (<b>g</b>) results for autumn season at the SEDUMA site (<b>h</b>) results for winter season at the SEDUMA site.</p> Full article ">Figure 5 Cont.
<p>Median concentrations (µg m<sup>−3</sup>) for BTEX and wind direction diagrams for both sampling sites. (<b>a</b>) results for spring season at the Cholul site (<b>b</b>) results for summer season at the Cholul site (<b>c</b>) results for autumn season at the Cholul site (<b>d</b>) results for winter season at the Cholul site (<b>e</b>) results for spring season at the SEDUMA site (<b>f</b>) results for summer season at the SEDUMA site (<b>g</b>) results for autumn season at the SEDUMA site (<b>h</b>) results for winter season at the SEDUMA site.</p> Full article ">
1676 KiB  
Article
Theoretical Model of Spiral Rain Clusters and Analysis of Their Horizontal Structure Equation
by Jie Yu, Jiquan Zhang and Ming Zhang
Atmosphere 2017, 8(6), 106; https://doi.org/10.3390/atmos8060106 - 15 Jun 2017
Cited by 2 | Viewed by 3673
Abstract
Rain clusters are mesoscale disaster weather systems, and some of rain clusters have spiral structures. In this paper, a theoretical model of spiral rain cluster is established under pseudo-adiabatic approximation, and its horizontal structure equation is obtained. The study shows that the horizontal [...] Read more.
Rain clusters are mesoscale disaster weather systems, and some of rain clusters have spiral structures. In this paper, a theoretical model of spiral rain cluster is established under pseudo-adiabatic approximation, and its horizontal structure equation is obtained. The study shows that the horizontal structure with spiral arm rain clusters has the following characteristics: at locations close enough to the center of the rain clusters, its shape is round and symmetrical; at locations far from the center, there may be spiral arms; the intensity of the vertical ascending motion of the rain cluster decreases with increasing distance from the center; and the vertical ascending motion is larger on the axis of the spiral arms of rain clusters. The conclusions obtained are consistent with not only the numerical results but also the observational facts. Full article
(This article belongs to the Section Meteorology)
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<p>Radar image for 1 h cumulative rainfall at 9:12 CST (25 August 2008) (the unit of the color bar is mm).</p> Full article ">Figure 2
<p>Image of the Bessel function of the first kind.</p> Full article ">Figure 3
<p>Horizontal structure of theoretical rain clusters with spiral arms. Positive values indicate ascending movement, negative values indicate descending movement. The unit of the coordinate X axis and Y axis is 10 km. The unit of the color bar is m·s<sup>−1</sup>. (<b>a</b>) <span class="html-italic">m</span> = 2, <span class="html-italic">k</span> = 1 km<sup>−1</sup> and <span class="html-italic">R</span> = 20 km; (<b>b</b>) <span class="html-italic">m</span> = 1, <span class="html-italic">k</span> = 1 km<sup>−1</sup> and <span class="html-italic">R</span> = 20 km; and (<b>c</b>) <span class="html-italic">m</span> = 2, <span class="html-italic">k</span> = 2 km<sup>−1</sup> and <span class="html-italic">R</span> = 20 km.</p> Full article ">
2941 KiB  
Article
Cryogenic Displacement and Accumulation of Biogenic Methane in Frozen Soils
by Gleb Kraev, Ernst-Detlef Schulze, Alla Yurova, Alexander Kholodov, Evgeny Chuvilin and Elizaveta Rivkina
Atmosphere 2017, 8(6), 105; https://doi.org/10.3390/atmos8060105 - 15 Jun 2017
Cited by 35 | Viewed by 7628
Abstract
Evidences of highly localized methane fluxes are reported from the Arctic shelf, hot spots of methane emissions in thermokarst lakes, and are believed to evolve to features like Yamal crater on land. The origin of large methane outbursts is problematic. Here we show, [...] Read more.
Evidences of highly localized methane fluxes are reported from the Arctic shelf, hot spots of methane emissions in thermokarst lakes, and are believed to evolve to features like Yamal crater on land. The origin of large methane outbursts is problematic. Here we show, that the biogenic methane (13C ≤ −71‰) which formed before and during soil freezing is presently held in the permafrost. Field and experimental observations show that methane tends to accumulate at the permafrost table or in the coarse-grained lithological pockets surrounded by the sediments less-permeable for gas. Our field observations, radiocarbon dating, laboratory tests and theory all suggest that depending on the soil structure and freezing dynamics, this methane may have been displaced downwards tens of meters during freezing and has accumulated in the lithological pockets. The initial flux of methane from the one pocket disclosed by drilling was at a rate of more than 2.5 kg C(CH4) m−2 h−1. The age of the methane was 8–18 thousand years younger than the age of the sediments, suggesting that it was displaced tens of meters during freezing. The theoretical background provided the insight on the cryogenic displacement of methane in support of the field and experimental data. Upon freezing of sediments, methane follows water migration and either dissipates in the freezing soils or concentrates at certain places controlled by the freezing rate, initial methane distribution and soil structure. Full article
(This article belongs to the Special Issue Atmospheric Methane)
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<p>Location of the methane sampling sites in the northeastern Siberia; the area squared on the Circum-Arctic permafrost map [<a href="#B29-atmosphere-08-00105" class="html-bibr">29</a>]. Reproduced with permission from Philippe Rekacewicz, visionscarto.net.</p> Full article ">Figure 2
<p>Soil composition, CH<sub>4</sub> concentration (shown by bars) and stable carbon isotopes (shown by circles) of CH<sub>4</sub>, radiocarbon dates of dissolved organic matter and CH<sub>4</sub> from the gas filling borehole 3,4-07. Key: 1, silts; 2, sands; 3, gravel; 4, peat; 5, radiocarbon dating samples; 6, permafrost table. Unit numbers define the geological layers, described in the text. Initial data are presented in <a href="#atmosphere-08-00105-t001" class="html-table">Table A1</a>.</p> Full article ">Figure 3
<p>CH<sub>4</sub> concentrations averaged by relative depth in the uppermost permafrost and active layer. (<b>a</b>) Yedoma and the Cover Layer (normalized by the Cover Layer thickness at the sampling sites). (<b>b</b>) Active layer and rarely thawed transient layer (normalized by the maximal annual thawing depth at the sampling sites). CH<sub>4</sub> was sampled from the permafrost in northeastern Siberia in 2004–2008 (<a href="#atmosphere-08-00105-t002" class="html-table">Table A2</a> and <a href="#atmosphere-08-00105-t003" class="html-table">Table A3</a>).</p> Full article ">Figure 4
<p>Methane concentration patterns in sand (<b>a</b>) and silt (<b>b</b>) after the freezing of unsaturated (moisture content 30%) methane-enriched soils.</p> Full article ">Figure 5
<p>Schematic representation of the water and methane flows in freezing soils. (<b>a</b>) Cryogenic suction case; (<b>b</b>) Ice penetration to pores with squeezing of the excess water and methane.</p> Full article ">Figure 6
<p>Direction of methane flux and water flux. (<b>a</b>) In unsaturated soils; (<b>b</b>) In saturated soils. Line labels denote dimensionless C<sub>1</sub>/C<sub>2</sub>, showing the initial methane distribution in a 0.6 m thick sample soil column, with C<sub>2</sub> = 178 mol, and equilibrium pressure. In saturated soils, methane flux always follows water flux.</p> Full article ">Figure 7
<p>Paleoenvironmental reconstruction of the sequence at Kolyma floodplain disclosed by borehole 3,4-07. (<b>a</b>) Soils of the thaw bulb under the floodplain lake with potential methane production; (<b>b</b>) Freezing of the methane-enriched soils of a thaw bulb after the lake drainage; (<b>c</b>) Methane trapped in the pores and cavities of permafrost after complete freezing of the thaw bulb. Key: 1, floodplain wetland; 2, warm riverine and lacustrine waters; 3, riverine alluvium, gravel layer; 4, riverine alluvium, sand layer; 5, floodplain alluvium, silt layer; 6, lacustrine deposits, peaty silts; 7, permafrost table; 8, free methane; 9, trapped methane; 10, borehole.</p> Full article ">
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Article
Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods
by Zengliang Zang, Weiqi Wang, Xinghong Cheng, Bin Yang, Xiaobin Pan and Wei You
Atmosphere 2017, 8(6), 104; https://doi.org/10.3390/atmos8060104 - 12 Jun 2017
Cited by 19 | Viewed by 5378
Abstract
The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM [...] Read more.
The aerosol optical depth (AOD) from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH) is a popular factor in the regression model of AOD and PM2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP) reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the regression coefficients of the BLH are positive and negative in the stable and convective regression models, respectively. Moreover, the effects of meteorological factors are indeed related to the types of BLHs. Full article
(This article belongs to the Section Air Quality)
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<p>The spatial distribution of the PM<sub>2.5</sub> station, radiosonde station and Aerosol Robotic Network (AERONET) station used in this study.</p> Full article ">Figure 2
<p>The average vertical temperature profiles of stable BLH and convective BLH for spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>) and winter (<b>d</b>).</p> Full article ">Figure 3
<p>Average BLHs of stable boundary layer (<b>a</b>) and convective boundary layer (<b>b</b>) in Beijing during 2014–2015 calculated by different methods.</p> Full article ">Figure 4
<p>Scatter plot of <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">SBI</mi> </mrow> <mrow> <mi mathvariant="normal">Sta</mi> </mrow> </msubsup> </mrow> </semantics> </math> vs. <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">RE</mi> </mrow> <mrow> <mi mathvariant="normal">Sta</mi> </mrow> </msubsup> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Cross-correlations between convective BLHs from six different methods.</p> Full article ">Figure 6
<p>Scatter plots of the observed vs. the predicted surface PM<sub>2.5</sub> concentrations for the M-I-Sta (<b>a</b>) and the M-I-Con (<b>b</b>).</p> Full article ">Figure 7
<p>Scatter plots of the observed vs. the predicted surface PM<sub>2.5</sub> concentrations from the M-II-Sta with <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">SBI</mi> </mrow> <mrow> <mi mathvariant="normal">Sta</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>a</b>) and <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">RE</mi> </mrow> <mrow> <mi mathvariant="normal">Sta</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>b</b>).</p> Full article ">Figure 8
<p>Scatter plots of the observed vs. the predicted surface PM<sub>2.5</sub> concentrations from the M-II-Con with <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <msub> <mi mathvariant="sans-serif">θ</mi> <mi mathvariant="normal">v</mi> </msub> </mrow> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>a</b>), <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">EI</mi> </mrow> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>b</b>), <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mi mathvariant="sans-serif">θ</mi> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>c</b>), <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">RH</mi> </mrow> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>d</b>), <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mi mathvariant="normal">N</mi> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>e</b>) and <math display="inline"> <semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">BLH</mi> </mrow> <mrow> <mi mathvariant="normal">RE</mi> </mrow> <mrow> <mi mathvariant="normal">Con</mi> </mrow> </msubsup> </mrow> </semantics> </math> (<b>f</b>).</p> Full article ">
590 KiB  
Article
Mitigation of Livestock Odors Using Black Light and a New Titanium Dioxide-Based Catalyst: Proof-of-Concept
by Wenda Zhu, Jacek A. Koziel and Devin L. Maurer
Atmosphere 2017, 8(6), 103; https://doi.org/10.3390/atmos8060103 - 10 Jun 2017
Cited by 30 | Viewed by 10456
Abstract
Concentrated livestock feeding operations have become a source of odorous gas emissions that impact air quality. Comprehensive and practical technologies are needed for a sustainable mitigation of the emissions. In this study, we advance the concept of using a catalyst for barn walls [...] Read more.
Concentrated livestock feeding operations have become a source of odorous gas emissions that impact air quality. Comprehensive and practical technologies are needed for a sustainable mitigation of the emissions. In this study, we advance the concept of using a catalyst for barn walls and ceilings for odor mitigation. Two catalysts, a new TiO2-based catalyst, PureTi Clean, and a conventional Evonik (formerly Degussa, Evonik Industries, Essen, Germany) P25 (average particle size 25 nm) catalyst, were studied for use in reducing simulated odorous volatile organic compound (VOC) emissions on a laboratory scale. The UV source was black light. Dimethyl disulfide (DMDS), diethyl disulfide (DEDS), dimethyl trisulfide (DMTS), butyric acid, p-cresol, and guaiacol were selected as model odorants. The effects of the environmental parameters, the presence of swine dust covering the catalyst, the catalyst type and layer density, and the treatment time were tested. The performance of the PureTi catalyst at 10 µg/cm2 was comparable to that of P25 at 250 µg/cm2. The odorant reduction ranged from 100.0 ± 0.0% to 40.4 ± 24.8% at a treatment time of 200 s, simulating wintertime barn ventilation. At a treatment time of 40 s (simulating summertime barn ventilation), the reductions were lower (from 27.4 ± 8.3% to 62.2 ± 7.5%). The swine dust layer on the catalyst surface blocked 15.06 ± 5.30% of UV365 and did not have a significant impact (p > 0.23) on the catalyst performance. Significant effects of relative humidity and temperature were observed. Full article
(This article belongs to the Section Air Quality)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of a UV treatment system (ISU Air Quality Lab).</p> Full article ">
9854 KiB  
Article
Improving Residential Wind Environments by Understanding the Relationship between Building Arrangements and Outdoor Regional Ventilation
by Wei You, Zhi Gao, Zhi Chen and Wowo Ding
Atmosphere 2017, 8(6), 102; https://doi.org/10.3390/atmos8060102 - 9 Jun 2017
Cited by 21 | Viewed by 5533
Abstract
This paper explores the method of assessing regional spatial ventilation performance for the design of residential building arrangements at an operational level. Three ventilation efficiency (VE) indices, Net Escape Velocity (NEV), Visitation Frequency (VF) and spatial-mean Velocity Magnitude (VM), are adopted to quantify [...] Read more.
This paper explores the method of assessing regional spatial ventilation performance for the design of residential building arrangements at an operational level. Three ventilation efficiency (VE) indices, Net Escape Velocity (NEV), Visitation Frequency (VF) and spatial-mean Velocity Magnitude (VM), are adopted to quantify the influence of design variation on VE within different regional spaces. Computational Fluid Dynamics (CFD) method is applied to calculate VE indices mentioned above. Several residential building arrangement cases are set to discuss the effect of different building length, lateral spacing and layouts on four typical space patterns under wind directions oblique or perpendicular to the main (long) building facade. The simulation results prove that NEV, VF and VM are useful VE indices, which can reflect different features of flow pattern in studied regional domains. Preliminary parametric studies indicate that wind direction might be the most important factor for improving spatial ventilation. When the angle between main building facade and wind direction is more than 30°, ventilation of different exterior spaces could improve evidently. When wind direction is perpendicular to main building façade, decreasing building length can increase NEV of the middle space by 50%, while decreasing lateral spacing would decrease NEV of the intersection space by 35%. Full article
(This article belongs to the Special Issue Recent Advances in Urban Ventilation Assessment and Flow Modelling)
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<p>View of typical residential building arrangements in China: (<b>a</b>) Shenzhen; (<b>b</b>) Nanjing; (<b>c</b>) Shanghai; and (<b>d</b>) Beijing.</p> Full article ">Figure 2
<p>Living units and unit combination in a residential building: (<b>a</b>) Residential building size; (<b>b</b>) Living unit types (unit: m); (<b>c</b>) Living unit combination.</p> Full article ">Figure 3
<p>Setup of calculation cases (Units m): (<b>a</b>) Calculated residential building model (B1 = 24, B2 = D3 = 30); (<b>b</b>) Building layout patterns.</p> Full article ">Figure 4
<p>Typical studied areas and space pattern classification (Unit: m): (<b>a</b>) Studied domain; (<b>b</b>) Space pattern classification of studied domain.</p> Full article ">Figure 5
<p>Computational domain and boundary conditions.</p> Full article ">Figure 6
<p>Gird resolution in the computational domain (Case A).</p> Full article ">Figure 7
<p>Wind flow patterns for five cases under two wind directions (θ = S 0° and θ = SE 30°) and studied domains for each case (<span class="html-italic">z</span> = 0.1 H).</p> Full article ">Figure 8
<p>Ventilation performance of different studied domains for five cases under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30°.</p> Full article ">Figure 9
<p>Concentration fields within the studied RM1 and RM2 domains (RM space) for different design variations under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 10
<p>Effect of building length on ventilation efficiency indices within RM1 and RM2 domains (RM space) under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 11
<p>Concentration fields within studied RS space for different arrangements and building spacing under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 12
<p>Effect of building spacing on ventilation efficiency indices within the RS space under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 13
<p>Concentration fields within studied ROD1 and ROD2 domains (ROD space) for different design variations under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 14
<p>Effect of design variations on ventilation efficiency indices within ROD1 and ROD2 domains (ROD space) under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (Unit: m).</p> Full article ">Figure 15
<p>Concentration fields within the studied RID1 and RID2 domains (RID space) for different design variations under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (unit: m).</p> Full article ">Figure 16
<p>Effect of design variations on ventilation efficiency indices within the RID1 and RID2 domains (RID space) under wind direction: (<b>a</b>) θ = S 0°; and (<b>b</b>) θ = SE 30° (unit: m).</p> Full article ">
3953 KiB  
Article
Comparison of Sensible Heat Fluxes Measured by a Large Aperture Scintillometer and Eddy Covariance System over a Heterogeneous Farmland in East China
by Xin Li, Zhiqiu Gao, Yubin Li and Bing Tong
Atmosphere 2017, 8(6), 101; https://doi.org/10.3390/atmos8060101 - 6 Jun 2017
Cited by 12 | Viewed by 4500
Abstract
The sensible heat is an important component in surface energy partitioning over the land surface. This paper compared the sensible heat fluxes measured by a large aperture scintillometer system (LAS) and an eddy covariance system (EC) over a rice paddy with a patch [...] Read more.
The sensible heat is an important component in surface energy partitioning over the land surface. This paper compared the sensible heat fluxes measured by a large aperture scintillometer system (LAS) and an eddy covariance system (EC) over a rice paddy with a patch of mulberry seedlings in the east China coastal region during the period from 13 September–11 October 2015. During the observation period, easterlies and northerlies prevailed, and 96% easterlies and northerlies had a speed of 0–6 m s−1. The sensible heat fluxes measured by the two systems reflected that the value of HLAS generally was inclined to be larger than HEC with the average difference of 20.30 W m−2, and the uncertainty for two instruments was less than 17 W m−2. Analysis of the average footprint resulted that the mulberry seedling field always had a higher contribution to LAS than that to EC, which could be the reason that HLAS was always larger than HEC. During the days when the contributions of the mulberry seedling field to the two systems were close to each other, the sensible heat flux measurements of the two instruments were similar. The case analysis on typical sunny days showed that there would be larger sensible heat fluxes over the mulberry seedling field than in the rice paddy field especially under larger net radiation conditions. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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<p>(<b>a</b>) Map of eastern China; the location of the experimental site is shown as a red star; (<b>b</b>) The experimental site. Points A and B are the transmitter and receiver of the large aperture scintillometer (LAS) with an optical path of 470 m, respectively; C is the eddy covariance (EC) system; and D is the automatic meteorological station (AWS). The mulberry seedling field and grove field are also shown with green and blue frames, respectively. Concrete roads are shown as white bands.</p> Full article ">Figure 2
<p>Solution of Equation (14) using fixed-point recursion, and the relationship of <span class="html-italic">M</span> and <math display="inline"> <semantics> <mrow> <mrow> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>z</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> <mo>−</mo> <mi>d</mi> </mrow> <mo>)</mo> </mrow> </mrow> <mo>/</mo> <mi>L</mi> </mrow> </mrow> </semantics> </math> is monotonic.</p> Full article ">Figure 3
<p>Uncertainty of (<b>a</b>) EC- and (<b>b</b>) LAS-derived sensible heat fluxes from measurements during this observation period.</p> Full article ">Figure 4
<p>Temporal variations of (<b>a</b>) wind speed at 3-, 5-, 8- and 10-m heights (lines); (<b>b</b>) air temperature at 3-, 5-, 8- and 10-m heights; (<b>c</b>) relative humidity at 3-, 5-, 8- and 10-m heights; (<b>d</b>) surface atmospheric pressure and (<b>e</b>) precipitation from 12 September–11 October 2015.</p> Full article ">Figure 5
<p>Distributions of (<b>a</b>) wind directions at 10-m height during the daytime and (<b>b</b>) wind directions at 10-m height during nighttime.</p> Full article ">Figure 6
<p>Variations of sensible heat fluxes (H), latent heat fluxes (LE) and net radiation (Rn) from 13 September–11 October 2015. The three days that are discussed in <a href="#sec4dot3-atmosphere-08-00101" class="html-sec">Section 4.3</a> are marked in yellow.</p> Full article ">Figure 7
<p>Variations of <span class="html-italic">H<sub>LAS</sub></span> and <span class="html-italic">H<sub>EC</sub></span> when the sensible heat fluxes were greater than zero from 12 September–11 October 2015. The red points represent the median values binned at 5 W m<sup>−2</sup> of the interval. The corresponding bars represent the interquartile ranges. The sample numbers are also shown in the figure. The regression lines are plotted in blue. The slopes and determination coefficients are also shown in the picture.</p> Full article ">Figure 8
<p>Relationship between LAS-derived and EC-derived sensible heat fluxes under (<b>a</b>) east wind; (<b>b</b>) west wind; (<b>c</b>) south wind and (<b>d</b>) north wind conditions is shown in the left panels. The slopes and determination coefficients are also shown in the picture. The regression lines are plotted in red. The corresponding average footprints for LAS and EC under (<b>e</b>) east wind; (<b>f</b>) west wind; (<b>g</b>) south wind and (<b>h</b>) north wind conditions are shown in the right panels. The blue rectangle represents the grove, and the green trapezoid represents the mulberry seedling field. The numbers (<span class="html-italic">n</span>) of data points are also given in the figure.</p> Full article ">Figure 9
<p>Relationship between LAS-derived and EC-derived sensible heat fluxes under (<b>a</b>) east wind and (<b>b</b>) north wind conditions. The different wind speed was plotted in black point (0–2 m s<sup>−1</sup>), green plus sign (2–4 m s<sup>−1</sup>) and blue asterisk (4–6 m s<sup>−1</sup>). The slopes and determination coefficients were also shown in the picture. The regression lines were plotted in red. The corresponding average footprints when east wind was 0–2 m s<sup>−1</sup> (<b>c</b>), 2–4 m s<sup>−1</sup> (<b>d</b>) and 4–6 m s<sup>−1</sup> (<b>e</b>). The corresponding average footprints when north wind was 0–2 m s<sup>−1</sup> (<b>f</b>), 2–4 m s<sup>−1</sup> (<b>g</b>) and 4–6 m s<sup>−1</sup> (<b>h</b>). The blue rectangle represents the grove, and the green trapezoid represents the mulberry seedling field. The numbers (n) of data points are also given in the figure.</p> Full article ">Figure 10
<p>Variations of <span class="html-italic">H<sub>LAS</sub></span> and <span class="html-italic">H<sub>EC</sub></span> on 2 (<b>a</b>) and 5 October (<b>b</b>). Footprints of sensible heat fluxes measured by EC and LAS systems on 2 October (<b>c</b>), and on 5 October (<b>d</b>). The blue rectangle represents the grove field, and the green trapezoid represents the mulberry seedling field. Additionally, the corresponding time is marked in the lower right corner of picture.</p> Full article ">Figure 11
<p>Variations of <span class="html-italic">H<sub>LAS</sub></span> and <span class="html-italic">H<sub>EC</sub></span> on 28 September were shown in (<b>a</b>,<b>b</b>) Footprints of sensible heat fluxes measured by the EC and LAS systems on 28 September. The blue rectangle represents the grove, and the green trapezoid represents the mulberry seedling field. Additionally, the corresponding time is marked in the lower right corner of picture.</p> Full article ">
9289 KiB  
Article
Air Quality and Control Measures Evaluation during the 2014 Youth Olympic Games in Nanjing and its Surrounding Cities
by Hui Zhao, Youfei Zheng and Ting Li
Atmosphere 2017, 8(6), 100; https://doi.org/10.3390/atmos8060100 - 4 Jun 2017
Cited by 21 | Viewed by 5183
Abstract
Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce [...] Read more.
Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce pollutant emissions in Nanjing and its surrounding cities during the Youth Olympic Games. The Air Quality Index (AQI) is an index of air quality which is used to inform the public about levels of air pollution and associated health risks. In this study, we use the AQI and air pollutant concentrations data to evaluate the effectiveness of the implementation of control measures. The results suggest that the emission reduction measures significantly improved air quality in Nanjing. In August 2014, the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 were 42.44 μg·m−3, 59.01 μg·m−3, 11.12 μg·m−3, 31.09 μg·m−3, 0.76 mg·m−3 and 38.39 μg·m−3, respectively, and fell by 35.92%, 36.75%, 20.40%, 15.05%, 8.54% and 47.15%, respectively, compared to the prophase mean before the emission reduction. After the emission reduction, the mean concentrations of PM2.5, PM10, SO2, NO2, and O3 increased by 20.81%, 41.84%, 22.84%, 21.16% and 60.93%, respectively, which is due to the cancellation of temporary atmospheric pollution control measures. The air pollutants diurnal variation curve during the emission reduction was lower than the other two periods, except for CO. In addition, the AQI of Nanjing and its surrounding cities showed a downward trend, compared with July 2014. The most of effective method to control air pollution is to implement the measures of regional cooperation and joint defense and control, and reduce local emissions during the polluted period, such as airborne dust, coal-burning, vehicle emissions, mobile sources and industrial production. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting)
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<p>Distribution of air quality monitoring stations in Nanjing and surrounding cities.</p> Full article ">Figure 2
<p>Daily variation of the AQI and air pollutant concentrations in different periods.</p> Full article ">Figure 3
<p>Diurnal variation of air pollutant concentrations in different periods.</p> Full article ">Figure 4
<p>Spatial variation of the AQI and air pollutants at each site before and during the emission reduction.</p> Full article ">Figure 5
<p>Variation of the AQI in Nanjing and its surrounding cities during the emission reduction period.</p> Full article ">Figure 6
<p>Decrease percentage of the AQI during the emission reduction period compared with July 2014 in Nanjing and its surrounding cities.</p> Full article ">
6174 KiB  
Article
Back-Calculation of Traffic-Related PM10 Emission Factors Based on Roadside Concentration Measurements
by Yuan Wang, Zihan Huang, Yujie Liu, Qi Yu and Weichun Ma
Atmosphere 2017, 8(6), 99; https://doi.org/10.3390/atmos8060099 - 2 Jun 2017
Cited by 11 | Viewed by 5974
Abstract
Many researchers have failed to utilize back-calculation to estimate traffic emissions effectively or have obtained unclear results. In this study, the back-calculation of traffic-related PM10 emission factors based on roadside concentration measurements was analyzed. Experimental conditions were considered to ensure the success [...] Read more.
Many researchers have failed to utilize back-calculation to estimate traffic emissions effectively or have obtained unclear results. In this study, the back-calculation of traffic-related PM10 emission factors based on roadside concentration measurements was analyzed. Experimental conditions were considered to ensure the success of back-calculation. Roadside measurements were taken in a street canyon in Shanghai, China. Concentrations from a background site were often found to exceed the measured concentrations at the roadside on polluted days as more errors occurred in the background concentrations. On clean days, these impacts were negligible. Thus, only samples collected on clean days were used in back-calculation. The mean value from back-calculation was 0.138 g/km, which was much smaller than the results obtained using official emission models. Emission factors for light-duty vehicles (LDV), medium-duty vehicles (MDV), heavy-duty vehicles (HDV), and motorcycles were approximately 0.121, 0.427, 0.445, and 0.096 g/km, respectively. The fleet-averaged non-exhaust emission factor was approximately 0.121 g/km, indicating that road dust accounted for 87.7% of the roadside concentration increments. According to the dispersion simulation of reserved samples, the concentrations simulated using back-calculated emission factors were in better agreement with the measured data than the concentrations derived using modeled emission factors. Full article
(This article belongs to the Section Air Quality)
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<p>Measurement and background sites. (<b>a</b>) Locations of measurement site, background site and Station YP; (<b>b</b>) Surroundings of Station YP; (<b>c</b>) Surroundings of measurement site and background site; (<b>d</b>) Measurement site in the street canyon.</p> Full article ">Figure 2
<p>Comparison of PM<sub>10</sub> concentrations at the background site and Station YP (μg/m<sup>3</sup>). (<b>a</b>) Concentrations at the background site and Station YP; (<b>b</b>) Average concentration ratios on individual days.</p> Full article ">Figure 3
<p>Roadside concentration increments (μg/m<sup>3</sup>) on clean days and polluted days.</p> Full article ">Figure 4
<p>PM<sub>10</sub> concentrations (μg/m<sup>3</sup>) and contribution rates of traffic to PM<sub>10</sub> concentrations (%) in the roadside environment.</p> Full article ">Figure 5
<p>Number of vehicles per hour (veh/h).</p> Full article ">Figure 6
<p>Comparison of OSPM-simulated and measured roadside concentrations of PM<sub>10</sub> (μg/m<sup>3</sup>). (<b>a</b>) Back-calculated emission factors for different kinds of vehicles were used; (<b>b</b>) Modeled emission factor was used.</p> Full article ">
40809 KiB  
Article
Characteristics of Strong Cold Air Outbreaks in China’s Central and Eastern Mongolian Region between 1970 and 2013
by Zongming Wang, Zhaobo Sun and Gang Zeng
Atmosphere 2017, 8(6), 98; https://doi.org/10.3390/atmos8060098 - 26 May 2017
Cited by 5 | Viewed by 4743
Abstract
Strong cold air outbreak tracking has been a key meteorological focal point over the years. With observational data and gridded datasets, we used the “three-dimensional wind speed trajectory inverse method” to trace cold air intrusion tracks that occurred during the winter half-years for [...] Read more.
Strong cold air outbreak tracking has been a key meteorological focal point over the years. With observational data and gridded datasets, we used the “three-dimensional wind speed trajectory inverse method” to trace cold air intrusion tracks that occurred during the winter half-years for the central and eastern parts of Inner Mongolia in 1970–2013. The results indicated that there were a total of 303 northwest and 32 westward tracks intruding from along the north end and southern side of the Altai Mountains, respectively, 118 northward tracks intruding from the two individual sides of the Yablonoi Mountains, and 16 occurrences of “other” tracks. The imminent circulation evolution pattern prior to outbreaks essentially causes three categories of cold air masses to undergo dramatic temperature increases, thereby reducing the impacts of source regional differences on the subject air masses. The measure of the annual frequency reduction in northwest tracks was determined to be 0.41 incidents every ten years, while other tracks’ annual frequencies increased, which essentially implies the probable increase of extreme cold in northeast and central China and an increase in the frequency of continuous cold weather exceeding a three-day span in southern China. Full article
(This article belongs to the Section Meteorology)
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<p>Map showing the study area.</p> Full article ">Figure 2
<p>72 h track before outbreaks. Horizontal views in (<b>a</b>): the grey are north tracks, the red are northwest tracks, and the black are west tracks. The bold lines and dots are average tracks and daily average positions, respectively. Vertical views in (<b>b</b>–<b>d</b>): (<b>b</b>) is northwest tracks, (<b>c</b>) is north tracks, and (<b>d</b>) is west tracks; 0 in the horizontal coordinate represents outbreak and “-” represents before the outbreak, with an associated time interval of 1 h.</p> Full article ">Figure 3
<p>Interannual variation of frequency (<b>a</b>–<b>c</b>) and interannual variation of frequency proportion (<b>d</b>–<b>f</b>) obtained by dividing the total frequencies. The dotted line is a simple-regression trend line.</p> Full article ">Figure 4
<p>Composite sea level pressure (contours, 5hPa interval) in (<b>d</b>–<b>f</b>). Composite anomalies of (<b>a</b>–<b>c</b>) geopotential height at 500 hPa (contours, 20 m interval, and the bolded items with a 95% reliability test) and 850 hPa (shadow, grey dots marked with 95% reliability test area), (<b>d</b>–<b>f</b>) temperature (shadow, grey dots marked with 95% reliability test area) and winds (vectors) at 850 hPa, and (<b>g</b>–<b>i</b>) vertical cross sections of geopotential height (contours, 20 m interval) and potential temperature (shadings) along red lines in (<b>a</b>–<b>c</b>) during ±2 days relative to 303 cold events of northwest tracks occurrences in the entire analysis period.</p> Full article ">Figure 5
<p>Same as <a href="#atmosphere-08-00098-f004" class="html-fig">Figure 4</a>, but for 118 cold events of north tracks.</p> Full article ">Figure 6
<p>Same as <a href="#atmosphere-08-00098-f004" class="html-fig">Figure 4</a>, but for 32 cold events of west tracks.</p> Full article ">Figure 7
<p>Composite Arctic Oscillation (AO) index of the 10 days before and after outbreaks.</p> Full article ">Figure 8
<p>Temperature-fall period caused by the three categories of strong cold air outbreaks (CAOs) (<b>a</b>–<b>c</b>), temperature anomaly (<b>d</b>–<b>f</b>), and lasting days (<b>g</b>–<b>i</b>). (<b>a</b>,<b>d</b>,<b>g</b>) are northwest tracks; (<b>b</b>,<b>e</b>,<b>h</b>) are north tracks; and (<b>c</b>,<b>f</b>,<b>i</b>) are west tracks.</p> Full article ">Figure 9
<p>Time evolution of each item three days before and after outbreaks on 850 hPa. (<b>a</b>–<b>d</b>) are northwest tracks; (<b>e</b>–<b>h</b>) are north tracks; and (<b>i</b>–<b>l</b>) are west tracks. Panels are arranged on the basis of each region’s geographic position (<a href="#atmosphere-08-00098-f001" class="html-fig">Figure 1</a>).</p> Full article ">
5697 KiB  
Article
Observed Effects of Vegetation Growth on Temperature in the Early Summer over the Northeast China Plain
by Xiaxiang Li, Xuezhen Zhang and Lijuan Zhang
Atmosphere 2017, 8(6), 97; https://doi.org/10.3390/atmos8060097 - 25 May 2017
Cited by 6 | Viewed by 4434
Abstract
The effect of vegetation on temperature is an emerging topic in the climate science community. Existing studies have mostly examined the effects of vegetation on daytime temperature (Tmax), whereas this study investigates the effects on nighttime temperature (Tmin [...] Read more.
The effect of vegetation on temperature is an emerging topic in the climate science community. Existing studies have mostly examined the effects of vegetation on daytime temperature (Tmax), whereas this study investigates the effects on nighttime temperature (Tmin). Ground measurements from 53 sites across northeastern China (NEC) from 1982 to 2006 show that early summer (June) Tmax and Tmin increased at mean rates of approximately 0.61 °C/10 year and 0.67 °C/10 year, respectively. Over the same period, the satellite-based Normalized Difference Vegetation Index (NDVI) decreased by approximately 0.10 (accounting for 18% of the climatological NDVI for 1982–1991). It is highlighted that a larger increase in Tmax (Tmin) co-occurred spatially with a larger (smaller) decrease in NDVI. Deriving from such spatial co-occurrences, we found that the spatial variability of changes in Tmax (i.e., ΔTmax) is negatively correlated with the spatial variability of changes in NDVI (i.e., ΔNDVI), while the spatial variability of changes in Tmin (i.e., ΔTmin) is positively correlated (r2 = 0.10; p < 0.05) with that of ΔNDVI. Similarly, we detected significant positive correlations between the spatial variability of ΔNDVI and the change in surface latent heat flux (r2 = 0.16; p < 0.01) and in surface air specific humidity (r2 = 0.28; p < 0.001). These findings on the spatial co-occurrences suggest that the vegetation growth intensifies the atmospheric water vapor through evapotranspiration, which enhances the atmospheric downward longwave radiation and strengthens the greenhouse warming effects at night. Thereby, the positive correlation between ΔNDVI and ΔTmin is better understood. These results indicate that vegetation growth may not only exert effects on daytime temperature but also exert warming effects on nighttime temperature by increasing atmospheric water vapor and thus intensifying the local greenhouse effect. This study presents new observation evidence of the effects of vegetation on local temperature. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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<p>Location of the study area in China and spatial distribution of the meteorological sites from the China Meteorological Administration (gray shading denotes the percentage of cropland within 10 km around the site in the year 2000).</p> Full article ">Figure 2
<p>Mean half-monthly Normalized Difference Vegetation Index (NDVI) for 1982–1991 and 1997–2006 within the study area (<b>a</b>) and the spatial pattern of NDVI changes (1997–2006 minus 1982–1991) for June (<b>b</b>).</p> Full article ">Figure 3
<p>Changes in the monthly mean daily maximum (<span class="html-italic">T</span><sub>max</sub>) and minimum (<span class="html-italic">T</span><sub>min</sub>) temperatures during June between 1982–1991 and 1997–2006 (1997–2006 minus 1982–1991) at each site.</p> Full article ">Figure 4
<p>Mean <span class="html-italic">T</span><sub>max</sub> and <span class="html-italic">T</span><sub>min</sub> changes at sites grouped by the amplitude of NDVI decrease (Δ<span class="html-italic">NDVI</span>), between 1982–1991 and 1997–2006 (top panel, (<b>a</b>,<b>b</b>), are derived directly from site observations; bottom panel, (<b>c</b>,<b>d</b>), are after removing the regional mean temperature changes).</p> Full article ">Figure 5
<p>Correlations between the spatial variability of NDVI changes (Δ<span class="html-italic">NDVI</span>) and local temperature changes (<b>a</b>), Δ<span class="html-italic">T</span><sub>max</sub>; (<b>b</b>), Δ<span class="html-italic">T</span><sub>min</sub> in June from 1982–1991 to 1997–2006 across all sites in northeastern China (shaded solid circles represent sites without significant land use and land cover change).</p> Full article ">Figure 6
<p>The same as in <a href="#atmosphere-08-00097-f005" class="html-fig">Figure 5</a>, but for the daily mean specific humidity ((<b>a</b>), Δ<span class="html-italic">SH</span>), surface latent heat flux ((<b>b</b>), Δ<span class="html-italic">λE</span>), and atmospheric downward longwave radiation ((<b>c</b>), Δ<span class="html-italic">L<sub>d</sub></span>).</p> Full article ">
11963 KiB  
Article
Sensitivity Study on High-Resolution WRF Precipitation Forecast for a Heavy Rainfall Event
by Joon-Bum Jee and Sangil Kim
Atmosphere 2017, 8(6), 96; https://doi.org/10.3390/atmos8060096 - 24 May 2017
Cited by 48 | Viewed by 10180
Abstract
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as domain size, sea surface temperature (SST) data, [...] Read more.
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as domain size, sea surface temperature (SST) data, initial conditions, and lead time. The numerical model employs one-way nesting with horizontal resolutions of 5 km and 1 km for the outer and inner domains, respectively. The model domain includes the capital city of Seoul and its suburban megacities in South Korea. The model performance is evaluated via statistical analysis using the correlation coefficient, deviation, and root mean squared error by comparing with observational data including, but not limited to, those from ground-based instruments. The sensitivity analysis conducted here suggests that SST data show negligible influence for a short range forecasting model, the data assimilated initial conditions show the more effective results rather than the non-assimilated high resolution initial conditions, and for a given domain size of the forecasting model, an appropriate outer domain size and lead time of <6 h for a 1-km high-resolution domain should be taken into consideration when optimizing the WRF configuration for regional torrential rainfall events around Seoul and its suburban area, Korea. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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<p>Default model domains for (<b>a</b>) the 5 km outer domain indicated by the black solid rectangular box identified as s1; (<b>b</b>) the 1 km inner domain (red rectangular box identified as d02 in <a href="#atmosphere-08-00096-f001" class="html-fig">Figure 1</a>a); and (<b>c</b>) Automated Weather Station (AWS) locations with red dots and the digital elevation model (DEM) with shading (black box in <a href="#atmosphere-08-00096-f001" class="html-fig">Figure 1</a>b). White dot and black dashed rectangular boxes in (<b>a</b>) respectively indicate the domain reference grid (36° N latitude, 126° E longitude) and numerical outer domains for the sensitivity experiments. The black dot in (<b>b</b>) and white dot in (<b>c</b>) indicate the AWS location of the Seocho meteorological station (37.49° N latitude, 127.02° E longitude). Red dots in (<b>c</b>) indicate AWS sites in urban and rural areas. The solid closed curve in (<b>b</b>,<b>c</b>) represent the administrative district for Seoul.</p> Full article ">Figure 2
<p>Weather chart provided by KMA for (<b>a</b>) 00 UTC 26 July 2011; (<b>b</b>) 12 UTC 26 July 2011; and (<b>c</b>) 00 UTC 27 July 2011.</p> Full article ">Figure 3
<p>KMA-observed hourly accumulated rainfall at 18 UTC 26 July 2011 for (<b>a</b>) radar; (<b>b</b>) AWSs; and (<b>c</b>) meteogram of the Seocho Meteorological Station (white dot in <a href="#atmosphere-08-00096-f001" class="html-fig">Figure 1</a>c). The color bar represents the rain rate (mm/h) in (<b>a</b>,<b>b</b>). Meteorological values in the meteogram include one-hour accumulated rainfall (blue shading), 15 min accumulated rainfall (magenta shading), rain detection (sky-blue shading), temperature (red line), wind speed (green line), and wind direction (orange dots) from 00 UTC 26 July 2011 to 15 UTC 27 July 2011.</p> Full article ">Figure 4
<p>One-hour accumulated rainfall at 18 UTC 26 July 2011 for (<b>a</b>) AWS analysis field; (<b>b</b>) 5 km outer domain part; and (<b>c</b>) 1 km inner domain. The color bar represents the rain rate (mm/h). The box in each panel indicates the region bounded by 37.3° N–37.8° N and 126.6° E–127.4° E.</p> Full article ">Figure 5
<p>Domain-averaged hourly accumulated rainfall averaged between latitudes 37.49° N and 37.8° N, and between longitudes 126.6° E and 127.4° E for the AWS analysis field (black line), outer domain (red line), and inner domain (blue line). The horizontal axis is the forecasting time starting at 00 UTC 26 July 2011. The two ellipses indicate the first and second onsets of rainfall at the beginning of the rainfall period.</p> Full article ">Figure 6
<p>Flowchart of the WRF sensitivity simulations. The left column is the order of the model run, and the right column shows the necessary data source incorporated into the WRF depending on the experiments shown in <a href="#atmosphere-08-00096-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 7
<p>Effect of outer domain size on the performance of the inner domain for hourly accumulated precipitation at 18 UTC 26 July 2011. (<b>a</b>) AWS rainfall analysis provided by KMA. Sensitivity results of the inner domain for (<b>b</b>) +150; (<b>c</b>) +120; (<b>d</b>) +90; (<b>e</b>) +60; (<b>f</b>) +30; (<b>g</b>) 0; (<b>h</b>) −30; and (<b>i</b>) −60, respectively, for the outer domain sizes of (s2), (s3), (s4), (s5), (s6), (s1), (s7), and (s8) in <a href="#atmosphere-08-00096-f001" class="html-fig">Figure 1</a>a. The color bar represents the rain rate (mm/h).</p> Full article ">Figure 8
<p>SST effects of daily accumulated rainfall between 00 UTC 26 July 2011 and 00 UTC 27 July 2011 for (<b>a</b>) AWS rainfall analysis by KMA, and model SST experiments for (<b>b</b>) OSTIA, (<b>c</b>) RTG-SST, (<b>d</b>) AVHRR, (<b>e</b>) AVHRR-AMSRE, and (<b>f</b>) G1-SST. The color bar represents the rain rate (mm/day).</p> Full article ">Figure 9
<p>Effect of the initial conditions on the daily accumulated rainfall between 00 UTC 26 July 2011 and 00 UTC 27 July 2011. (<b>a</b>) UMR (12 km); (<b>b</b>) UMG (23 km); (<b>c</b>) ECMWF-interim; (<b>d</b>) NCEP-FNL. See <a href="#atmosphere-08-00096-f007" class="html-fig">Figure 7</a>a for a comparison with the AWS rainfall analysis. The color bar represents the rain rate (mm/day).</p> Full article ">Figure 10
<p>Lead time effect for the hourly accumulated rainfall at 18 UTC 26 July 2011, starting at (<b>a</b>) 00 UTC 26 July 2011 (+18 h); (<b>b</b>) 06 UTC 26 July 2011; (<b>c</b>) 12 UTC 26 July 2011, which respectively correspond to the lead time effect simulations of +18 h, +12 h, and +06 experiments in <a href="#atmosphere-08-00096-t002" class="html-table">Table 2</a>. The color bar represents the rain rate (mm/h). The box in each panel indicates the region bounded by 37.3° N–37.8° N and 126.6° E–127.4° E. See <a href="#atmosphere-08-00096-f005" class="html-fig">Figure 5</a>a for a comparison to the AWS rainfall analysis.</p> Full article ">Figure 11
<p>Domain-averaged hourly rainfall calculated for the region bounded by 37.49° N to 37.8° N latitude and 126.6° E and 127.4° E longitude for the AWS analysis field (black line), default simulation started at 00 UTC (blue line), +12 simulation started at 06 UTC (red line), and +6 simulation started at 12 UTC (sky blue line).</p> Full article ">
8396 KiB  
Article
Intercomparison of MODIS and VIIRS Fire Products in Khanty-Mansiysk Russia: Implications for Characterizing Gas Flaring from Space
by Ambrish Sharma, Jun Wang and Elizabeth M. Lennartson
Atmosphere 2017, 8(6), 95; https://doi.org/10.3390/atmos8060095 - 23 May 2017
Cited by 24 | Viewed by 5959
Abstract
Gas flaring is commonly used by industrial plants for processing oil and natural gases in the atmosphere, and hence is an important anthropogenic source for various pollutants including CO2, CO, and aerosols. This study evaluates the feasibility of using satellite data [...] Read more.
Gas flaring is commonly used by industrial plants for processing oil and natural gases in the atmosphere, and hence is an important anthropogenic source for various pollutants including CO2, CO, and aerosols. This study evaluates the feasibility of using satellite data to characterize gas flaring from space by focusing on the Khanty-Mansiysk Autonomous Okrug in Russia, a region that is well known for its dominant gas flaring activities. Multiple satellite-based thermal anomaly data products at night are intercompared and analyzed, including MODIS (Moderate Resolution Imaging Spectroradiometer) Terra level 2 Thermal Anomalies product (MOD14), MODIS Aqua level 2 Thermal Anomalies product (MYD14), VIIRS (Visible Infrared Imaging Radiometer Suite) Active Fires Applications Related Product (VAFP), and VIIRS level 2 Nightfire product (VNF). The analysis compares and contrasts the efficacy of these sensor products in detecting small, hot sources like flares on the ground in extremely cold environments such as Russia. We found that the VNF algorithm recently launched by the National Oceanic and Atmospheric Administration (NOAA) has the unprecedented accuracy and efficiency in characterizing gas flares in the region owing primarily to the use of Shortwave Infrared (SWIR) bands. Reconciliation of VNF’s differences and similarities with other nighttime fire products is also conducted, indicating that MOD14/MYD14 and VAFP data are only effective in detecting those gas flaring pixels that are among the hottest in the region; incorporation of shortwave infrared (1.6 µm) band used in VNF may improve the detection of relatively cooler gas flares. The gas flaring locations from the VNF product are validated using Google Earth images. It is shown that VNF’s estimates of the area of gas flaring agree well with the Google image counterparts with a linear correlation of 0.91, highlighting its potential use for routinely monitoring emissions of gas flaring from space. Full article
(This article belongs to the Special Issue Biomass Burning)
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<p>Projected map of test region (in red boundaries) enclosing Khanty-Mansiysk Autonomous Okrug, Russia. The background map is from GoogleEarth<sup>©</sup> with modification.</p> Full article ">Figure 2
<p>Nighttime fire detections by different products over the study area during Summer 2013. The considerable difference in detections between VNF and other products is due to VNF’s ability to detect more gas flares in the region.</p> Full article ">Figure 3
<p>Nocturnal detections by different sensors over the gas flaring regions in the study area (April–August 2013). <span class="html-italic">X</span>- and <span class="html-italic">Y</span>-axis show the longitude and latitude, respectively.</p> Full article ">Figure 4
<p>NOAA VNF (<b>a</b>) and VNF Replica (<b>b</b>): Nighttime M10 band detections. See the text for details.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>f</b>) Reconciliation between VAFP and NOAA VNF, 4 July 2013. See the text for details.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>f</b>) Reconciliation between VAFP and NOAA VNF, 2 August 2013. See the text for details.</p> Full article ">Figure 7
<p>(<b>a</b>) Simulation of 4 µm (top left) and (<b>b</b>) 1.6 µm (top right) radiances for varying fire temperature and fire area fraction, as well as (bottom left) (<b>c</b>) their corresponding difference (1.6–4 µm) for varying fire temperature and fire area fraction. The background brightness temperature is considered uniformly at 300 K.</p> Full article ">Figure 8
<p>(<b>a</b>) A test site in Khanty-Mansiysk, Russia (Image courtesy: Google maps); (<b>b</b>) Fire area, temperature, and distance of detected pixel from the flare location for this site over five months in 2013, as retrieved by NOAA VNF version 1.0. The red lines represent the fire temperature reported by VNF for hot pixels found in proximity to the flare, whereas the blue and navy blue lines represent the fire area and distance from the flare to the center of the detected pixel, respectively.</p> Full article ">Figure 9
<p>Histogram for fire temperature reported by VNF for ten chosen flaring sites in the study region over the five month period (April–August 2013).</p> Full article ">Figure 10
<p>Histogram for view zenith angles for the same period and sites reported by VNF. It is noticeable that most flare detections are happening for viewing angles very close to nadir.</p> Full article ">Figure 11
<p>Scatter plot for fire areas reported by NOAA VNF for different flare sites versus the area estimated using Google imagery for the respective sites.</p> Full article ">
2406 KiB  
Article
Evaluation and Parameter Optimization of Monthly Net Long-Wave Radiation Climatology Methods in China
by Wen Cao, Chunfeng Duan, Shuanghe Shen and Yun Yao
Atmosphere 2017, 8(6), 94; https://doi.org/10.3390/atmos8060094 - 23 May 2017
Cited by 3 | Viewed by 3990
Abstract
Based on surface radiation balance data and meteorological observations at 19 radiation stations in China from 1993 to 2012, we assessed the applicability of seven empirical formulas for the estimation of monthly surface net long-wave radiation (Rnl). We then established [...] Read more.
Based on surface radiation balance data and meteorological observations at 19 radiation stations in China from 1993 to 2012, we assessed the applicability of seven empirical formulas for the estimation of monthly surface net long-wave radiation (Rnl). We then established a revised method applicable to China by re-fitting the formula using new observational data. The iterative solution method and the multivariate regression analysis method with the minimum root mean square error (RMSE) were used as the objective functions in the revised method. Meanwhile, the accuracy of the CERES (Clouds and the Earth’s Radiant Energy System) estimated Rnl was also evaluated. Results show that monthly Rnl over China was underestimated by the seven formulas and the CERES data. The Tong Hongliang formula with lowest errors was the best among the seven formulas for estimating Rnl over China as a whole, followed by the Penman and the Deng Genyun formulas. The estimated Rnl based on the CERES data also showed relatively higher precision in accordance with the three formulas mentioned above. The FAO56-PM formula (Penman–Monteith formula recommended in the No. 56 report of the Food and Agriculture Organization) without calibration was not applicable to China due to its low accuracy. For individual stations, the Deng Genyun formula was the most accurate in the eastern plain area, while the Tong Hongliang formula was suitable for the plateau. Regional formulas were established based on the geographical distribution of water vapor pressure and elevation over China. The revised national and regional formulas were more accurate than the seven original formulas and the CERES data. Furthermore, the regional formulas produced smaller errors than the national formula at most of the stations. The regional formulas were clearly more accurate than the Deng Genyun formula at stations in Northwestern China and on the Tibetan Plateau. They were also more accurate than the Tong Hongliang formula at the stations located in the eastern area. Therefore, the regional formulas developed in this study are recommended as the standard climatology formulas to calculate monthly Rnl over China. Full article
(This article belongs to the Section Meteorology)
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<p>Distribution of the nineteen radiation stations in China.</p> Full article ">Figure 2
<p>Evolution of daily net long-wave radiation for the period from January 1993 to December 2012 at Beijing (<b>a</b>) and Ejin Banner (<b>b</b>).</p> Full article ">Figure 3
<p>Correlation between the standard and estimated monthly net long-wave radiation based on the seven existing empirical formulas (<b>a</b>) Brunt; (<b>b</b>) Penman; (<b>c</b>) Bepлянд; (<b>d</b>) FAO24; (<b>e</b>) FAO56-PM; (<b>f</b>) Deng Genyun; (<b>g</b>) Tong Hongliang) and CERES data (<b>h</b>) at the nineteen radiation stations.</p> Full article ">Figure 4
<p>Correlation between the standard and validated monthly net long-wave radiation (<b>a</b>) national formula; (<b>b</b>) regional formula) at the nineteen radiation stations.</p> Full article ">Figure 5
<p>Cumulative frequency of MAPE between the standard and estimated monthly net long-wave radiation based on the nine empirical formulas and the CERES data.</p> Full article ">Figure 6
<p>RMSE of the regional, Deng Genyun, and Tong Hongliang formulas compared with the standard net long-wave radiation at nineteen stations in China.</p> Full article ">
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