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Atmosphere
Volume 15
Issue 2
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Atmosphere, Volume 15, Issue 2 (February 2024) – 99 articles

Cover Story (view full-size image): Methane is a key driver of near-term climate change, and studies suggest methane loss from oil and gas production calculated by bottom-up methods may be underestimated. To investigate this, an emissions inventory based on EPA emission factors was compared to an inventory using contemporary/region-specific measurement data. The EPA-based inventory estimated emissions at 315 Gg CH4 y−1, while the updated inventory estimated emissions of 1.5 Tg CH4 y−1. The largest changes resulted from large fugitives (+430 Gg), maintenance activity (+214 Gg), inefficient flares (+174 Gg), and associated gas venting (+136 Gg). This suggests systematic underestimates probably exist in current emissions inventories, and emission factors could be improved through direct comparison with measurement data. View this paper
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16 pages, 10989 KiB  
Article
Study on the Vertical Distribution and Transport of Aerosols in the Joint Observation of Satellite and Ground-Based LiDAR
by Hao Yang, Xiaomeng Zhu, Zhiyuan Fang, Duoyang Qiu, Yalin Hu, Chunyan Tian and Fei Ming
Atmosphere 2024, 15(2), 240; https://doi.org/10.3390/atmos15020240 - 19 Feb 2024
Viewed by 1395
Abstract
The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the [...] Read more.
The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution. Full article
(This article belongs to the Section Air Quality)
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<p>Study area: geographical location (<b>a</b>,<b>b</b>); terrain elevation distribution (<b>c</b>); population distribution (<b>d</b>).</p> Full article ">Figure 2
<p>Statistical results of aerosol pollution weather pre- and post-lockdown (2019, 2020, and 2021) at Beijing monitoring station: total air pollution days and days with moderate pollution or above (<b>a</b>); distribution of PM2.5 and PM10 concentrations (<b>b</b>,<b>c</b>); probability density distribution of the meteorological parameters (<b>d</b>–<b>h</b>). (The blue dotted line represents the cumulative probability).</p> Full article ">Figure 3
<p>The relationship between particulate matter and meteorological elements during instances of polluted weather from February to March before and after the epidemic (2019, 2020, and 2021) at the Beijing stations: (<b>a</b>) particulate matter (PM); (<b>b</b>) temperature (T); (<b>c</b>) relative humidity (RH); (<b>d</b>) atmospheric pressure (AP); (<b>e</b>) wind speed (WS); (<b>f</b>) wind direction (WD).</p> Full article ">Figure 4
<p>Distribution of PM2.5 and PM10 at inland stations in China. (The red circle represents the Beijing-Tianjin-Hebei region).</p> Full article ">Figure 5
<p>MODIS true color images from 28 February to 5 March 2019.</p> Full article ">Figure 6
<p>Aerosol optical depth (AOD) based on 550 nm VIIRS observations. (The black circle represents the Beijing-Tianjin-Hebei region).</p> Full article ">Figure 7
<p>Aerosol distribution and types depicted when using CALIPSO satellite LiDAR: (<b>a</b>,<b>c</b>,<b>e</b>) aerosol distribution; (<b>b</b>,<b>d</b>,<b>f</b>) aerosol types. (Arrows represent the direction of satellite transit).</p> Full article ">Figure 8
<p>Vertical distribution of aerosols from ground-based LiDAR range-corrected signal inversion.</p> Full article ">Figure 9
<p>Temperature profile of the sounding data.</p> Full article ">Figure 10
<p>Air quality data taken from the Beijing monitoring stations during pollution period: (<b>a</b>) PM2.5, PM10 and PM2.5/PM10 data; (<b>b</b>) CO data; (<b>c</b>) NO<sub>2</sub> and O<sub>3</sub> data; (<b>d</b>) SO<sub>2</sub> and O<sub>3</sub> data.</p> Full article ">Figure 11
<p>Wind speed, wind direction and wind rose taken from Beijing monitoring stations: (<b>a</b>) wind speed and wind direction; (<b>b</b>) wind rose. (Blue and pink symbols represent wind speed and wind direction respectively).</p> Full article ">Figure 12
<p>ERA5 surface weather situation map during the aerosol pollution period.</p> Full article ">Figure 13
<p>Simulation analysis based on particle backward trajectory.</p> Full article ">
15 pages, 5414 KiB  
Article
Multi-Scale Analysis of Grain Size in the Component Structures of Sediments Accumulated along the Desert-Loess Transition Zone of the Tengger Desert and Implications for Sources and Aeolian Dust Transportation
by Xinran Yang, Jun Peng, Bing Liu and Yingna Liu
Atmosphere 2024, 15(2), 239; https://doi.org/10.3390/atmos15020239 - 19 Feb 2024
Viewed by 1152
Abstract
Aeolian sediments accumulated along the desert-loess transition zone of the Tengger Desert include heterogeneous textures and complex component structures in their grain-size distributions (GSD). However, the sources of these aeolian sediments have not been resolved due to the lack of large reference GSD [...] Read more.
Aeolian sediments accumulated along the desert-loess transition zone of the Tengger Desert include heterogeneous textures and complex component structures in their grain-size distributions (GSD). However, the sources of these aeolian sediments have not been resolved due to the lack of large reference GSD sample datasets from adjacent regions that contain various types of sediments; such datasets could be used for fingerprinting based on grain-size properties. This lack of knowledge hinders our understanding of the mechanism of aeolian dust releases in these regions and the effects of forcing of atmospheric circulations on the transportation and accumulation of sediments in this region. In this study, we employed a multi-scale grain-size analysis method, i.e., a combination of the single-sample unmixing (SSU) and the parametric end-member modelling (PEMM) techniques, to resolve the component structures of sediments that had accumulated along the desert-loess transition zone of the Tengger Desert. We have also analyzed the component structures of GSDs of various types of sediments, including mobile and fixed sand dunes, lake sediments, and loess sediments from surrounding regions. Our results demonstrate that the patterns observed in coarser fractions of sediments (i.e., sediments with a mode grain size of >100 μm) from the transition zone match well with the patterns of component structures of several types of sediments from the interior of the Tengger Desert, and the patterns seen in the finer fractions (i.e., fine, medium, and coarse silts with a modal size of <63 μm) were broadly consistent with those of loess sediments from the Qilian Mountains. The deflation/erosion of loess from the Qilian Mountains by wind was the most important mechanism underlying the production of these finer grain-size fractions. The East Asia winter monsoon (EAWM) played a key role in transportation of the aeolian dust from these source regions to the desert-loess transition zone of the desert. Full article
(This article belongs to the Topic Environmental Change, Geomorphological and Sedimentological Processes in Asian Hinterlands)
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<p>(<b>a</b>) grain-size sampling sites around the Tengger Desert. Different types of sediments are shown in different colors. (<b>b</b>–<b>j</b>) representative photos of sampling sites for various types of sediments, including (<b>b</b>) mobile dunes, (<b>c</b>) semi-mobile dunes, (<b>d</b>) interdune depressions, (<b>e</b>) fixed/semi-fixed dunes, (<b>f</b>) nebkhas, (<b>g</b>) interdune lake surfaces, (<b>h</b>) loess from the Qilian Mountains, (<b>i</b>,<b>j</b>) sediments from the desert-loess transition zone.</p> Full article ">Figure 2
<p>Ternary plot showing abundances of clay, silt, and sand for various types of sediments.</p> Full article ">Figure 3
<p>Variations in (<b>a</b>) sorting and (<b>b</b>) skewness as a function of mean grain size for various types of sediments.</p> Full article ">Figure 4
<p>SSU results of grain-size distribution for various types of sediments, using the skew normal distribution. “SN0” is the transformed skew normal distribution; “mrsl” is the minimum degree of overlapping between adjacent subpopulations; “RSS” and “FOM” are the residual sum of squares and the figure-of-merit value, respectively. Note that the <span class="html-italic">x</span>-axis is plotted on a log scale for better visualization.</p> Full article ">Figure 5
<p>Simplified radial plots showing the classification of the means of grain-size components extracted from the pooled unmixing results using the finite mixture model-based Bayesian clustering algorithm [<a href="#B22-atmosphere-15-00239" class="html-bibr">22</a>] for various types of sediments, including (<b>a</b>) mobile dunes, (<b>b</b>) semi-mobile dunes, (<b>c</b>) interdune depressions, (<b>d</b>) fixed/semi-fixed dunes, (<b>e</b>) nebkhas, (<b>f</b>) interdune lake surfaces, (<b>g</b>) loess from the Qilian Mountains, (<b>h</b>) sediments from the desert-loess transition zone. The gray region indicates the two-sigma range of a cluster.</p> Full article ">Figure 6
<p>Comparison of the means and abundances for clusters of grain-size components classified using the finite mixture model-based Bayesian clustering algorithm [<a href="#B22-atmosphere-15-00239" class="html-bibr">22</a>] for various types of sediments. Note that the <span class="html-italic">y</span>-axis is plotted on a log scale for better visualization.</p> Full article ">Figure 7
<p>Variations of probability density distributions of end-members for various types of sediments. Note that the <span class="html-italic">x</span>-axis is plotted on a log scale for better visualization.</p> Full article ">Figure 8
<p>Comparison of the modal sizes and abundances of end-members for various types of sediments.</p> Full article ">
18 pages, 5720 KiB  
Article
High Gravity-Enhanced Direct Air Capture: A Leap Forward in CO2 Adsorption Technology
by Shufei Wang, Youzhi Liu, Chengqian Zhang, Shuwei Guo and Yuliang Li
Atmosphere 2024, 15(2), 238; https://doi.org/10.3390/atmos15020238 - 18 Feb 2024
Viewed by 2989
Abstract
Given the global pressure of climate change and ecological equilibrium, there is an urgent need to develop effective carbon dioxide (CO2) capture technology. Due to its comprehensiveness and flexibility, Direct Air Capture (DAC) technology has emerged as a vital supplement to [...] Read more.
Given the global pressure of climate change and ecological equilibrium, there is an urgent need to develop effective carbon dioxide (CO2) capture technology. Due to its comprehensiveness and flexibility, Direct Air Capture (DAC) technology has emerged as a vital supplement to traditional emission reduction methods. This study aims to innovate Direct Air Capture (DAC) technology by utilizing the ultrasonic impregnation method to load Tetraethylenepentamine (TEPA) onto alumina (Al2O3) as the adsorbent. Furthermore, high gravity adsorption technology is integrated to significantly enhance the efficiency of DAC. Characterization tests, including BET, FTIR, TG, XRD, and SEM-EDS, confirm the structural stability and high capture capacity of the adsorbent. Additionally, this study demonstrates the rapid and efficient capture of CO2 from the air using TEPA-Al2O3 adsorbent under high gravity conditions for the first time. Under optimal conditions with TEPA loading at 15.06%, a high gravity factor of 2.67, and a gas flow rate of 30 L/min, TEPA-Al2O3 achieves a CO2 adsorption capacity of 48.5 mg/g in RAB, which is an improvement of 15.56 mg/g compared to traditional fixed-bed technology. Moreover, it reaches adsorption saturation faster under high gravity conditions, exhibiting a significantly higher adsorption rate compared to traditional fixed-bed systems. Furthermore, the adsorption process better conforms to the Avrami model. Steam stripping regeneration is utilized to regenerate the adsorbent, demonstrating excellent regeneration performance and stable adsorption capacity, thereby proving its feasibility and economic benefits as a DAC technology. Full article
(This article belongs to the Section Air Pollution Control)
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<p>The cycle of amine-modified adsorbents absorbing CO<sub>2</sub> in air.</p> Full article ">Figure 2
<p>Schematic of the high gravity enhanced direct air CO<sub>2</sub> capture process using amine-modified adsorbents. 1—Fan; 2, 5, 7—Valves; 3—Flowmeter; 4—Rotating Adsorption Bed; 6—Gas Detector.</p> Full article ">Figure 3
<p>Nitrogen adsorption/desorption isotherms of Al<sub>2</sub>O<sub>3</sub> and TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">Figure 4
<p>Infrared spectra of Al<sub>2</sub>O<sub>3</sub> and TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">Figure 5
<p>The curve of XRD of Al<sub>2</sub>O<sub>3</sub> and TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">Figure 6
<p>The curve of TG and DTG of Al<sub>2</sub>O<sub>3</sub> and TEPA-Al<sub>2</sub>O<sub>3</sub> in the temperature range of 30~1000 °C.</p> Full article ">Figure 7
<p>(<b>a</b>) Scanning Electron Microscope (SEM) micrograph of Al<sub>2</sub>O<sub>3</sub>; (<b>b</b>) SEM micrograph of TEPA-Al<sub>2</sub>O<sub>3</sub>; (<b>c</b>) Energy-Dispersive X-ray Spectroscopy (EDS) spectrum for nitrogen (N) on Al<sub>2</sub>O<sub>3</sub>; (<b>d</b>) Elemental mapping for Al<sub>2</sub>O<sub>3</sub>; (<b>e</b>) EDS spectrum for nitrogen (N) on TEPA-Al<sub>2</sub>O<sub>3</sub>; (<b>f</b>) Elemental mapping for TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) Scanning Electron Microscope (SEM) micrograph of Al<sub>2</sub>O<sub>3</sub>; (<b>b</b>) SEM micrograph of TEPA-Al<sub>2</sub>O<sub>3</sub>; (<b>c</b>) Energy-Dispersive X-ray Spectroscopy (EDS) spectrum for nitrogen (N) on Al<sub>2</sub>O<sub>3</sub>; (<b>d</b>) Elemental mapping for Al<sub>2</sub>O<sub>3</sub>; (<b>e</b>) EDS spectrum for nitrogen (N) on TEPA-Al<sub>2</sub>O<sub>3</sub>; (<b>f</b>) Elemental mapping for TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">Figure 8
<p>The influence of TEPA impregnation solution concentration on the CO<sub>2</sub> adsorption performance of amine-modified adsorbents.</p> Full article ">Figure 9
<p>The impact of the high gravity factor on the CO<sub>2</sub> adsorption performance of amine-modified adsorbents.</p> Full article ">Figure 10
<p>The impact of gas flow rate on the CO<sub>2</sub> adsorption performance of amine-modified adsorbents.</p> Full article ">Figure 11
<p>The comparison of kinetic model results and experimental CO<sub>2</sub> adsorption amounts for 4-TEPA-Al<sub>2</sub>O<sub>3</sub> in the Rotating Adsorption Bed (RAB) and fixed bed.</p> Full article ">Figure 12
<p>Cyclic adsorption performance of TEPA-Al<sub>2</sub>O<sub>3</sub>.</p> Full article ">
15 pages, 6324 KiB  
Article
Characteristics and Source Apportionment of Volatile Organic Compounds in an Industrial Area at the Zhejiang–Shanghai Boundary, China
by Xiang Cao, Jialin Yi, Yuewu Li, Mengfei Zhao, Yusen Duan, Fei Zhang and Lian Duan
Atmosphere 2024, 15(2), 237; https://doi.org/10.3390/atmos15020237 - 18 Feb 2024
Cited by 1 | Viewed by 1815
Abstract
As “fuel” for atmospheric photochemical reactions, volatile organic compounds (VOCs) play a key role in the secondary generation of ozone (O3) and fine particulate matter (PM2.5, an aerodynamic diameter ≤ 2.5 μm). To determine the characteristics of VOCs in [...] Read more.
As “fuel” for atmospheric photochemical reactions, volatile organic compounds (VOCs) play a key role in the secondary generation of ozone (O3) and fine particulate matter (PM2.5, an aerodynamic diameter ≤ 2.5 μm). To determine the characteristics of VOCs in a high-level ozone period, comprehensive monitoring of O3 and its precursors (VOCs and NOx) was continuously conducted in an industrial area in Shanghai from 18 August to 30 September 2021. During the observation period, the average concentration of VOCs was 47.33 ppb, and alkanes (19.64 ppb) accounted for the highest proportion of TVOCs, followed by oxygenated volatile organic compounds (OVOCs) (13.61 ppb), alkenes (6.92 ppb), aromatics (4.65 ppb), halogenated hydrocarbons (1.60 ppb), and alkynes (0.91 ppb). Alkenes were the predominant components that contributed to the ozone formation potential (OFP), while aromatics such as xylene, toluene, and ethylbenzene contributed the most to the secondary organic aerosol production potential (SOAFP). During the study period, O3, NOx, and VOCs showed significant diurnal variations. Industrial processes were the main source of VOCs, and the second largest source of VOCs was vehicle exhaust. While the largest contribution to OFP was from vehicle exhaust, the second largest contribution was from liquid petroleum gas (LPG). High potential source contribution function (PSCF) values were observed in western and southeastern areas near the sampling sites. The results of a health risk evaluation showed that the Hazard Index was less than 1 and there was no non-carcinogenic risk, but 1,3-butadiene, benzene, chloroform, 1,2-dibromoethane, and carbon tetrachloride pose a potential carcinogenic risk to the population. Full article
(This article belongs to the Special Issue Advances in Atmospheric Aqueous-Phase Chemistry)
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<p>Satellite imagery showing the sampling site and surrounding area.</p> Full article ">Figure 2
<p>Time series of meteorological parameters and pollutants during the observation period. The black solid line in the graph of daily ozone change indicates an ozone concentration of 200 μg/m<sup>3</sup>.</p> Full article ">Figure 3
<p>Characteristics of diurnal variations in meteorological parameters on polluted and non-polluted days.</p> Full article ">Figure 4
<p>VOCs ranked in the top 15 in terms of (<b>a</b>) concentration, (<b>b</b>) the OFP, and (<b>c</b>) the SOAFP.</p> Full article ">Figure 5
<p>Diurnal variation characteristics of ozone, nitrogen dioxide, ethylene, isoprene and toluene on polluted and non-polluted days.</p> Full article ">Figure 6
<p>Source profiles and contributions of VOCs.</p> Full article ">Figure 7
<p>Contribution of each emission source to (<b>a</b>) VOCs and (<b>b</b>) OFP.</p> Full article ">Figure 8
<p>PSCF analysis of O<sub>3</sub>, NO<sub>2</sub>, and TVOC.</p> Full article ">
18 pages, 11338 KiB  
Article
Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China
by Chongxun Mo, Xiaoyu Wan, Xingbi Lei, Xinru Chen, Rongyong Ma, Yi Huang and Guikai Sun
Atmosphere 2024, 15(2), 236; https://doi.org/10.3390/atmos15020236 - 17 Feb 2024
Viewed by 1129
Abstract
Reliable precipitation forecasts are essential for weather-related disaster prevention and water resource management. Multi-source weather (MSWX), a recently released ensemble meteorological dataset, has provided new opportunities with open access, fine horizontal resolution (0.1°), and a lead time of up to seven months. However, [...] Read more.
Reliable precipitation forecasts are essential for weather-related disaster prevention and water resource management. Multi-source weather (MSWX), a recently released ensemble meteorological dataset, has provided new opportunities with open access, fine horizontal resolution (0.1°), and a lead time of up to seven months. However, few studies have comprehensively evaluated the performance of MSWX in terms of precipitation forecasting and hydrological modeling, particularly in hill-karst basins. The key concerns and challenges are how precipitation prediction performance relates to elevation and how to evaluate the hydrologic performance of MSWX in hill-karst regions with complex geographic heterogeneity. To address these concerns and challenges, this study presents a comprehensive evaluation of MSWX at the Chengbi River Basin (Southwest China) based on multiple statistical metrics, the Soil and Water Assessment Tool (SWAT), and a multi-site calibration strategy. The results show that all ensemble members of MSWX overestimated the number of precipitation events and tended to have lower accuracies at higher altitudes. Meanwhile, the error did not significantly increase with the increased lead time. The “00” member exhibited the best performance among the MSWX members. In addition, the multi-site calibration-enhanced SWAT had reliable performance (Average Nash–Sutcliffe value = 0.73) and hence can be used for hydrological evaluation of MSWX. Furthermore, MSWX achieved satisfactory performance (Nash–Sutcliffe value > 0) in 22% of runoff event predictions, but the error increased with longer lead times. This study gives some new hydrometeorological insights into the performance of MSWX, which can provide feedback on its development and applications. Full article
(This article belongs to the Section Meteorology)
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<p>The structure chart of the study.</p> Full article ">Figure 2
<p>Approximate profiles of the CR basin: (<b>a</b>) location of CR basin in China, (<b>b</b>) hydrometeorological station distribution and altitude topographic map under MSWX grids, and (<b>c</b>) hydrometeorological station distribution under the hypsometric curve of the CR Basin. The “relative area” refers to the percentage ratio of the area of a specific region, which is at an altitude lower than a certain value, to the total area of the entire watershed.</p> Full article ">Figure 3
<p>Statistical analysis of precipitation data from different sources: (<b>a</b>) percentage of precipitation events in each precipitation dataset, and (<b>b</b>) cumulative probability distribution of observed and forecast precipitation under different intensities (exclusion intensity &lt; 1 mm/day).</p> Full article ">Figure 4
<p>Performance characteristics of MSWX members at different lead times.</p> Full article ">Figure 5
<p>Statistical analysis of the accuracy fluctuation of different MSWX members. The upper and lower lines of the box and the middle line represent the 25th and 75th percentile and median, respectively, while “whiskers” indicate extreme values. The (<b>a</b>–<b>f</b>) represent POD, FAR, CSI, Corr, Bias, and RMSE metrics, respectively.</p> Full article ">Figure 6
<p>Scatterplot and the fitted line of accuracy metrics versus elevation for various MSWX memberships.</p> Full article ">Figure 7
<p>Performance of the multi-site calibration-based SWAT for the calibration stage and validation stage.</p> Full article ">Figure 8
<p>Performance of different precipitation data in runoff prediction. T1–T24 represents different initialization times from 2014 to 2019 at 3-month intervals. For example, T1 and T24 stand for 1 January 2014 and 1 October 2019, respectively.</p> Full article ">Figure 9
<p>Performance of different precipitation data in runoff prediction benchmarked against gauge-precipitation simulations. The upper and lower lines of the box and the middle (orange) line represent the 25th and 75th percentile and median, respectively, while “whiskers” indicate extreme values. Some of the extremes are not fully shown, as the distance from the percentile is too large.</p> Full article ">
18 pages, 7045 KiB  
Article
A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor
by Xiangyang Tan, Kaixue Ma and Fangli Dou
Atmosphere 2024, 15(2), 235; https://doi.org/10.3390/atmos15020235 - 17 Feb 2024
Cited by 1 | Viewed by 1301
Abstract
As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study [...] Read more.
As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>SeeborV5 data distribution in the Asian region.</p> Full article ">Figure 2
<p>GRAPS temperature gird data distribution at 60–150° E, 10–80° N.</p> Full article ">Figure 3
<p>(<b>a</b>) By employing the cumulative information content method, we have selected 268 temperature profile retrieval channels distributed around 60 GHz, 118 GHz, and 425 GHz. (<b>b</b>) Channel weighting function we selected.</p> Full article ">Figure 4
<p>Through the method of cumulative information content, 268 oxygen-sensitive channels were selected. (<b>a</b>) shows 155 channels in the range of 50–60 GHz, (<b>b</b>) shows 59 channels in the range of 108–128 GHz, and (<b>c</b>) shows 54 channels in the range of 415–435 GHz.</p> Full article ">Figure 5
<p>We selected a sample with smooth brightness temperature variations near 60 GHz as an example for convolution kernel computation. In reality, channel frequencies are more densely spaced, but for the purpose of illustration, we chose a bandwidth of 50 MHz, a kernel size of 5, and a stride of 2. The window represents the region for each computation.</p> Full article ">Figure 6
<p>Schematic of the CNN-LAA. Green color represents the input and output data. Blue color represents the layers.</p> Full article ">Figure 7
<p>Schematic of the LAA layer. (<b>a</b>) illustrates a local attention mechanism, taking the 50–70 GHz frequency range as an example with a sliding window size of 3. (<b>b</b>) depicts the architecture of the Agent Attention model.</p> Full article ">Figure 8
<p>(<b>a</b>) Comparison of temperature profiles generated by CNN1d, BPNN, XGBoost, and SVM retrieval methods for a special sample (63°68′ E,70°54′ N) in SeeborV5. In this sample, the data exhibit significant fluctuations or oscillations, indicating high volatility. (<b>b</b>) Retrieval bias of temperature by different methods from special sample in (<b>a</b>).</p> Full article ">Figure 9
<p>The figure displays the temperature retrieval biases on the test set. Subfigures (<b>a</b>–<b>f</b>) shows the retrieval bias of temperature (the retrieval temperature minus testdata temperature) generated by six different deep learning retrieval methods. The yellow solid line represents the median, while the blue dashed line represents the mean.</p> Full article ">Figure 10
<p>(<b>a</b>) depicts the gridded temperature over the Asian region at 1000 hPa, segmented into 144, 30 × 30 images. Meanwhile, in figure (<b>b</b>), the brightness temperature image of the 58.50 GHz frequency channel is presented, also divided into 144, 30 × 30 images.</p> Full article ">Figure 11
<p>(<b>a</b>) illustrates the <span class="html-italic">RMSE</span> and <span class="html-italic">MAE</span> of temperature retrieval at different pressure levels. (<b>b</b>) shows the correlation coefficient between the validation set and the predicted data.</p> Full article ">Figure 12
<p>Bias of Temperature in selected channels and all channels.</p> Full article ">Figure 13
<p>(<b>a</b>) CNN-LAA training loss (blue line) and validation loss (yellow line) with epochs, and (<b>b</b>) shows CNN losses.</p> Full article ">
15 pages, 5925 KiB  
Article
Topographic Elevation’s Impact on Local Climate and Extreme Rainfall: A Case Study of Zhengzhou, Henan
by Zhi Jin, Jinhua Yu and Kan Dai
Atmosphere 2024, 15(2), 234; https://doi.org/10.3390/atmos15020234 - 16 Feb 2024
Viewed by 1436
Abstract
The topography significantly influences local climate precipitation and the intensity of precipitation events, yet the specific differences in its elevational effects require further understanding. This study focuses on precipitation in Zhengzhou City, Henan Province, utilizing hourly data and a topographic elevation precipitation increment [...] Read more.
The topography significantly influences local climate precipitation and the intensity of precipitation events, yet the specific differences in its elevational effects require further understanding. This study focuses on precipitation in Zhengzhou City, Henan Province, utilizing hourly data and a topographic elevation precipitation increment model to assess the impact of topography on local climate precipitation and extreme heavy rainfall events. The results indicate that the daily precipitation attributed to topographic elevation in Zhengzhou in July was 0.21 mm, accounting for 4.9% of the total precipitation. In the extreme heavy rainfall event on 20 July 2021 (“7.20” event), the precipitation due to topographic elevation reaches 48.7 mm, constituting 15.8% of the total precipitation. Additionally, numerical simulations using the Weather Research and Forecasting (WRF) model for the 20–21 July 2021 rainfall event in Zhengzhou show that the WRF model effectively reproduces the spatiotemporal characteristics of the precipitation process. The simulated topographic elevation precipitation intensity is 49.8 mm/day, accounting for 16.6% of daily precipitation, closely resembling observational data. Sensitivity experiments further reveal that reducing the heights of the Taihang Mountains and Funiu Mountains weakens the low-level easterly winds around Zhengzhou. Consequently, as the center of the heavy rainfall shifts northward or westward, the intensity of topographic elevation-induced precipitation decreases to 7.3 mm/day and 12.9 mm/day. Full article
(This article belongs to the Section Climatology)
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<p>Vertical velocity calculation schematic for the slope surface (<b>a</b>) and sectional view (<b>b</b>).</p> Full article ">Figure 2
<p>Spatial distribution of slope (<b>a</b>) and aspect (<b>b</b>) in Henan Province.</p> Full article ">Figure 3
<p>Henan’s daily average precipitation in July from 1981 to 2010 is depicted in (<b>a</b>) (contour lines, unit: mm/day), while (<b>b</b>) shows the 925 hPa water vapor transport flux (vectors, unit: g/(cm * s * hPa)) with terrain height shaded (unit: m). The red dots represent the center of Zhengzhou, and this convention applies throughout.</p> Full article ">Figure 4
<p>The precipitation series in Zhengzhou for July from 1981 to 2021 is shown in <a href="#atmosphere-15-00234-f004" class="html-fig">Figure 4</a> (unit: mm). The dashed line represents the average precipitation for July from 1981 to 2010.</p> Full article ">Figure 5
<p>The precipitation anomaly ((<b>a</b>,<b>c</b>) isoline, unit: mm) and 925 hPa water vapor transport flux anomaly ((<b>b</b>,<b>d</b>) vector, unit: g/(cm * s * hPa) in Henan in July 2021 and 2008 are both relative to the average in July 1981–2010, and the shadow is the terrain height (unit: m). The red dots represent the center of Zhengzhou.</p> Full article ">Figure 6
<p>The spatial distribution of daily average terrain elevation precipitation from 1981 to 2010 July ((<b>a</b>) shadow, unit: mm/d) and from 00:00 on 20 July 2021 to 00:00 on 21 July 2021 ((<b>b</b>) shadow, unit: mm/d).in Henan; The area enclosed by the black box is Zhengzhou, the same below.</p> Full article ">Figure 7
<p>Distribution of accumulated precipitation (unit: mm) from 00:00 on 20 July 2021 to 00:00 on 21 July 2021; ((<b>a</b>) Observation, (<b>b</b>) Simulation).</p> Full article ">Figure 8
<p>Regional averages hourly precipitation series (unit: mm/h) of actual (blue) and simulated (yellow) of Zhengzhou from 00:00 20 July 2021 to 00:00 21 July 2021.</p> Full article ">Figure 9
<p>A 925 hPa circulation field(vector, unit: m/s) at 09:00 on 20 July 2021 in Henan and terrain height (shadow, unit: m). ((<b>a</b>) ERA5, (<b>b</b>) Simulation). The red dots represent the center of Zhengzhou.</p> Full article ">Figure 10
<p>EXP1 (purple frame) and EXP2 (blue frame) mountain area. The area enclosed by the red solid line is Henan Province, the black dot is the rainfall center (34.7° N, 113.7° E), A and B are Taihang Mountain and Funiu Mountain.</p> Full article ">Figure 11
<p>Distribution of 24 h accumulated precipitation simulated by sensitivity experiments (unit: mm) from 00:00 on 20 July 2021 to 00:00 on 21 July 2021. ((<b>a</b>) EXP1, (<b>b</b>) EXP2). The area enclosed by the black box is Zhengzhou.</p> Full article ">Figure 12
<p>Regional averages hourly precipitation sequence of Zhengzhou output by the model (unit: mm/h) from 00:00 20 July 2021 to 00:00 21 July 2021.</p> Full article ">Figure 13
<p>Spatial distribution of terrain elevation precipitation (unit: mm/h) output by the model ((<b>a</b>) SIM, (<b>b</b>) EXP1, (<b>c</b>) EXP2) on 20 July 2021 in Henan. The area enclosed by the black box is Zhengzhou.</p> Full article ">Figure 14
<p>The 925 hPa wind field difference (arrow, unit: m/s) between the sensitivity test ((<b>a</b>) EXP1, (<b>b</b>) EXP2) and the control test in Henan Province on July 20, 2021, terrain height (shadow, unit: m). The red dots represent the center of Zhengzhou.</p> Full article ">
19 pages, 3273 KiB  
Review
Sources, Occurrences, and Risks of Polycyclic Aromatic Hydro-Carbons (PAHs) in Bangladesh: A Review of Current Status
by Mohammad Mazbah Uddin and Fuliu Xu
Atmosphere 2024, 15(2), 233; https://doi.org/10.3390/atmos15020233 - 15 Feb 2024
Viewed by 2359
Abstract
Polycyclic aromatic hydrocarbons (PAHs) pollution has emerged as a significant environmental issue in Bangladesh in the recent years, driven by both economic and population growth. This review aims to investigate the current trends in PAHs pollution research, covering sediments, water, aquatic organisms, air [...] Read more.
Polycyclic aromatic hydrocarbons (PAHs) pollution has emerged as a significant environmental issue in Bangladesh in the recent years, driven by both economic and population growth. This review aims to investigate the current trends in PAHs pollution research, covering sediments, water, aquatic organisms, air particles, and associated health risks in Bangladesh. A comparative analysis with PAHs research in other countries is conducted, and potential future research directions are explored. This review suggests that the research on PAHs pollution in Bangladesh is less well studied and has fewer research publications compared to other countries. Dominant sources of PAHs in Bangladesh are fossil fuel combustion, petroleum hydrocarbons, urban discharges, industrial emissions, shipbreaking, and shipping activities. The concentrations of PAHs in sediments, water, air particles, and aquatic organisms in Bangladesh were found to be higher than those in most of the other countries around the world. Therefore, coastal sediments showed higher PAHs pollution than urban areas. Health risk assessments reveal both carcinogenic and non-carcinogenic risks to residents in Bangladesh due to the consumption of aquatic organisms. According to this investigation, it can be concluded that there are considerably higher PAHs concentrations in different environmental compartments in Bangladesh, which have received less research attention compared with other countries of the world. Considering these circumstances, this review recommends that future PAHs pollution research directions should focus on aquatic ecosystems, shipbreaking areas, air particles, and direct exposure to human health risks. Therefore, this study recommends addressing the identification of PAH sources, bioaccumulation, biomagnification in the food web, and biomarker responses of benthic organisms in future PAHs pollution research. Full article
(This article belongs to the Section Air Quality)
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<p>The selection process (PRISMA method) of research articles for the review of PAHs pollution research status in Bangladesh.</p> Full article ">Figure 2
<p>The number of PAHs pollution research publications in different years from all over the world (Data source: Web of Science) [<a href="#B49-atmosphere-15-00233" class="html-bibr">49</a>].</p> Full article ">Figure 3
<p>The number of PAHs pollution research publications in different countries around the world (Data source: Web of Science) [<a href="#B49-atmosphere-15-00233" class="html-bibr">49</a>].</p> Full article ">Figure 4
<p>The major sources of PAHs pollution in Bangladesh.</p> Full article ">Figure 5
<p>The number of dismantled ships in these countries in the year 2022 (Data source) [<a href="#B113-atmosphere-15-00233" class="html-bibr">113</a>].</p> Full article ">Figure 6
<p>The annual average PM<sub>2.5</sub> concentrations of air in Bangladesh (Data source) [<a href="#B82-atmosphere-15-00233" class="html-bibr">82</a>].</p> Full article ">
15 pages, 7339 KiB  
Article
Causes of Summer Ozone Pollution Events in Jinan, East China: Local Photochemical Formation or Regional Transport?
by Baolin Wang, Yuchun Sun, Lei Sun, Zhenguo Liu, Chen Wang, Rui Zhang, Chuanyong Zhu, Na Yang, Guolan Fan, Xiaoyan Sun, Zhiyong Xia, Hongyu Xu, Guang Pan, Zhanchao Zhang, Guihuan Yan and Chongqing Xu
Atmosphere 2024, 15(2), 232; https://doi.org/10.3390/atmos15020232 - 15 Feb 2024
Cited by 1 | Viewed by 1279
Abstract
Simultaneous measurements of atmospheric volatile organic compounds (VOCs), conventional gases and meteorological parameters were performed at an urban site in Jinan, East China, in June 2021 to explore the formation and evolution mechanisms of summertime ozone (O3) pollution events. O3 [...] Read more.
Simultaneous measurements of atmospheric volatile organic compounds (VOCs), conventional gases and meteorological parameters were performed at an urban site in Jinan, East China, in June 2021 to explore the formation and evolution mechanisms of summertime ozone (O3) pollution events. O3 Episode Ⅰ, O3 Episode II, and non-O3 episodes were identified based on the China Ambient Air Quality Standards and the differences in precursor concentrations. The O3 concentrations in Episode I and Episode II were 145.4 μg/m3 and 166.4 μg/m3, respectively, which were significantly higher than that in non-O3 episode (90 μg/m3). For O3 precursors, VOCs and NOx concentrations increased by 48% and 34% in Episode I, and decreased by 21% and 27% in Episode II compared to non-O3 episode days. The analysis of the m,p-xylene to ethylbenzene ratio (X/E) and OH exposure demonstrated that the aging of the air masses in Episode II was significantly higher than the other two episodes, and the differences could not be explained by localized photochemical consumption. Therefore, we speculate that the high O3 concentrations in Episode II were driven by the regional transport of O3 and its precursors. Backward trajectory simulations indicated that the air masses during Episode II were concentrated from the south. In contrast, the combination of high precursor concentrations and favorable meteorological conditions (high temperatures and low humidity) led to an excess of O3 in Episode I. Positive matrix factorization (PMF) model results indicated that increased emissions from combustion and gasoline vehicle exhausts contributed to the elevated concentrations of VOCs in Episode I, and solvent usage may be an important contributor to O3 formation. The results of this study emphasize the importance of strengthening regional joint control of O3 and its precursors with neighboring cities, especially in the south, which is crucial for Jinan to mitigate O3 pollution. Full article
(This article belongs to the Special Issue Ozone Pollution and Effects in China)
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<p>Time series of O<sub>3</sub>, VOCs, NO<sub>X</sub> and meteorological parameters during the campaign.</p> Full article ">Figure 2
<p>Diurnal variations of meteorological parameters, O<sub>3</sub> and its precursors during different O<sub>3</sub> episodes.</p> Full article ">Figure 3
<p>Diurnal variations of X/E and OH exposure. The red dashed lines represent the initial emission ratio of X/E.</p> Full article ">Figure 4
<p>Correlations of VOCs and NO<sub>X</sub> during different episodes in Jinan.</p> Full article ">Figure 5
<p>Concentrations of VOCs, OFP and their major group contributions during different O<sub>3</sub> episodes.</p> Full article ">Figure 6
<p>Top 10 species that contributed to OFP during different periods.</p> Full article ">Figure 7
<p>Source profiles resolved with PMF.</p> Full article ">Figure 8
<p>The contributions of six sources to the measured VOC concentrations during different events.</p> Full article ">Figure 9
<p>Backward trajectory analysis during the observation in the study area.</p> Full article ">
15 pages, 27731 KiB  
Article
Yearly Elevation Change and Surface Velocity Revealed from Two UAV Surveys at Baishui River Glacier No. 1, Yulong Snow Mountain
by Leiyu Li, Yuande Yang, Shijin Wang, Chuya Wang, Qihua Wang, Yuqiao Chen, Junhao Wang, Songtao Ai and Yanjun Che
Atmosphere 2024, 15(2), 231; https://doi.org/10.3390/atmos15020231 - 14 Feb 2024
Cited by 2 | Viewed by 1355
Abstract
Glaciers play an important role in understanding the climate, water resources, and surrounding natural change. Baishui River Glacier No. 1, a temperate glacier in the monsoon-influenced Southeastern Qinghai–Tibet Plateau, has experienced significant ablation due to regional warming during the past few decades. However, [...] Read more.
Glaciers play an important role in understanding the climate, water resources, and surrounding natural change. Baishui River Glacier No. 1, a temperate glacier in the monsoon-influenced Southeastern Qinghai–Tibet Plateau, has experienced significant ablation due to regional warming during the past few decades. However, little is known about the yearly changes in Baishui River Glacier No. 1. To investigate how Baishui River Glacier No. 1 has changed in recent years, digital orthophoto maps and digital elevation models were obtained from an unmanned aerial vehicle on 20 October 2018 and 22 July 2021, covering 84% and 47% of the total area, respectively. The results of the Baishui River Glacier No. 1 changes were obtained by differencing the digital elevation models, manual tracking, and terminus-retreat calculation methods. Our results showed that the surveyed area had a mean elevation change of −4.26 m during 2018 and 2021, and the lower area lost more ice than other areas. The terminus of Baishui River Glacier No. 1 has retreated by 16.35 m/a on average, exhibiting spatial variation with latitude. Moreover, we initially found that there was a high correlation between surface velocity and elevation gradient in this high-speed glacier. The surface velocity of Baishui River Glacier No. 1 was derived with the manual feature tracking method and ranged from 10.48 to 32.00 m/a, which is slightly smaller than the seasonal average. However, the snow coverage and ice melting of the two epochs led to the underestimation of our elevation change and velocity results, which need further investigation. Full article
(This article belongs to the Special Issue Polar Glacier Mass Balance and Climate Change)
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<p>(<b>a</b>) Yulong Snow Mountain is located in the Southeastern Qinghai–Tibetan Plateau and the southern end of the Hengduan Mountains. (<b>b</b>) The location of Baishui River Glacier No. 1 on Yulong Snow Mountain. (<b>c</b>) Baishui River Glacier No. 1, the areas of different unmanned aerial vehicle surveys (the 2018 survey is shown as a red rectangle, and the 2021 survey is shown as a yellow rectangle), and the position where unmanned aerial vehicles were launched.</p> Full article ">Figure 2
<p>(<b>a</b>) Terminus of Baishui River Glacier No. 1 in 2018 and 2021; (<b>b</b>) elevation changes in Baishui River Glacier No. 1 from 20 October 2018 to 22 July 2021—Area1 is enclosed by the blue line, and Area2 is enclosed by the black line; (<b>c</b>) elevation change in Area1; (<b>d</b>) 8 selected feature points from glacier terminus.</p> Full article ">Figure 3
<p>Digital orthophoto maps acquired in (<b>a</b>) 2018 and (<b>b</b>) 2021.</p> Full article ">Figure 4
<p>(<b>a</b>) Gradient of study area; (<b>b</b>) relationship between surface velocity and gradient; (<b>c</b>) derived surface velocity; (<b>d</b>) the surface velocity along the mainstream line.</p> Full article ">Figure 5
<p>(<b>a</b>) The precipitation and temperature data collected between 22 July and 20 October 2018 from the meteorological station. (<b>b</b>) Surface elevation data between 22 July and 20 October 2021 from a real-time platform.</p> Full article ">Figure 6
<p>(<b>a</b>) Relationship between elevation and elevation change; (<b>b</b>) elevation change distribution along the elevation in different regions.</p> Full article ">Figure A1
<p>Glacier surface velocity extracted by ImGRAFT.</p> Full article ">Figure A2
<p>(<b>a</b>) The 151 homologous points selected; (<b>b</b>) relationship between velocity and elevation gradient.</p> Full article ">Figure A3
<p>(<b>a</b>) Relationship between velocity and elevation gradient of 40 points selected from <a href="#atmosphere-15-00231-f0A2" class="html-fig">Figure A2</a>; (<b>b</b>) the selected homologous points: red points represent 2021, and blue points represent 2018.</p> Full article ">
19 pages, 7616 KiB  
Article
Objective Algorithm for Detection and Tracking of Extratropical Cyclones in the Southern Hemisphere
by Carina K. Padilha Reinke, Jeferson P. Machado, Mauricio M. Mata, José Luiz L. de Azevedo, Jaci Maria Bilhalva Saraiva and Regina Rodrigues
Atmosphere 2024, 15(2), 230; https://doi.org/10.3390/atmos15020230 - 14 Feb 2024
Viewed by 1547
Abstract
In this study, we propose an easy and robust algorithm to identify and track extratropical cyclone events using 850 hPa relative vorticity data, gaussian filter and connected-component labeling technique, which recognize the cyclone as areas under a threshold. Before selecting the events, the [...] Read more.
In this study, we propose an easy and robust algorithm to identify and track extratropical cyclone events using 850 hPa relative vorticity data, gaussian filter and connected-component labeling technique, which recognize the cyclone as areas under a threshold. Before selecting the events, the algorithm can include essential characteristics that are good metrics of intensity, like minimum mean sea level pressure and maximum 10-m winds. We implemented the algorithm in the Southern Hemisphere, using a 41-year high resolution dataset. Sensitivity tests were performed to determine the best parameters for detection and tracking, such as degree of smoothing, thresholds of relative vorticity at 850 hPa and the minimum area within the threshold. Two case studies were used to assess the positive and negative points of the methodology. The results showed that it is efficient in obtaining the position of extratropical cyclones in their most intense stage, but it does not always perform well during cyclolysis. We compare the methodology using 1-h temporal resolution to that using a 6-hours temporal resolution, and their reproducibility regarding the literature. The extratropical cyclone climatology in the Southern Hemisphere is provided and discussed. The algorithm developed here can be applied to datasets with good spacial and temporal resolution, providing a better inventory of extratropical cyclones. Full article
(This article belongs to the Special Issue Understanding and Simulating Air–Sea Interactions under Extreme Weather and Climate Conditions (2nd Edition))
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<p>Example of points center of mass of areas that match the relative vorticity threshold-sequence of hourly data.</p> Full article ">Figure 2
<p>Mean sea level pressure and relative vorticity at 850 hPa under −10<sup>−4</sup> s<sup>−1</sup>, on 27 October 2016, 06 UTC. The green square is the “effective area” to search for the more intense characteristics of the cyclone. The yellow point is the center of mass identified by the algorithm and the blue line and points are the following time frames of the event.</p> Full article ">Figure 3
<p>Flowchart of the detection algorithm of extratropical cyclones proposed in this study.</p> Full article ">Figure 4
<p>Mean sea level pressure and relative vorticity at 850 hPa under −10<sup>−4</sup> s<sup>−1</sup>, on 1 July 2020, 12 UTC. (<b>A</b>) raw data and with different standard deviations of the Gaussian filter: (<b>B</b>) 0.5, (<b>C</b>) 1.0 and (<b>D</b>) 3.0.</p> Full article ">Figure 5
<p>Study of the features of cyclones for the three sensitivity tests of filter degree, computed from 2013 to 2022: (<b>A</b>) Frequency distribution of cyclone lifetime, (<b>B</b>) frequency distribution of cyclone mean speed and (<b>C</b>) time series of total annual number of extratropical cyclones.</p> Full article ">Figure 6
<p>Mean sea level pressure (contours) and relative vorticity at 850 hPa (shading) with thresholds of: (<b>A</b>) −10<sup>−5</sup> s<sup>−1</sup>; (<b>B</b>) −0.5 × 10<sup>−4</sup> s<sup>−1</sup>; (<b>C</b>) −10<sup>−4</sup> s<sup>−1</sup> and (<b>D</b>) −2 × 10<sup>−4</sup> s<sup>−1</sup>, on 1 July 2020, 12 UTC, using a Gaussian filter parameter of 0.5.</p> Full article ">Figure 7
<p>Mean Cyclone centers per month detected between 2013 and 2022, using a Gaussian filter parameter of 0.5, a threshold of relative vorticity of −10<sup>−4</sup> s<sup>−1</sup> and different sets of minimum area (number of pixels).</p> Full article ">Figure 8
<p>Study of the features of cyclones for the four sensitivity tests of area criterion, computed between 2013 and 2022: (<b>A</b>) Frequency distribution of cyclone lifecicle, (<b>B</b>) frequency distribution of cyclone mean speed and (<b>C</b>) time series of total annual number of extratropical cyclones.</p> Full article ">Figure 9
<p>Extratropical cyclone during 26 to 28 October 2016. (<b>A</b>) Location of Hermenegildo Beach and automatic station of Chui (A899). (<b>B</b>) Cyclone track from 26 October 2016, 20 UTC to 28 October 2016, 6 UTC obtained from the program (blue line) and visual inspection (green line). (<b>C</b>) Mean sea level pressure and relative vorticity at 850 hPa, on 26 October 2016, 20 UTC, the green point is the position obtained from the algorithm. (<b>D</b>) Same as (<b>C</b>) except for: 27 October 2016, 16 UTC. (<b>E</b>) Minimum mean sea level pressure (green line) and maximum 10-m wind (red line) detected by the algorithm during the event. (<b>F</b>) Minimum relative vorticity (green line) and total area under the threshold (blue histograms) detected by the algorithm during the event.</p> Full article ">Figure 10
<p>Extratropical Cyclone of 15 to 17 August 2020. (<b>A</b>) Cyclone track from 15 August 2020, 20 UTC to 17 August 2020, 06 UTC, obtained from the algorithm (blue line) and from visual inspection (green line). (<b>B</b>) Mean sea level pressure and relative vorticity at 850 hPa, 15 August 2020, 20 UTC, the green point is the position obtained from the algorithm. (<b>C</b>) Same as (<b>B</b>) except for: 16 August 2020, 06 UTC. (<b>D</b>) Same as (<b>B</b>) except for: 17 August 2020, 02 UTC. (<b>E</b>) Minimum mean sea level pressure (green line) and maximum 10-m wind (red line) detected by the algorithm during the event. (<b>F</b>) Minimum relative vorticity (green line) and total area under the threshold (blue bars) detected by the algorithm during the event.</p> Full article ">Figure 11
<p>Study of the features of cyclones in the Southern Hemisphere using 1-hourly (blue) and 6-hourly (orange) temporal resolutions, for the period of 1982–2021: (<b>A</b>) Frequency distribution of cyclone lifecicle, (<b>B</b>) frequency distribution of cyclone mean speed and (<b>C</b>) time series of total annual number of extratropical cyclones.</p> Full article ">Figure 12
<p>Violin plot of extratropical cyclones tracking in the Southern Hemisphere using 1-hourly (blue) and 6-hourly (orange) temporal resolutions, for the period of 1982–2022. (<b>A</b>) Events per year. (<b>B</b>) Duration in hours. (<b>C</b>) Track length in km. (<b>D</b>) Average speed in km/h. (<b>E</b>) Minimum relative vorticity of events in s<sup>−1</sup>. (<b>F</b>) Maximum area with relative vorticity under the threshold.</p> Full article ">Figure 13
<p>The average number of cyclogenesis per month in the Southern Hemisphere, with a cap area of 10<sup>6</sup> km<sup>2</sup>. (<b>A</b>) Winter: June, July and August, (<b>B</b>) Summer: December, January and February, (<b>C</b>) Spring: September, October and November, (<b>D</b>) Autumn: March, April and May.</p> Full article ">
12 pages, 2923 KiB  
Communication
CO2 Absorption by Solvents Consisting of TMG Protic Ionic Liquids and Ethylene Glycol: The Influence of Hydrogen Bonds
by Bohao Lu, Yixing Zeng, Mingzhe Chen, Shaoze Zhang and Dezhong Yang
Atmosphere 2024, 15(2), 229; https://doi.org/10.3390/atmos15020229 - 14 Feb 2024
Cited by 1 | Viewed by 1455
Abstract
Herein, the absorption of CO2 by the TMG-based (TMG: 1,1,3,3-tetramethylguanidine) ionic liquids (ILs) and the absorbents formed by TMG ILs and ethylene glycol (EG) is studied. The TMG-based ILs used are formed by TMG and 4-fluorophenol (4-F-PhOH) or carvacrol (Car), and their [...] Read more.
Herein, the absorption of CO2 by the TMG-based (TMG: 1,1,3,3-tetramethylguanidine) ionic liquids (ILs) and the absorbents formed by TMG ILs and ethylene glycol (EG) is studied. The TMG-based ILs used are formed by TMG and 4-fluorophenol (4-F-PhOH) or carvacrol (Car), and their viscosities are low at 25 °C. The CO2 uptake capacities of [TMGH][4-F-PhO] and [TMGH][Car] are low (~0.09 mol CO2/mol IL) at 25 °C and 1.0 atm. However, the mixtures [TMGH][4-F-PhO]-EG and [TMGH][Car]-EG show much higher capacities (~1.0 mol CO2/mol IL) than those of parent ILs, which is unexpected because of the low CO2 capacity of EG (0.01 mol CO2/mol EG) in the same conditions. NMR spectra and theoretical calculations are used to determine the reason for these unexpected absorption behaviors. The spectra and theoretical results show that the strong hydrogen bonds between the [TMGH]+ cation and the phenolate anions make the used TMG-based ILs unreactive to CO2, resulting in the low CO2 capacity. In the Ils-EG mixtures, the hydrogen bonds formed between EG and phenolate anions can weaken the [TMGH]+–anion hydrogen bond strength, so ILs-EG mixtures can react with CO2 and present high CO2 capacities. Full article
(This article belongs to the Special Issue Advances in CO2 Capture and Absorption)
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<p>CO<sub>2</sub> uptake by [TMGH][4-F-PhO]-EG and [TMGH][4-F-PhO] at 25 °C and 1.0 atm.</p> Full article ">Figure 2
<p>The <sup>1</sup>H (<b>a</b>) and <sup>13</sup>C (<b>b</b>) NMR spectra of [TMGH][4−F−PhO]:EG (1:3) with and without CO<sub>2</sub>.</p> Full article ">Figure 3
<p>The <sup>1</sup>H (<b>a</b>) and <sup>13</sup>C (<b>b</b>) NMR spectra of [TMGH][4-F-PhO] with and without CO<sub>2</sub>.</p> Full article ">Figure 4
<p>The FTIR spectra of [TMGH][4−F−PhO]:EG (1:3) (<b>a</b>) and [TMGH][4−F−PhO] (<b>b</b>) before and after absorption.</p> Full article ">Figure 5
<p>(<b>a</b>) The <sup>1</sup>H NMR spectra of EG, [TMGH][4-F-PhO]:EG (1:3), [TMGH][4-F-PhO], and 4-F-PhOH; (<b>b</b>) <sup>13</sup>C NMR spectra of [TMGH][4-F-PhO]:EG (1:3), [TMGH][4-F-PhO], and 4-F-PhOH.</p> Full article ">Figure 6
<p>The ESP on the vdW surface (isosurface = 0.001 a.u.) of anion [4−F−PhO]<sup>−</sup>, cation [TMGH]<sup>+</sup>, and <span class="html-italic">V<sub>s,max</sub></span> and <span class="html-italic">V<sub>s,min</sub></span> for ions and molecules.</p> Full article ">Figure 7
<p>The hydrogen bonds in [TMGH][4-F-PhO] and [TMGH][4-F-PhO]-EG.</p> Full article ">Figure 8
<p>The interactions between CO<sub>2</sub> and absorbents: (<b>a</b>) [TMGH][4-F-PhO] + CO<sub>2</sub>; (<b>b</b>) [TMGH][4-F-PhO]-EG + CO<sub>2</sub>.</p> Full article ">Scheme 1
<p>The possible reaction mechanism between CO<sub>2</sub> and [TMGH][4−F−PhO]−EG solvents used in this work.</p> Full article ">
15 pages, 5782 KiB  
Article
Pacific Decadal Oscillation Modulation on the Relationship between Moderate El Niño-Southern Oscillation and East Asian Winter Temperature
by Jingwen Ge, Xiaojing Jia and Hao Ma
Atmosphere 2024, 15(2), 228; https://doi.org/10.3390/atmos15020228 - 14 Feb 2024
Cited by 1 | Viewed by 4453
Abstract
Based on observation data from 1958 to 2020, the current study explores the interdecadal modulation effects on moderate El Niño-Southern Oscillation (ENSO) episodes and East Asian (EA) winter surface air temperature (SAT) through the Pacific Decadal Oscillation (PDO). Strong and moderate ENSO episodes [...] Read more.
Based on observation data from 1958 to 2020, the current study explores the interdecadal modulation effects on moderate El Niño-Southern Oscillation (ENSO) episodes and East Asian (EA) winter surface air temperature (SAT) through the Pacific Decadal Oscillation (PDO). Strong and moderate ENSO episodes are classified by their amplitudes. The current work investigates the influence of moderate ENSO episodes on the EA winter SAT, especially moderate La Niña episodes, which show a close relationship with the EA winter SAT. To explore the PDO modulation effect on the influence of ENSO episodes, these ENSO episodes are further divided into two categories in terms of warm or cold PDO phases. The composite results show that in the warm phase of the PDO, the moderate La Niña signal is relatively strong and stable, with a profound impact on the EA winter SAT variability, whereas in the cold PDO phase, the relationship between the EA winter SAT and moderate La Niña episodes becomes ambiguous. Further studies show that the PDO modulates the moderate La Niña impacts on EA winter SAT primarily through varying the East Asian winter monsoon (EAWM). While moderate La Niña episodes take place in a warm PDO phase, positive and negative anomalies of sea level pressure (SLP) are observed in the Eurasian continent and mid–high-latitude North Pacific, respectively, favoring anomalous northerlies along the eastern coast of East Asia and therefore a colder-than-normal EA winter. In contrast, in a moderate La Niña winter during the cold PDO phase, the mid–high-latitude North Pacific is controlled by an anomalous high-pressure system with southerly anomalies along its western flank, and therefore, a weak warm pattern is observed for the EA winter SAT. Full article
(This article belongs to the Special Issue Multi-Year ENSO Events: Dynamics, Predictability, Teleconnections, and Impacts)
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<p>The normalized winter-mean Niño3.4 index (bars) with the decadal component (longer than 10 years) of PDO index overlaid by thick black curve. Dark (light) bars represent strong (moderate) ENSO episodes and blue (red) bars denote La Niña (El Niño) episodes. Gray bars refer to neutral years without evident ENSO signal occurring in the tropical central-eastern Pacific Ocean. Dotted and dashed lines denote the criteria of moderate and strong ENSO episodes, respectively.</p> Full article ">Figure 2
<p>Composite SAT anomalies during (<b>a</b>) strong El Niño, (<b>b</b>) moderate El Niño, (<b>c</b>) strong La Niña, and (<b>d</b>) moderate La Niña winters. Anomalies significant at the 95% confidence level are dotted.</p> Full article ">Figure 3
<p>Composite (<b>a</b>) SST, (<b>b</b>) precipitation, (<b>c</b>) divergent wind at 850 hPa (vector) together with velocity potential (contour), (<b>d</b>) SLP, (<b>e</b>) Z500, and (<b>f</b>) 850 hPa wind anomalies during moderate La Niña winters. Anomalies significant at the 95% confidence level are dotted in (<b>a</b>,<b>b</b>,<b>d</b>,<b>e</b>). Dashed (solid) lines in (<b>e</b>) represent negative (positive) values, with zero lines omitted, and the contour interval is 2 × 10<sup>5</sup> m<sup>2</sup>s<sup>−1</sup>. Light and dark shading areas in (<b>c</b>,<b>f</b>) indicate the anomalies passing the 95% and 99% confidence levels, respectively.</p> Full article ">Figure 4
<p>Composite differences of the winter average (<b>a</b>) SST and (<b>b</b>) SLP anomalies between the warm and cold phases of PDO. Anomalies significant at the 95% confidence level are dotted.</p> Full article ">Figure 5
<p>Composite SSTAs during moderate La Niña winters in (<b>a</b>) warm and (<b>b</b>) cold PDO phases. Anomalies significant at the 95% confidence level are dotted.</p> Full article ">Figure 6
<p>Composite (<b>a</b>,<b>b</b>) SAT and (<b>c</b>,<b>d</b>) 850 hPa wind anomalies during moderate La Niña winters in (<b>a</b>,<b>c</b>) warm and (<b>b</b>,<b>d</b>) cold PDO phases. Anomalies significant at the 95% confidence level are dotted in (<b>a</b>,<b>b</b>), while light and dark shading areas in (<b>c</b>,<b>d</b>) indicate the anomalies passing the confidence levels of 90% and 95%, respectively.</p> Full article ">Figure 7
<p>Composite (<b>a</b>,<b>b</b>) SLP, (<b>c</b>,<b>d</b>) Z500, and (<b>e</b>,<b>f</b>) U200 anomalies during moderate La Niña winters in (<b>a</b>,<b>c</b>,<b>e</b>) warm and (<b>b</b>,<b>d</b>,<b>f</b>) cold PDO phases. Anomalies significant at the 95% confidence level are dotted.</p> Full article ">Figure 8
<p>Composite CRU SAT anomalies during (<b>a</b>) strong El Niño, (<b>b</b>) moderate El Niño, (<b>c</b>) strong La Niña, and (<b>d</b>) moderate La Niña winters. Anomalies significant at the 90% confidence level are dotted.</p> Full article ">Figure 9
<p>Composite CRU SAT anomalies during moderate La Niña winters in (<b>a</b>) warm and (<b>b</b>) cold PDO phases. Anomalies significant at the 90% confidence level are dotted.</p> Full article ">
36 pages, 8466 KiB  
Article
A Novel Evaluation Approach for Emissions Mitigation Budgets and Planning towards 1.5 °C and Alternative Scenarios
by Joseph Akpan and Oludolapo Olanrewaju
Atmosphere 2024, 15(2), 227; https://doi.org/10.3390/atmos15020227 - 14 Feb 2024
Cited by 1 | Viewed by 1942
Abstract
Achieving ambitious climate targets, such as the 1.5 °C goal, demands significant financial commitment. While technical feasibility exists, the economic implications of delayed action and differing scenarios remain unclear. This study addresses this gap by analyzing the investment attractiveness and economic risks/benefits of [...] Read more.
Achieving ambitious climate targets, such as the 1.5 °C goal, demands significant financial commitment. While technical feasibility exists, the economic implications of delayed action and differing scenarios remain unclear. This study addresses this gap by analyzing the investment attractiveness and economic risks/benefits of different climate scenarios through a novel emissions cost budgeting model. A simplified model is developed using five global scenarios: announced policies (type 1 and 2), 2.0 °C, and 1.5 °C. A unit marginal abatement cost estimated the monetary value of avoided and unavoided emissions costs for each scenario. Net present value (NPV) and cost–benefit index (BI) were then calculated to compare the scenario attractiveness of the global emission budgets. The model was further applied to emissions budgets for China, the USA, India, and the European Union (EU). Increasing discount rates and gross domestic product (GDP) led to emission increases across all scenarios. The 1.5 °C scenario achieved the lowest emissions, while the baseline scenario showed the highest potential emissions growth (between 139.48% and 146.5%). Therefore, emphasis on the need for further financial commitment becomes important as the emissions’ abatement cost used as best case was estimated at USD 2.4 trillion per unit of 1 Gtons CO2 equivalent (eq.). Policy delays significantly impacted NPV and BI values, showcasing the time value of investment decisions. The model’s behavior aligns with real-world observations, including GDP growth influencing inflation and project costs. The simplified model could be coupled to existing integrated assessment frameworks or models (IAMs) as none offer cost–benefit analysis of climate scenarios to the best of our knowledge. Also, the model may be used to examine the economic attractiveness of carbon reduction programs in various nations, cities, and organizations. Thus, the model and analytical approach presented in this work indicate promising applications. Full article
(This article belongs to the Section Air Pollution Control)
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<p>Comparison of two <span class="html-italic">TCRE</span> estimations’ cumulative CO<sub>2</sub> emissions and CO<sub>2</sub>-induced temperature change, according to H.D. Matthews et al. in [<a href="#B21-atmosphere-15-00227" class="html-bibr">21</a>]. <span class="html-italic">TCRE</span>—transient climate response to cumulative emissions, Obs—observations, and CIMP5—coupled model intercomparison projections version 5.</p> Full article ">Figure 2
<p>Comparison of global and top emitting countries’ emissions budgets. Data from <a href="#app1-atmosphere-15-00227" class="html-app">Table S1</a>.</p> Full article ">Figure 3
<p>The general form of capital finance budgeting indices. Source: authors’ elaboration.</p> Full article ">Figure 4
<p>The framework of the simplified EB model for the policy scenarios. <b>Note:</b> A—Emissions budgets for baseline case, B—Emissions budgets for announced policy type 1, C—Emissions budgets for announced policy type 2, D—Emissions budgets for 2.0 °C Scenario, and E—Emissions budgets for 1.5 °C Scenario.</p> Full article ">Figure 5
<p>EB modelling process.</p> Full article ">Figure 6
<p>Plot of model fitting using the 1.5 °C scenario investment. (<b>a</b>) Real GDP from 2000 to 2050. (<b>b</b>) Cost–benefit index versus investment start period. (<b>c</b>) Net present value versus investment start period.</p> Full article ">Figure 6 Cont.
<p>Plot of model fitting using the 1.5 °C scenario investment. (<b>a</b>) Real GDP from 2000 to 2050. (<b>b</b>) Cost–benefit index versus investment start period. (<b>c</b>) Net present value versus investment start period.</p> Full article ">Figure 7
<p>Case 1 (initial <span class="html-italic">R<sub>p</sub></span>): emissions budgeting indices of the global policy scenarios.</p> Full article ">Figure 8
<p>Case 2 (at 10% increase in <span class="html-italic">R<sub>p</sub></span>): emissions budgeting indices of the global policy scenarios.</p> Full article ">Figure 9
<p>Case 3 (at 15% increase in <span class="html-italic">R<sub>p</sub></span>): emissions budgeting indices of the global policy scenarios.</p> Full article ">Figure 10
<p>Comparison of the avoided emissions rate for the global policy scenarios.</p> Full article ">Figure 11
<p>Emissions budgeting indices of the policy scenarios for China, the USA, India, and the EU.</p> Full article ">Figure 11 Cont.
<p>Emissions budgeting indices of the policy scenarios for China, the USA, India, and the EU.</p> Full article ">Figure 12
<p>Comparison of the avoided emissions rates for China, USA, India, and the EU.</p> Full article ">Figure 13
<p>Model validation with the global scenarios. Note: Red numbered texts in (<b>b</b>,<b>c</b>) imply negative NPV and BI values, respectively.</p> Full article ">Figure 13 Cont.
<p>Model validation with the global scenarios. Note: Red numbered texts in (<b>b</b>,<b>c</b>) imply negative NPV and BI values, respectively.</p> Full article ">Figure A1
<p>Different simulated policies versus global temperature rise.</p> Full article ">
21 pages, 8578 KiB  
Article
Assessing the Effects of Urban Canopy on Extreme Rainfall over the Lake Victoria Basin in East Africa Using the WRF Model
by Joan Birungi, Jinhua Yu, Abdoul Aziz Saidou Chaibou, Nyasulu Matthews and Emmanuel Yeboah
Atmosphere 2024, 15(2), 226; https://doi.org/10.3390/atmos15020226 - 14 Feb 2024
Cited by 1 | Viewed by 1594
Abstract
The model simulation focuses on an extreme rainfall event that triggered a flood hazard in the Lake Victoria basin region of East Africa from June 24th to 26th, 2022. This study investigates the impacts of its urban canopy on the extreme rainfall events [...] Read more.
The model simulation focuses on an extreme rainfall event that triggered a flood hazard in the Lake Victoria basin region of East Africa from June 24th to 26th, 2022. This study investigates the impacts of its urban canopy on the extreme rainfall events over the Lake Victoria basin in East Africa, employing the Weather Research and Forecasting (WRF) model at a convective-permitting resolution. The rapid urbanization of the region has given rise to an urban canopy, which has notable effects on local weather patterns, including the intensity and distribution of rainfall. The model incorporates high-resolution land use and urban canopy parameters to accurately capture the influences of urbanization on local weather patterns. This research comprises three sets of experiments, two with urban areas and one without, using the WRF model; the experiments focus on three days of an extreme rainfall event in the Lake Victoria basin. Satellite-based precipitation products and reanalysis datasets are employed for a synoptic analysis and model evaluation. The results demonstrate the model’s effectiveness in capturing meteorological variables during an extreme event compared to observed data. The synoptic patterns reveal that, during the extreme event, the Mascarene and St. Helena influenced rainfall conditions over the Lake Victoria Basin by directing moist air toward the northwest. This led to increased moisture convergence from the urban–rural interface toward urban areas, enhancing convection and processes that result in extreme rainfall. Moreover, this study indicates that the urban canopy, specifically the building effect parameterization, significantly amplifies the intensity and duration of rainfall in the urban areas of the region. This research also indicates a general increase in air temperature, relative humidity, latent heat flux, and surface sensible heat flux due to the urban canopy. These findings highlight the substantial influence of urbanization on rainfall patterns in the urban environment. Full article
(This article belongs to the Special Issue Weather and Climate Extremes: Observations, Modeling, and Impacts)
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<p>The simulated domain is shown on the right within the location of the Lake Victoria basin, showing land-use-category data from WRF model simulations for the year 2022.</p> Full article ">Figure 2
<p>Study methodology.</p> Full article ">Figure 3
<p>Spatial distribution of rainfall of the observation data: (<b>a</b>) CMORPH and the control simulations; (<b>b</b>) urban; and (<b>c</b>) BEPurban.</p> Full article ">Figure 4
<p>The diurnal cycle of the average spatial rainfall over the Lake Victoria basin for the observation (CMORPH) and the control experiments with the urban canopy models (urban and BEPurban).</p> Full article ">Figure 5
<p>Spatially distributed bias and RMSE of the simulated rainfall: (<b>a</b>,<b>c</b>) bias, RMSE for UCM (urban canopy model)and (<b>b</b>,<b>d</b>) bias, RMSE for BEP–urban and time series, and (<b>e</b>) RMSE and (<b>f</b>) Bias.</p> Full article ">Figure 6
<p>Scatter plot showing the correlation coefficient results between the control simulations (urban and BEPurban) and the observations (CMORPH dataset) (<b>a</b>) represents the Urban and (<b>b</b>) is the BEP–urban.</p> Full article ">Figure 7
<p>Rainfall simulations from the model, where (<b>a</b>) represents the simulation without urban physics (non-urban), (<b>b</b>) represents the simulation with building effect parameterization (BEP–urban), and (<b>c</b>) shows the comparison between the non-urban simulation (non-urban) and the control BEPurban (non-urban–BEPurban).</p> Full article ">Figure 8
<p>Shows the moisture convergence from (<b>a</b>) non-urban areas and the control simulation; (<b>b</b>) BEPurban; and (<b>c</b>) the difference between the non-urban experiments and the control simulations (non-urban–BEPurban).</p> Full article ">Figure 9
<p>Spatially distributed 2 m temperature: (<b>a</b>) non-urban, (<b>b</b>) control (BEPurban), and (<b>c</b>) the difference between the non-urban experiments and the control simulations (non-urban–BEPurban).</p> Full article ">Figure 10
<p>Spatially distributed relative humidity (RH): (<b>a</b>) non-urban and (<b>b</b>) control (BEPurban). (<b>c</b>) The difference between the non-urban and the control simulations (non-urban–BEPurban) and time series of the spatial average diurnal variation in RH over the Lake Victoria basin.</p> Full article ">Figure 11
<p>Spatially distributed upward sensible heat flux (hfx): (<b>a</b>) non-urban, (<b>b</b>) BEPurban, and (<b>c</b>) the difference between the non-urban experiment and the control simulation (non-urban–BEPurban).</p> Full article ">Figure 12
<p>Spatially distributed latent heat flux (l h): (<b>a</b>) non-urban, (<b>b</b>) BEPurban, and (<b>c</b>) the difference between the non-urban and control simulations (non-urban–BEPurban) and time series of the average diurnal spatial variation across l h over the Lake Victoria basin.</p> Full article ">Figure 13
<p>Vertical profiles of the integrated moisture flux (shaded, in kg m<sup>−1</sup>s<sup>−1</sup>) and horizontal wind vectors (in the u and v directions in m/s) over the storm period, where (<b>a</b>) is the non-urban, (<b>b</b>) BEPurban, and (<b>c</b>) the difference between non-urban and BEPurban.</p> Full article ">
15 pages, 2861 KiB  
Article
Gridded Assessment of Mainland China’s Solar Energy Resources Using the Typical Meteorological Year Method and China Meteorological Forcing Dataset
by Zongpeng Song, Bo Wang, Hui Zheng, Shuanglong Jin, Xiaolin Liu and Shenbing Hua
Atmosphere 2024, 15(2), 225; https://doi.org/10.3390/atmos15020225 - 14 Feb 2024
Cited by 1 | Viewed by 1120
Abstract
The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under [...] Read more.
The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under climatological weather conditions. However, there are very few TMY-based solar energy assessments on the scale of China. On the national scale, the difference between the TMY and MYA methods, the requirement of the data record length, and the impacts of the selection of meteorological variables on the TMY-based assessment are still unclear. This study aims to fill these gaps by assessing mainland China’s solar energy resources using the TMY method and China Meteorological Forcing Dataset. The results show that the data record length could significantly influence annual total solar radiation estimation when the record length is shorter than 30 years. Whereas, the estimation becomes stable when the length is greater or equal to 30 years, suggesting a thirty-year data record is preferred. The difference between the MYA and TMY methods is exhibited primarily in places with modest or low abundance of solar radiation. The difference is nearly independent of the examined data record lengths, hinting at the role of regional-specific weather characteristics. The TMY and MYA methods differ more pronounced when assessing the seasonal stability grade. A total of 7.4% of the area of China experiences a downgrade from the TMY relative to the MYA methods, while a 3.15% area experiences an upgrade. The selection of the meteorological variables has a notable impact on the TMY-based assessment. Among the three meteorological variables examined, wind speed has the most considerable impact on both the annual total and seasonal stability, dew point has the second most significant impact, and air temperature has the least. The results are useful for guiding future research on solar energy assessment in China and could be helpful for solar energy development planning. Full article
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<p>Schematic diagram of the TMY generation procedure. The five three-hourly variables shown at the top are from CMFD.</p> Full article ">Figure 2
<p>Study domain. Blue lines denote the Yangtze and Huai Rivers. Black lines denote the province boundaries.</p> Full article ">Figure 3
<p>China’s solar energy resources assessed using the TMY method for a thirty-year period (1991 to 2020). (<b>a</b>) annual global horizontal solar radiation, and (<b>b</b>) abundance grade of the solar energy resources. The definitions of the grades A, B, C, and D are described in <a href="#sec2dot3-atmosphere-15-00225" class="html-sec">Section 2.3</a>.</p> Full article ">Figure 4
<p>Relative changes in the solar radiation estimates using the reference periods of different lengths. The changes are relative to the thirty-year period from 1991 to 2020: (<b>a</b>) 10 years from 2011 to 2020; (<b>b</b>) 20 years from 2001 to 2020; (<b>c</b>) 40 years from 1981 to 2020; and (<b>d</b>) 50 years from 1971 to 2020.</p> Full article ">Figure 5
<p>Relative difference between the MYA and TMY methods in calculating annual solar radiation. The left panels present the spatial distribution, whereas the right panels present the probability distribution function (PDF).</p> Full article ">Figure 6
<p>Seasonal radiation estimated with the TMY method for the thirty-year period (1991 to 2020). The values are fractions of the annual totals. (<b>a</b>) MAM for March, April, and May; (<b>b</b>) JJA for June, July, and August; (<b>c</b>) SON for September, October, and November; and (<b>d</b>) DJF for December, January, and February.</p> Full article ">Figure 7
<p>Seasonal stability index and grade of the solar energy resource assessed using the TMY method for the thirty-year period (1991 to 2020). The seasonal stability index is defined as the ratio of the minimum monthly radiation to its maximum value. The stability grades A, B, C, and D are defined in <a href="#sec2dot3-atmosphere-15-00225" class="html-sec">Section 2.3</a>.</p> Full article ">Figure 8
<p>Difference between the MYA and TMY methods in calculating seasonal solar radiation for the thirty-year reference period (1991 to 2020). The values are presented as the percentage of the annual solar radiation estimated using the TMY method (<a href="#atmosphere-15-00225-f003" class="html-fig">Figure 3</a>a).</p> Full article ">Figure 9
<p>Difference in the seasonal stability index and grade between the MYA and TMY methods: (<b>a</b>) Difference of the MYA method relative to TMY; (<b>b</b>) Difference in the seasonal stability grade of the MYA method relative to TMY. Red denotes an upgrade (up arrow), whereas blue denotes a downgrade (down arrow); (<b>c</b>) Probability distribution function (PDF) of the difference in the seasonal stability index.</p> Full article ">Figure 10
<p>Sensitivity of annual solar radiation to the considered meteorological variables. The three rows present the impacts of wind speed, air temperature, and dew point, respectively. The right panels present the probability distribution function of the relative differences corresponding to the left panels.</p> Full article ">Figure 11
<p>Sensitivity of the seasonal stability index to the considered meteorological variables. The three rows present the impacts of wind speed, air temperature, and dew point, respectively. The right panels present the cumulative distribution function of the relative changes corresponding to the left panels. The lower and upper numbers in the right panels denote the CDFs of the area with negative and non-negative changes, respectively.</p> Full article ">
27 pages, 11172 KiB  
Article
A Practical Approach for On-Road Measurements of Brake Wear Particles from a Light-Duty Vehicle
by Jon Andersson, Louisa J. Kramer, Michael Campbell, Ian Marshall, John Norris, Jason Southgate, Simon de Vries and Gary Waite
Atmosphere 2024, 15(2), 224; https://doi.org/10.3390/atmos15020224 - 13 Feb 2024
Cited by 1 | Viewed by 1727
Abstract
Brake wear particles are generated through frictional contact between the brake disc or brake drum and the brake pads. Some of these particles may be released into the atmosphere, contributing to airborne fine particulate matter (PM2.5). In this study, an onboard [...] Read more.
Brake wear particles are generated through frictional contact between the brake disc or brake drum and the brake pads. Some of these particles may be released into the atmosphere, contributing to airborne fine particulate matter (PM2.5). In this study, an onboard system was developed and tested to measure brake wear particles emitted under real-world driving conditions. Brake wear particles were extracted from a fixed volume enclosure surrounding the pad and disc installed on the front wheel of a light-duty vehicle. Real-time data on size distribution, number concentration, PM2.5 mass, and the contribution of semi-volatiles were obtained via a suite of instruments sub-sampling from the constant volume sampler (CVS) dilution tunnel. Repeat measurements of brake particles were obtained from a 42 min bespoke drive cycle on a chassis dynamometer, from on-road tests in an urban area, and from braking events on a test track. The results showed that particle emissions coincided with braking events, with mass emissions around 1 mg/km/brake during on-road driving. Particle number emissions of low volatility particles were between 2 and 5 × 109 particles/km/brake. The highest emissions were observed under more aggressive braking. The project successfully developed a proof-of-principle measurement system for brake wear emissions from transient vehicle operation. The system shows good repeatability for stable particle metrics, such as non-volatile particle number (PN) from the solid particle counting system (SPCS), and allows for progression to a second phase of work where emissions differences between commercially available brake system components will be assessed. Full article
(This article belongs to the Special Issue Study of Brake Wear Particle Emission)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of final sampling system and instrumentation.</p> Full article ">Figure 2
<p>Brake enclosure design.</p> Full article ">Figure 3
<p>PG42 cycle.</p> Full article ">Figure 4
<p>On-road real driving cycle.</p> Full article ">Figure 5
<p>Moderate test track braking, showing road speed (kph) and braking pressure (bar).</p> Full article ">Figure 6
<p>Particle Transmission through the final sampling system at 300 L/min. Uncertainty in the measurements is shown by the error bars.</p> Full article ">Figure 7
<p>(<b>a</b>) Repeatability, as CoV, of PG42 on-dyno cycle distance; (<b>b</b>) CO<sub>2</sub> emissions. Error bars show one standard deviation.</p> Full article ">Figure 8
<p>Repeatability of cold (blue lines) and hot (red lines) ELPI particle size distributions: (<b>a</b>) log scale; (<b>b</b>) linear scale. The measurements during each cycle are represented by the line thickness.</p> Full article ">Figure 9
<p>Illustration of hot ELPI particle number emissions isolated from an ~80 kph test track braking experiment.</p> Full article ">Figure 10
<p>(<b>a</b>) Real-time brake PM (eFilter) and (<b>b</b>) PN (SPCS), emissions during PG42 cycle.</p> Full article ">Figure 11
<p>(<b>a</b>) Real-time brake PN emissions from cold ELPI and (<b>b</b>) hot ELPI during PG42 cycle.</p> Full article ">Figure 12
<p>Volatile particle release, between 2100 and 2500 s of PG42, during high-speed cruise, after hard braking.</p> Full article ">Figure 13
<p>Real-time braking events on an urban road measured by the (<b>a</b>) eFilter, (<b>b</b>) hot ELPI, and (<b>c</b>) cold ELPI. Also shown is the brake pressure (bar) and road speed (kph) during the cycle.</p> Full article ">Figure 14
<p>Brake mass and number emissions from PG42 and urban driving; error bars show one standard deviation. Note, the SPCS was not measuring during the urban driving test.</p> Full article ">Figure 15
<p>Image of three filters collected during PG42 brake emissions testing.</p> Full article ">Figure 16
<p>Results of thermogravimetric analyses of brake sample filters for the PG42 and urban cycles.</p> Full article ">Figure 17
<p>Average particle/s emissions from moderate braking events compared with background levels: (<b>a</b>) cold ELPI, (<b>b</b>) hot ELPI, (<b>c</b>) eFilter, and (<b>d</b>) eFilter low speeds only.</p> Full article ">Figure 18
<p>Relationship between particle emission rates and peak speed of braking event: (<b>a</b>) hot ELPI, (<b>b</b>) cold ELPI, (<b>c</b>) eFilter.</p> Full article ">Figure 19
<p>There is no consistent relationship between average brake pad temperature and PM emissions rate (µg/s) below ~170 °C for braking events from (<b>a</b>) ~80 kph, (<b>b</b>) ~64 kph, (<b>c</b>) ~48 kph, (<b>d</b>) ~32 kph, and (<b>e</b>) ~16 kph.</p> Full article ">Figure 20
<p>There is no consistent relationship between average brake pad temperature and PN emissions rate (#/s) below ~170 °C for braking events from (<b>a</b>) ~80 kph, (<b>b</b>) ~64 kph, (<b>c</b>) ~48 kph, (<b>d</b>) ~32 kph, and (<b>e</b>) ~16 kph.</p> Full article ">Figure 21
<p>Cold ELPI and hot ELPI PN emissions rates, vehicle speed, brake pad and disc temperatures and brake pressures, from the first aggressive braking experiment.</p> Full article ">Figure 22
<p>Two nominally identical braking events (circled as 1 and 2) executed with different initial pad and disc temperatures.</p> Full article ">Figure 23
<p>Comparative (<b>a</b>) cold and hot ELPI PN and (<b>b</b>) eFilter PM emissions from drive cycles and test-track braking experiments.</p> Full article ">Figure 24
<p>Hot ELPI size distributions, all cycle types, averaged data.</p> Full article ">Figure 25
<p>Cold ELPI size distributions, all cycle types, averaged data.</p> Full article ">
16 pages, 1614 KiB  
Article
Prioritization of Volatile Organic Compound Reduction in the Tire Manufacturing Industry through Speciation of Volatile Organic Compounds Emitted at the Fenceline
by Hyo Eun Lee, Jeong Hun Kim, Daram Seo and Seok J. Yoon
Atmosphere 2024, 15(2), 223; https://doi.org/10.3390/atmos15020223 - 13 Feb 2024
Cited by 1 | Viewed by 1302
Abstract
Volatile organic compounds (VOCs), with their ubiquitous presence across numerous global industries, pose multifaceted challenges, influencing air pollution and health outcomes. In response, countries such as the United States and Canada have implemented fenceline monitoring systems, enabling real-time tracking of organic solvents, including [...] Read more.
Volatile organic compounds (VOCs), with their ubiquitous presence across numerous global industries, pose multifaceted challenges, influencing air pollution and health outcomes. In response, countries such as the United States and Canada have implemented fenceline monitoring systems, enabling real-time tracking of organic solvents, including benzene. Initially, this focus was predominantly placed on the petroleum refining industry, but it has gradually been broadening. This investigation seeks to identify and analyze the specific VOCs produced in the tire manufacturing sector by utilizing both active and passive monitoring methodologies. The findings of the present study aim to recommend prioritized reduction strategies for specific VOCs. Percentage means the ratio of VOCs detected at the research site. At research target facility A, active monitoring demonstrated the presence of Methylene chloride (20.7%) and Carbon tetrachloride (15.3%), whereas passive monitoring identified Carbon tetrachloride (43.4%) and m,p-Xylene (20.8%). After converting these substances to their equivalent concentrations, we found a noteworthy correlation between the active and passive methodologies. At research target facility B, active monitoring detected n-Pentane (45.5%) and Isoprene (11.4%), while passive monitoring revealed Toluene (21.3%) and iso-Hexane (15.8%). Interestingly, even at sites like warehouses and test tracks where VOC concentrations were projected to be low, we observed VOC levels comparable to those in process areas. This underlines the fact that the dispersal of VOCs is considerably influenced by wind direction and speed. Specifically, in the tire manufacturing industry, emissions of Xylene and 3-Methylhexane, both having high photochemical ozone creation potential (POCP), contribute significantly to air pollution. However, the overall detection concentration in the tire manufacturing industry was detected at a low concentration of less than 2 μg/m3. This is less than 9 μg/m3, which is the standard for benzene, which has strong carcinogenicity regulations. This suggests that additional research is needed on synthetic rubber manufacturing rather than tire manufacturing. Full article
(This article belongs to the Special Issue Mechanisms of Urban Ozone Pollution)
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<p>Locations and measurement points of the target tire manufacturing facilities: (<b>a</b>) research target facility A, (<b>b</b>) research target facility B.</p> Full article ">Figure 2
<p>VOC Capture Methods: (<b>a</b>) Active Sampling (<b>b</b>) Passive Sampling. The meaning of the phrase in the figure is ‘Measuring VOCs’.</p> Full article ">Figure 3
<p>Total VOCs in active and passive modes at research target facility A. (<b>a</b>) Total VOCs, (<b>b</b>) wind rose diagram.</p> Full article ">Figure 4
<p>Total VOCs in active and passive modes at research target facility B. (<b>a</b>) Total VOCs, (<b>b</b>) wind rose diagram.</p> Full article ">
22 pages, 9756 KiB  
Article
Investigation of the Synoptic and Dynamical Characteristics of Cyclone Shaheen (2021) and Its Influence on the Omani Coastal Region
by Petros Katsafados, Pantelis-Manolis Saviolakis, George Varlas, Haifa Ben-Romdhane, Kosmas Pavlopoulos, Christos Spyrou and Sufian Farrah
Atmosphere 2024, 15(2), 222; https://doi.org/10.3390/atmos15020222 - 12 Feb 2024
Cited by 1 | Viewed by 1269
Abstract
Tropical Cyclone Shaheen (TCS), originating in the Arabian Sea on 30 September 2021, followed an east-to-west trajectory and made landfall as a category-1 cyclone in northern Oman on 3 October 2021, causing severe floods and damages before dissipating in the United Arab Emirates. [...] Read more.
Tropical Cyclone Shaheen (TCS), originating in the Arabian Sea on 30 September 2021, followed an east-to-west trajectory and made landfall as a category-1 cyclone in northern Oman on 3 October 2021, causing severe floods and damages before dissipating in the United Arab Emirates. This study aims to analyze the synoptic and dynamical conditions influencing Shaheen’s genesis and evolution. Utilizing ERA5 reanalysis data, SEVIRI-EUMETSAT imagery, and Sorbonne University Atmospheric Forecasting System (SUAFS) outputs, it was found that Shaheen manifested as a warm-core cyclone with moderate vertical wind shear within the eyewall. Distinctive features included a trajectory aligned with rising sea surface temperatures and increased specific humidity levels at 700 hPa in the Arabian Sea. As Shaheen approached the Gulf of Oman, a significant increase in rainfall rates occurred, correlated with variations in sea surface temperatures and vertical wind shear. Comparative analysis between SUAFS and ERA5 data revealed a slight northward shift in the SUAFS track and landfall. Advance warnings highlighted heavy rainfall, rough seas, and strong winds. This study provides valuable insights into the meteorological factors contributing to Shaheen’s formation and impact. Full article
(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (3rd Edition))
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<p>The computational domain of SUAFS. The topography (m) of the area is also illustrated.</p> Full article ">Figure 2
<p>Tracks of cyclones and depressions in the North Indian Ocean for the year 2021. (Source: Cyclone e-atlas RSMC India Meteorological Department).</p> Full article ">Figure 3
<p>The development of Shaheen through satellite images in infrared. (<b>a</b>) is on 30/09/2021 at 12:00 UTC, (<b>b</b>) on 01/10/2021 at 00:00 UTC, (<b>c</b>) on 02/10/2021 at 00:00 UTC, (<b>d</b>) on 03/10/2021 at 00:00 UTC (Source: EUMETSAT Images, High Rate SEVIRI IR10.8μm).</p> Full article ">Figure 4
<p>(<b>a</b>) The SST (K) in the region on 30/09/2021 at 12:00 UTC. (<b>b</b>) The SST (K) on 03/10/2021 at 12:00 UTC. (<b>c</b>) The total precipitation (mm/h) on 03/10/2021 12:00 UTC (Source Copernicus ERA5 data).</p> Full article ">Figure 5
<p>Wind shear between 200−850 hPa (in m s<sup>−1</sup>) on 30/09/2021 at 12:00 UTC.</p> Full article ">Figure 6
<p>Phase Space Hart Diagram for TCS (estimated using Copernicus ERA5 Data). The time interval between successive points is 6 h from 30/9/2021 12:00 UTC up to 03/10/2021 12:00 UTC.</p> Full article ">Figure 7
<p>(<b>a</b>) Temperature at 850 hPa (K) and Geopotential height (m) at 500 hPa on 30/09/2021 12:00 UTC. (<b>b</b>) Temperature at 850 hPa (K) and Geopotential height (m) at 500 hPa on 01/10/2021 at 06:00 UTC. (<b>c</b>) Vertical velocity in a horizontal cross section of the cyclone and constant Lat 23.25° on 01/10/2021 at 06:00 UTC. (Source: Copernicus ERA5 data).</p> Full article ">Figure 8
<p>(<b>a</b>) Specific humidity (kg/kg) and wind vectors at 700 hPa on 30/09/2021 12:00 UTC Based on Copernicus ERA5 data. (<b>b</b>) Potential vorticity at 850 hPa (1 PV = 10<sup>−6</sup> m<sup>2</sup> s<sup>−1</sup> K kg<sup>−1</sup>) and geopotential height (gpm) on 30/09/2021 12:00 UTC. (Source Copernicus ERA5 data).</p> Full article ">Figure 9
<p>Wind shear between 200–850 hPa (in m s<sup>−1</sup>) from 30/09/2021 12:00 UTC to 03/10/2021 00:00 UTC. (<b>a</b>) 30/09/2021 12:00 UTC, (<b>b</b>) 01/10/2021 00:00 UTC, (<b>c</b>) 02/10/2021 00:00 UTC, (<b>d</b>) 03/10/2021 00:00 UTC. (Source Copernicus ERA5 data).</p> Full article ">Figure 10
<p>IVT analysis in kg m<sup>−1</sup> s<sup>−1</sup>. (<b>a</b>) on 30/09/2021 12:00 UTC, (<b>b</b>) on 01/10/2021 00:00 UTC, (<b>c</b>) on 02/10/2021 00:00 UTC, (<b>d</b>) on 03/10/2021 00:00 UTC (Source Copernicus ERA5 data).</p> Full article ">Figure 11
<p>(<b>a</b>) The locations and the WMO stations (showing the WMO ID number) used for the comparison against SUAFS model outputs, (<b>b</b>) Time plots of the modeled and observed temperature at 2 m, (<b>c</b>) Time plots of the modeled and observed wind speed at 10 m, (<b>d</b>) Time plots of the modeled and observed mean sea level pressure.</p> Full article ">Figure 12
<p>Wind speed at 10 m (m s<sup>−1</sup>) from SUAFS (<b>a</b>) and from Copernicus ERA5 (<b>b</b>) on 03/10/2021 at 9:00 UTC. The vectors depict the wind direction at 10m.</p> Full article ">Figure 13
<p>Simulated (<b>a</b>) latent heat flux and (<b>b</b>) sensible heat flux from SUAFS in W m<sup>−2</sup> on 03/10/2021 at 9:00 UTC. The vectors depict the wind direction at 10m.</p> Full article ">Figure 14
<p>(<b>a</b>) Vertical velocity, (<b>b</b>) vertical profile of condensational heating rate, (<b>c</b>) vertical profile of potential vorticity on 03/10/2021 at 9:00 UTC, as simulated by SUAFS, (<b>d</b>) Potential Vorticity at 850 hPa from the SUAFS, on 03/10/2021 09:00 UTC in Potential Vorticity Units (1 PVU = 10<sup>−6</sup> m<sup>2</sup> s<sup>−1</sup>⋅K kg<sup>−1</sup>).</p> Full article ">Figure 15
<p>The track of cyclone Shaheen as extracted from Copernicus ERA5 data (red) and the SUAFS model (blue). Dots correspond to 3 h time intervals.</p> Full article ">Figure 16
<p>The drainage basin of the wadi Hawasnah and the wadi Bani (red line), where the fieldwork took place during November 2021.</p> Full article ">Figure 17
<p>(<b>Top</b>): The flood plain of Wadi Hawasnah one month after the cyclone Shaheen passed as viewed from eastwards. (<b>Bottom</b>): Drainage basin as viewed from downstream/westwards. The wadi bedload deposits destroyed the main road.</p> Full article ">
18 pages, 6188 KiB  
Article
Sensitivity of the Land–Atmosphere Coupling to Soil Moisture Anomalies during the Warm Season in China and its Surrounding Areas
by Lan Wang, Shuwen Zhang, Xinyang Yan and Chentao He
Atmosphere 2024, 15(2), 221; https://doi.org/10.3390/atmos15020221 - 12 Feb 2024
Viewed by 1260
Abstract
Significant temporal and spatial variability in soil moisture (SM) is observed during the warm season in China and its surrounding regions. Because of the existence of two different evapotranspiration regimes, i.e., soil moisture-limited and energy-limited, averaging the land–atmosphere (L–A) coupling strength for all [...] Read more.
Significant temporal and spatial variability in soil moisture (SM) is observed during the warm season in China and its surrounding regions. Because of the existence of two different evapotranspiration regimes, i.e., soil moisture-limited and energy-limited, averaging the land–atmosphere (L–A) coupling strength for all soil wetness scenarios may result in the loss of coupling signals. This study examines the daytime-only L–A interactions under different soil moisture conditions, by using two-legged metrics in the warm season from May to September 1981–2020, partitioning the interactions between SM and latent heat flux (SM–LH, the land leg) from the interactions between latent heat flux and the lifting condensation level (LH–LCL, the atmospheric leg). The statistical results reveal large regional differences in warm-season daytime L–A feedback in China and its surrounding areas. As the soil becomes wetter, the positive SM–LH coupling strength increases in arid regions (e.g., northwest China, Hetao, and the Great Indian Desert) and the positive feedback shifts to the negative one in semi-arid/semi-humid regions (northeast and northern China). The negative LH–LCL coupling is most pronounced in wet soil months in arid regions, while the opposite is true for the Tibetan Plateau. In terms of intraseasonal variation, the large variability of SM in north China, the Tibetan Plateau, and India due to the influence of the summer monsoon leads to the sign change in the land segment coupling index, comparing pre- and post-monsoon periods. To further examine the impact of SM anomalies on L–A coupling and to explore evapotranspiration regimes in the North China Plain, four sets of sensitivity experiments with different soil moisture levels over a period of 10 years were conducted. Under relatively dry soil conditions, evapotranspiration is dominated by the soil moisture-limited regime with positive L–A coupling, regardless of external moisture inflow. The critical soil moisture value separating a soil moisture-limited and an energy-limited regime lies between 0.24 m3/m3 and 0.29 m3/m3. Stronger positive feedback under negative soil moisture anomalies may increase the risk of drought in the North China Plain. Full article
(This article belongs to the Special Issue New Insights into the Response and Adaptation of Agriculture to Climate Change)
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<p>Topography (units: m) in China and its surrounding areas. The elevation data are from the Global 30 Arc-Second Elevation (GTOPO30 10 min) dataset: <a href="https://lta.cr.usgs.gov/GTOPO30" target="_blank">https://lta.cr.usgs.gov/GTOPO30</a> (accessed on 22 January 2024). The blue box represents the model simulation domain. The inner red box represents the area where soil moisture is modified in four sensitivity experiments.</p> Full article ">Figure 2
<p>Spatial distribution of SM–LH ((<b>a</b>–<b>c</b>); W/m<sup>2</sup>) and LH–LCL ((<b>d</b>–<b>f</b>); m) coupling indices for dry, neutral, and wet soil months. The region with the slash is the area that passes the 95% significance test, indicating that there is a significant difference between the samples’ coupling strength and the whole.</p> Full article ">Figure 3
<p>Scatterplots and fitted curves of the coupling strength of SM–LH ((<b>a</b>–<b>d</b>); W/m<sup>2</sup>) and LH–LCL ((<b>e</b>–<b>h</b>); m) for dry, neutral, and wet months as a function of the spatial percentile of warm-season soil wetness climatology. The curves are obtained from a 10-degree polynomial curve fitting.</p> Full article ">Figure 4
<p>Distribution of spatial percentiles of the warm-season climatological mean soil wetness (<b>a</b>) and average standard deviation of SM over 40 warm seasons ((<b>b</b>), m<sup>3</sup>/m<sup>3</sup>).</p> Full article ">Figure 5
<p>Relationships between the regionally averaged standardized SM anomalies and SM–LH coupling index for each month of the 40-year warm season in nine subregions.</p> Full article ">Figure 6
<p>Relationships between the regionally averaged standardized SM anomalies and LH–LCL coupling index for each month of the 40-year warm season in nine subregions.</p> Full article ">Figure 7
<p>Area-averaged LH in May–September of different years at four soil moisture levels (<b>a1</b>–<b>d5</b>). The rows represent the months from May to September. The columns represent four sets of experiments. A line with a greener color indicates higher moisture inflow in that month.</p> Full article ">Figure 8
<p>Area-averaged SH in May–September of different years at four soil moisture levels (<b>a1</b>–<b>d5</b>). The rows represent the months from May to September. The columns represent four sets of experiments. A line with a greener color indicates higher moisture inflow in that month.</p> Full article ">Figure 9
<p>Area- and daytime-averaged EF in May (<b>a</b>), June (<b>b</b>), July (<b>c</b>), August (<b>d</b>) and September (<b>e</b>) of different years at four SM levels. A greener colored dot represents the month with higher moisture inflow. The four soil moisture values on the horizontal axis for each month represent the average of the model output data from four sets of experiments conducted during that month.</p> Full article ">Figure 10
<p>The evolution of area-averaged LCL (solid lines) and PBL (dashed lines) in May (<b>a</b>), June (<b>b</b>), July (<b>c</b>), August (<b>d</b>) and September (<b>e</b>) under different soil moisture sensitivity experiments.</p> Full article ">Figure 11
<p>Probability of crossover events between LCL and PBL in four experiments for each warm-season month (<b>a1</b>–<b>d5</b>). The rows represent the months from May to September. The columns represent four sets of experiments.</p> Full article ">
22 pages, 27158 KiB  
Article
Growth and Breakdown of Kelvin–Helmholtz Billows in the Stable Atmospheric Boundary Layer
by Qingfang Jiang
Atmosphere 2024, 15(2), 220; https://doi.org/10.3390/atmos15020220 - 12 Feb 2024
Viewed by 1076
Abstract
The development and breakdown of Kelvin–Helmholtz (KH) waves (billows) in the stable atmospheric boundary layer (SABL) and their impact on vertical transport of momentum and scalars have been examined utilizing large eddy simulations. These simulations are initialized with a vertically uniform geostrophic wind [...] Read more.
The development and breakdown of Kelvin–Helmholtz (KH) waves (billows) in the stable atmospheric boundary layer (SABL) and their impact on vertical transport of momentum and scalars have been examined utilizing large eddy simulations. These simulations are initialized with a vertically uniform geostrophic wind and a constant potential temperature lapse rate. An Ekman type of boundary layer develops, and an inflection point forms in the SABL, which triggers the KH instability (KHI). KHI develops with the kinetic energy (KE) in the KH billows growing exponentially with time. The subsequent onset of secondary shear instability along S-shaped braids leads to the turbulent breakdown of the KH billow cores and braids. The frictional ground surface tends to slow down the growth of KE near the surface, reduce the KH billow core depth, and likely suppress other types of secondary instability. KH billows induce substantial down-gradient transport of momentum and sensible heat, which can be further enhanced by the onset of secondary shear instability. Although the KHI-induced strong transport only lasts for around 10–20 min, it reduces vertical shear and stratification in the SABL, enhances surface winds, and results in a 2–3-fold increase in the SABL depth. Full article
(This article belongs to the Section Meteorology)
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<p>Vertical cross-sections of <span class="html-italic">u</span> (leftmost, color incr. = 0.5 m/s), potential temperature (col 2, <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>−</mo> <msub> <mi>θ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, color incr. = 0.025 K), normalized span-wise vorticity (col 3, incr. = 0.05), and normalized buoyancy frequency squared (rightmost, incr. = 0.05) valid at (top to bottom rows) <span class="html-italic">t<sub>KH</sub></span> = 5 (<b>a</b>–<b>d</b>), 11 (<b>e</b>–<b>h</b>), 16 (<b>i</b>–<b>l</b>), 17.5 (<b>m</b>–<b>p</b>), and 18.5 (<b>q</b>–<b>t</b>) min, respectively. The vorticity is normalized by 0.35 (top row), 0.49 (row 2), and 0.78 s<sup>−1</sup> (rows 3–5), respectively, and <span class="html-italic">N</span><sup>2</sup> is normalized by 0.001 s<sup>−2</sup>. These figures are created by stitching together two identical cross-sections for the sake of description.</p> Full article ">Figure 2
<p>Horizontal (X-Y) cross-sections of <span class="html-italic">u</span> at levels z = 6 (<b>a</b>,<b>c</b>) and 21 m (<b>b</b>,<b>d</b>) valid at <span class="html-italic">t<sub>KH</sub></span> = 15 and 17 min, respectively. The color shading increments are (<b>a</b>) 0.2 m/s, (<b>b</b>) 0.6 m/s, (<b>c</b>) 0.5 m/s, and (<b>d</b>) 0.5 m/s, respectively. The graphics are stretched in the Y direction by a factor of two. These figures are created by stitching two horizontal sections together in the X-direction for the sake of description.</p> Full article ">Figure 3
<p>Distance–time sections, namely (<b>a</b>) X-<span class="html-italic">t<sub>KH</sub></span> (along a given Y-section) and (<b>b</b>) Y-<span class="html-italic">t<sub>KH</sub></span> (along a given X section) of <span class="html-italic">u</span> at <span class="html-italic">z</span> = 18.6 m normalized by <span class="html-italic">Ug</span>. The dashed lines are used to estimate the KH wave propagation speed.</p> Full article ">Figure 4
<p>Time–height (t<sub>KH</sub>-z) sections of (<b>a</b>) u-wind (<span class="html-italic">u</span>, m/s), (<b>b</b>) potential temperature (<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>−</mo> <msub> <mi>θ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, K), and (<b>c</b>) vertical velocity (w, m/s) sampled near the center of the domain. Only the lowest 200 m portion and for t<sub>KH</sub> between 5–23 min is shown.</p> Full article ">Figure 5
<p>Profiles of (<b>a</b>) horizontal wind components U and V (red), (<b>b</b>) potential temperature (<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>−</mo> <msub> <mi>θ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, K), (<b>c</b>) vertical wind shears <math display="inline"><semantics> <mrow> <mo>∂</mo> <mi>U</mi> <mo>/</mo> <mo>∂</mo> <mi>z</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>∂</mo> <mi>V</mi> <mo>/</mo> <mo>∂</mo> <mi>z</mi> </mrow> </semantics></math> (red), and (<b>d</b>) Richardson number before KH billows fully developed (i.e., <span class="html-italic">t<sub>KH</sub></span> = 8 min, solid curves, denoted with subscript “B”) and after their breakdown (i.e., <span class="html-italic">t<sub>KH</sub></span> = 20 min, dashed curves, denoted with subscript “A”). The V−wind is multiplied by 10. An inflection point is located at z~30 m, above the V maximum at the beginning of the KH event.</p> Full article ">Figure 6
<p>Time–height sections of (<b>a</b>) kinetic energy (KE, normalized by 5 m<sup>2</sup>/s<sup>2</sup>), (<b>b</b>) momentum flux in the geostrophic wind direction (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> normalized by 0.8 m<sup>2</sup>/s<sup>2</sup>), (<b>c</b>) dynamic heat flux (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msup> <mi>w</mi> <mo>′</mo> </msup> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> normalized by 0.064 K m/s), (<b>d</b>) <span class="html-italic">K<sub>m</sub></span> (normalized by 20 m<sup>2</sup>/s), (<b>e</b>) <span class="html-italic">K<sub>h</sub></span> (normalized by 20 m<sup>2</sup>/s), (<b>f</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msup> <mi>w</mi> <mrow> <mo>′</mo> <mn>3</mn> </mrow> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> (normalized by 0.6 m<sup>3</sup>/s<sup>3</sup>), (<b>g</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msup> <mi>θ</mi> <mrow> <mo>′</mo> <mn>2</mn> </mrow> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math> (normalized by 0.044 K<sup>2</sup>), (<b>h</b>) Richardson number, (<b>i</b>) U (normalized by U<sub>g</sub>), and (<b>j</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>−</mo> <msub> <mi>θ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (K). Only the lowest 160 m is shown.</p> Full article ">Figure 7
<p>Profiles of the domain-averaged (<b>a</b>) kinetic energy (KE, m<sup>2</sup>/s<sup>2</sup>), (<b>b</b>) momentum flux (<math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>m</mi> </msub> <mo>=</mo> <msqrt> <mrow> <msup> <mrow> <mover accent="true"> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mover accent="true"> <mrow> <msup> <mi>v</mi> <mo>′</mo> </msup> <msup> <mi>w</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </semantics></math>, m<sup>2</sup>/s<sup>2</sup>), (<b>c</b>) dynamic heat flux (<math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>θ</mi> </msub> <mo>=</mo> <mover accent="true"> <mrow> <msup> <mi>w</mi> <mo>′</mo> </msup> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>, K m/s), and (<b>d</b>) eddy diffusivity of potential temperature (<span class="html-italic">K<sub>h</sub></span>, m<sup>2</sup>/s) at three different times, <span class="html-italic">t</span><sub>1</sub> = 8, <span class="html-italic">t</span><sub>2</sub> = 11, and <span class="html-italic">t</span><sub>3</sub> = 78 min. Those at <span class="html-italic">t</span><sub>1</sub> = 8 min are multiplied by 10 for the sake of comparison.</p> Full article ">Figure 8
<p>Evolution of the (<b>a</b>) vertically integrated kinetic energy (IKE, m<sup>3</sup>/s<sup>2</sup>, top) in log scale, (<b>b</b>) vertically integrated shear production rate of KE (ISPR, m<sup>3</sup>/s<sup>3</sup>, middle; resolved and SGS are shown in black and red respectively), and (<b>c</b>) integrated dissipation rate (IDR, ε, m<sup>3</sup>/s<sup>3</sup>, bottom; IDRs in the lowest 30 m, above, and total are shown in black, red, and green, respectively).</p> Full article ">Figure 9
<p>Evolution of the domain-averaged (<b>a</b>) 10 m wind speed (m/s), (<b>b</b>) surface sensible heat flux (W/m<sup>2</sup>), and (<b>c</b>) SABL height during a 40 min period.</p> Full article ">Figure 10
<p>Dimensionless power spectra of (<b>a</b>–<b>c</b>) <span class="html-italic">u</span> and (<b>d</b>–<b>f</b>) <span class="html-italic">w</span>, and dimensionless co-spectra of (<b>g</b>–<b>i</b>) u–w and (<b>j</b>–<b>l</b>) <math display="inline"><semantics> <mrow> <mi>w</mi> <mo>−</mo> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> </semantics></math> valid at t<sub>KH</sub> = 13 (<b>left</b>), 18 (<b>middle</b>), and 23 min (<b>right</b>), respectively, are shown in nondimensional wave number (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> <mi>H</mi> </msub> </mrow> </semantics></math>) and height sections. Only the lowest 160 m and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>K</mi> <mo stretchy="false">^</mo> </mover> <mi>H</mi> </msub> </mrow> </semantics></math> up to 50 is shown.</p> Full article ">Figure 11
<p>Dimensionless power spectra of (<b>a</b>) <span class="html-italic">u</span> (i.e., P<sub>u</sub>), (<b>b</b>) <span class="html-italic">w</span> (i.e., P<sub>w</sub>), and (<b>c</b>) <span class="html-italic">u–w</span> co-spectrum (i.e., F<sub>m</sub>) at <span class="html-italic">z</span> = 21 m valid at <span class="html-italic">t<sub>KH</sub></span> = 11, 18, and 23 min, respectively. Both P<sub>u</sub> and P<sub>w</sub> are shown in logarithm and the blue dashed lines with -5/3 slope are included for comparison.</p> Full article ">Figure 12
<p>Time–height sections of (<b>a</b>) KE (normalized by 1.7 m<sup>2</sup>/s<sup>2</sup>), (<b>b</b>) momentum flux (normalized by 0.32 m<sup>2</sup>/s<sup>2</sup>), (<b>c</b>) U (normalized by Ug = 9 m/s), and (<b>d</b>) potential temperature (<math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>−</mo> <msub> <mi>θ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, incr. = 0.05 K).</p> Full article ">Figure 13
<p>The X–Y sections of <span class="html-italic">u</span> at <span class="html-italic">z</span> = 10.6 m from simulation B valid at <span class="html-italic">t<sub>KH</sub></span> = 5.5, 6.9, 8.3, and 9.7 min (<b>a</b>–<b>d</b>), respectively. The color increment = 0.4 m/s. The misalignment portions (or knots) are highlighted by dashed ovals.</p> Full article ">Figure 14
<p>Vertical cross-sections of (<b>a</b>,<b>c</b>) <span class="html-italic">u</span> (m/s, normalized by Ug = 9 m/s, incr. = 0.05) and (<b>b</b>,<b>d</b>) span-wise vorticity (normalized by 0.6 s<sup>−1</sup> and 1.01 s<sup>−1</sup>, color incr. = 0.1), valid at <span class="html-italic">t<sub>KH</sub></span> = 6.9 (<b>a</b>,<b>b</b>) and 8.3 (<b>c</b>,<b>d</b>) min, respectively.</p> Full article ">
17 pages, 10031 KiB  
Article
A Case Study of Pc1 Waves Observed at the Polar Cap Associated with Proton Precipitation at Subauroral Latitudes
by Giulia D’Angelo, Patrizia Francia, Marcello De Lauretis, Alexandra Parmentier, Tero Raita and Mirko Piersanti
Atmosphere 2024, 15(2), 219; https://doi.org/10.3390/atmos15020219 - 11 Feb 2024
Viewed by 1275
Abstract
The importance of ElectroMagnetic Ion Cyclotron (EMIC) ultra-low-frequency (ULF) waves (and their Pc1 counterparts) is connected to their critical role in triggering energetic particle precipitation from the magnetosphere to the conjugated ionosphere via pitch angle scattering. In addition, as a prominent element of [...] Read more.
The importance of ElectroMagnetic Ion Cyclotron (EMIC) ultra-low-frequency (ULF) waves (and their Pc1 counterparts) is connected to their critical role in triggering energetic particle precipitation from the magnetosphere to the conjugated ionosphere via pitch angle scattering. In addition, as a prominent element of the ULF zoo, EMIC/Pc1 waves can be considered a perfect tool for the remote diagnosis of the topologies and dynamic properties of near-Earth plasmas. Based on the availability of a comprehensive set of instruments, operating on the ground and in the top-side ionosphere, the present case study provides an interesting example of the evolution of EMIC propagation to both ionospheric hemispheres up to the polar cap. Specifically, we report observations of Pc1 waves detected on 30 March 2021 under low Kp, low Sym-H, and moderate AE conditions. The proposed investigation shows that high-latitude ground magnetometers in both hemispheres and the first China Seismo-Electromagnetic Satellite (CSES-01) at a Low Earth Orbit (LEO) detected in-synch Pc1 waves. In strict correspondence to this, energetic proton precipitation was observed at LEO with a simultaneous appearance of an isolated proton aurora at subauroral latitudes. This supports the idea of EMIC wave-induced proton precipitation contributing to energy transfer from the magnetosphere to the ionosphere. Full article
(This article belongs to the Section Upper Atmosphere)
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<p>Interplanetary and geomagnetic conditions during the time interval 27–31 March 2021. From the top: the IMF strength (<b>a</b>) and north–south component (<b>b</b>), the solar wind speed (<b>c</b>), density (<b>d</b>), temperature (<b>e</b>), dynamic pressure (<b>f</b>), and the Sym-H index (<b>g</b>). The blue shaded area marks the specific Pc1 event simultaneously observed at DMC in Antarctica and by the LEO CSES-01 spacecraft. Red dashed horizontal line corresponds to the zero value.</p> Full article ">Figure 2
<p>The dynamic spectra of the H (panels (<b>a</b>,<b>c</b>)) and D (panels (<b>b</b>,<b>d</b>)) components at MZS (first and second panel from the top) and DMC (third and fourth panel from the top) during the time interval 20:42–21:06 UT on 30 March 2021.</p> Full article ">Figure 3
<p>The time–frequency dependence of the polarization ratio (<b>a</b>) and ellipticity (<b>b</b>) at DMC during the time interval 20:42–21:05 UT on 30 March 2021.</p> Full article ">Figure 4
<p>The dynamic spectra of the magnetic field observations by CSES–01 along the mean field-aligned coordinate system (from top to bottom: compressional (<b>a</b>), poloidal (<b>b</b>), and toroidal (<b>c</b>) components), and the ellipticity (<b>d</b>) during the time interval 20:58–21:02 UT on 30 March 2021. The high values of ellipticity between 21:01:00 UT and 21:01:30 UT correspond to a negligible PSD in the field components (not visible in the selected color scale).</p> Full article ">Figure 5
<p>The CSES–01 electric (red) and magnetic field (black) waveforms, filtered at 1 Hz. From top to bottom: compressional, poloidal, and toroidal components.</p> Full article ">Figure 6
<p>The dynamic power spectra of the horizontal geomagnetic component measured across the Finnish network during the time interval 20:44–21:06 UT on 30 March 2021: (<b>a</b>) Kevo station; (<b>b</b>) Kilpisjärvi station; (<b>c</b>) Nurmijärvi station; (<b>d</b>) Oulu station.</p> Full article ">Figure 7
<p>The time–frequency dependence of the polarization ratio (<b>a</b>) and ellipticity (<b>b</b>) at KEV during the time interval 20:44–21:06 UT on March 2021.</p> Full article ">Figure 8
<p>Stack plot of MetOp–01/TED total proton flux integrated at 120 km (<b>upper panel</b>) and MEPED–0° differential proton flux in channels P1 (30–80 keV; <b>mid panel</b>) and P2 (80–240 keV; <b>bottom panel</b>).</p> Full article ">Figure 9
<p>Polar-view maps, in AACGM latitude and MLT, of the 10-min (between 20:50 UT and 21:00 UT) integrated FAC density ((<b>a</b>), red/blue shaded areas) as provided by AMPERE, and the auroral emission in the hydrogen line HI (121.6 nm) as measured by the SSUSI instrument on board DMSP ((<b>b</b>), colored area). Map in panel a also reports the total proton flux integrated at 120 km, as measured by the TED on board the MetOp–01 satellite between 20:57 UT and 20:59 UT (thick black curve). In panel b, the CSES–01 track between 20:57 UT and 20:59 UT (red full curve) and the Altitude Adjusted Corrected Geomagnetic (AACGM) coordinates of DMC (black triangle, 89.03° S), MZS (blue triangle, 79.88° S), and Syowa (magenta triangle, 66.49° S) stations at 20:58 UT are also reported. Each map covers 00:00–24:00 MLT and |50°|–|90°| AACGM Lat; the magnetic noon/midnight is at the top/bottom.</p> Full article ">Figure 10
<p>A north–south optical keogram produced by all-sky white-light cameras located at Syowa station between 18:00 UT on 30 March 2021 and 06:00 UT on next day.</p> Full article ">Figure 11
<p>Same plot as in <a href="#atmosphere-15-00219-f009" class="html-fig">Figure 9</a>a for the northern hemisphere, superimposed onto AACGM coordinates of four ground magnetometers in the Finnish Pulsation Magnetometer Array, evaluated at 20:58 UT.</p> Full article ">Figure 12
<p>Plasmapause location in the second half of the event day (between 03:30 and 06:30 MLT), as estimated using the Liu and Liu [<a href="#B45-atmosphere-15-00219" class="html-bibr">45</a>] model.</p> Full article ">
15 pages, 5137 KiB  
Communication
An Ensemble-Based Model for Specific Humidity Retrieval from Landsat-8 Satellite Data for South Korea
by Sungwon Choi, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Jongho Woo, Nayeon Kim, Sungwoo Park and Kyung-soo Han
Atmosphere 2024, 15(2), 218; https://doi.org/10.3390/atmos15020218 - 11 Feb 2024
Viewed by 971
Abstract
Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data [...] Read more.
Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites. Full article
(This article belongs to the Special Issue Precipitation Monitoring and Databases)
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<p>ASOS points in South Korea.</p> Full article ">Figure 2
<p>Flowchart of this study.</p> Full article ">Figure 3
<p>Result of validation of 4 machine learning methods. (<b>a</b>) Multiple Linear Regression, (<b>b</b>) K-Nearest Neighbor, (<b>c</b>) Random Forest, (<b>d</b>) Deep Neural Network.</p> Full article ">Figure 4
<p>Results of Specific Humidity using Random Forest in South Korea. (<b>a</b>) Seoul 19 March 2017, (<b>b</b>) Busan 19 April 2016.</p> Full article ">Figure 5
<p>Importance of input variables in RF model.</p> Full article ">Figure 6
<p>Result of comparison to ASOS Specific Humidity from 2018 to 2019.</p> Full article ">Figure 7
<p>Result of test using ASOS point (<b>a</b>) Seoul, (<b>b</b>) Busan, (<b>c</b>) Incheon.</p> Full article ">Figure 8
<p>Result of test using ASOS point (<b>a</b>) Andong, (<b>b</b>) Gyeongju, (<b>c</b>) Chuncheon.</p> Full article ">
25 pages, 4117 KiB  
Article
Understanding Rainfall Distribution Characteristics over the Vietnamese Mekong Delta: A Comparison between Coastal and Inland Localities
by Huynh Vuong Thu Minh, Bui Thi Bich Lien, Dang Thi Hong Ngoc, Tran Van Ty, Nguyen Vo Chau Ngan, Nguyen Phuoc Cong, Nigel K. Downes, Gowhar Meraj and Pankaj Kumar
Atmosphere 2024, 15(2), 217; https://doi.org/10.3390/atmos15020217 - 10 Feb 2024
Cited by 4 | Viewed by 1498
Abstract
This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season [...] Read more.
This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season rainfall, as well as daily rainfall events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual and seasonal rainfall compared to the further inland stations of Can Tho and Moc Hoa. Interestingly, Ca Mau experienced a notable decrease in annual rainfall. Conversely, Can Tho, showed an overall decrease in some months of the wet season and an increase in dry season rainfall. Furthermore, Moc Hoa showed an increase in the number of rainy days, especially during the dry season. Principal component analysis (PCA) further revealed strong correlations between annual rainfall and extreme weather events, particularly for Ca Mau, emphasizing the complex interplay of geographic and climatic factors within the region. Our findings offer insights for policymakers and planners, thus aiding the development of targeted interventions to manage water resources and prepare for changing climate conditions. Full article
(This article belongs to the Section Climatology)
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<p>Location of three studied meteorological stations in the VMD.</p> Full article ">Figure 2
<p>Contribution of the variables and observation of rainfall at Ca Mau station.</p> Full article ">Figure 3
<p>Contribution of the variables and observation of rainfall at Can Tho station.</p> Full article ">Figure 4
<p>Contribution of the variables and observation of rainfall at Moc Hoa station.</p> Full article ">Figure 5
<p>The comparison of daily rainfall and accumulated rainfall during the periods from 1978 to 2000 and 2011 to 2022 for Ca Mau (<b>a</b>,<b>d</b>), Can Tho (<b>b</b>,<b>e</b>), and Moc Hoa (<b>c</b>,<b>f</b>).</p> Full article ">Figure 6
<p>Rainfall trend patterns: (<b>a</b>) Annual rainfall decreasing trend in Ca Mau using S-MK. (<b>b</b>) Annual rainfall decreasing trend in Ca Mau using ITA. (<b>c</b>) Increased dry season rainfall in Can Tho using S-MK test. (<b>d</b>) Increased dry season rainfall in Can Tho using ITA. (<b>e</b>) Increased number of dry season rainfall days in Moc Hoa using S-MK test. (<b>f</b>) Increased number of dry season rainfall days in Moc Hoa using ITA.</p> Full article ">Figure 6 Cont.
<p>Rainfall trend patterns: (<b>a</b>) Annual rainfall decreasing trend in Ca Mau using S-MK. (<b>b</b>) Annual rainfall decreasing trend in Ca Mau using ITA. (<b>c</b>) Increased dry season rainfall in Can Tho using S-MK test. (<b>d</b>) Increased dry season rainfall in Can Tho using ITA. (<b>e</b>) Increased number of dry season rainfall days in Moc Hoa using S-MK test. (<b>f</b>) Increased number of dry season rainfall days in Moc Hoa using ITA.</p> Full article ">Figure 7
<p>Decreased August rainfall at Ca Mau station using S-MK test (<b>a</b>) and ITA (<b>b</b>). (<b>a</b>) Decreased August rainfall at Ca Mau station using S-MK test. (<b>b</b>) Decreased August rainfall at Ca Mau station using ITA.</p> Full article ">Figure 8
<p>Decreased August rainfall in Can Tho station using the S-MK test (<b>a</b>) and ITA (<b>b</b>). (<b>a</b>) Decreased August rainfall in Can Tho using S-MK test. (<b>b</b>) Decreased August rainfall in Can Tho using ITA.</p> Full article ">Figure A1
<p>Rainfall trend patterns in Ca Mau station using Mann-Kendall test: (<b>a</b>) Annual rainfall decreasing trend in Ca Mau using Mann–Kendall test with a significance level of 95%. (<b>b</b>) Wet season rainfall decreasing trend in Ca Mau using Mann–Kendall test with a significance level of 90%. (<b>c</b>) Jun rainfall decreasing trend in Ca Mau using Mann–Kendall test with a significance level of 90%. (<b>d</b>) Jun rainfall decreasing trend in Ca Mau using Mann–Kendall test with a significance level of 90%.</p> Full article ">Figure A2
<p>Rainfall trend patterns in Can Tho station using Mann-Kendall test: (<b>a</b>) Dry season rainfall day increasing trend in Can Tho using Mann–Kendall test with a significance level of 95%. (<b>b</b>) No. dry season rainfall day increasing trend in Can Tho using Mann–Kendall test with a significance level of 90%. (<b>c</b>) August rainfall decreasing trend in Can Tho using Mann–Kendall test with a significance level of 95%. (<b>d</b>) Jun rainfall decreasing trend in Can Tho using Mann–Kendall test with a significance level of 90%.</p> Full article ">Figure A3
<p>Rainfall trend patterns in Moc Hoa station using Mann-Kendall test: (<b>a</b>) Number of rainfall days increasing trend in Moc Hoa using Mann–Kendall test with a significance level of 95%. (<b>b</b>) Number of dry season rainfall days increasing trend in Moc Hoa using Mann–Kendall test with a significance level of 95%.</p> Full article ">
13 pages, 799 KiB  
Article
Long-Term Exposure to PM10 Air Pollution Exaggerates Progression of Coronary Artery Disease
by Tomasz Urbanowicz, Krzysztof Skotak, Anna Olasińska-Wiśniewska, Krzysztof J. Filipiak, Jakub Bratkowski, Michał Wyrwa, Jędrzej Sikora, Piotr Tyburski, Beata Krasińska, Zbigniew Krasiński, Andrzej Tykarski and Marek Jemielity
Atmosphere 2024, 15(2), 216; https://doi.org/10.3390/atmos15020216 - 9 Feb 2024
Cited by 5 | Viewed by 1754
Abstract
(1) Background: The increase in cardiovascular risk related to air pollution has been a matter of interest in recent years. The role of particulate matter 2.5 (PM2.5) has been postulated as a possible factor for premature death, including cardiovascular death. The role of [...] Read more.
(1) Background: The increase in cardiovascular risk related to air pollution has been a matter of interest in recent years. The role of particulate matter 2.5 (PM2.5) has been postulated as a possible factor for premature death, including cardiovascular death. The role of long-term exposure to PM10 is less known. The aim of the study was to assess the individual relationship between air pollution in habitation and the development of coronary artery disease. (2) Methods: Out of 227 patients who underwent coronary angiography, 63 (38 men and 25 women) with a mean age of 69 (63–74) years, with nonsignificant atherosclerotic changes at the initial examination, were included in the study. The baseline and repeated coronary angiography were compared to reveal patients with atherosclerotic progression and its relation to demographic and clinical factors and exposure to air pollution in the habitation place. (3) Results: In the performed analysis, we found a significant correlation between Syntax score in de novo lesions and BMI (Spearman’s rho −0.334, p = 0.008). The significant and strong correlation between median annual PM10 values of 20 µg/m3 and at least 25 µg/m3 in air pollution and the risk of de novo coronary disease was noticed (Spearman’s rho = 0.319, p = 0.011 and Spearman’s rho = 0.809, p < 0.001, respectively). (4) Conclusions: There is a positive correlation between long-term exposure to PM10 air pollution and coronary artery disease progression, demonstrated by the increase in Syntax score. The presented analysis revealed increased morbidity at lower PM10 concentrations than generally recommended thresholds. Therefore, further investigations concerning air pollution’s influence on cardiovascular risk should be accompanied by promoting lifestyle changes in the population and revisiting the needs for environmental guidelines. Full article
(This article belongs to the Special Issue Composition Analysis and Health Effects of Atmospheric Particulate Matter)
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<p>Flow chart of patients enrolled into the analysis.</p> Full article ">Figure 2
<p>Individuals’ PM10 long-term exposure in relation to their habitation place in analyzed group.</p> Full article ">
12 pages, 21031 KiB  
Article
UV Exposure during Cycling as a Function of Solar Elevation and Orientation
by Philipp Weihs, Sarah Helletzgruber, Sofie Kranewitter, Lara Langer, Zacharias Lumerding, Viktoria Luschin, Philipp Schmidt, Jakob Heydenreich and Alois W. Schmalwieser
Atmosphere 2024, 15(2), 215; https://doi.org/10.3390/atmos15020215 - 9 Feb 2024
Cited by 1 | Viewed by 1322
Abstract
Although cycling is the most prevalent means of locomotion in the world, little research has been done in evaluating the ultraviolet (UV) radiation exposure of cyclists. In this study, a volunteer using a men’s bike was equipped with 10 miniature UV-meters at different [...] Read more.
Although cycling is the most prevalent means of locomotion in the world, little research has been done in evaluating the ultraviolet (UV) radiation exposure of cyclists. In this study, a volunteer using a men’s bike was equipped with 10 miniature UV-meters at different body sites. Besides erythemally effective irradiance, the ratio of personal UV exposure to ambient UV radiation was determined for solar elevations up to 65°, taking into account different orientations with respect to the sun. This method provides a universal model that allows for the calculation of UV exposure whenever ambient UV radiation and solar elevation are available. Our results show that the most exposed body sites are the back, forearm, upper arm, and anterior thigh, receiving between 50% and 75% of ambient UV radiation on average. For certain orientations, this percentage can reach 105% to 110%. However, the risk of UV overexposure depends on ambient UV radiation. At lower solar elevations (<40°), the risk of UV overexposure clearly decreases. Full article
(This article belongs to the Special Issue Solar UV Radiation)
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<p>Electronic miniature UV-meter of the Sunsaver type [<a href="#B27-atmosphere-15-00215" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>Measurement setup: men’s model bicycle on a roller trainer and cyclist equipped with UV-meters (Sunsaver type) at Danube Island, Vienna, Austria.</p> Full article ">Figure 3
<p>Erythemally effective irradiance (mean values over 10 min) during the measurement campaign (19 May, 31 May, and 19 June 2022) in Vienna (48.247° N, 16.389° E, 165 m a.s.l.), Austria.</p> Full article ">Figure 4
<p>First measuring cycle on 19 May 2021, 12:30 MEST. The Exposure Ratio To Ambient (ERTA) for the forehead, back, shin, upper arm, and the anterior (frontal side of the) thigh is shown for the different cycling directions at a solar elevation of 61°.</p> Full article ">Figure 5
<p>Mean Exposure Ratio To Ambient (ERTA): (<b>a</b>) back, forehead, calf, shin, chest, posterior thigh; and (<b>b</b>) forearm, upper arm, anterior thigh, and neck, dependent on the solar elevation. Mean values were calculated as the average over all four cardinal directions on all 3 days.</p> Full article ">Figure 6
<p>(<b>a</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 60° and 65°. (<b>b</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 40° and 45°. (<b>c</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 10° and 25°.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 60° and 65°. (<b>b</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 40° and 45°. (<b>c</b>) Mean ERTA of different body sites dependent on orientation for solar elevations between 10° and 25°.</p> Full article ">Figure 7
<p>Box plot of the ERTA (all orientations, including facing the sun, the sun behind the cyclist, and the four cardinal directions) at the different body sites as a function of solar elevation. Maxima are indicated by thin vertical lines above the boxes and minima by thin vertical lines below. The boxes span the range from the 25th to the 75th quantiles. Averages are depicted by filled squares and medians by horizontal lines inside the box. The 1st and 99th percentiles are indicated by “x”.</p> Full article ">Figure 8
<p>(<b>a</b>) Box plot of the mean sunburn time (averaged over all orientations: facing the sun, the sun behind the cyclist, and the four cardinal directions) from different days for skin photo type I at the different body sites as a function of the solar elevation. Maxima are indicated by thin vertical lines above the boxes and minima by thin vertical lines below. The boxes range from the 25th to 75th quantiles. Averages are depicted by crosses and medians by horizontal lines inside the boxes. (<b>b</b>) Same as (<b>a</b>), but for sunburn times of less than 120 min only.</p> Full article ">
19 pages, 3475 KiB  
Article
Assessment of Outdoor Thermal Comfort in a Hot Summer Region of Europe
by José Luis Sánchez Jiménez and Manuel Ruiz de Adana
Atmosphere 2024, 15(2), 214; https://doi.org/10.3390/atmos15020214 - 9 Feb 2024
Cited by 1 | Viewed by 2756
Abstract
Heat waves are increasingly frequent in Europe, especially in South European countries during the summer season. The intensity and frequency of these heat waves have increased significantly in recent years. Spain, as one of the southern European countries most affected by these recurring [...] Read more.
Heat waves are increasingly frequent in Europe, especially in South European countries during the summer season. The intensity and frequency of these heat waves have increased significantly in recent years. Spain, as one of the southern European countries most affected by these recurring heat waves, particularly experiences this phenomenon in touristic cities such as Cordoba. The aim of this study was to perform an experimental assessment of outdoor thermal comfort in a typical three-hour tourist walkable path of the historical center of Cordoba. The experimental study was carried out in the three-hour period of higher temperatures from 16:30 to 19:30 h CEST (UTC+2) on a normal summer day (6 July 2023), a day with a heat wave (28 June 2023) and a day with a higher heat intensity, called a super heat wave (10 August 2023). Environmental conditions such as a radiant temperature, ambient temperature, wet bulb temperature, air velocity and relative humidity were measured at three different heights corresponding to 0.1 (ankles), 0.7 (abdomen) and 1.7 (head) m. The results show extremely high levels of heat stress in all bioclimatic indices throughout the route. Cumulative heat stress ranged from “very hot” conditions at the beginning of the route to becoming “highly sweltering” at the end of the route. The average temperature excess over the thermal comfort threshold was very high and increased with the heat intensity. In addition, a correlation analysis was carried out between the bioclimatic indices studied, with the UTCI index standing out for its strong correlation with other thermal comfort indices. The findings emphasize the need for interventions to improve the urban environment and promote better outdoor thermal comfort for city dwellers through measures such as green infrastructure, UHI mitigation and increasing public awareness. Full article
(This article belongs to the Special Issue Urban Microclimate: Thermal Comfort, Air Quality and Green Building as Drivers of Healthy Urban Design)
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<p>Touristic route conducted in the experimental study with a total length of 1850 m.</p> Full article ">Figure 2
<p>Days and time-period selected according to ambient temperature measured by WS2.</p> Full article ">Figure 3
<p>Tourist simulated by a measurement pole.</p> Full article ">Figure 4
<p>Ambient dry temperature at WS1 and WS2.</p> Full article ">Figure 5
<p>Temporal register of temperature indices for NSD, HD and SHD.</p> Full article ">Figure 6
<p>Frequency of assessment classes of temperature indices.</p> Full article ">Figure 7
<p>Color map of cumulative stress for: (<b>a</b>) NSD; (<b>b</b>) HD; (<b>c</b>) SHD.</p> Full article ">Figure 8
<p>Heat stress exposure indices for different days.</p> Full article ">
20 pages, 24035 KiB  
Article
Composition and Reactivity of Volatile Organic Compounds and the Implications for Ozone Formation in the North China Plain
by Saimei Hao, Qiyue Du, Xiaofeng Wei, Huaizhong Yan, Miao Zhang, Youmin Sun, Shijie Liu, Lianhuan Fan and Guiqin Zhang
Atmosphere 2024, 15(2), 213; https://doi.org/10.3390/atmos15020213 - 9 Feb 2024
Cited by 1 | Viewed by 1502
Abstract
Enhanced ozone (O3) pollution has emerged as a pressing environmental concern in China, particularly for densely populated megacities and major city clusters. However, volatile organic compounds (VOCs), the key precursors to O3 formation, have not been routinely measured. In this [...] Read more.
Enhanced ozone (O3) pollution has emerged as a pressing environmental concern in China, particularly for densely populated megacities and major city clusters. However, volatile organic compounds (VOCs), the key precursors to O3 formation, have not been routinely measured. In this study, we characterize the spatial and temporal patterns of VOCs and examine the role of VOCs in O3 production in five cities (Dongying (DY), Rizhao (RZ), Yantai (YT), Weihai (WH), and Jinan (JN)) in the North China Plain (NCP) for two sampling periods (June and December) in 2021 through continuous field observations. Among various VOC categories, alkanes accounted for the largest proportion of VOCs in the cities. For VOCs, chemical reactivities, aromatic hydrocarbons, and alkenes were dominant contributors to O3 formation potential (OFP). Unlike inland regions, the contribution to OFP from OVOCs increased greatly at high O3 concentrations in coastal regions (especially YT). Model simulations during the O3 episode show that the net O3 production rates were 27.87, 10.24, and 10.37 ppbv/h in DY, RZ, and JN. The pathway of HO2 + NO contributed the most to O3 production in JN and RZ, while RO2 + NO was the largest contributor to O3 production in DY. The relative incremental reactivity (RIR) revealed that O3 formation in DY was the transitional regime, while it was markedly the VOC-limited regime in JN and RZ. The O3 production response is influenced by NOx concentration and has a clear daily variation pattern (the sensitivity is greater from 15:00 to 17:00). The most efficiencies in O3 reduction could be achieved by reducing NOx when the NOx concentration is low (less than 20 ppbv in this study). This study reveals the importance of ambient VOCs in O3 production over the NCP and demonstrates that a better grasp of VOC sources and profiles is critical for in-depth O3 regulation in the NCP. Full article
(This article belongs to the Special Issue Air Pollution Control in China: Progress, Challenges, and Perspectives (2nd Edition))
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Figure 1
<p>Locations of five monitoring sites (Jinan (JN), Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH)) across the NCP region.</p> Full article ">Figure 2
<p>Seasonal distribution of VOC concentrations and proportions in Jinan (JN), Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH).</p> Full article ">Figure 3
<p>Diurnal variation of VOC concentrations in coastal cities (Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH)) and an inland city (Jinan (JN)) in June and December.</p> Full article ">Figure 3 Cont.
<p>Diurnal variation of VOC concentrations in coastal cities (Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH)) and an inland city (Jinan (JN)) in June and December.</p> Full article ">Figure 4
<p>Seasonal distribution of the top 10 VOC species concentrations in Jinan (JN), Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH) in June and December.</p> Full article ">Figure 5
<p>OFP<sub>MIR</sub> proportions for different types of VOCs in Jinan (JN), Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH) in June and December.</p> Full article ">Figure 6
<p>Variation in the proportions of OFP<sub>MIR</sub> with their O<sub>3</sub> concentration in Jinan (JN), Dongying (DY), Rizhao (RZ), Yantai (YT), and Weihai (WH).</p> Full article ">Figure 7
<p>O<sub>3</sub> production and destruction pathways in Dongying (DY), Rizhao (RZ), and Jinan (JN) on days with high O<sub>3</sub> concentration.</p> Full article ">Figure 8
<p>Diurnal variation of the net O<sub>3</sub> production rate (O<sub>3-Chem</sub>) and regional transport O<sub>3</sub>(O<sub>3-Tran</sub>) as well as the observed O<sub>3</sub> production change rate (d(O<sub>3</sub>/dt)) in Dongying (DY), Rizhao (RZ), and Jinan (JN) during the O<sub>3</sub> pollution process.</p> Full article ">Figure 9
<p>Spatial distribution of temperature and wind vectors of a typical pollution process in Dongying (DY), Rizhao (RZ), and Jinan (JN).</p> Full article ">Figure 10
<p>Relative incremental reactivity (RIR) of the major O<sub>3</sub> precursor VOCs and NO<sub>X</sub> in Dongying (DY), Rizhao (RZ), and Jinan (JN).</p> Full article ">Figure 11
<p>Relative incremental reactivity (RIR) of VOC species in Dongying (DY), Rizhao (RZ), and Jinan (JN).</p> Full article ">Figure 12
<p>O<sub>3</sub> production rates for different reduction ratios of NO<sub>X</sub> and VOCs in Dongying (DY), Rizhao (RZ), and Jinan (JN).</p> Full article ">Figure 13
<p>Distribution of NO<sub>X</sub> concentration variation and RIR during the daytime (7:00–19:00) in Dongying (DY), Rizhao (RZ), and Jinan (JN).</p> Full article ">
27 pages, 9447 KiB  
Review
Salt Lake Aerosol Overview: Emissions, Chemical Composition and Health Impacts under the Changing Climate
by Muhammad Subtain Abbas, Yajuan Yang, Quanxi Zhang, Donggang Guo, Ana Flavia Locateli Godoi, Ricardo Henrique Moreton Godoi and Hong Geng
Atmosphere 2024, 15(2), 212; https://doi.org/10.3390/atmos15020212 - 8 Feb 2024
Cited by 1 | Viewed by 1898
Abstract
Salt Lakes, having a salt concentration higher than that of seawater and hosting unique extremophiles, are predominantly located in drought-prone zones worldwide, accumulating diverse salts and continuously emitting salt dust or aerosols. However, knowledge on emission, chemical composition, and health impacts of Salt [...] Read more.
Salt Lakes, having a salt concentration higher than that of seawater and hosting unique extremophiles, are predominantly located in drought-prone zones worldwide, accumulating diverse salts and continuously emitting salt dust or aerosols. However, knowledge on emission, chemical composition, and health impacts of Salt Lake aerosols under climate change is scarce. This review delves into the intricate dynamics of Salt Lake aerosols in the context of climate change, pointing out that, as global warming develops and weather patterns shift, Salt Lakes undergo notable changes in water levels, salinity, and overall hydrological balance, leading to a significant alteration of Salt Lake aerosols in generation and emission patterns, physicochemical characteristics, and transportation. Linked to rising temperatures and intensified evaporation, a marked increase will occur in aerosol emissions from breaking waves on the Salt Lake surface and in saline dust emission from dry lakebeds. The hygroscopic nature of these aerosols, coupled with the emission of sulfate aerosols, will impart light-scattering properties and a cooling effect. The rising temperature and wind speed; increase in extreme weather in regard to the number of events; and blooms of aquatic microorganisms, phytoplankton, and artemia salina in and around Salt Lakes, will lead to the release of more organic substances or biogenic compounds, which contribute to the alteration of saline aerosols in regard to their quantitative and chemical composition. Although the inhalation of saline aerosols from Salt Lakes and fine salt particles suspended in the air due to salt dust storms raises potential health concerns, particularly causing respiratory and cardiovascular disease and leading to eye and skin discomfort, rock salt aerosol therapy is proved to be a good treatment and rehabilitation method for the prevention and treatment of pneumoconiosis and chronic obstructive pulmonary disease (COPD). It is implied that the Salt Lake aerosols, at a certain exposure concentration, likely can delay the pathogenesis of silicosis by regulating oxidative stress and reducing interstitial fibrosis of the lungs. It emphasizes the interconnectedness of climate changes, chemical composition, and health aspects, advocating for a comprehensive and practical approach to address the challenges faced by Salt Lake aerosols in an ever-changing global climate. Full article
(This article belongs to the Special Issue Composition Analysis and Health Effects of Atmospheric Particulate Matter)
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<p>The relationship between lake salinity and inflow salinity.</p> Full article ">Figure 2
<p>(<b>A</b>) Global distribution of Salt Lakes (shaded areas using black lines and dots). (<b>B</b>) Worldwide Salt Lake areas with the main hypersaline hotspots. (<b>a</b>) Great Salt Lake (Utah, USA), (<b>b</b>) Dead Sea (Israel), (<b>c</b>) Crimean Salt Lake (Crimea), (<b>d</b>) Dangxiong Co Salt Lake (Tibet, China), (<b>e</b>) Laguna Puilar, Salar de Atacama (Chile), (<b>f</b>) Gaet’ale Pond (Ethiopia), (<b>g</b>) Kati Thanda-Lake Eyre (Australia), and (<b>h</b>) Deep Lake (Antarctica). Oceania is illustrated in black within the rectangle at the bottom left corner of the map (adapted from Mattia Saccò 2021 [<a href="#B33-atmosphere-15-00212" class="html-bibr">33</a>]).</p> Full article ">Figure 3
<p>The ethereal beauty of the Yuncheng Salt Lake (located in Shanxi Province, China), combined with its ecological and economic value, establishes it as a site of both natural wonder and cultural significance. (<b>a</b>) Location of Yuncheng Salt Lake. (<b>b</b>) Color pools due to different salinity and algal growth. (<b>c</b>) Salt Lake biodiversity. (<b>d</b>) Salt crystallization and accumulation under low temperature.</p> Full article ">Figure 4
<p>Mechanism of Salt Lake aerosol generation: (<b>a</b>) aerosols generated from film and jet droplets; (<b>b</b>) possible organic and inorganic components of Salt Lake aerosols.</p> Full article ">Figure 5
<p>A few examples of morphology and chemical composition of Salt Lake aerosols (SLAs). (<b>A</b>) The secondary electron images (SEIs) of Salt Lake aerosols collected over the Yuncheng Salt Lake, Shanxi Province, China, in September 2022. (<b>B</b>) The typical SEIs by SEM-EDX and elemental atomic concentrations of (<b>a</b>) a NaCl-containing particle; (<b>b</b>) a Na<sub>2</sub>SO<sub>4</sub>-containing particle, in Yuncheng Salt Lake aerosols collected on Al foil in September 2022 by the authors. The high aluminum peak in the EDX spectrum is attributed to the Al foil, used to collect the atmospheric aerosols. (<b>C</b>) The SEIs and EDX spectra of atmospheric aerosols collected during a dust storm episode: (<b>a</b>) a Na-, S-, and Cl-rich particle, likely from dried salt-lakes and saline soils; and (<b>b</b>) a common dust particle (Zhang et al., 2009 [<a href="#B7-atmosphere-15-00212" class="html-bibr">7</a>]).</p> Full article ">Figure 5 Cont.
<p>A few examples of morphology and chemical composition of Salt Lake aerosols (SLAs). (<b>A</b>) The secondary electron images (SEIs) of Salt Lake aerosols collected over the Yuncheng Salt Lake, Shanxi Province, China, in September 2022. (<b>B</b>) The typical SEIs by SEM-EDX and elemental atomic concentrations of (<b>a</b>) a NaCl-containing particle; (<b>b</b>) a Na<sub>2</sub>SO<sub>4</sub>-containing particle, in Yuncheng Salt Lake aerosols collected on Al foil in September 2022 by the authors. The high aluminum peak in the EDX spectrum is attributed to the Al foil, used to collect the atmospheric aerosols. (<b>C</b>) The SEIs and EDX spectra of atmospheric aerosols collected during a dust storm episode: (<b>a</b>) a Na-, S-, and Cl-rich particle, likely from dried salt-lakes and saline soils; and (<b>b</b>) a common dust particle (Zhang et al., 2009 [<a href="#B7-atmosphere-15-00212" class="html-bibr">7</a>]).</p> Full article ">Figure 6
<p>Emission pathways of Salt Lake aerosols.</p> Full article ">Figure 7
<p>Aerosol generation under temperature ((<b>A</b>), adapted from [<a href="#B59-atmosphere-15-00212" class="html-bibr">59</a>]). (<b>B</b>) Lake spray aerosol emission flux under wind speed (<b>a</b>–<b>c</b>) (adapted from [<a href="#B92-atmosphere-15-00212" class="html-bibr">92</a>]). (<b>C</b>) Aerosol types and wind velocity in March 2016 (<b>a</b>,<b>b</b>) and June 2016 (<b>c</b>,<b>d</b>) (adapted from [<a href="#B93-atmosphere-15-00212" class="html-bibr">93</a>]).</p> Full article ">Figure 7 Cont.
<p>Aerosol generation under temperature ((<b>A</b>), adapted from [<a href="#B59-atmosphere-15-00212" class="html-bibr">59</a>]). (<b>B</b>) Lake spray aerosol emission flux under wind speed (<b>a</b>–<b>c</b>) (adapted from [<a href="#B92-atmosphere-15-00212" class="html-bibr">92</a>]). (<b>C</b>) Aerosol types and wind velocity in March 2016 (<b>a</b>,<b>b</b>) and June 2016 (<b>c</b>,<b>d</b>) (adapted from [<a href="#B93-atmosphere-15-00212" class="html-bibr">93</a>]).</p> Full article ">Figure 8
<p>Aerosolized toxins emission and health implications (adapted from study of Lim et al., 2023 [<a href="#B111-atmosphere-15-00212" class="html-bibr">111</a>]).</p> Full article ">Figure 9
<p>Some possible health benefits of salt aerosols.</p> Full article ">Figure 10
<p>(<b>a</b>,<b>b</b>) Dimethylsulfide emission and sulfate enhancement. (<b>c</b>) Salt Lake aerosols as cloud albedo agent (Modified from Sarwar et al., 2023 [<a href="#B124-atmosphere-15-00212" class="html-bibr">124</a>]).</p> Full article ">
19 pages, 6116 KiB  
Article
The Intermittency of Turbulence in Magneto-Hydrodynamical Simulations and in the Cosmos
by Pierre Lesaffre, Edith Falgarone and Pierre Hily-Blant
Atmosphere 2024, 15(2), 211; https://doi.org/10.3390/atmos15020211 - 8 Feb 2024
Cited by 1 | Viewed by 1158
Abstract
Turbulent dissipation is a central issue in the star and galaxy formation process. Its fundamental property of space–time intermittency, well characterised in incompressible laboratory experiments, remains elusive in cosmic turbulence. Progress requires the combination of state-of-the-art modelling, numerical simulations and observations. The power [...] Read more.
Turbulent dissipation is a central issue in the star and galaxy formation process. Its fundamental property of space–time intermittency, well characterised in incompressible laboratory experiments, remains elusive in cosmic turbulence. Progress requires the combination of state-of-the-art modelling, numerical simulations and observations. The power of such a combination is illustrated here, where the statistical method intended to locate the extrema of velocity shears in a turbulent field, which are the signposts of intense dissipation extrema, is applied to numerical simulations of compressible magneto-hydrodynamical (MHD) turbulence dedicated to dissipation scales and to observations of a turbulent molecular cloud. We demonstrate that increments of several observables computed at the smallest lags can detect coherent structures of intense dissipation. We apply this statistical method to the observations of a turbulent molecular cloud close to the Sun in our galaxy and disclose a remarkable structure of extremely large velocity shear. At the location of the largest velocity shear, this structure is found to foster 10× more carbon monoxide molecules than standard diffuse molecular gas, an enrichment supported by models of non-equilibrium warm chemistry triggered by turbulent dissipation. In our simulations, we also compute structure functions of various synthetic observables and show that they verify Extended Self-Similarity. This allows us to compute their intermittency exponents, and we show how they constrain some properties of the underlying three-dimensional turbulence. The power of the combination of modelling and observations is also illustrated by the observations of the CH+ cation that provide unique quantitative information on the kinetic energy trail in the massive, multi-phase and turbulent circum-galactic medium of a galaxy group at redshift z=2.8. Full article
(This article belongs to the Special Issue Turbulence from Earth to Planets, Stars and Galaxies—Commemorative Issue Dedicated to the Memory of Jackson Rea Herring)
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<p>Temperature–density cycle of baryonic matter from the various thermal phases of the interstellar medium (ISM), the stars themselves and its ejection back to the ISM through winds, jets and supernova explosions. The different thermal phases are the hot ionised medium (HIM), the warm neutral medium (WNM), the cold (atomic) neutral medium (CNM) and denser molecular phases, noted as diffuse and dense (see <a href="#atmosphere-15-00211-t001" class="html-table">Table 1</a> for the characteristics of each of these thermal phases). The energy and processes driving the evolution along each branch of the cycle are indicated. Note that turbulence is an actor along the cooling branch on the left of the cycle and that all the thermal phases of the ISM, except the densest, which are gravitationally bound, are in thermal pressure equilibrium. (Figure adapted from Lesaffre [<a href="#B21-atmosphere-15-00211" class="html-bibr">21</a>]).</p> Full article ">Figure 2
<p>Integrated dissipation <math display="inline"> <semantics> <mrow> <mo>∫</mo> <mi>ε</mi> <mi mathvariant="normal">d</mi> <mi>z</mi> </mrow> </semantics> </math> along the line of sight coordinate <span class="html-italic">z</span> near dissipation peak for an initial r.m.s. Mach 4 simulation of decaying compressible MHD turbulence, starting from a perturbed Orszag–Tang initial configuration with a resolution of <math display="inline"> <semantics> <msup> <mn>1024</mn> <mn>3</mn> </msup> </semantics> </math> pixels (see [<a href="#B48-atmosphere-15-00211" class="html-bibr">48</a>]). The values of <math display="inline"> <semantics> <mrow> <mo>∫</mo> <mi>ε</mi> <mi mathvariant="normal">d</mi> <mi>z</mi> </mrow> </semantics> </math> are normalised by <math display="inline"> <semantics> <mrow> <mo>&lt;</mo> <mi>ρ</mi> <mo>&gt;</mo> <msubsup> <mi>u</mi> <mrow> <mrow> <mi mathvariant="normal">r</mi> <mo>.</mo> <mi mathvariant="normal">m</mi> <mo>.</mo> <mi mathvariant="normal">s</mi> <mo>.</mo> </mrow> </mrow> <mn>3</mn> </msubsup> </mrow> </semantics> </math>, where <math display="inline"> <semantics> <mrow> <mo>&lt;</mo> <mi>ρ</mi> <mo>&gt;</mo> </mrow> </semantics> </math> is the average mass density and <math display="inline"> <semantics> <msub> <mi>u</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>.</mo> <mi mathvariant="normal">m</mi> <mo>.</mo> <mi mathvariant="normal">s</mi> <mo>.</mo> </mrow> </msub> </semantics> </math> is the initial r.m.s. velocity in the computational domain. The total intensity of pixels is coded according to the total dissipation <math display="inline"> <semantics> <mrow> <mo>∫</mo> <mi>ε</mi> <mi mathvariant="normal">d</mi> <mi>z</mi> </mrow> </semantics> </math>, while red, green and blue color fractions of pixels scale according to the line-of-sight integrated relative fractions of Ohmic dissipation <math display="inline"> <semantics> <mrow> <mi>η</mi> <msup> <mrow> <mo>(</mo> <mo>∇</mo> <mo>×</mo> <mi>B</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics> </math> (<span class="html-italic">red</span>), viscous shear dissipation <math display="inline"> <semantics> <mrow> <mi>ρ</mi> <mi>ν</mi> <msup> <mrow> <mo>(</mo> <mo>∇</mo> <mo>×</mo> <mi>u</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics> </math> (<span class="html-italic">green</span>) and compressible dissipation <math display="inline"> <semantics> <mrow> <mfrac> <mn>4</mn> <mn>3</mn> </mfrac> <mi>ρ</mi> <mi>ν</mi> </mrow> </semantics> </math> (<math display="inline"> <semantics> <mrow> <mo>∇</mo> <mo>.</mo> <mi>u</mi> </mrow> </semantics> </math>)<math display="inline"> <semantics> <msup> <mrow><mspace width="-2.pt"/><mo> </mo></mrow> <mn>2</mn> </msup> </semantics> </math> (<span class="html-italic">blue</span>), where <math display="inline"> <semantics> <mi>η</mi> </semantics> </math> and <math display="inline"> <semantics> <mi>ν</mi> </semantics> </math> are the resistive and viscous coefficients.</p> Full article ">Figure 3
<p>Fraction of energy dissipation for normalised velocity convergence (the opposite of velocity divergence). A normalised convergence value around unity indicates that <math display="inline"> <semantics> <mrow> <mo>−</mo> <mo>∇</mo> <mo>.</mo> <mi>u</mi> </mrow> </semantics> </math>∼<math display="inline"> <semantics> <mrow> <msub> <mi>u</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>.</mo> <mi mathvariant="normal">m</mi> <mo>.</mo> <mi mathvariant="normal">s</mi> <mo>.</mo> </mrow> </msub> <mo>/</mo> <mi>L</mi> </mrow> </semantics> </math>, where <math display="inline"> <semantics> <msub> <mi>u</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>.</mo> <mi mathvariant="normal">m</mi> <mo>.</mo> <mi mathvariant="normal">s</mi> <mo>.</mo> </mrow> </msub> </semantics> </math> and <span class="html-italic">L</span> are the initial r.m.s. velocity and the size of the periodic domain, respectively. Most of the energy is dissipated at compression levels lower than this, although large values of the convergence exist (for example, in strongly compressive shocks). More than half of the energy is dissipated for normalised convergence values below 1. This graph is at a time near dissipation peak, when the compressive motions are maximal, and for initial Orszag–Tang conditions, which are known to generate large-scale shocks.</p> Full article ">Figure 4
<p>Same as <a href="#atmosphere-15-00211-f002" class="html-fig">Figure 2</a> for the background, overlaid with 2-<math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> contours of 1-pixel increments of integrated observables <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mi>I</mi> </mrow> </semantics> </math> (<span class="html-italic">white</span>, column density), <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msub> <mi>v</mi> <mi>z</mi> </msub> </mrow> </semantics> </math> (<span class="html-italic">green</span>, centroid velocity), <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>(</mo> <mi>Q</mi> <mo>/</mo> <mi>I</mi> <mo>)</mo> </mrow> </semantics> </math> (<span class="html-italic">blue</span>, relative Stokes Q parameter) and <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>(</mo> <mi>U</mi> <mo>/</mo> <mi>I</mi> <mo>)</mo> </mrow> </semantics> </math> (<span class="html-italic">red</span>, relative Stokes U parameter). See text for more detailed definitions of <math display="inline"> <semantics> <mrow> <mi>I</mi> <mo>,</mo> <mi>Q</mi> <mo>,</mo> <mi>U</mi> <mo>,</mo> <msub> <mi>v</mi> <mi>z</mi> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Probability distribution functions of the increments of the radial velocity <math display="inline"> <semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics> </math> at dissipation peak (Orszag–Tang initial conditions) for a collection of lags ranging from small (1 pixel, <span class="html-italic">blue</span>) to large (256 pixels, <span class="html-italic">red</span>, or one quarter of the computational domain).</p> Full article ">Figure 6
<p>Dependence of structure functions for the radial velocity <math display="inline"> <semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics> </math> at dissipation peak (Orszag–Tang initial conditions) versus lag <span class="html-italic">ℓ</span> in pixels (<b>left</b>) and versus <math display="inline"> <semantics> <mrow> <mi>S</mi> <mo>(</mo> <mn>3</mn> <mo>,</mo> <mi>ℓ</mi> <mo>)</mo> </mrow> </semantics> </math> (<b>right</b>), where logarithmic scaling is seen to be extended to a larger range of scales. The order <span class="html-italic">p</span> ranges from 0 (<span class="html-italic">blue</span>) to 8 (<span class="html-italic">red</span>) in steps of 1/3.</p> Full article ">Figure 7
<p>On the left panel, we show intermittency exponents measured for four variables, column density <span class="html-italic">I</span>, projected velocity <math display="inline"> <semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics> </math>, <span class="html-italic">U</span> and <span class="html-italic">Q</span> Stokes parameters, probing scales within the range of lags 12 to 48 pixels for a simulation of 1024 pixels of side. ESS (see text) intermittency exponents (computed for the whole range of lags between 1 and 256 pixels) are displayed on the right panel. Error bars show the 1-<math display="inline"> <semantics> <mi>σ</mi> </semantics> </math> standard deviation of the fit residuals over the selected range of scales. Error bars are significantly reduced when using ESS even though the lag range of the fit is much larger. These exponents are computed on a snapshot of a compressible MHD simulation of decaying turbulence (Orszag–Tang initial conditions), at a time near the dissipation peak, about a third of the initial non-linear turnover time [<a href="#B69-atmosphere-15-00211" class="html-bibr">69</a>,<a href="#B70-atmosphere-15-00211" class="html-bibr">70</a>,<a href="#B71-atmosphere-15-00211" class="html-bibr">71</a>].</p> Full article ">Figure 8
<p>Comparison of our intermittency exponents to the model of Grauer et al. [<a href="#B69-atmosphere-15-00211" class="html-bibr">69</a>] or Politano and Pouquet [<a href="#B70-atmosphere-15-00211" class="html-bibr">70</a>] (P&amp;P) and Boldyrev et al. [<a href="#B71-atmosphere-15-00211" class="html-bibr">71</a>] and to the observed ESS coefficients by Hily-Blant et al. [<a href="#B49-atmosphere-15-00211" class="html-bibr">49</a>] (PHB+(2008)) in the Polaris and Taurus regions. OT initial conditions for left panels, ABC flow for the right ones. At dissipation peak for top panels (at about 1/3 turnover time), after one initial turnover time for bottom ones.</p> Full article ">Figure 9
<p>From left to right: Parsec-scale maps in the Polaris Flare of (1) the integrated <math display="inline"> <semantics> <mrow> <msup> <mrow><mspace width="-2.pt"/><mo> </mo></mrow> <mn>12</mn> </msup> <mi>CO</mi> </mrow> </semantics> </math>(2-1) line emission (expressed in K km s<math display="inline"> <semantics> <msup> <mrow><mspace width="-2.pt"/><mo> </mo></mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math>) [<a href="#B50-atmosphere-15-00211" class="html-bibr">50</a>], (2) the dust continuum emission (in MJy/sr) measured at 250 <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math>m by the SPIRE bolometers aboard the <span class="html-italic">Herschel</span> satellite [<a href="#B33-atmosphere-15-00211" class="html-bibr">33</a>], (3) the <math display="inline"> <semantics> <mrow> <msup> <mrow><mspace width="-2.pt"/><mo> </mo></mrow> <mn>12</mn> </msup> <mi>CO</mi> </mrow> </semantics> </math>(2-1) line centroid velocity increments (CVI, in km s<math display="inline"> <semantics> <msup> <mrow><mspace width="-2.pt"/><mo> </mo></mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math>) measured at a lag of 60 arcsec (or 0.1 pc at <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>350</mn> </mrow> </semantics> </math> pc). Rightmost panel: Blow-up of the same quantities within the box drawn on the dust emission map and rotated by 30 deg: it encompasses three dust filaments among the weakest detected by <span class="html-italic">Herschel</span>/SPIRE. The yellow curves provide the quantities averaged along the filament directions: they show that the central filament, F2, barely detected in the dust emission, is the brightest in the CO(2-1) and CVI maps.</p> Full article ">
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