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Atmosphere
Volume 14
Issue 7
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Atmosphere, Volume 14, Issue 7 (July 2023) – 140 articles

Cover Story (view full-size image): Notwithstanding decades of international research and debate and increasingly ominous scientific warnings since the first IPCC assessment (in 1990), the failure of climate change mitigation through carbon emissions reduction is depressingly clear. Many reasons have been identified for our collective failure to bend the global emissions curve. These reasons include economic, geo-political, psychological and sociological factors. This review discusses societal progress to address the climate crisis since the landmark 2015 COP21 meeting and examines the likelihood of keeping warming below 1.5C and what remaining actions may yet be taken to ameliorate the problem. View this paper
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15 pages, 3043 KiB  
Article
Impacts of UHI on Heating and Cooling Loads in Residential Buildings in Cities of Different Sizes in Beijing–Tianjin–Hebei Region in China
by Fanchao Meng, Guoyu Ren and Ruixue Zhang
Atmosphere 2023, 14(7), 1193; https://doi.org/10.3390/atmos14071193 - 24 Jul 2023
Cited by 1 | Viewed by 1337
Abstract
The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating [...] Read more.
The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating and cooling energy consumption levels of buildings. However, there is a lack of research on the impact of the UHI on the heating and cooling energy consumption of buildings in cities of different sizes in the Beijing–Tianjin–Hebei urban agglomeration, which is the most urbanized region in northern China. We selected rural reference stations using the remote sensing method, and applied an hourly data set from automatic weather stations, to examine the impact of the UHI on the typical residential building heating and cooling loads in three cities of varied sizes in the Beijing–Tianjin–Hebei urban agglomeration through building energy simulation. The main conclusions were as follows. As the UHI intensity (UHII) increased, the heating load difference between urban and rural areas decreased, while the cooling load difference between urban and rural areas increased in the cities. The average daily heating loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 8.14, 10.71, and 2.79% lower than those in their rural areas, respectively, while the average daily cooling loads in the urban areas were 6.88, 6.70, and 0.27% higher than those in their rural areas, respectively. Moreover, the absolute hourly load differences between urban and rural areas were significantly larger during the heating periods than during the cooling periods, with the former characterized by being strong at night and weak during the day. During the peak energy load period, the contribution of the UHI to the peak load of residential buildings varied between the cities. During the stable high-load period, from 18:00 to 07:00 the next day in the heating periods (from 18:00 to 05:00 the next day in the cooling periods), the hourly loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 3.15 (2.48), 3.88 (1.51), and 1.07% (1.09%) lower (higher) than those in their rural areas, respectively. Our analysis highlights the necessity to differentiate the energy supplies for the heating and cooling of urban buildings in different sized cities in the region. Full article
(This article belongs to the Special Issue Urban Heat Islands and Global Warming (2nd Edition))
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Figure 1
<p>Location of the study area and the distribution of the urban weather station (red dots) and the RRWS (purple stars) in BTH region, China.</p> Full article ">Figure 2
<p>Daily variations in annual mean values of the temperature in urban and rural areas and UHII in Beijing, Tianjin and Shijiazhuang for the period 2011–2019.</p> Full article ">Figure 3
<p>Daily variations in annual mean values of the heating/cooling loads (<b>A1</b>–<b>A3</b>,<b>C1</b>–<b>C3</b>) and the correlation between the loads and UHII (<b>B1</b>–<b>B3</b>,<b>D1</b>–<b>D3</b>) for 2011–2019.</p> Full article ">Figure 4
<p>Hourly variations of annual mean values of the heating (<b>A</b>) cooling (<b>B</b>) load difference between the urban and rural areas for 2011–2019.</p> Full article ">Figure 5
<p>Correlation between the hourly UHII and the hourly heating (<b>A</b>,<b>C</b>,<b>E</b>) cooling (<b>B</b>,<b>D</b>,<b>F</b>) load differences in buildings in Beijing, Tianjin and Shijiazhuang for the period 2011–2019.</p> Full article ">
17 pages, 3380 KiB  
Article
Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting
by Vateanui Sansine, Pascal Ortega, Daniel Hissel and Franco Ferrucci
Atmosphere 2023, 14(7), 1192; https://doi.org/10.3390/atmos14071192 - 24 Jul 2023
Cited by 2 | Viewed by 1203
Abstract
For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with [...] Read more.
For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year’s worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)
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<p>Total sky imager with a digital camera Axis 212 PTZ (<b>left</b>). Sky image taken at the University on 3 November 2019 (<b>right</b>).</p> Full article ">Figure 2
<p>Pearson correlation test for the measured meteorological data.</p> Full article ">Figure 3
<p>GHI autocorrelation using the Pearson correlation, for a time-shift ranging from 0 to 300 min. Red cross representing the Pearson coefficient.</p> Full article ">Figure 4
<p>Implemented CNN, with inputs being image sequences and meteorological data. The output is the predicted GHI for the next hour.</p> Full article ">Figure 5
<p>Implemented CNN-LSTM. The sky images are first processed by a CNN, then by an LSTM. The meteorological data are only processed by the LSTM. The two LSTMs are concatenated into a dense layer for GHI predictions.</p> Full article ">Figure 6
<p>Distribution of residuals at 10 am (<b>left</b>) and 13 pm (<b>right</b>).</p> Full article ">Figure 7
<p>GHI measurements versus predictions for data test n°1.</p> Full article ">Figure 8
<p>Probabilistic predictions for the hybrid model for PI(38%), PI(68%), PI(95%), and PI(99%).</p> Full article ">
25 pages, 11887 KiB  
Article
Spider Lightning Characterization: Integrating Optical, NLDN, and GLM Detection
by Gilbert Green and Naomi Watanabe
Atmosphere 2023, 14(7), 1191; https://doi.org/10.3390/atmos14071191 - 24 Jul 2023
Viewed by 1400
Abstract
Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the [...] Read more.
Here, we investigate the characteristics of spider lightning analyzing individual lightning flashes as well as the overall electric storm system. From July to November 2022, optical camera systems captured the visually spectacular spider lightning in Southwest Florida. The aspects and activities of the discharges were analyzed by merging the video images with lightning flash data from the National Detection Lightning Network (NLDN) and the Geostationary Lightning Mapper (GLM). Spider lightning discharges primarily occurred during the later stages of the overall lightning activity when there was a decrease in the flash count and flash locations were drifting apart. The propagation path of the spider discharge was predominantly luminous and exhibited an extended duration, ranging from 300 ms to 1720 ms, with most of the path remaining continuously illuminated. Occasionally, observed discharges produced cloud-to-ground flashes (CG) along their propagation paths. This study represents the first attempt to utilize video images, NLDN, and GLM data to investigate the correlation between visual observed spider lightning events and detection networks. These combined datasets facilitated the characterization of the observed spider lightning discharges. Full article
(This article belongs to the Special Issue Lightning Flashes: Detection, Forecasting and Hazards)
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Figure 1
<p>(<b>a</b>) Location of Bonita Springs (26.3° N, 81.8° W) on a map of South Florida. (<b>b</b>) Locations of Camera 1 (26.3° N, 81.8° W) and Camera 2 (26.4° N, 81.8° W) and their fields of view. The red dots on the map represent the locations of the cameras, with numbers 1 and 2 indicating Camera 1 and Camera 2, respectively. The light blue triangles show the field of view of each camera. (<b>c</b>) View captured by Camera 1 (<b>upper panel</b>) and photograph depicting the camera’s setting and outside view (<b>lower panel</b>).</p> Full article ">Figure 2
<p>(<b>a</b>) Lightning locations reported by the NLDN on 13 July 2022 from 21:00:00 UTC to 23:00:00 UTC, in a view of the coordinate of 25.2° N–27° N and 81° W–82° W, with a color scale from red to violet in the 15 min time window. The range in the box represents the time frame in hours and minutes (UTC), and the numbers in brackets indicate the flash counts that occurred within each time frame. (<b>b</b>) The maximum peak current per minute within the time interval. (<b>c</b>) Histogram of the peak current distribution of the flashes occurring during the time interval, color-coded by ICP, ICN, CGP, and CGN. The quantities presented are the flash count, arithmetic mean (AM), median, geometric mean (GM), standard deviation (SD), minimum (Min), and maximum (Max) peak current.</p> Full article ">Figure 3
<p>(<b>a</b>) Discharge development recorded on 13 July 2022, at 22:15:56 UTC. Specific video frames are selected. The video recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 13 July 2022, from 22:15:56.25 UTC to 22:15:57.30 UTC. The map displays a view of the coordinates of 26° N–27° N and 81° W–82° W, with a color scale ranging from red to violet in a 150 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the flash counts within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>) Discharge development recorded on 13 July 2022, at 22:15:56 UTC. Specific video frames are selected. The video recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 13 July 2022, from 22:15:56.25 UTC to 22:15:57.30 UTC. The map displays a view of the coordinates of 26° N–27° N and 81° W–82° W, with a color scale ranging from red to violet in a 150 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the flash counts within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure 4
<p>(<b>a</b>) Discharge development recorded on 13 July 2022, at 22:22:58 UTC. Specific video frames are selected. The video recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 13 July 2022, from 22:22:58.4 UTC to 22:22:59.8 UTC. The map displays a view of the coordinates of 25.8° N–27° N and 81.4° W–82° W, with a color scale ranging from red to violet in a 200 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the flash counts within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure 5
<p>(<b>a</b>) Discharge development recorded on 13 July 2022, at 22:25:11 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) A zoomed-in frame of 360 ms of (<b>a</b>) and a position of each branch’s tip at different times (220 ms, 260, ms, 280 ms, and 340 ms) are displayed. (<b>c</b>) Lightning locations reported by the GLM data on 13 July, from 22:30:10.00 UTC to 22:30:11.75 UTC, in a view of the coordinates of 25.8° N–27° N and 81.4° W–82° W, with a color scale from red to violet in each 250 ms time window in the time interval. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the flash counts within each time frame.</p> Full article ">Figure 6
<p>(<b>a</b>) Lightning locations reported by the NLDN on 23 July 2022 from 00:45:00 UTC to 02:30:00 UTC, in a view of the coordinates of 25° N–27° N and 81° W–82° W, with a color scale from red to violet in each time window. The range in the box represents the time frame in hours and minutes (UTC), and the numbers in brackets indicate the flash counts that occurred within a 15 min time frame. (<b>b</b>) The maximum peak current per minute within the time interval. (<b>c</b>) Histogram of the peak current distribution of the flashes occurring during the time interval, color-coded by ICP, ICN, CGP, and CGN. The quantities presented are the flash count, arithmetic mean (AM), median, geometric mean (GM), standard deviation (SD), minimum (Min), and maximum (Max) peak current.</p> Full article ">Figure 7
<p>(<b>a</b>) Discharge development recorded on 23 July 2022, at 02:08:57 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 23 July 2022 from 22:08:57.2 UTC to 22:08:58.8 UTC. The map displays the coordinates of 25.9° N–26.4° N and 81.6° W–81.9° W, with a color scale ranging from red to violet in the 200 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) Discharge development recorded on 23 July 2022, at 02:08:57 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 23 July 2022 from 22:08:57.2 UTC to 22:08:58.8 UTC. The map displays the coordinates of 25.9° N–26.4° N and 81.6° W–81.9° W, with a color scale ranging from red to violet in the 200 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure 8
<p>(<b>a</b>) Lightning locations reported by the NLDN on 8 September 2022 from 00:30:00 UTC to 01:10:00 UTC, in a view of the coordinates of 25.2° N–26.6° N and 81.0° W–81.9° W, with a color scale from red to violet in the 5 min time window. The range in the box represents the time frame in hours and minutes (UTC), and the numbers in brackets indicate the flash counts that occurred within the 5 min time frame. (<b>b</b>) The maximum peak current per minute within the time interval. (<b>c</b>) Histogram of the peak current distribution of the flashes occurring during the time interval, color-coded by ICP, ICN, CGP, and CGN. The quantities presented are the flash count, arithmetic mean (AM), median, geometric mean (GM), standard deviation (SD), minimum (Min), and maximum (Max) peak current.</p> Full article ">Figure 9
<p>(<b>a</b>) Discharge development recorded on 8 September 2022, at approximately 00:57:50.60 UTC. Specific video frames are selected. The recording has a frame rate of 60 frames per second, corresponding to approximately 17 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 8 September 2022 from 00:57:50.8 UTC to 00:57:52.2 UTC, in a view of the coordinates of 26.1° N–26.35° N and 81.4° W–81.9° W, with a color scale from red to violet in each 200 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure 10
<p>(<b>a</b>) Lightning locations reported by the NLDN on 16 October 2022 from 02:00:00 UTC to 02:49:00 UTC, in a view of the coordinate boundaries of 26.2° N–26.5° N and 81.8° W–82.0° W, with a color scale from red to violet in the 7 min time window. The range in the box represents the time frame in hours and minutes (UTC), and the numbers in brackets indicate the count of flashes that occurred within the 7 min time frame. (<b>b</b>) The maximum peak current every minute within the time interval. (<b>c</b>) Histogram of the peak current distribution of the flashes occurring during the time interval, color-coded for ICP, ICN, CGP, and CGN. The quantities presented are the count, arithmetic mean (AM), median, geometric mean (GM), standard deviation (SD), minimum (Min), and maximum (Max) peak current.</p> Full article ">Figure 11
<p>(<b>a</b>) Discharge development recorded on 16 October 2022 at 02:45:51 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 16 October 2022 from 02:45:50.0 UTC to 02:45:52.0 UTC, in a view of the coordinates of 26.20° N–26.35° N and 81.84° W–81.76° W, with a color scale from red to violet in each 100 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure 11 Cont.
<p>(<b>a</b>) Discharge development recorded on 16 October 2022 at 02:45:51 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 16 October 2022 from 02:45:50.0 UTC to 02:45:52.0 UTC, in a view of the coordinates of 26.20° N–26.35° N and 81.84° W–81.76° W, with a color scale from red to violet in each 100 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure A1
<p>(<b>a</b>) Discharge development recorded on 13 July 2022 at approximately 22:19:47 UTC. Specific video frames are selected. The recording has a frame rate of 50 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 13 July 2022, from 22:19:47.00 UTC to 22:19:49.00 UTC. The map displays a view of the coordinates of 26° N–27° N and 81° W–82° W, with a color scale ranging from red to violet in the 250 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the flash counts within each time frame. The right panel shows a zoomed-in view of the box displayed in the left panel.</p> Full article ">Figure A2
<p>(<b>a</b>) Discharge development recorded on 8 September 2022, at 00:55:34 UTC. Specific video frames are selected. The recording has a frame rate of 60 frames per second, corresponding to approximately 17 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 8 September 2022, from 00:55:34.40 UTC to 00:55:35.10 UTC, in a view of the coordinates of 26.10° N–26.45° N and 81.4° W–81.8° W, with a color scale from red to violet in the 100 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure A2 Cont.
<p>(<b>a</b>) Discharge development recorded on 8 September 2022, at 00:55:34 UTC. Specific video frames are selected. The recording has a frame rate of 60 frames per second, corresponding to approximately 17 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 8 September 2022, from 00:55:34.40 UTC to 00:55:35.10 UTC, in a view of the coordinates of 26.10° N–26.45° N and 81.4° W–81.8° W, with a color scale from red to violet in the 100 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure A3
<p>(<b>a</b>) Discharge development recorded on 8 September 2022, at 01:01:29 UTC. Specific video frames are selected. The recording has a frame rate of 60 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 8 September 2022, from 01:01:29.00 UTC to 01:01:29.84 UTC, in a view of the coordinates of 26.10° N–26.35° N and 81.2° W–81.8° W, with a color scale from red to violet in the 120 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure A3 Cont.
<p>(<b>a</b>) Discharge development recorded on 8 September 2022, at 01:01:29 UTC. Specific video frames are selected. The recording has a frame rate of 60 frames per second, corresponding to approximately 20 ms per frame. Photo courtesy of G. Green. (<b>b</b>) Lightning locations reported by the GLM data on 8 September 2022, from 01:01:29.00 UTC to 01:01:29.84 UTC, in a view of the coordinates of 26.10° N–26.35° N and 81.2° W–81.8° W, with a color scale from red to violet in the 120 ms time window. The range in the box represents the time frame in seconds, and the numbers in brackets indicate the count of flashes within each time frame.</p> Full article ">Figure A4
<p>Snapshots of the NCEP North American Regional Reanalysis Precipitation Composition (kg/m<sup>2</sup>/s) mean, from the NOAA Physical Science Laboratory, on (<b>a</b>) 13 July 2022, (<b>b</b>) 23 July 2022, (<b>c</b>) 8 September 2022, and (<b>d</b>) 16 October 2022.</p> Full article ">Figure A5
<p>Snapshots of the NCEP North American Regional Reanalysis Air Temperature (°C) Composition mean, from the NOAA Physical Science Laboratory, at the pressure levels: (<b>a</b>) 450 mb, (<b>b</b>) 500 mb, (<b>c</b>) 550 mb, and (<b>d</b>) 600 mb. (<b>e</b>) A zoomed-in view of the yellow box shown in the panel (<b>a</b>). (<b>f</b>) The plot of the relationship between height and temperature, which was used to estimate the temperature at specific vertical heights.</p> Full article ">Figure A6
<p>(<b>a</b>) Locations of canals and rivers in Southwest Florida from the canal network of South Florida. Adapted from <a href="http://sfwnd.gov" target="_blank">sfwnd.gov</a>, reported on June 2020. (<b>b</b>) Scatter plot of the locations of flashes occurring on 13 July at 22:22:58 UTC.</p> Full article ">
16 pages, 10032 KiB  
Article
A Comparative Analysis between Radar and Human Observations of the Giant Hail Event of 30 August 2022 in Catalonia
by Tomeu Rigo and Carme Farnell
Atmosphere 2023, 14(7), 1190; https://doi.org/10.3390/atmos14071190 - 24 Jul 2023
Cited by 1 | Viewed by 1105
Abstract
Three facts characterise the hailstorm of 30 August 2022 in the Catalan village of La Bisbal d’Empordà and its surroundings: first, the most dramatic, the death of a child hit by a hailstone; second, the damage to most of the roofs and cars [...] Read more.
Three facts characterise the hailstorm of 30 August 2022 in the Catalan village of La Bisbal d’Empordà and its surroundings: first, the most dramatic, the death of a child hit by a hailstone; second, the damage to most of the roofs and cars in the town; finally, the highest recorded amount of hail (more than 10 cm) in Catalonia in at least the last 30 years. This research focuses on the radar field comparison and the observations provided by an electronic survey of the study area. The results reveal that weather radar underestimated the hail size because of different factors. Conversely, some reporters provided an inaccurate hour. The difference of three months between the hail event and the electronic survey is the probable cause of this mistake in the time estimation. However, the survey delay helped to avoid answers with larger hail sizes than those provided by the official spotters. Full article
(This article belongs to the Section Meteorology)
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Figure 1

Figure 1
<p>Area hit by the hailstorm on 30 August 2022. The green pins indicate the locations of the three points of the field work, the orange points show the positions with information provided thorough the electronic form, and the solid lines delimit the areas of Vertical Integrated Liquid (VIL) from radar of 15 mm (orange), 35 mm (red), and 55 mm (purple). The top-right panel marks the area (white rectangle) in a map of Europe.</p> Full article ">Figure 2
<p>Zoomed-in image of La Bisbal d’Empordà (marked with a red line) and Corçà (delimited by a blue line). The green pins correspond to the locations of the field work and the orange points mark the positions with information provided in the form.</p> Full article ">Figure 3
<p>(<b>Above</b>) Track of the hailstorm delimited by the purple solid lines. The yellow line indicates the section of the vertical profile of the elevation. (<b>Below</b>) Vertical profile of the elevation.</p> Full article ">Figure 4
<p>(<b>Left</b>) Maximum daily VIL field (in mm) of the event. The black dots correspond to the registers and the largest black and grey dots indicate the location of the closest radar. (<b>Below</b>) Zoomed-in image of the region with registers, corresponding to the black square in the left panel.</p> Full article ">Figure 5
<p>(<b>Left</b>) Pie chart with the time (local time, +2 h with respect to UTC) of all the registers. (<b>Right</b>) The same as the left panel, but only for the La Bisbal de l’Empordà cases.</p> Full article ">Figure 6
<p>(<b>Left</b>) Pie chart with the duration of the event for all registers. (<b>Right</b>) The same but only for La Bisbal d’Empordà observations.</p> Full article ">Figure 7
<p>(<b>Left</b>) Pie chart with the maximum size of the stones for all surveys. (<b>Right</b>) The same, but only for La Bisbal d’Empordà observations.</p> Full article ">Figure 8
<p>Boxplot of the different pixels with ground registers grouped by size for the VIL (<b>above</b>) and the maximum reflectivity (<b>below</b>) daily fields.</p> Full article ">Figure 9
<p>The same as <a href="#atmosphere-14-01190-f004" class="html-fig">Figure 4</a>, only using the maximum reflectivity.</p> Full article ">Figure 10
<p>Maximum hail size field obtained from universal co-Krigging: (<b>a</b>) Using VIL for the epicenter; (<b>b</b>) Using maximum reflectivity for the epicenter; (<b>c</b>) as (<b>a</b>) but for the full area; (<b>d</b>) as (<b>b</b>) but for the full area. A0 indicates “No hail”, A1: “hail ≤1 cm”, A2: “hail 1–2 cm”, A3: “hail 2–4 cm”, A4: “hail 4–8 cm”, and A5: “hail ≥8 cm”.</p> Full article ">Figure 11
<p>Different behaviors between VIL and the electronic survey. Left column: VIL evolution of the percentiles 25, 50, 75, 90, and the maximum over the location (cyan point on the right column maps). The cyan rectangles indicate the hail time (provided by the survey). The size and the duration are shown below the X-axis label “Time (UTC)”. Right column: Maps showing the maximum daily VIL field in the surroundings of each observation. (<b>Top</b>) The survey indicated that the event occurred before the real time. (<b>Middle</b>) A case of simultaneity. (<b>Below</b>) A case of delay in the observation.</p> Full article ">
15 pages, 3188 KiB  
Article
Influence of the Madden–Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific
by Moleni Tu’uholoaki, Antonio Espejo, Krishneel K. Sharma, Awnesh Singh, Moritz Wandres, Herve Damlamian and Savin Chand
Atmosphere 2023, 14(7), 1189; https://doi.org/10.3390/atmos14071189 - 24 Jul 2023
Viewed by 1600
Abstract
The modulating influence of the Madden–Julian oscillation (MJO) on tropical cyclones (TCs) has been examined globally, regionally, and subregionally, but its impact on the island scale remains unclear. This study investigates how TC activity affecting the Tonga region is being modulated by the [...] Read more.
The modulating influence of the Madden–Julian oscillation (MJO) on tropical cyclones (TCs) has been examined globally, regionally, and subregionally, but its impact on the island scale remains unclear. This study investigates how TC activity affecting the Tonga region is being modulated by the MJO, using the Southwest Pacific Enhanced Archive of Tropical Cyclones (SPEArTC) and the MJO index. In particular, this study investigates how the MJO modulates the frequency and intensity of TCs affecting the Tonga region relative to the entire study period (1970–2019; hereafter referred to as all years), as well as to different phases of the El Niño southern oscillation (ENSO) phenomenon. Results suggest that the MJO strongly modulates TC activity affecting the Tonga region. The frequency and intensity of TCs is enhanced during the active phases (phases six to eight) in all years, including El Niño and ENSO-neutral years. The MJO also strongly influences the climatological pattern of genesis of TCs affecting the Tonga region, where more (fewer) cyclones form in the active (inactive) phases of the MJO and more genesis points are clustered (scattered) near (away from) the Tonga region. There were three regression curves that best described the movement of TCs in the region matching the dominant steering mechanisms in the Southwest Pacific region. The findings of this study can provide climatological information for the Tonga Meteorological Service (TMS) and disaster managers to better understand the TC risk associated with the impact of the MJO on TCs affecting the Tonga region and support its TC early warning system. Full article
(This article belongs to the Special Issue Weather and Climate Extremes: Observations, Modeling, and Impacts)
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<p>A 5-degree radius circle centred on Nuku’alofa, referred to as the Tonga region, with 128 TC tracks (in coloured plots) passing through the region between 1970–2019.</p> Full article ">Figure 2
<p>(<b>a</b>) Number of TC geneses per day (%) during 1970 and 2019 for all years (in black), El Niño years (in red), La Niña years (in blue), and ENSO-neutral years (in green). (<b>b</b>) The spatial distribution of genesis points (El Niño years (in red), La Niña years (in blue), and ENSO-neutral years (in green)) and corresponding tracks for the eight MJO phases. Asterisk (*) and plus (+) signs in (<b>a</b>) indicate statistical significance at 99% and 95% levels, respectively.</p> Full article ">Figure 3
<p>The spatial distribution of TC genesis points (El Niño in red, La Niña in blue, and ENSO-neutral in green) and the accompanied tracks corresponding with each of the clusters of TCs affecting Tonga. Mean regression curves (thick black lines) are also shown.</p> Full article ">Figure 4
<p>Frequency by TC intensity during the different phases of the MJO for (<b>a</b>) all years, (<b>b</b>) El Niño years, (<b>c</b>) La Niña years, and (<b>d</b>) ENSO-neutral years within the Tonga region between 1970–2019. Asterisk (*) and plus (+) signs indicate statistical significance at the 99% and 95% level, respectively.</p> Full article ">Figure 5
<p>Trends of frequency of TCs per MJO active phase (phases six to eight) for (a) all years (black dashed line), (b) El Niño years (red dashed line), (c) La Niña years (blue dashed line), and (d) ENSO-neutral years (green dashed line) within the Tonga region between 1970–2019. Asterisk (*) sign indicates statistical significance at the 95% level, respectively.</p> Full article ">
14 pages, 3023 KiB  
Article
Spatiotemporal Characteristics of Drought in Northwest China Based on SPEI Analysis
by Yongqin Peng, Tao Peng and Yan Li
Atmosphere 2023, 14(7), 1188; https://doi.org/10.3390/atmos14071188 - 23 Jul 2023
Cited by 7 | Viewed by 1308
Abstract
Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little [...] Read more.
Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little rain. It is of great significance to make full use of agricultural resources to clarify the temporal and spatial distribution characteristics of the drought regime in Northwest China. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this paper used the methods of Mann–Kendall non-parameter trend, mutation test, and Morlet wavelet analysis to explore the drought characteristics in Northwest China from 1961 to 2017. The results showed that the spatial distribution of SPEI on annual and seasonal scales differed slightly in different regions, but from northwest to southeast, the distribution was generally wetter to drier. The drought intensity (Sij) had a step-like distribution with a range of 1.14–1.98. Based on Sij analysis, the frequency of drought in Northwest China was moderate, followed by extreme drought, severe drought, and light drought. The inter-annual drought station proportion (Pj) ranged from 7.4% to 84.1%. A total of 25, 18, 7, and 5 years of pan-regional drought, regional drought, partial region drought, and local drought occurred, respectively, based on Pj analysis. Moreover, from the whole study period, the regional drought changes tended to cause humidification to different degrees. The results of Morlet wavelet analysis showed that there were multiple time scales of 33–52, 11–19, and 4–7 years of SPEI in the entire time domain, and dry and wet trends occurred. The results of the present research can provide a reference for the efficient utilization of water resources, drought monitoring and early warning, drought prevention, and drought relief in Northwest China. Full article
(This article belongs to the Special Issue Climate Extremes in China)
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<p>Study area and distribution map of studied weather stations.</p> Full article ">Figure 2
<p>Spatial distribution of climate tendency rate of SPEI on annual and seasonal scales in Northwest China.</p> Full article ">Figure 3
<p>Spatial distribution of different drought degrees in Northwest China.</p> Full article ">Figure 4
<p>Annual variation of SPEI in annual, spring, summer, autumn, winter, and their M-K test result in Northwest China.</p> Full article ">Figure 5
<p>Variation of drought intensity (<b>a</b>) and drought station proportion (<b>b</b>) in Northwest China.</p> Full article ">Figure 6
<p>Results of Morlet wavelet transform for the annual SPEI in Northwest China.</p> Full article ">Figure 7
<p>Wavelet transformation variance and specific real part of annual SPEI in Northwest China.</p> Full article ">
12 pages, 3465 KiB  
Article
Comprehensive Efficiency Evaluation of Aircraft Artificial Cloud Seeding in Hunan Province, China, Based on Numerical Simulation Catalytic Method
by Xiecheng Wan, Sheng Zhou and Zhichao Fan
Atmosphere 2023, 14(7), 1187; https://doi.org/10.3390/atmos14071187 - 23 Jul 2023
Viewed by 2329
Abstract
Aircraft cloud seeding refers to the use of equipment on aircraft to release chemicals into clouds, changing their physical and chemical properties to increase rainfall or snowfall. The purpose of precipitation enhancement is to alleviate drought and water scarcity issues. Due to the [...] Read more.
Aircraft cloud seeding refers to the use of equipment on aircraft to release chemicals into clouds, changing their physical and chemical properties to increase rainfall or snowfall. The purpose of precipitation enhancement is to alleviate drought and water scarcity issues. Due to the complexity of the technology, the precise control of factors such as cloud characteristics and chemical release amounts is necessary. Therefore, a scientific evaluation of the potential of aircraft cloud seeding can help to improve the effectiveness of the process, and is currently a technical challenge in weather modification. This study used the mesoscale numerical model WRF coupled with a catalytic process to simulate and evaluate the seven aircraft cloud seeding operations conducted in Hunan Province in 2021. The results show that WRF can effectively evaluate the effectiveness of cloud seeding. When the water vapor conditions are suitable, the airborne dispersion of silver iodide (AgI) can significantly increase the content of large particles of high-altitude ice crystals, snow, and graupel, resulting in an increase in low-level rainwater content and, correspondingly, an increase in ground precipitation. When the water vapor conditions are insufficient, the dispersion of AgI does not trigger effective precipitation, consistent with the results of station observations and actual flight evaluations. This study provides an effective method for scientifically evaluating the potential and effectiveness of aircraft cloud seeding operations. Full article
(This article belongs to the Special Issue Atmospheric Ice Nucleating Particles, Cloud and Precipitation)
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<p>Model domain and elevation (unit: m).</p> Full article ">Figure 2
<p>Flight route map for aircraft cloud seeding operations for artificial rainfall in Hunan Province from August to September 2021.</p> Full article ">Figure 3
<p>Distribution of cumulative precipitation during seven aircraft cloud seeding operations in Hunan Province from August to September 2021 (unit: mm) (left column), with the red box indicating the area affected by aircraft rainfall. The middle column shows the cumulative precipitation without considering catalytic effects, and the right column shows the cumulative precipitation involving catalytic effects (unit: mm).</p> Full article ">Figure 4
<p>Vertical cross-sections of the ice-mixing ratio (<b>a</b>,<b>b</b>), snow-mixing ratio (<b>c</b>,<b>d</b>), graupel-mixing ratio (<b>e</b>,<b>f</b>), and cloud-water-mixing ratio (<b>g</b>,<b>h</b>) over the area influenced by aircraft cloud seeding with (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and without (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) the catalytic effect at 10:00–16:00 (UTC) on 25 August 2021 (units: g/kg). The ordinate is the pressure level heights (hPa).</p> Full article ">
15 pages, 1787 KiB  
Article
Computational Domain Size Effects on Large-Eddy Simulations of Precipitating Shallow Cumulus Convection
by Oumaima Lamaakel, Ravon Venters, Joao Teixeira and Georgios Matheou
Atmosphere 2023, 14(7), 1186; https://doi.org/10.3390/atmos14071186 - 22 Jul 2023
Cited by 2 | Viewed by 1645
Abstract
Idealized large-eddy simulations of shallow convection often utilize horizontally periodic computational domains. The development of precipitation in shallow cumulus convection changes the spatial structure of convection and creates large-scale organization. However, the limited periodic domain constrains the horizontal variability of the atmospheric boundary [...] Read more.
Idealized large-eddy simulations of shallow convection often utilize horizontally periodic computational domains. The development of precipitation in shallow cumulus convection changes the spatial structure of convection and creates large-scale organization. However, the limited periodic domain constrains the horizontal variability of the atmospheric boundary layer. Small computational domains cannot capture the mesoscale boundary layer organization and artificially constrain the horizontal convection structure. The effects of the horizontal domain size on large-eddy simulations of shallow precipitating cumulus convection are investigated using four computational domains, ranging from 40×40km2 to 320×320km2 and fine grid resolution (40 m). The horizontal variability of the boundary layer is captured in computational domains of 160×160km2. Small LES domains (≤40 km) cannot reproduce the mesoscale flow features, which are about 100km long, but the boundary layer mean profiles are similar to those of the larger domains. Turbulent fluxes, temperature and moisture variances, and horizontal length scales are converged with respect to domain size for domains equal to or larger than 160×160km2. Vertical velocity flow statistics, such as variance and spectra, are essentially identical in all domains and show minor dependence on domain size. Characteristic horizontal length scales (i.e., those relating to the mesoscale organization) of horizontal wind components, temperature and moisture reach an equilibrium after about hour 30. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>LES domain sizes and cloud liquid water path (LWP) at the end of the run, <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math>. The computational domain area quadruples as the LES computational domain increases. Axes ticks correspond to 50-km intervals. Axes labels are not shown to maximize the plot area.</p> Full article ">Figure 2
<p>Rain water path (RWP) at <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math>. Axes ticks correspond to 50-km intervals. Axes labels are not shown to maximize the plot area. See <a href="#atmosphere-14-01186-f001" class="html-fig">Figure 1</a> for the corresponding LWP field.</p> Full article ">Figure 3
<p>Evolution of vertically integrated turbulent kinetic energy (VTKE), cloud-liquid water path (LWP), rain water path (RWP), cloud base <math display="inline"><semantics><msub><mi>z</mi><mi>b</mi></msub></semantics></math>, cloud top height <math display="inline"><semantics><msub><mi>z</mi><mi>c</mi></msub></semantics></math>, inversion height <math display="inline"><semantics><msub><mi>z</mi><mi>i</mi></msub></semantics></math>, cloud cover <math display="inline"><semantics><mrow><mi>c</mi><mi>c</mi></mrow></semantics></math> and surface precipitation rate.</p> Full article ">Figure 4
<p>Profiles of (<b>a</b>) <span class="html-italic">u</span>-component wind, (<b>b</b>) potential temperature <math display="inline"><semantics><mi>θ</mi></semantics></math>, (<b>c</b>) total water mixing ratio <math display="inline"><semantics><msub><mi>q</mi><mi>t</mi></msub></semantics></math>, (<b>d</b>) cloud liquid water mixing ratio <math display="inline"><semantics><msub><mi>q</mi><mi>l</mi></msub></semantics></math>, (<b>e</b>) turbulent kinetic energy (TKE), (<b>f</b>) horizontal component of TKE, (<b>g</b>) vertical velocity variance and (<b>h</b>) vertical total water flux at the end of the LES runs, <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math>.</p> Full article ">Figure 5
<p>(<b>a</b>) Resolved-scale total water mixing ratio, and (<b>b</b>) liquid water potential temperature variance at <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math> for all LES domains.</p> Full article ">Figure 6
<p>(<b>a</b>) Time evolution of inversion strength for all runs. (<b>b</b>) Inversion height and inversion strength in the <math display="inline"><semantics><mrow><mn>8</mn><mo>×</mo><mn>8</mn></mrow></semantics></math> subdomains of run D at <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math> with open circles. Filled circle corresponds to the entire-domain average, which is the same value as the line for run D at <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math> of (<b>a</b>).</p> Full article ">Figure 7
<p>Premultiplied one-dimensional spectra. All spectra are computed at <math display="inline"><semantics><mrow><mi>z</mi><mo>=</mo><mn>360</mn><mspace width="0.277778em"/><mi mathvariant="normal">m</mi></mrow></semantics></math> along the zonal direction. The <span class="html-italic">x</span>-axis is converted to length scale to assist the physical interpretation. In each panel, spectra from all four runs are shown, lines are as in <a href="#atmosphere-14-01186-f004" class="html-fig">Figure 4</a>. Panel rows from top to bottom correspond to a different time <math display="inline"><semantics><mrow><mi>t</mi><mo>=</mo><mn>24</mn></mrow></semantics></math> (<b>a</b>–<b>d</b>), 30 (<b>e</b>–<b>h</b>) and <math display="inline"><semantics><mrow><mn>36</mn><mspace width="0.277778em"/><mi mathvariant="normal">h</mi></mrow></semantics></math> (<b>i</b>–<b>l</b>). In each row, panels correspond to different variables: from left to right, zonal wind, vertical velocity, liquid water potential temperature, and total water mixing ratio.</p> Full article ">Figure 8
<p>Horizontal length scales computed from runs C (black symbols) and D (orange). (<b>a</b>) Length scales of zonal wind <math display="inline"><semantics><msub><mi>l</mi><mi>u</mi></msub></semantics></math>. (<b>b</b>) Meridional wind <math display="inline"><semantics><msub><mi>l</mi><mi>v</mi></msub></semantics></math>. (<b>c</b>) Liquid water potential temperature <math display="inline"><semantics><msub><mi>l</mi><mi>θ</mi></msub></semantics></math>. (<b>d</b>) Total water mixing ratio <math display="inline"><semantics><msub><mi>l</mi><mi>q</mi></msub></semantics></math> and radii of two cold pools <math display="inline"><semantics><msub><mi>r</mi><mrow><mi>c</mi><mi>p</mi><mn>1</mn></mrow></msub></semantics></math> and <math display="inline"><semantics><msub><mi>r</mi><mrow><mi>c</mi><mi>p</mi><mn>2</mn></mrow></msub></semantics></math> from run C. (<b>e</b>) Vertical velocity <math display="inline"><semantics><msub><mi>l</mi><mi>w</mi></msub></semantics></math>.</p> Full article ">Figure A1
<p>Vertical profiles averaged in the horizontal directions and in time between <span class="html-italic">t</span> = 35–36 h for run A.</p> Full article ">Figure A2
<p>Vertical profiles averaged in the horizontal directions and in time between <span class="html-italic">t</span> = 35–36 h for run B.</p> Full article ">Figure A3
<p>Vertical profiles averaged in the horizontal directions and in time between <span class="html-italic">t</span> = 35–36 h for run C.</p> Full article ">Figure A4
<p>Vertical profiles averaged in the horizontal directions and in time between <span class="html-italic">t</span> = 35–36 h for run D.</p> Full article ">
12 pages, 3466 KiB  
Article
Why Does a Stronger El Niño Favor Developing towards the Eastern Pacific while a Stronger La Niña Favors Developing towards the Central Pacific?
by Jiahui Yu, Tim Li and Leishan Jiang
Atmosphere 2023, 14(7), 1185; https://doi.org/10.3390/atmos14071185 - 22 Jul 2023
Cited by 1 | Viewed by 1522
Abstract
By decomposing observed El Niño and La Niña events into a strong group and a weak group, respectively, we discovered that the strong La Niña group has its peak center more towards the west compared to the weak La Niña group, whereas the [...] Read more.
By decomposing observed El Niño and La Niña events into a strong group and a weak group, respectively, we discovered that the strong La Niña group has its peak center more towards the west compared to the weak La Niña group, whereas the strong El Niño group has its peak center more towards the east compared to the weak El Niño group. The cause of this structure asymmetry is investigated through an ocean mixed-layer heat budget analysis. It was found that the asymmetry is closely linked to the longitudinal distribution of SST anomaly (SSTA) skewness along the equator, and is fundamentally caused by nonlinear dynamic heating, especially nonlinear horizontal temperature advection. It was demonstrated that near the equatorial central Pacific, the anomalous zonal and meridional currents generate negative nonlinear zonal and meridional temperature advection anomalies for both the El Niño and La Niña events, thus favoring a stronger La Niña and a weaker El Niño. Over the eastern Pacific, due to the dominant geostrophic zonal current anomalies and the southward shift of SSTA centers, nonlinear horizontal temperature advection anomalies tend to be positive for both the El Niño and La Niña, thus favoring a stronger growth of El Niño than La Niña. Nonlinear vertical temperature advection anomalies play minor roles in the central Pacific and tend to partially offset the nonlinear horizontal advection effect in the equatorial eastern Pacific. Full article
(This article belongs to the Section Meteorology)
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<p>Time series of normalized DJF Niño−3.4 index from 1958 to 2019. Dashed lines indicate 0.5 (−0.5) and 1.0 (−1.0) standard deviations, respectively. Light red (light blue) bars denote relatively weaker El Niño (La Niña) events and red (blue) bars denote relatively stronger El Niño (La Niña) events.</p> Full article ">Figure 2
<p>Composite DJF SSTA (shading, °C) for strong and weak El Niño (<b>a</b>,<b>b</b>)/La Niña (<b>c</b>,<b>d</b>). The dotted region denotes above 90% confidence level.</p> Full article ">Figure 3
<p>(<b>a</b>) DJF SSTA skewness; (<b>b</b>) asymmetric component of composite SSTA (shading, °C) for total El Niño and La Niña events. Red box denotes the central Pacific (5° N–5° S, 160° E–170° W) and blue box represents the eastern Pacific (5° N–5° S, 120°–90° W).</p> Full article ">Figure 4
<p>The mixed−layer budget results over the (<b>a</b>) equatorial central Pacific (5° S–5° N, 160° E–170° W) and (<b>b</b>) the eastern Pacific (5° S–5° N, 120°–90° W). Along the x axis is the observed MLT tendency, the MLT tendency calculated by (Equation (1)), the three-dimensional total ocean temperature advection, the surface heat flux. Hatched bar and dotted bar represent composite results for El Niño and La Niña events, respectively.</p> Full article ">Figure 5
<p>The advection terms over the (<b>a</b>) equatorial central Pacific (5° S–5° N, 160° E–170° W) and the (<b>b</b>) eastern Pacific (5° S–5° N, 120°–90° W). Along the x axis is the total advection terms, linear advection terms and nonlinear advection terms. (<b>c</b>,<b>d</b>) Decomposition of nonlinear advection terms. Along the x axis is the total nonlinear advection, zonal nonlinear advection, meridional nonlinear advection and vertical nonlinear advection. Hatched bar and dotted bar represent composite results for El Niño and La Niña events, respectively.</p> Full article ">Figure 6
<p>Longitude−depth cross−section of the anomalous ocean temperature (shading; unit: °C) and zonal currents (vector; unit: m∙s<sup>−1</sup>)/vertical currents (vector; unit: 10<sup>4</sup> m∙s<sup>−1</sup>) along the equatorial region (within ±5°) at the development stage (June−November) of (<b>a</b>) El Niño and (<b>b</b>) La Niña.</p> Full article ">Figure 7
<p>Latitudinal−depth cross−section of the anomalous ocean temperature (shading; unit: °C) and meridional currents (vector; unit: m∙s<sup>−1</sup>)/vertical currents (vector; unit: 10<sup>4</sup> m∙s<sup>−1</sup>) averaged over the central Pacific (160° E–170° W) at the development stage (June–November) of (<b>a</b>) El Niño and (<b>b</b>) La Niña. (<b>c</b>,<b>d</b>) are the same as (<b>a</b>,<b>b</b>) but are averaged over the eastern Pacific (120°–90° W).</p> Full article ">Figure 8
<p>Schematic diagram of nonlinear horizontal advection in causing spatial structure asymmetry for El Niño (<b>a</b>) and La Niña (<b>b</b>). Black/Green vectors indicate anomalous zonal/meridional currents; red/blue shading represents the El Niño−related/La Niña−related mixed−layer temperature anomaly during ENSO developing June–November).</p> Full article ">
17 pages, 9898 KiB  
Article
Variability of River Runoff in Poland and Its Connection to Solar Variability
by Dariusz Wrzesiński, Leszek Sobkowiak, Ileana Mares, Venera Dobrica and Constantin Mares
Atmosphere 2023, 14(7), 1184; https://doi.org/10.3390/atmos14071184 - 22 Jul 2023
Cited by 3 | Viewed by 1041
Abstract
The aim of this research was to determine relationships between solar activity and variability of discharges of three Central European rivers: the Vistula, Odra and Warta in Poland in the multi-annual period of 1901–2020. Changes in precipitation and air temperature at Poznań meteorological [...] Read more.
The aim of this research was to determine relationships between solar activity and variability of discharges of three Central European rivers: the Vistula, Odra and Warta in Poland in the multi-annual period of 1901–2020. Changes in precipitation and air temperature at Poznań meteorological station in the same period were also analyzed. The long-term variations in river runoff were investigated both from the point of view of temporal variability in relation to climate variations in the study area, and from the point of view of linear/non-linear links to solar activity, as described by the Wolf sunspot number. The wavelet transform analysis was used to highlight the frequency-time distribution of the coherences between solar and discharge variability. It was found that most of the links between solar activity and discharges were non-linear. Full article
(This article belongs to the Section Climatology)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Trends of changes in the discharges of the studied rivers in the multi-annual period of 1901–2020 with the 10 year moving average.</p> Full article ">Figure 3
<p>Trends of changes in the seasonal discharges of the studied rivers in the multi-annual period of 1901–2020 with the 10 year moving average.</p> Full article ">Figure 4
<p>Trends of changes in precipitation and air temperature in Poznań in the multi-annual period of 1901–2020. The blue bars represent precipitation in Poznań (P Poznań), and the red curve represents air temperature in Poznań (Tair Poznań).</p> Full article ">Figure 5
<p>Trends of changes in the discharges of the studied rivers, air temperature and precipitation in Poznań in the 20 year sub-periods of the multi-year period of 1901–2020; <span class="html-italic">p</span>—statistical significance, R—coefficient of correlation.</p> Full article ">Figure 6
<p>Standardized cumulative annual deviations (SCAD) from the average of annual river discharges, precipitation (P) and air temperature (T<sub>air</sub>).</p> Full article ">Figure 7
<p>Deviations of monthly discharges in the 20 year sub-periods from the average values from 1901 to 2020 and their statistical significance.</p> Full article ">Figure 8
<p>The linear |R| and nonlinear correlation coefficient (NLR), between the discharges (Qs) from three rivers in Poland and Wolf number with lags 0:5.</p> Full article ">Figure 9
<p>Relationship between solar activity (SSN) and the Warta discharge for spring at lag = 0: (<b>a</b>) WTC (wavelet coherence), (<b>b</b>) boxplot for the coherence obtained by WTC, (<b>c</b>) Global Coherence (GC), solid line, and Significance of GC (SGC), dashed line. SGC indicates a 95% CL.</p> Full article ">Figure 10
<p>Relationship between solar activity (SSN) and the Vistula discharge for fall at lag = 4: (<b>a</b>) WTC (wavelet coherence), (<b>b</b>) Global Coherence (GC), solid line, and Significance of GC (SGC), dashed line. SGC indicates a 95% CL. In (<b>a</b>) the arrows with an orientation from west to east indicate that the two time series are in phase, i.e. they are positively correlated, and the arrows with an orientation from east to west indicate that the series are in anti-phase, i.e. there is a negative correlation between them.</p> Full article ">Figure 11
<p>Relationship between solar activity (SSN) and the Warta discharge for summer at lag = 1: (<b>a</b>) WTC (wavelet coherence), (<b>b</b>) Global Coherence (GC), solid line, and Significance of GC (SGC), dashed line. SGC indicates a 95% CL.</p> Full article ">Figure 12
<p>Relationship between solar activity (SSN) and the Vistula discharge for winter at lag = 3: (<b>a</b>) WTC (wavelet coherence), (<b>b</b>) Global Coherence (GC), solid line, and Significance of GC (SGC), dashed line. SGC indicates a 95% CL.</p> Full article ">
16 pages, 511 KiB  
Communication
Climate Change: An Issue That Should Be Part of Workers’ Information and Training Duties Envisaged by EU Directives on Occupational Health and Safety
by Carlo Grandi, Andrea Lancia and Maria Concetta D’Ovidio
Atmosphere 2023, 14(7), 1183; https://doi.org/10.3390/atmos14071183 - 21 Jul 2023
Cited by 2 | Viewed by 1243
Abstract
The impact of climate change on the physical environment, ecosystems, and human societies is increasingly recognized as the most important global challenge. Climate change may alter, among others, the thermal environment, the occurrence of extreme weather events, and the human exposure to physical, [...] Read more.
The impact of climate change on the physical environment, ecosystems, and human societies is increasingly recognized as the most important global challenge. Climate change may alter, among others, the thermal environment, the occurrence of extreme weather events, and the human exposure to physical, chemical, and biological pollutants, thus affecting human health with several potential outcomes. The impact of climate change on occupational health and safety has been receiving increasing attention in last years. In the European Union, the health and safety of workers is under the rule of Directive 89/391 and its daughters. In a changing climate, compliance with all requirements of the existing EU regulation entails an additional effort to implement preventive and protective measures. A central role in workers’ health protection is played by proper workers’ information and training, which is partly in charge of the occupational physicians. This paper provides a basic proposal on topics related to climate change to update workers’ information and training and to integrate the curricula of occupational physicians. Importantly, suitable information and training may contribute to promoting workers’ health and to implement adaptation measures, which are part of the individual, societal, and global responses to climate change. Full article
(This article belongs to the Special Issue Aerobiology and Health Impacts)
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<p>An essential scheme showing the very complex and only partially known interactions between climate change, stratospheric ozone, and urban heat islands, with regard to occupational exposure to physical, chemical, and biological agents in both outdoor and indoor settings. The figure’s content is qualitative. A quick view highlights that most of the reported risk factors acts (directly or indirectly) in both outdoor and indoor environment(s), with the exception of pollutants typical of the indoor environment(s) (e.g., radon and molds). Different types of risk factors may interact and modulate each other (for instance increasing or decreasing their concentration into the environment or their action on biological targets), and this is represented by a blue line, with some dashes connecting the different classes of physical, chemical, and biological agents. In this context, the so-called “Urban heat island” may act on both outdoor and indoor settings, with the exception of those located in extra urban areas, potentially amplifying the effects due to a severe thermal environment and, indirectly, to other risk factors. Urban heat island may also modify, among others, individual lifestyles. In summary, workers’ health is affected by multiple exposures. The final health outcome(s) is (are) qualitatively and quantitatively dependent on activity sector, job performed, time spent indoors/outdoors, agents to which workers are exposed, their relative and changing concentrations, and their interactions. Health effects on the single worker are modulated by individual susceptibility, lifestyles, and socio-economic determinants (reported in the figure). The continuous blue circle represents the generality of the outdoor setting (with the outdoor workers), whereas the dotted blue circle indicates the generality of indoor settings (with the indoor workers). Importantly, exchanges between outdoors and indoors do exist and are represented in the figure by two blue arrows in opposite directions. Exchanges are principally due to air flowing and to people movement inward or outward (not represented in the figure). People act as carriers of, for instance, biological agents and/or allergens like pollen. The figure indicates that the general gradient of exchange is usually inward (thicker arrow), i.e., toward indoor settings, due to the higher outdoor concentration of several chemical and biological pollutants. Continuous red arrows mark what refers to the outdoors (with the exception of the red arrow indicating health effects, which is cumulative), whereas, for the indoors, dotted red arrows were chosen.</p> Full article ">
14 pages, 10287 KiB  
Article
Application of the Self-Organizing Map Method in February Temperature and Precipitation Pattern over China: Comparison between 2021 and 2022
by Zengping Zhang, Yu Gu, Zhikuan Wang, Siyuan Luo, Siyuan Sun, Shuting Wang and Guolin Feng
Atmosphere 2023, 14(7), 1182; https://doi.org/10.3390/atmos14071182 - 21 Jul 2023
Viewed by 1180
Abstract
In this study, we compared two anomalous wet February periods in 2021 and 2022 in China. The same anomalies appeared in the spatial distribution of precipitation, with anomalous precipitation centered over the southeast coast. However, temperature discrepancies appeared in most of China, with [...] Read more.
In this study, we compared two anomalous wet February periods in 2021 and 2022 in China. The same anomalies appeared in the spatial distribution of precipitation, with anomalous precipitation centered over the southeast coast. However, temperature discrepancies appeared in most of China, with anomalously high temperatures in 2021 and lower temperatures in 2022. Both instances of increased precipitation were attributed to warm and moist advection from the south, with transport in 2021 being partly enhanced by the South China Sea cyclone, whereas transport in 2022 was mainly due to the subtropical western North Pacific anticyclone. Therefore, in this study, we aimed to compare and analyze temperature and precipitation anomalies in February 2021 and 2022 using the self-organizing map method. Warm events in East Asia and cold events in Siberia and the Tibetan Plateau types were obtained by mode 1, which contained 2021. Mode 6 exhibited opposite warm types in Siberia and cold types in southern Asia, including February temperature and precipitation anomalies in 2022. Based on the results of this study, we can conclude that precipitation anomalies in February 2021 and 2022 occurred under different temperature and circulation anomalies, and both were influenced by La Niña events. Autumn sea ice loss in the Barents Sea contributed significantly to warm and rainy events in February 2021. However, the cold and rainy events of February 2022 were closely related to the strengthening of the Siberian High. Full article
(This article belongs to the Special Issue Extreme Climate in Arid and Semi-arid Regions)
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<p>Topographic map of China (shading; Units: m) and location map of 2128 selected stations (red dots). The lower-right picture shows the South China Sea.</p> Full article ">Figure 2
<p>Diagram of the SOM training results. The numbers in brackets in the figure represent the number of each model after the classification, and the numbers above represent the sample size, where 2021 and 2022 are located in Model 1 and Model 6, respectively.</p> Full article ">Figure 3
<p>Temperature anomaly (scatter; Units: °C) and precipitation anomaly percentage (scatter; Units: %) over China in February of (<b>a</b>,<b>b</b>) 2021 and (<b>c</b>,<b>d</b>) 2022, respectively. Time series of anomalous temperature (<b>e</b>) and precipitation (<b>f</b>) over China in February from 1961 to 2022.</p> Full article ">Figure 4
<p>Characteristics of the anomalous atmospheric circulation in February: (<b>a</b>,<b>b</b>) 200 hPa zonal wind (shading, Units: m·s−1; the dashed and solid green lines represent the 30 m·s−1 isoline in the climatological mean) and wind field (vectors, Units: m s−1); (<b>c</b>,<b>d</b>) 500 hPa geopotential height (shading, Units: gpm; the solid brown lines are the climatological mean of geopotential height) and wind field (vectors, Units: m s−1); (<b>e</b>,<b>f</b>) WVT (vectors, Units: kg·m−1 s−1). The left column is for 2021, and the right column is for 2022, respectively.</p> Full article ">Figure 5
<p>Temporal evolution of the BMU for the nine (3 × 3) SOM modes.</p> Full article ">Figure 6
<p>Nine (3 × 3) SOM modes of the February T2M anomaly (Units: °C) from 1961 to 2022. The dots denote regions in which the differences exceed the 95% confidence level, which is based on the student’s <span class="html-italic">t</span>-tests.</p> Full article ">Figure 7
<p>Composites of the precipitation anomaly percentage (Units: %) of the SOM patterns. The dots denote regions in which the differences exceed the 95% confidence level.</p> Full article ">Figure 8
<p>Circulation configuration of nine SOM patterns in a 500 hPa geopotential height (shading, Units: gpm), 200 hPa wind field (vectors, Units: m s−1) and 50 m·s−1 zonal wind isoline (purple lines, Units: m s−1). The dots denote regions, and green vectors mean they exceed the 95% confidence level.</p> Full article ">Figure 9
<p>Spring SIC (shading, Units: %) and 500 hPa geopotential height (contours, Units: gpm) anomalies of nine SOM. The dots denote regions in which the differences exceed the 95% confidence level. The solid red line represents the positive value of the 500 hPa geopotential height, the blue dashed line represents the positive value of the 500 hPa geopotential height.</p> Full article ">
16 pages, 4024 KiB  
Article
Development and Application of a Novel Snow Peak Sighting Forecast System over Chengdu
by Chengwei Lu, Ting Chen, Xinyue Yang, Qinwen Tan, Xue Kang, Tianyue Zhang, Zihang Zhou, Fumo Yang, Xi Chen and Yuancheng Wang
Atmosphere 2023, 14(7), 1181; https://doi.org/10.3390/atmos14071181 - 21 Jul 2023
Viewed by 1085
Abstract
As air quality has improved rapidly in recent years, the public has become more interested in whether a famous snow peak, Yaomei Feng on the Tibetan Plateau, can be seen from Chengdu, a megacity located on the western plain of the Sichuan Basin, [...] Read more.
As air quality has improved rapidly in recent years, the public has become more interested in whether a famous snow peak, Yaomei Feng on the Tibetan Plateau, can be seen from Chengdu, a megacity located on the western plain of the Sichuan Basin, east of the plateau. Therefore, a threshold-method-based forecasting system for snow peak sighting was developed in this study. Variables from numerical models, including cloud–water mixing ratio, cloud cover over snow peak, water mixing ratio, PM2.5 concentration, and ground solar radiation, were used in the snow peak sighting forecast system. Terrain occlusion rate of each model grid was calculated. Monte Carlo simulations were applied for threshold determination. A WRF-CMAQ hindcast was conducted for 2020, owing to insufficient observation data, hindcast results on the snow peak sighting were compared with posts collected from social media. Estimations showed that the snow peak sighting forecast system performed well in reflecting the monthly trend of snow peak sightings, and the hindcast results matched the daily observations, especially from May to August. Accuracy of the snow peak sighting forecast model was 78.9%, recall value was 57.1%, and precision was 24.4%. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Model domain setup, with a photograph of Yaomei Feng taken in Wenjiang District, Chengdu shown in the dashed red box.</p> Full article ">Figure 2
<p>Spatial distribution of air quality and meteorological observing stations.</p> Full article ">Figure 3
<p>Example of terrain occlusion judgment. In the right plot, elevation of the red solid line was shown as the black solid line, the green dashed line showed the sight line from observation point to the peak, and the red dashed line showed the first terrain occlusion height.</p> Full article ">Figure 4
<p>Correlation coefficients distribution of modeled and observed 10 m wind speed, 2 m temperature, atmospheric pressure, and relative humidity for 13 national observation stations.</p> Full article ">Figure 5
<p>Correlation coefficient distribution of modeled and observed NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub> for 35 municipal and national environmental monitoring stations.</p> Full article ">Figure 6
<p>Spatial distribution of terrain occlusion rates.</p> Full article ">Figure 7
<p>Annual spatial distribution of snow peak sighting probability.</p> Full article ">Figure 8
<p>Monthly spatial distribution of snow peak sighting probability.</p> Full article ">Figure 9
<p>Comparisons of modeled and posted snow peak sighting days in months.</p> Full article ">Figure 10
<p>Daily comparison of modeled and posted snow peak sighting.</p> Full article ">
20 pages, 3476 KiB  
Review
The Impact of Shipping on Air Quality in the Port Cities of the Mediterranean Area: A Review
by Domenico Toscano
Atmosphere 2023, 14(7), 1180; https://doi.org/10.3390/atmos14071180 - 21 Jul 2023
Cited by 10 | Viewed by 2965
Abstract
Shipping emissions contribute significantly to air pollution at the local and global scales and will do so even more in the future because global maritime transport volumes are projected to increase. The Mediterranean Sea contains the major routes for short sea shipping within [...] Read more.
Shipping emissions contribute significantly to air pollution at the local and global scales and will do so even more in the future because global maritime transport volumes are projected to increase. The Mediterranean Sea contains the major routes for short sea shipping within Europe and between Europe and East Asia. For this reason, concern about maritime emissions from Mediterranean harbours has been increasing on the EU and IMO (International Maritime Organization, London, UK) agenda, also supporting the implementation of a potential Mediterranean Emission Control Area (MedECA). Many studies are concerned with the impact of ship emissions in port cities. Studies of the contributions of ship emissions to air quality at the local scale include several monitoring and modelling techniques. This article presents a detailed review of the contributions of ship emissions of NO2, SO2, PM10, and PM2.5 on air quality in the main ports in the Mediterranean area. The review extracts and summarises information from published research. The results show a certain variability that suggests the necessity of harmonisation among methods and input data in order to compare results. The analysis illustrates the effects of this pollution source on air quality in urban areas, which could be useful for implementing effective mitigation strategies. Full article
(This article belongs to the Special Issue Advances in Urban Air Pollution Observation and Simulation)
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<p>Extent of the countries in Mediterranean area.</p> Full article ">Figure 2
<p>Location of the analysed published studies focusing on air quality over harbours.</p> Full article ">Figure 3
<p>Location of the ports across the Mediterranean area.</p> Full article ">Figure 4
<p>Minimum (<b>left</b>) and maximum (<b>right</b>) PM<sub>10</sub> concentration values measured for each Mediterranean case study during the corresponding sampling period.</p> Full article ">Figure 5
<p>Minimum (<b>left</b>) and maximum (<b>right</b>) PM<sub>2.5</sub> concentration values measured for each Mediterranean case study, during the corresponding sampling period.</p> Full article ">Figure 6
<p>Minimum (<b>left</b>) and maximum (<b>right</b>) NO<sub>2</sub> concentration values measured for each Mediterranean case study during the corresponding sampling period.</p> Full article ">Figure 7
<p>Minimum (<b>left</b>) and maximum (<b>right</b>) SO<sub>2</sub> concentration values measured for each Mediterranean case study during the corresponding sampling period.</p> Full article ">
18 pages, 6585 KiB  
Article
Impact of Temperature Extremes on Carbon Emissions from Crop Production in Hebei Province, China
by Shuai Shao and Hongwu Qiao
Atmosphere 2023, 14(7), 1179; https://doi.org/10.3390/atmos14071179 - 21 Jul 2023
Viewed by 1036
Abstract
The study investigated the impact of temperature extremes on carbon emissions (CE) from crop production. (1) Background: Many scholars have studied climate extremes. However, the research on the relationship between temperature extremes and CE is not extensive, which deserves attention. (2) Methods: The [...] Read more.
The study investigated the impact of temperature extremes on carbon emissions (CE) from crop production. (1) Background: Many scholars have studied climate extremes. However, the research on the relationship between temperature extremes and CE is not extensive, which deserves attention. (2) Methods: The study adopted a fixed-effect model to analyze the impact of temperature extremes on CE from crop production, and the moderating effect was tested using total factor productivity (TFP) in agriculture. (3) Results: Temperature extremes in Hebei Province were mainly reflected in a decline in the cold day index (TX10p) and a rise in the warm spell duration index (WSDI) and the number of summer days (SU25). Additionally, TX10p was positively correlated with CE. For every 1% reduction in TX10p, CE dropped by 0.237%. There was no significant correlation between WSDI and CE. Finally, the agricultural TFP had a significant moderating effect on CE, with each 1% increase resulting in a corresponding 0.081% decrease in CE. (4) Conclusions: The results indicated a warming trend in Hebei Province, which resulted in a decrease in the number of winter days, and reduced CE from crop production. The improvement of input efficiency in agricultural production factors helped moderate the CE. Full article
(This article belongs to the Section Biometeorology)
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<p>Location and temperature conditions of Hebei Province. (<b>a</b>) The location of Hebei Province, China. (<b>b</b>) The temporal trend in mean annual temperature (MAT) from 2001 to 2020.</p> Full article ">Figure 2
<p>The number of cities whose extreme temperature index passed the significance test.</p> Full article ">Figure 3
<p>Change trend in the Cold Days Index (TX10p) in prefecture-level cities. (<b>a</b>) Shijiazhuang, (<b>b</b>) Handan, (<b>c</b>) Baoding, (<b>d</b>) Cangzhou, (<b>e</b>) Langfang, and (<b>f</b>) Hengshui.</p> Full article ">Figure 4
<p>Change trend of Warm Spell Duration Index (WSDI) in prefecture-level cities. (<b>a</b>) Shijiazhuang, (<b>b</b>) Tangshan, (<b>c</b>) Qinhuangdao, (<b>d</b>) Handan, (<b>e</b>) Xingtai, (<b>f</b>) Baoding, (<b>g</b>) Langfang, and (<b>h</b>) Hengshui.</p> Full article ">Figure 5
<p>Carbon emissions (CE) and the growth rate of CE from crop production in Hebei Province from 2001 to 2020.</p> Full article ">Figure 6
<p>Agricultural total factor productivity (TFP) and the growth rate of TFP in Hebei Province from 2001 to 2020.</p> Full article ">
20 pages, 13271 KiB  
Article
Velocity Fluctuations Spectra in Experimental Data on Rayleigh–Taylor Mixing
by Kurt C. Williams and Snezhana I. Abarzhi
Atmosphere 2023, 14(7), 1178; https://doi.org/10.3390/atmos14071178 - 21 Jul 2023
Viewed by 984
Abstract
Rayleigh–Taylor (RT) interfacial mixing plays an important role in nature and technology, including atmospheric flows. In this work, we identify the physics properties of Rayleigh–Taylor mixing through the analysis of unprocessed experimental data. We consider the fluctuations spectra of the specific kinetic energy [...] Read more.
Rayleigh–Taylor (RT) interfacial mixing plays an important role in nature and technology, including atmospheric flows. In this work, we identify the physics properties of Rayleigh–Taylor mixing through the analysis of unprocessed experimental data. We consider the fluctuations spectra of the specific kinetic energy of each of the velocity components, and identify their spectral shapes, by employing the group theory guided foundations and the rigorous statistical method. We find the spectral shape parameters, including their mean values and relative errors, and apply the Anderson–Darling test to inspect the residuals and the goodness-of-fit. We scrupulously study the effect of the fitting window and identify, for each velocity component, the best fit interval, where the relative errors are small and the goodness of fit is excellent. We reveal that the fluctuations spectra in RT mixing experiments can be described by a compound function, being a product of a power-law and an exponential. The data analysis results unambiguously discovered the dynamic anisotropy and the dynamic bias of RT mixing and displayed the necessity to improve the design of experiments on RT mixing. 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>Configuration of the experimental tank. The gas inflow is stream-wise (in the <span class="html-italic">x</span>-direction), with the gravity driving the flow against the cross-stream direction (<span class="html-italic">y</span>-direction) and with both the inflow and gravity being incident to the cross-tank (<span class="html-italic">z</span>) direction.</p> Full article ">Figure 2
<p>(<b>a</b>) The estimated fluctuation spectra <math display="inline"><semantics><mrow><mi>S</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></semantics></math> (black line) found tight agreement with the periodogram of the streamwise (<math display="inline"><semantics><msup><mi>u</mi><mn>2</mn></msup></semantics></math>) velocity fluctuation spectra in the region considered (magenta). (<b>b</b>) The Cumulative Distribution Function of the residuals closely followed that of the <math display="inline"><semantics><msubsup><mi>χ</mi><mrow><mn>2</mn></mrow><mn>2</mn></msubsup></semantics></math> distribution.</p> Full article ">Figure 3
<p>Variations in the estimation of <math display="inline"><semantics><mi>α</mi></semantics></math> (<b>left</b>) and <math display="inline"><semantics><mi>β</mi></semantics></math> (<b>right</b>) as a function of the fit window for the stream-wise velocity <math display="inline"><semantics><msup><mi>u</mi><mn>2</mn></msup></semantics></math> fluctuations.</p> Full article ">Figure 4
<p>Measures of uncertainty in obtaining <math display="inline"><semantics><mi>α</mi></semantics></math> (<b>a</b>), the uncertainty in the estimation of <math display="inline"><semantics><mi>β</mi></semantics></math> (<b>c</b>) and the goodness of fit score <math display="inline"><semantics><msub><mi>p</mi><mrow><mi>A</mi><mi>D</mi></mrow></msub></semantics></math> (<b>b</b>) for stream-wise velocity <math display="inline"><semantics><msup><mi>u</mi><mn>2</mn></msup></semantics></math> fluctuations.</p> Full article ">Figure 5
<p>(<b>a</b>) The estimated fluctuation spectra <math display="inline"><semantics><mrow><mi>S</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></semantics></math> (black line) found tight agreement with the periodogram of the cross-tank velocity (<math display="inline"><semantics><msup><mi>w</mi><mn>2</mn></msup></semantics></math>) fluctuation spectra in the region considered (gray). (<b>b</b>) The Cumulative Distribution Function of the residuals closely followed that of the <math display="inline"><semantics><msubsup><mi>χ</mi><mrow><mn>2</mn></mrow><mn>2</mn></msubsup></semantics></math> distribution.</p> Full article ">Figure 6
<p>Variations in the estimation of <math display="inline"><semantics><mi>α</mi></semantics></math> (<b>left</b>) and <math display="inline"><semantics><mi>β</mi></semantics></math> (<b>right</b>) as a function of the fit window for the cross-tank velocity <math display="inline"><semantics><msup><mi>w</mi><mn>2</mn></msup></semantics></math> fluctuations.</p> Full article ">Figure 6 Cont.
<p>Variations in the estimation of <math display="inline"><semantics><mi>α</mi></semantics></math> (<b>left</b>) and <math display="inline"><semantics><mi>β</mi></semantics></math> (<b>right</b>) as a function of the fit window for the cross-tank velocity <math display="inline"><semantics><msup><mi>w</mi><mn>2</mn></msup></semantics></math> fluctuations.</p> Full article ">Figure 7
<p>Measures of uncertainty in obtaining <math display="inline"><semantics><mi>α</mi></semantics></math> (<b>a</b>), the uncertainty in the estimation of <math display="inline"><semantics><mi>β</mi></semantics></math> (<b>c</b>) and the goodness of fit score <math display="inline"><semantics><msub><mi>p</mi><mrow><mi>A</mi><mi>D</mi></mrow></msub></semantics></math> (<b>b</b>) for the cross-tank velocity <math display="inline"><semantics><msup><mi>w</mi><mn>2</mn></msup></semantics></math> fluctuations.</p> Full article ">
19 pages, 4625 KiB  
Article
Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data
by Shin Akatsuka
Atmosphere 2023, 14(7), 1177; https://doi.org/10.3390/atmos14071177 - 21 Jul 2023
Viewed by 841
Abstract
Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages [...] Read more.
Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages and disadvantages in terms of spatiotemporal resolution. This study uses high temporal resolution numerical prediction data and high spatial resolution elevation to reproduce PW distributions with high spatiotemporal resolution. This study also focuses on the threshold for elevation correction, improving temporal resolution, and reproducing PW distributions in near real time. Results show that using the water vapor content in intervals between the ground surface and 1000-hPa isobaric surface as the threshold value for elevation correction and generating hourly numerical prediction data using the Akima spline interpolation method enabled the reproduction of hourly PW distributions for 75% of the global navigation satellite system observation stations in the target region throughout the year with a root mean square error of 3 mm or less. These results suggest that using the mean value of monthly correction coefficients for the past years enables the reproduction of PW distributions in near real time following the acquisition of numerical prediction data. Full article
(This article belongs to the Section Meteorology)
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<p>Locations of the target region, along with GNSS observation stations. The base map shows the elevation, which was mapped using ASTER GDEM data. The red dots indicate the locations of the GNSS observation stations.</p> Full article ">Figure 2
<p>Relationship between difference in elevation within 5-km grid squares between ASTER GDEM data with a spatial resolution of 90 m and MSM-GPV data and variables used to calculate PW amount.</p> Full article ">Figure 3
<p>Relationship between monthly mean difference (bias) between MSM-PW and GNSS-PW amount at each GNSS observation station in 2014 prior to elevation correction and elevation at each GNSS observation station. The configuration of points differs depending on whether <span class="html-italic">W<sub>s</sub></span><sub>,1000</sub> is equal to or not equal to zero. The straight line indicates the linear regression expression for the monthly mean bias of the GNSS observation stations, where the value of <span class="html-italic">W<sub>s</sub></span><sub>,1000</sub> is equal to 0, and the elevation of each GNSS observation station.</p> Full article ">Figure 4
<p>Relationship between monthly mean RMSE for MSM-PW and GNSS-PW amounts at each GNSS observation station in 2014 prior to elevation correction and elevation at each GNSS observation station.</p> Full article ">Figure 5
<p>Relationship between monthly difference (bias) of MSM-PW and GNSS-PW amounts at each GNSS observation station in 2014 before and after elevation correction (EC), and elevation at each GNSS observation station. The blue symbols indicate the relationship before elevation correction. The red symbols indicate the relationship after elevation correction.</p> Full article ">Figure 6
<p>Comparison of accuracy of MSM high-resolution PW at 3-h intervals (midnight, 3 a.m., 6 a.m., 9 a.m., noon, 3 p.m., 6 p.m., and 9 p.m.) and accuracy of MSM high-resolution PW at 1-h intervals (1 a.m., 2 a.m., 4 a.m., 5 a.m., 7 a.m., 8 a.m., 10 a.m., 11 a.m., 1 p.m., 2 p.m., 4 p.m., 5 p.m., 7 p.m., 8 p.m., 10 p.m., and 11 p.m.) estimated with barometric surface data and ground surface data created using two spline interpolation methods. (The “×” symbol indicates the mean value for all GNSS observation stations.).</p> Full article ">Figure 7
<p>Monthly mean RMSE at each GNSS observation station from 2014 to 2018. Red indicates the RMSE before the elevation correction. Blue indicates the RMSE after elevation correction. The “×” and “+” symbols indicate the mean value for all GNSS observation stations.</p> Full article ">Figure 8
<p>Monthly mean RMSE at each GNSS observation station from 2019 to 2022. The red box plots show the calculation results of elevation correction coefficients for each period and the reproduction of PW distributions from 2019 to 2022. The blue box plots show the calculation results of elevation correction coefficients for each period from 2014 to 2018 and the use of these monthly mean values to reproduce the PW distributions from 2019 to 2022. The “×” and “+” symbols indicate the mean value for all GNSS observation stations.</p> Full article ">
3 pages, 193 KiB  
Editorial
Climate Change and Its Impacts on Terrestrial Ecosystems: Recent Advances and Future Directions
by Cheng Li, Fan Yang, Qitao Xiao and Yao Gao
Atmosphere 2023, 14(7), 1176; https://doi.org/10.3390/atmos14071176 - 21 Jul 2023
Cited by 1 | Viewed by 2472
Abstract
With the increasing concentration of greenhouse gases in the atmosphere, climate change is now an indisputable fact and has strong impacts on various terrestrial ecosystems (e [...] Full article
(This article belongs to the Special Issue Climate Change and Its Impacts on Terrestrial Ecosystems: Recent Advances and Future Directions)
19 pages, 4496 KiB  
Article
Volatility of a Ship’s Emissions in the Baltic Sea Using Modelling and Measurements in Real-World Conditions
by Oskari Kangasniemi, Pauli Simonen, Jana Moldanová, Hilkka Timonen, Luis M. F. Barreira, Heidi Hellén, Jukka-Pekka Jalkanen, Elisa Majamäki, Barbara D’Anna, Grazia Lanzafame, Brice Temime-Roussel, Johan Mellqvist, Jorma Keskinen and Miikka Dal Maso
Atmosphere 2023, 14(7), 1175; https://doi.org/10.3390/atmos14071175 - 20 Jul 2023
Cited by 1 | Viewed by 1410
Abstract
Shipping emissions are a major source of particulate matter in the atmosphere. The volatility of gaseous and particulate phase ship emissions are poorly known despite their potentially significant effect on the evolution of the emissions and their secondary organic aerosol (SOA) formation potential. [...] Read more.
Shipping emissions are a major source of particulate matter in the atmosphere. The volatility of gaseous and particulate phase ship emissions are poorly known despite their potentially significant effect on the evolution of the emissions and their secondary organic aerosol (SOA) formation potential. An approach combining a genetic optimisation algorithm with volatility modelling was used on volatility measurement data to study the volatility distribution of a ship engine’s emissions in real-world conditions. The fuels used were marine gas oil (MGO) and methanol. The engine was operated with 50% and 70% loads with and without active NOx after-treatment with selective catalytic reduction (SCR). The volatility distributions were extended to higher volatilities by combining the speciation information of the gas phase volatile organic compounds with particle phase volatility distributions and organic carbon measurements. These measurements also provided the emission factors of the gas and particle phase emissions. The results for the particle phase volatility matched well with the existing results placing most of the volatile organic mass in the intermediate volatile organic compounds (IVOC). The IVOCs also dominated the speciated gas phase. Partitioning of the emissions in the gas and particle phases was affected significantly by the total organic mass concentration, underlining the importance of the effect of the dilution on the phase of the emissions. Full article
(This article belongs to the Special Issue Atmospheric Shipping Emissions and Their Environmental Impacts)
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<p>Setup used in the volatility measurements. The sample from the stack is diluted before it either enters or bypasses the thermodenuder. From there, the sample is led to an aethalometer, SP-AMS, and two SMPS setups.</p> Full article ">Figure 2
<p>Flowchart describing the basic process of the genetic optimisation algorithm. An initial population of randomly generated individuals, i.e., volatility distributions, produces children taking one half of the volatility properties from each parent. The child can also mutate, which means drawing a completely new volatility distribution. These children are either accepted into the population or discarded based on how well their volatility behaviour matches a measured volatility when used as input in a volatility model. A new generation of individuals is created this way that, in turn, goes through the same process. The population of volatility distributions keeps on improving by discarding the worst individuals and replacing those with ones whose volatility behaviour better matches the measurement data. The aim of the process is to produce a volatility distribution that reproduces measurement data as accurately as possible when used in a volatility model.</p> Full article ">Figure 3
<p>Schematic of the algorithm used to construct a full volatility distribution from the OM and THC measurements. Volatility distribution is created using the thermodenuder measurements with the OM concentration and the identified gas phase species together with the THC concentration. Partitioning to the gas and particle phases is then solved for all the bins and for each of the two distributions. For each bin, the particle and the gas phase concentration is chosen to be the larger contribution. The fraction of mass in the particle phase in this new volatility distribution is then compared to the fraction in the particle phase according to the partitioning equation using the same volatility distribution.</p> Full article ">Figure 4
<p>The evaporation curves obtained from the optimisation algorithm and the thermodenuder data compared to the measured data points from the thermodenuder measurements.</p> Full article ">Figure 5
<p>The volatility distributions of the particle phase samples used in the thermodenuder experiments shown as mass fractions from the total particle phase mass. The genetic optimisation algorithm was run 50 times for each case. The mass fraction from the particle phase organic mass is the average of these runs in each bin, and the error estimation comes from standard deviation of these runs.</p> Full article ">Figure 6
<p>The combined volatility distributions as mass fractions from the total organic mass based on the OM and THC concentration measurements. The OM concentrations were applied to the VBS from the thermodenuder measurements and the THC to the VBS derived from gas phase measurements from the engine.</p> Full article ">Figure 7
<p>An example of the partitioning of a volatility distribution to the particle phase for an MGO with 75% engine load and urea treatment, constructed as described in the Methods section. The black crosses are the measured mass fractions in the particle phase based on the thermodenuder and the total hydrocarbon concentration measurements. These are compared to what the mass in the particle phase should be according to the partitioning equation.</p> Full article ">Figure 8
<p>Partitioning of each of the measurement cases to the particle phase due to dilution. The total organic mass concentration of the emission is reduced with the dilution, which drives more mass from the particle to the gas phase.</p> Full article ">Figure 9
<p>Bin-specific partitioning calculated for the five measurement cases with dilution ratios of 1, 10, 100, and 1000. The low-volatility bins, where all the mass is in the particle phase, have been combined as one bin. The most volatile bins have similarly been combined to one volatile bin.</p> Full article ">Figure 10
<p>The volatility distribution from the ship’s engine measurement compared to the volatility distribution from the STEAM model for the (<b>a</b>) 50% engine load and the (<b>b</b>) 70% engine load.</p> Full article ">
20 pages, 1559 KiB  
Article
Classification of Weather Conditions Based on Supervised Learning for Swedish Cities
by Mohamad Safia, Rodi Abbas and Mohammad Aslani
Atmosphere 2023, 14(7), 1174; https://doi.org/10.3390/atmos14071174 - 20 Jul 2023
Cited by 1 | Viewed by 6572
Abstract
Weather forecasting has always been challenging due to the atmosphere’s complex and dynamic nature. Weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently [...] Read more.
Weather forecasting has always been challenging due to the atmosphere’s complex and dynamic nature. Weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently used to determine weather conditions, but they have their limitations, particularly in terms of computing time. In recent years, supervised machine learning methods have shown great potential in predicting weather events accurately. These methods use historical weather data to train a model, which can then be used to predict future weather conditions. This study enhances weather forecasting by employing four supervised machine learning techniques—artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and k-nearest neighbors (KNN)—on three distinct datasets obtained from the Weatherstack database. These datasets, with varying temporal spans and uncertainty levels in their input features, are used to train and evaluate the methods. The results show that the ANN has superior performance across all datasets. Furthermore, when compared to Weatherstack’s weather prediction model, all methods demonstrate significant improvements. Interestingly, our models show variance in performance across different datasets, particularly those with predicted rather than observed input features, underscoring the complexities of handling data uncertainty. The study provides valuable insights into the use of supervised machine learning techniques for weather forecasting and contributes to the development of more precise prediction models. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Maximum margin hyperplane.</p> Full article ">Figure 2
<p>Example of a decision tree of an RF.</p> Full article ">Figure 3
<p>Example of weather classification using KNN for 3 and 5 nearest neighbors.</p> Full article ">Figure 4
<p>Histogram of the weather conditions in the dataset from 2009 to 2022.</p> Full article ">Figure 5
<p>Workflow for evaluation of the performance of the classifiers.</p> Full article ">Figure 6
<p>Architecture of the ANN.</p> Full article ">Figure 7
<p>Epoch accuracy and loss accuracy on the training set and validation set.</p> Full article ">Figure 8
<p>Class scores for precision, recall, and F1 in the ANN model obtained from dataset 2.</p> Full article ">Figure 9
<p>Class scores for precision, recall, and F1 in the RF model obtained from dataset 2.</p> Full article ">Figure 10
<p>Class scores for precision, recall, and F1 in the KNN model obtained from dataset 2.</p> Full article ">Figure 11
<p>Class scores for precision, recall, and F1 in the SVM model obtained from dataset 2.</p> Full article ">Figure 12
<p>Comparison of models’ accuracy on the outer test set, dataset 2, and dataset 3, and weather condition predictions of Weatherstack.</p> Full article ">
12 pages, 2070 KiB  
Article
Inhibition of Dust Re-Deposition for Filter Cleaning Using a Multi-Pulsing Jet
by Quanquan Wu, Xiaohai Li, Zhenqiang Xing, Qin Kuang, Jianlong Li, Shan Huang, Hong Huang, Zhifei Ma and Daishe Wu
Atmosphere 2023, 14(7), 1173; https://doi.org/10.3390/atmos14071173 - 20 Jul 2023
Cited by 1 | Viewed by 1051
Abstract
The re-deposition of detached dust during online pulse-jet cleaning is an important issue encountered during filter regeneration. To reduce dust re-deposition, multi-pulsing jet cleaning schemes were designed and experimentally tested. A pilot-scale pulse-jet cleaning dust collector was built with one vertically installed pleated [...] Read more.
The re-deposition of detached dust during online pulse-jet cleaning is an important issue encountered during filter regeneration. To reduce dust re-deposition, multi-pulsing jet cleaning schemes were designed and experimentally tested. A pilot-scale pulse-jet cleaning dust collector was built with one vertically installed pleated filter cartridge. The effects of pulse duration and interval on the pulse pressure were tested, and the dust re-deposition rate and mechanism were studied and analyzed. It was found that, for the single-pulsing jet, the pulse duration had a critical value of approximately 0.080 s in this test, above which the pulse pressure remained at approximately 0.75 kPa and did not increase further. For the multi-pulsing jet with a small pulse interval (less than approximately 0.10 s), the pulse flows superimposed and reached a higher pulse pressure with a slight inhibition of dust re-deposition. For the multi-pulsing jet with a long pulse interval (over 0.15 s), dust re-deposition was clearly inhibited. The re-deposition rate decreased from 63.8% in the single-pulsing scheme to 24.4% in the multi (five)-pulsing scheme with the same total pulse duration of 0.400 s. The multi-pulsing scheme lengthens the duration of reverse pulse flow, resulting in more elapsed time for the detached dust to freely fall, and inhibiting the re-deposition of dust. The elapsed time in the five-pulsing jet scheme with the recommended pulse duration of 0.080 s and interval of 0.25 s was 2.8 times higher than that of the single-pulsing jet with the same total pulse duration. Full article
(This article belongs to the Section Air Pollution Control)
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<p>Schematic view of the test rig.</p> Full article ">Figure 2
<p>Diagram of the input signal for the magnetic valve in the multi-pulsing scheme.</p> Full article ">Figure 3
<p>Evolution of the transient pulse pressure on the inner surface of the filter during a single-pulsing jet with pulse duration varying from 0.020 s to 0.500 s in offline cleaning mode.</p> Full article ">Figure 4
<p>Peaks and durations of pulse pressures with varying pulse durations in the offline single-pulsing scheme.</p> Full article ">Figure 5
<p>Pulse pressure on the inner surface of the filter during the (<b>a</b>) seven-pulsing, (<b>b</b>) five-pulsing, and (<b>c</b>) three-pulsing jet schemes in the offline cleaning modes, and (<b>d</b>) seven-pulsing jet scheme in the online cleaning mode with a pulse duration of 0.080 s and interval of 0.05–0.40 s.</p> Full article ">Figure 6
<p>Evolutions of the filtration pressure drop and fallen dust mass under the five-pulsing jet scheme with a pulse duration <span class="html-italic">t</span><sub>d</sub> of 0.080 s and interval Δt of 0.05 to 0.15 s.</p> Full article ">Figure 7
<p>Re-deposition rate for three- and five-pulsing jet cleaning.</p> Full article ">Figure 8
<p>Comparison of the face velocities under the single- and multi-pulsing jet modes.</p> Full article ">
19 pages, 4763 KiB  
Article
Seasonal Distribution and Source Apportionment of Chemical Compositions in PM2.5 in Nanchang, Inland Area of East China
by Hong Huang, Xin Yin, Yuan Tang, Changwei Zou, Jianlong Li, Chenglong Yu and Fangxu Zhu
Atmosphere 2023, 14(7), 1172; https://doi.org/10.3390/atmos14071172 - 20 Jul 2023
Cited by 2 | Viewed by 1480
Abstract
PM2.5 was sampled in four seasons of 2021 in Nanchang. Organic carbon (OC), elemental carbon (EC), and water-soluble ions were the main chemical compositions in PM2.5. The annual average of OC/PM2.5 and EC/PM2.5 was 17.1% and 2.1%, respectively, [...] Read more.
PM2.5 was sampled in four seasons of 2021 in Nanchang. Organic carbon (OC), elemental carbon (EC), and water-soluble ions were the main chemical compositions in PM2.5. The annual average of OC/PM2.5 and EC/PM2.5 was 17.1% and 2.1%, respectively, while nine water-soluble ions were 56.7%. The order of each ion percentage in PM2.5 was NO3− > SO42− > K+ > Na+ > NH4+ > Cl− > NO2− > Ca2+ > Mg2+. The OC/EC (6.54, 13.17, 8.95, 7.99) and Char-EC/Soot-EC (0.88, 0.64, 1.32, 3.74) indicated that the carbon aerosols mainly originated from coal combustion, biomass combustion, and motor-vehicle emissions. High concentrations of Cl− and Ca2+ in spring were associated with dust sources. A good correlation between Na+, SO42−, and NO3− suggests the formation of Na2SO4 and NaNO3. The results of PM2.5 source apportionment by positive matrix factorisation (PMF) showed five main sources: motor-vehicle sources (18–33%), secondary sources (16–36%), coal combustion sources (16–30%), biomass-combustion sources (10–28%), and dust sources (5–7%). Backward trajectory clustering analysis showed PM2.5 in spring and autumn were more influenced by medium distance and local air but mainly influenced by local sources in winter. Full article
(This article belongs to the Special Issue Characteristics and Control of Particulate Matter)
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<p>Distribution of OC, EC, and water-soluble ions in PM<sub>2.5</sub> during the sampling period throughout the year and in spring, summer, autumn, and winter.</p> Full article ">Figure 2
<p>Seasonal distribution of OC and OC<sub>1</sub>, OC<sub>2</sub>, OC<sub>3</sub>, OC<sub>4</sub>, and OPC in PM<sub>2.5</sub> of Nanchang.</p> Full article ">Figure 3
<p>Seasonal distribution of EC, Char-EC, and Soot-EC in PM<sub>2.5</sub> of Nanchang.</p> Full article ">Figure 4
<p>Relationship between the correlation coefficient R<sup>2</sup> of SOC and EC and the assumed r(OC/EC)<sub>pri</sub>.</p> Full article ">Figure 5
<p>SOC, POC, and r(SOC/POC), r(POC/OC), and r(SOC/OC) seasonal averages.</p> Full article ">Figure 6
<p>The proportion of eight carbon fractions in TC throughout the year and in four seasons.</p> Full article ">Figure 7
<p>Correlation between OC and EC in four seasons.</p> Full article ">Figure 8
<p>Relationship of water-soluble ions in PM<sub>2.5</sub> (* <span class="html-italic">p</span> &lt;= 0.05, ** <span class="html-italic">p</span> &lt;= 0.01).</p> Full article ">Figure 9
<p>Seasonal distribution of (<b>a</b>) OC/EC, (<b>b</b>) EC/TC, and (<b>c</b>) Char-EC/Soot-EC in this study.</p> Full article ">Figure 10
<p>The component spectra by PMF model: (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p> Full article ">Figure 11
<p>The source contribution rate by PMF model in four seasons.</p> Full article ">Figure 12
<p>Backward trajectories diagrams for spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p> Full article ">Figure 12 Cont.
<p>Backward trajectories diagrams for spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p> Full article ">
24 pages, 51226 KiB  
Article
Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression
by Marcin Kawka, Joanna Struzewska and Jacek W. Kaminski
Atmosphere 2023, 14(7), 1171; https://doi.org/10.3390/atmos14071171 - 20 Jul 2023
Cited by 1 | Viewed by 1804
Abstract
High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50 μg/m3 is the permissible threshold for a daily average PM10 concentration. The number of people affected by [...] Read more.
High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50 μg/m3 is the permissible threshold for a daily average PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive detailed-resolution PM10 concentration maps, we propose a technique that utilizes the results of a regional CTM (GEM-AQ, with 2.5 km resolution) and a local Gaussian plume model. As a result, we receive a detailed, 250 m resolution PM10 distribution, which represents the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentrations. We also discuss different strategies of training random forest on data using additional features and selecting target variables. Full article
(This article belongs to the Special Issue Machine Learning in Air Pollution)
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<p>Location of the study area with meteorological and air quality stations.</p> Full article ">Figure 2
<p>Landuse classes within the study area.</p> Full article ">Figure 3
<p>Land elevation of the study area.</p> Full article ">Figure 4
<p><math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> annual emissions: (<b>a</b>) traffic, (<b>b</b>) industrial, (<b>c</b>) domestic.</p> Full article ">Figure 4 Cont.
<p><math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> annual emissions: (<b>a</b>) traffic, (<b>b</b>) industrial, (<b>c</b>) domestic.</p> Full article ">Figure 5
<p>Observed meteorological conditions at Bielsko Biała: (<b>a</b>) temperature; (<b>b</b>) wind speed.</p> Full article ">Figure 6
<p>R-squared coefficients between observations and GEM-AQ results: (<b>a</b>) hourly concentrations; (<b>b</b>) daily averaged concentrations.</p> Full article ">Figure 7
<p>R-squared coefficients between observations and Gaussian plume model results: (<b>a</b>) hourly concentrations; (<b>b</b>) daily averaged concentrations.</p> Full article ">Figure 8
<p>Annual average <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> concentration: (<b>a</b>) obtained by GEM-AQ model, (<b>b</b>) obtained by Gaussian plume model, and (<b>c</b>) obtained by random forest.</p> Full article ">Figure 9
<p>The 90.2% percentile of daily averaged <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> concentration timeseries: (<b>a</b>) obtained by GEM-AQ results, (<b>b</b>) obtained by Gaussian plume model, and (<b>c</b>) obtained by random forest model.</p> Full article ">Figure 9 Cont.
<p>The 90.2% percentile of daily averaged <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> concentration timeseries: (<b>a</b>) obtained by GEM-AQ results, (<b>b</b>) obtained by Gaussian plume model, and (<b>c</b>) obtained by random forest model.</p> Full article ">Figure 10
<p>Number of days with average daily concentration exceeding 50 <math display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math>g/m<math display="inline"><semantics><msup><mrow/><mn>3</mn></msup></semantics></math>: (<b>a</b>) obtained from GEM-AQ model; (<b>b</b>) obtained by random forest results.</p> Full article ">Figure A1
<p>Hourly time series of meteorological parameters bserved and modelled by GEM-AQ: (<b>a</b>) temperature; (<b>b</b>) wind speed.</p> Full article ">Figure A2
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> hourly concentration time series at (<b>a</b>) SlBielKossak, (<b>b</b>) SlCiesChopin, and (<b>c</b>) SlWodzGalczy.</p> Full article ">Figure A3
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> hourly concentration time series at (<b>a</b>) SlRybniBorki, (<b>b</b>) SlUstronSana, and (<b>c</b>) SlGoczaUzdroMOB.</p> Full article ">Figure A4
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> hourly concentration time series at (<b>a</b>) SlZywieKoper, (<b>b</b>) MpOswiecBema, and (<b>c</b>) MpSuchaNiesz.</p> Full article ">Figure A5
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> daily concentration time series at (<b>a</b>) SlBielKossak, (<b>b</b>) SlCiesChopin, and (<b>c</b>) SlWodzGalczy.</p> Full article ">Figure A6
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> daily concentration time series at (<b>a</b>) SlRybniBorki, (<b>b</b>) SlUstronSana, and (<b>c</b>) SlGoczaUzdroMOB.</p> Full article ">Figure A7
<p>Observed and modelled <math display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mn>10</mn></msub></mrow></semantics></math> daily concentration time series at (<b>a</b>) SlZywieKoper, (<b>b</b>) MpOswiecBema, and (<b>c</b>) MpSuchaNiesz.</p> Full article ">
14 pages, 7481 KiB  
Article
Vulnerability Identification and Analysis of Contributors to Desertification in Inner Mongolia
by Yang Chen, Long Ma, Tingxi Liu, Xing Huang and Guohua Sun
Atmosphere 2023, 14(7), 1170; https://doi.org/10.3390/atmos14071170 - 19 Jul 2023
Cited by 1 | Viewed by 1175
Abstract
Desertification vulnerability and contributing factors are of global concern. This study analyzed the spatial and temporal distribution of net primary productivity (NPP), precipitation, and temperature from 1985 to 2015. The rain use efficiency (RUE) of vegetation was selected as an indicator; and desertification [...] Read more.
Desertification vulnerability and contributing factors are of global concern. This study analyzed the spatial and temporal distribution of net primary productivity (NPP), precipitation, and temperature from 1985 to 2015. The rain use efficiency (RUE) of vegetation was selected as an indicator; and desertification vulnerability and contributors were evaluated with the Mann–Kendall test (M–K test) and the Thornthwaite–Memorial model. The results showed that NPP was lower in that years that had lower precipitation and higher temperatures, and vice versa. NPP was spatially consistent with precipitation distribution and roughly opposite to the spatial distribution of the annual change rate of temperature. The desertification vulnerability decreased from west to east, among which both the western sub–region (WSR) and the central sub–region (CSR) had the largest proportion of regions with high desertification vulnerability. On the other hand, the eastern sub–region (ESR) mostly comprises areas with extremely low or low desertification vulnerability. The vulnerability contributors for desertification differed among each sub–region. The desertified regions in WSR and ESR were mainly influenced by human activity (HA), but primarily driven by the combined impact of Precipitation–Temperature (PT) and HA in CSR. The south–east part of the CSR was only affected by HA, whereas the lesser affected regions in the study area were affected by PT and HA simultaneously. The study provides recommendations for the improvement of regional ecological environments to prevent future disasters. Full article
(This article belongs to the Special Issue Response of Vegetation to Climatic and Anthropogenic Drivers in the Plateau)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>The process of identification and attribution of desertification vulnerability.</p> Full article ">Figure 3
<p>(<b>a</b>) The regional mean value of NPP for WSR, CSR, and ESR. (<b>b</b>) The regional mean value of precipitation and temperature for WSR, CSR, and ESR. (<b>c</b>) Regionalization of WSR, CSR, and ESR.</p> Full article ">Figure 4
<p>(<b>a</b>) The regional mean value of NPP for WSR. (<b>b</b>) The regional mean value of NPP for CSR. (<b>c</b>) The regional mean value of NPP for ESR. (<b>d</b>) Spatial distribution of NPP’s muti-year mean value in different sub-regions. (<b>e</b>) Spatial distribution of NPP’s annual change rate in different sub-regions.</p> Full article ">Figure 5
<p>(<b>a</b>) The regional mean value of precipitation for WSR. (<b>b</b>) The regional mean value of precipitation for CSR. (<b>c</b>) The regional mean value of precipitation for ESR. (<b>d</b>) Spatial distribution of precipitation’s muti-year mean value in different sub-regions. (<b>e</b>) Spatial distribution of precipitation’s annual change rate in different sub-regions.</p> Full article ">Figure 6
<p>(<b>a</b>) The regional mean value of temperature for WSR. (<b>b</b>) The regional mean value of temperature for CSR. (<b>c</b>) The regional mean value of temperature for ESR. (<b>d</b>) Spatial distribution of temperature’s muti-year mean value in different sub-regions. (<b>e</b>) Spatial distribution of temperature’s annual change rate in different sub-regions.</p> Full article ">Figure 7
<p>(<b>a</b>) Spatial distribution of desertification vulnerability in each sub-region. (<b>b</b>) Spatial distribution of RUE’s annual change rate in different sub-regions.</p> Full article ">Figure 8
<p>(<b>a</b>) Spatial distribution of contributors to desertification vulnerability in different sub-regions. (<b>b</b>) Spatial distribution of HA<sub>RUE</sub>’s annual change rate in different sub-regions. (<b>c</b>) Spatial distribution of PT<sub>RUE</sub>’s annual change rate in different sub-regions.</p> Full article ">Figure 9
<p>The proportion of areas with different desertification vulnerability in each sub-region.</p> Full article ">
17 pages, 4101 KiB  
Article
Assessing the Impact of Climate Change on the Biodeterioration Risk in Historical Buildings of the Mediterranean Area: The State Archives of Palermo
by Elena Verticchio, Francesca Frasca, Donatella Matè, Federico Maria Giammusso, Matilde Sani, Maria Letizia Sebastiani, Maria Carla Sclocchi and Anna Maria Siani
Atmosphere 2023, 14(7), 1169; https://doi.org/10.3390/atmos14071169 - 19 Jul 2023
Cited by 5 | Viewed by 1134
Abstract
The growing sensitivity towards environmental sustainability, particularly in the light of climate change, requires a reflection on the role that historical buildings can play in heritage conservation. This research proposed an interdisciplinary approach combining climate and biological expertise to evaluate the biodeterioration risk [...] Read more.
The growing sensitivity towards environmental sustainability, particularly in the light of climate change, requires a reflection on the role that historical buildings can play in heritage conservation. This research proposed an interdisciplinary approach combining climate and biological expertise to evaluate the biodeterioration risk associated with different IPCC outdoor climate scenarios. Conduction heat transfer functions and dose–response functions were used to model the indoor climate of a historical building and the related climate-induced risk of mould and pest proliferation. The approach was applied to a case study in the Mediterranean area, i.e., the State Archives of Palermo (Italy) housed in a 15th-century convent. In 2018, a survey conducted by ICPAL-MiC experts warned about past infestations and risks deriving from climate conditions. An environmental monitoring campaign conducted in 2021 allowed for the characterisation of the buffering effect in a historical building in response to the outdoor climate and the simulation of future indoor climate. Since indoor temperature and mixing ratio are expected to raise in future scenarios, it was found that there is an increased risk of insects’ proliferation, combined with a decreased risk of spore germination and mould growth. Such evidence-based evaluation allows for the design of tailored preventive conservation measures to enhance the durability of both the archival collections and the building. Full article
(This article belongs to the Special Issue Microclimate of the Heritage Buildings)
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Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>,<b>b</b>) External view (Google Earth source) and plan of the first floor of La Gangia housing the State Archives of Palermo, Room 12 is located in the red area; (<b>c</b>,<b>d</b>) pictures taken during the technical survey conducted by the experts from <span class="html-italic">Istituto Centrale per la Patologia degli Archivi e del Libro</span> (namely ICPAL-MiC).</p> Full article ">Figure 2
<p>Daily outdoor temperatures in 2021 (light grey) and 2022 (dark grey) and the climatological band (coloured area) delimited by the 5th and 95th percentiles together with the climatological median values of the 30-year temperature data in RP (1981–2010).</p> Full article ">Figure 3
<p>(<b>a</b>,<b>d</b>) Box-and-whisker plots of outdoor (out) and indoor (in) temperature (T) and mixing ratio (MR); (<b>b</b>,<b>e</b>) time plots of indoor (black) and outdoor (grey) T and MR; (<b>c</b>,<b>f</b>) indoor versus outdoor T and MR (grey dots) and the ellipse (red solid line) describing the annual hysteresis cycle in the Archives. In the box-and-whisker plots, the horizontal lines dividing each box indicate the medians.</p> Full article ">Figure 4
<p>(<b>a</b>) Box-and-whisker plots of outdoor (out) and indoor (in) relative humidity; (<b>b</b>) time plots of indoor (black) and outdoor (grey) RH. In the box-and-whisker plots, the horizontal lines dividing each box indicate the medians, the black dots represent the outliers, s, i.e., the values above or below 1.5 × IQR (IQR = interquartile range).</p> Full article ">Figure 5
<p>Box-and-whisker plots of indoor temperature (T), mixing ratio (MR), and relative humidity (RH) in scenarios SSP1-2.6 (orange), SSP2-4.5 (blue), and SSP5-8.5 (green) from Recent Past (RP, 1981–2010) to Near Future (NF, 2021–2050) and Far Future (FF, 2071–2100). In the box-and-whisker plots, the horizontal lines dividing each box indicate the medians.</p> Full article ">Figure 6
<p>Monthly temperature (T) and relative humidity (RH) plotted together with the lowest isopleths for spore germination (solid lines, Equation (3)), mycelial growth (dashed lines, Equation (4)) according to Sedlbauer isopleths and the critical RH* (dotted lines, Equation (5)) according to the VTT model in measured conditions and scenarios SSP1-2.6 (<b>a</b>), SSP2-4.5 (<b>b</b>), and SSP5-8.5 (<b>c</b>) from Recent Past (RP, 1981–2010) to Near Future (NF, 2021–2050) and Far Future (FF, 2071–2100). Letter “J” in scatter plots indicates January with a clockwise direction of the annual cycle.</p> Full article ">Figure 7
<p>(<b>a</b>) Number of eggs laid (Equation (6)) over a representative year by webbing cloth moths (<span class="html-italic">T. biselliella</span>) and (<b>b</b>) annual growing degree days (GDDs) for temperature-dependent (<span class="html-italic">S. paniceum</span>) and humidity-dependent (<span class="html-italic">A. punctatum</span>) adult male individuals (Equation (7)) from measured conditions, Recent-Past (RP, 1981–2010) to Near-Future (NF, 2021-2050) and Far-Future (FF, 2071–2100) climates under scenarios SSP1-2.6 (orange), SSP2-4.5 (blue), and SSP5-8.5 (green). (<b>c</b>–<b>e</b>) Monthly temperature and relative humidity conditions (coloured dots) plotted on development time curves (t<sub>D</sub>, Equation (9)) indicating the time in days it would take for drugstore beetle (<span class="html-italic">S. paniceum</span>) to develop in a given year representative of measured conditions and scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Letter “J” in scatter plots indicates January with a clockwise direction of the annual cycle.</p> Full article ">
18 pages, 8332 KiB  
Article
Application and Improvement of the Particle Swarm Optimization Algorithm in Source-Term Estimations for Hazardous Release
by Jinshu Lu, Mengqing Huang, Wenfeng Wu, Yonghui Wei and Chong Liu
Atmosphere 2023, 14(7), 1168; https://doi.org/10.3390/atmos14071168 - 19 Jul 2023
Viewed by 1059
Abstract
Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion [...] Read more.
Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model; however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters. Full article
(This article belongs to the Section Air Quality and Human Health)
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<p>Framework of the PSO algorithm.</p> Full article ">Figure 2
<p>Arrangement of the sensors.</p> Full article ">Figure 3
<p>Comparison of observed and simulated concentrations by the Gaussian model utilizing experimental parameters: (<b>a</b>) R16 with stability class A; (<b>b</b>) R10 with stability class B; (<b>c</b>) R43 with stability class C; (<b>d</b>) R30 with stability class D; (<b>e</b>) R66 with stability class E; (<b>f</b>) R39 with stability class F. Sensors are disposed of in five semicircular concentric arcs, which are represented with alternating gray and white backgrounds, and the sensors are disposed counterclockwise on each semicircular arc.</p> Full article ">Figure 3 Cont.
<p>Comparison of observed and simulated concentrations by the Gaussian model utilizing experimental parameters: (<b>a</b>) R16 with stability class A; (<b>b</b>) R10 with stability class B; (<b>c</b>) R43 with stability class C; (<b>d</b>) R30 with stability class D; (<b>e</b>) R66 with stability class E; (<b>f</b>) R39 with stability class F. Sensors are disposed of in five semicircular concentric arcs, which are represented with alternating gray and white backgrounds, and the sensors are disposed counterclockwise on each semicircular arc.</p> Full article ">Figure 4
<p>Estimated results of the source parameters of trials under various stability classes based on 10 independent runs of the PSO, denoted by mean values (gray bars) and 95% confidence intervals (red error bars): (<b>a</b>) estimation results of source strength (Q<sub>0</sub>); (<b>b</b>) estimation results of location (x<sub>0</sub>); (<b>c</b>) estimation results of location (y<sub>0</sub>); (<b>d</b>) estimation results of location (z<sub>0</sub>); (<b>e</b>) comprehensive scoring index (S).</p> Full article ">Figure 4 Cont.
<p>Estimated results of the source parameters of trials under various stability classes based on 10 independent runs of the PSO, denoted by mean values (gray bars) and 95% confidence intervals (red error bars): (<b>a</b>) estimation results of source strength (Q<sub>0</sub>); (<b>b</b>) estimation results of location (x<sub>0</sub>); (<b>c</b>) estimation results of location (y<sub>0</sub>); (<b>d</b>) estimation results of location (z<sub>0</sub>); (<b>e</b>) comprehensive scoring index (S).</p> Full article ">Figure 5
<p>Concentration distribution of the prairie grass experiment under stability classes A to F. (<b>Left</b>) concentration distribution simulated by the Gaussian model utilizing experimental parameters; (<b>Right</b>) concentration distribution of actual observations of the prairie grass experiment.</p> Full article ">Figure 5 Cont.
<p>Concentration distribution of the prairie grass experiment under stability classes A to F. (<b>Left</b>) concentration distribution simulated by the Gaussian model utilizing experimental parameters; (<b>Right</b>) concentration distribution of actual observations of the prairie grass experiment.</p> Full article ">Figure 5 Cont.
<p>Concentration distribution of the prairie grass experiment under stability classes A to F. (<b>Left</b>) concentration distribution simulated by the Gaussian model utilizing experimental parameters; (<b>Right</b>) concentration distribution of actual observations of the prairie grass experiment.</p> Full article ">Figure 6
<p>Comparison of experimental data and simulated results of Gaussian models under stability classes A to F: (<b>a</b>) fitting for data under stability class A; (<b>b</b>) fitting for data under stability class B; (<b>c</b>) fitting for data under stability class C; (<b>d</b>) fitting for data under stability class D; (<b>e</b>) fitting for data under stability class E; (<b>f</b>) fitting for data under stability class F.</p> Full article ">Figure 6 Cont.
<p>Comparison of experimental data and simulated results of Gaussian models under stability classes A to F: (<b>a</b>) fitting for data under stability class A; (<b>b</b>) fitting for data under stability class B; (<b>c</b>) fitting for data under stability class C; (<b>d</b>) fitting for data under stability class D; (<b>e</b>) fitting for data under stability class E; (<b>f</b>) fitting for data under stability class F.</p> Full article ">Figure 7
<p>Comparison of observation and simulation concentrations of models (Gaussian, linear modified Gaussian, and polynomial modified Gaussian models) for R16 and R66: (<b>a</b>) R16; (<b>b</b>) R66.</p> Full article ">Figure 8
<p>Different score indexes of R16 and R66 with Equations (2), (10) and (11).</p> Full article ">Figure 9
<p>Score indexes of different objective functions for source-term parameters under stability classes A to F: (<b>a</b>) score index S<sub>Q</sub> of source strength Q<sub>0</sub> of different objective functions under stability classes A to F; (<b>b</b>) score index S<sub>x</sub> of horizontal location x<sub>0</sub> of different objective functions under stability classes A to F; (<b>c</b>) score index S<sub>y</sub> of horizontal location y<sub>0</sub> of different objective functions under stability classes A to F; (<b>d</b>) score index S<sub>z</sub> of location parameter z<sub>0</sub> of different objective functions under stability classes A to F; (<b>e</b>) comprehensive score index S of all source parameters [Q<sub>0</sub>, x<sub>0</sub>, y<sub>0</sub>, z<sub>0</sub>] of different objective functions under stability classes A to F.</p> Full article ">
15 pages, 1595 KiB  
Article
Estimation of Road Transportation Emissions in Colombia from 2010 to 2021
by Laura Mantilla-Romo, Yiniva Camargo-Caicedo, Sindy Bolaño-Diaz, Fredy Tovar-Bernal and Angélica Garrido-Galindo
Atmosphere 2023, 14(7), 1167; https://doi.org/10.3390/atmos14071167 - 19 Jul 2023
Cited by 1 | Viewed by 1482
Abstract
This work aimed to estimate the emissions associated with the transport sector in Colombia during the 2010–2021 period for the following four groups of pollutants: greenhouse gases or GHG (CO2, CH4, N2O), ozone precursors (CO, NMVOC, NO [...] Read more.
This work aimed to estimate the emissions associated with the transport sector in Colombia during the 2010–2021 period for the following four groups of pollutants: greenhouse gases or GHG (CO2, CH4, N2O), ozone precursors (CO, NMVOC, NOx), acidifying gases (NH3, SO2), and aerosols (PM, BC), based on the data provided by the Ministry of Mines and Energy. The estimate of emissions from road transportation was calculated using a standardized method with a top-down approach consistent with the 2006 IPCC Guidelines for National GHG Inventories and the EEA/EMEP Emission Inventory Guidebook 2019. Total annual emissions and the emissions for regions were estimated, and a comparison was made between estimated emissions and the emissions calculated by the Emissions Database for Global Atmospheric Research (EDGAR). Total annual emissions by road transport showed a progressive increase except for the annual emissions in 2020, which registered a reduction due to the COVID-19 lockdown. The highest yearly emissions were reported in 2021, with the most significant contributions by GHG (33,109.29 Gg CO2, 201.55 Gg CO2 Eq. CH4, and 512.43 Gg CO2 Eq. N2O). The Andean region was the one with the highest contributions of total emissions within the four groups of pollutants (57–66%), followed by the Caribbean (12–20%) and the Pacific region (14–18%). The most-used fuel was gasoline, with an increase of 103% for personal cars and motorcycles throughout the study period. These results contribute to decision-making at local, regional, and national levels regarding energy transition opportunities and strategies to adopt in the transport sector. Full article
(This article belongs to the Special Issue Traffic Related Emission)
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<p>Summary methodology for the estimation of emissions.</p> Full article ">Figure 2
<p>Total emissions estimated (Gg) of the four groups of pollutants: (<b>a</b>) GHG, (<b>b</b>) ozone precursors, (<b>c</b>) acidifying gases, and (<b>d</b>) aerosols during the period 2010–2021.</p> Full article ">Figure 2 Cont.
<p>Total emissions estimated (Gg) of the four groups of pollutants: (<b>a</b>) GHG, (<b>b</b>) ozone precursors, (<b>c</b>) acidifying gases, and (<b>d</b>) aerosols during the period 2010–2021.</p> Full article ">Figure 3
<p>Estimated total GHG emissions of Colombian regions (Andean, Amazon, Pacific, Caribbean, and Orinoquia) during the period 2010–2021: (<b>a</b>) CO<sub>2</sub>, (<b>b</b>) CH<sub>4</sub>, (<b>c</b>) N<sub>2</sub>O.</p> Full article ">Figure 4
<p>Fuel consumption by vehicle type: (<b>a</b>) gasoline, (<b>b</b>) diesel.</p> Full article ">
3 pages, 167 KiB  
Editorial
CO2 Geological Storage and Utilization
by Liang Huang
Atmosphere 2023, 14(7), 1166; https://doi.org/10.3390/atmos14071166 - 19 Jul 2023
Cited by 3 | Viewed by 1066
Abstract
With increasing greenhouse gas emissions caused by human activities, climate change is affecting the survival and development of human society [...] Full article
(This article belongs to the Special Issue CO2 Geological Storage and Utilization)
27 pages, 7064 KiB  
Article
Intelligent Identification and Verification of Flutter Derivatives and Critical Velocity of Closed-Box Girders Using Gradient Boosting Decision Tree
by Neyu Chen, Yaojun Ge and Claudio Borri
Atmosphere 2023, 14(7), 1165; https://doi.org/10.3390/atmos14071165 - 18 Jul 2023
Cited by 2 | Viewed by 1301
Abstract
Flutter derivatives (FDs) of the bridge deck are basic aerodynamic parameters by which flutter analysis determines critical flutter velocity (CFV), and they are traditionally identified by sectional model wind tunnel tests or computational fluid dynamics (CFD) numerical simulation. Based on some wind tunnel [...] Read more.
Flutter derivatives (FDs) of the bridge deck are basic aerodynamic parameters by which flutter analysis determines critical flutter velocity (CFV), and they are traditionally identified by sectional model wind tunnel tests or computational fluid dynamics (CFD) numerical simulation. Based on some wind tunnel testing results and numerical simulation data, the machine learning models for identifying FDs of closed-box girders are trained and developed via a gradient boosting decision tree in this study. The models can explore the underlying input–output transfer relationship of datasets and realize rapid intelligent identification of FDs without wind tunnel tests or numerical simulation. This method also provides a convenient and feasible option for expanding datasets of FDs, and the distribution of FDs can be analyzed through the post-interpretation of trained models. Combined with FD sensitivity analysis, the models can be verified by the calculation error of CFV. In addition, the proposed method can help determine the appropriate shape of the box girder cross-section in the preliminary design stage of long-span bridges and provide the necessary reference for aerodynamic shape optimization by modifying the local geometric features of the cross-section. Full article
(This article belongs to the Special Issue Advances in Computational Wind Engineering and Wind Energy)
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<p>Technology roadmap.</p> Full article ">Figure 2
<p>Reference system for displacements and self-excited forces.</p> Full article ">Figure 3
<p>Architecture of GBDT.</p> Full article ">Figure 4
<p>Architecture of a closed-box girder.</p> Full article ">Figure 5
<p>8 FDs under different reduced wind speeds of 20 sets of cross-sections from database.</p> Full article ">Figure 5 Cont.
<p>8 FDs under different reduced wind speeds of 20 sets of cross-sections from database.</p> Full article ">Figure 6
<p>8 FDs under different reduced wind speeds of 20 sets of cross-sections from literature.</p> Full article ">Figure 7
<p>8 FDs under different reduced wind speeds of 14 sets of supplementary cross-sections.</p> Full article ">Figure 7 Cont.
<p>8 FDs under different reduced wind speeds of 14 sets of supplementary cross-sections.</p> Full article ">Figure 8
<p>Cross-section distribution histogram of sample set.</p> Full article ">Figure 8 Cont.
<p>Cross-section distribution histogram of sample set.</p> Full article ">Figure 9
<p>Trend of FDs changing with reduced wind speed: (<b>a</b>) the comparison between the numerical simulation results and the wind tunnel test results of cross-section 1; (<b>b</b>) the comparison between the numerical simulation results in this study (CFD1) and other researcher’s calculation results (CFD2) of cross-section 21.</p> Full article ">Figure 10
<p>Fitting degree of training set: (<b>a</b>) based on 20 sets of wind tunnel test data; (<b>b</b>) based on 54 sets of hybrid data.</p> Full article ">Figure 11
<p>Prediction results of cross-section 1: (<b>a</b>) based on 20 sets of wind tunnel test data (model 1); (<b>b</b>) based on 54 sets of hybrid data (model 2).</p> Full article ">Figure 12
<p>Average fitting degree.</p> Full article ">Figure 13
<p>Generalization ability of test set: (<b>a</b>) the best prediction results (cross-section 29); (<b>b</b>) the worst prediction results (cross-section 26).</p> Full article ">Figure 14
<p>SHAP model explanation.</p> Full article ">Figure 14 Cont.
<p>SHAP model explanation.</p> Full article ">Figure 15
<p>Cross-section diagram of Runyang Bridge.</p> Full article ">Figure 16
<p>Prediction results of FDs for Runyang Bridge.</p> Full article ">Figure 17
<p>Error analysis. The <span class="html-italic">x</span> value of the blue dot represents the average error of the predicted FDs of each model, while the <span class="html-italic">y</span> value represents the calculation error of the corresponding CFV. The red dashed line indicates the overall trend of the CFV calculation error change with the FDs prediction error.</p> Full article ">Figure 18
<p>Influence of FDs on CFV.</p> Full article ">Figure 19
<p>Influence of shape parameters on CFV: (<b>a</b>) width-to-height ratio [<a href="#B61-atmosphere-14-01165" class="html-bibr">61</a>]; (<b>b</b>) wind fairing angle [<a href="#B62-atmosphere-14-01165" class="html-bibr">62</a>]; (<b>c</b>) inclined web slope [<a href="#B63-atmosphere-14-01165" class="html-bibr">63</a>].</p> Full article ">
20 pages, 2328 KiB  
Review
Impact of Modern Vehicular Technologies and Emission Regulations on Improving Global Air Quality
by Sai Sudharshan Ravi, Sergey Osipov and James W. G. Turner
Atmosphere 2023, 14(7), 1164; https://doi.org/10.3390/atmos14071164 - 18 Jul 2023
Cited by 9 | Viewed by 5759
Abstract
Over the past few decades, criteria emissions such as carbon monoxide (CO), hydrocarbons (HCs), nitrogen oxides (NOx) and particulate matter (PM) from transportation have decreased significantly, thanks to stricter emission standards and the widespread adoption of cleaner technologies. While air quality is a [...] Read more.
Over the past few decades, criteria emissions such as carbon monoxide (CO), hydrocarbons (HCs), nitrogen oxides (NOx) and particulate matter (PM) from transportation have decreased significantly, thanks to stricter emission standards and the widespread adoption of cleaner technologies. While air quality is a complex problem that is not solely dependent on transportation emissions, it does play a significant role in both regional and global air quality levels. Emission standards such as Euro 1–6 in Europe, Corporate Average Fuel Economy (CAFE) regulations, Tier I—III standards in the US and the low emission vehicle (LEV) program in California have all played a huge role in bringing down transportation emissions and hence improving air quality overall. This article reviews the effect of emissions from transportation, primarily focusing on criteria emissions from road transport emissions and highlights the impact of some of the novel technological advances that have historically helped meet these strict emission norms. The review also notes how modern road engine vehicles emissions compare with national and international aviation and shipping and discusses some of the suggested Euro 7 emissions standards and their potential to improve air quality. Full article
(This article belongs to the Special Issue Internal Combustion Engine Emissions: Formation, Reduction and Measurement)
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<p>(<b>a</b>) Historical fleet CO<sub>2</sub> emissions performance and current standards (g CO<sub>2</sub>/km normalized to NEDC) for passenger cars. Reproduced from [<a href="#B16-atmosphere-14-01164" class="html-bibr">16</a>]. (<b>b</b>) A case comparison of air quality in terms of PM10 in Santa Maria-Santa Barbara, California and Delhi [<a href="#B17-atmosphere-14-01164" class="html-bibr">17</a>,<a href="#B18-atmosphere-14-01164" class="html-bibr">18</a>,<a href="#B19-atmosphere-14-01164" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Euro 1–6 emission standards for PM and NOx from heavy-duty vehicles.</p> Full article ">Figure 3
<p>Criteria emissions and petroleum consumption in the US between 1970 and 2021 showing some of the key policy changes and technology adoptions that affected the emission trends. Data taken from [<a href="#B72-atmosphere-14-01164" class="html-bibr">72</a>,<a href="#B73-atmosphere-14-01164" class="html-bibr">73</a>].</p> Full article ">Figure 4
<p>Reduction in HC and CO<sub>2</sub> emissions with engine coolant preheating and its effect on the catalyst temperature. Reproduced from [<a href="#B76-atmosphere-14-01164" class="html-bibr">76</a>].</p> Full article ">Figure 5
<p>Comparison of FC and pollutant emissions with S&amp;S-on and S&amp;S-off for cold starts and hot starts at 28 °C and 5 °C ambient temperature. Reproduced from [<a href="#B91-atmosphere-14-01164" class="html-bibr">91</a>].</p> Full article ">Figure 6
<p>Filtration efficiency measured over the FTP-75 cycle (<b>left</b>) and US06 cycle (<b>right</b>) (reproduced from [<a href="#B104-atmosphere-14-01164" class="html-bibr">104</a>]).</p> Full article ">
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