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Volume 12
Issue 4
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Atmosphere, Volume 12, Issue 4 (April 2021) – 111 articles

Cover Story (view full-size image): Under the framework of Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project that was launched by governments of China, Japan, and Korea, three modeling groups simulated PM2.5 concentrations targeting the remote exit-and-entrance areas of transboundary transport over three countries. The employed models are WRF-CAMx (by China), NHM-RAQM2 (by Japan), and WRF-CMAQ (by Korea), and the results showed apparent bias that remains unexplored in both exit and entrance areas. Nevertheless, three models indicated higher NO3–/SO42– ratios in exit-and-entrance areas especially in winter under the favorable environments for ammonium nitrate formation, and also suggested that gas-aerosol partitioning for semi-volatile species of ammonium nitrate could be achieved at the entrance areas of transboundary transport over China, Japan, and Korea. View this paper.
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17 pages, 7391 KiB  
Article
Proposal to Refine Solar Radiation of Typical Meteorological Year Database and Evaluation on the Influence of Air-Conditioning Load
by Jihui Yuan, Kazuo Emura and Craig Farnham
Atmosphere 2021, 12(4), 524; https://doi.org/10.3390/atmos12040524 - 20 Apr 2021
Cited by 4 | Viewed by 2447
Abstract
The Typical meteorological year (TMY) database is often used to calculate air-conditioning loads, and it directly affects the building energy savings design. Among four kinds of TMY databases in China—including Chinese Typical Year Weather (CTYW), International Weather for Energy Calculations (IWEC), Solar Wind [...] Read more.
The Typical meteorological year (TMY) database is often used to calculate air-conditioning loads, and it directly affects the building energy savings design. Among four kinds of TMY databases in China—including Chinese Typical Year Weather (CTYW), International Weather for Energy Calculations (IWEC), Solar Wind Energy Resource Assessment (SWERA) and Chinese Standard Weather Data (CSWD)—only CSWD is measures solar radiation, and it is most used in China. However, the solar radiation of CSWD is a measured daily value, and its hourly value is separated by models. It is found that the cloud ratio (diffuse solar radiation divided by global solar radiation) of CSWD is not realistic in months of May, June and July while compared to the other sets of TMY databases. In order to obtain a more accurate cloud ratio of CSWD for air-conditioning load calculation, this study aims to propose a method of refining the cloud ratio of CSWD in Shanghai, China, using observed solar radiation and the Perez model which is a separation model of high accuracy. In addition, the impact of cloud ratio on air-conditioning load has also been discussed in this paper. It is shown that the cloud ratio can yield a significant impact on the air conditioning load. Full article
(This article belongs to the Special Issue Zero Energy Building and Indoor Thermal)
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Figure 1

Figure 1
<p>Calculated monthly cloud ratios among four kinds of TMY database in Shanghai, China.</p> Full article ">Figure 2
<p>Comparison of monthly cloud ratios among eight main sites of Japan.</p> Full article ">Figure 3
<p>Matrix of sky condition classification for sky index (<span class="html-italic">Si</span>) by Igawa et al [<a href="#B23-atmosphere-12-00524" class="html-bibr">23</a>].</p> Full article ">Figure 4
<p>Installation situation of on-site pyranometer with totary shielding band.</p> Full article ">Figure 5
<p>Actual observed solar radiation (diffuse irradiance and global irradiance) from January to September, 2011 in Shanghai, China; please note that the observation that zero values of irradiance in the figures correspond to night hours.</p> Full article ">Figure 5 Cont.
<p>Actual observed solar radiation (diffuse irradiance and global irradiance) from January to September, 2011 in Shanghai, China; please note that the observation that zero values of irradiance in the figures correspond to night hours.</p> Full article ">Figure 6
<p>Daily cloud ratios, <span class="html-italic">Kc</span> and <span class="html-italic">Cle</span> of a relatively clear day selected from each month (from January to September) in Shanghai, China.</p> Full article ">Figure 6 Cont.
<p>Daily cloud ratios, <span class="html-italic">Kc</span> and <span class="html-italic">Cle</span> of a relatively clear day selected from each month (from January to September) in Shanghai, China.</p> Full article ">Figure 6 Cont.
<p>Daily cloud ratios, <span class="html-italic">Kc</span> and <span class="html-italic">Cle</span> of a relatively clear day selected from each month (from January to September) in Shanghai, China.</p> Full article ">Figure 7
<p>Monthly average cloud ratios calculated by observed solar radiation and CSWD database in Shanghai, China.</p> Full article ">Figure 8
<p>Observed diffuse solar radiation and diffuse solar radiation separated by the Perez model on a representative day (20 August) in Osaka, Japan.</p> Full article ">Figure 9
<p>The steps of authors’ refinement method.</p> Full article ">Figure 10
<p>Comparison of diffuse solar radiation and daily cloud ratios among original CSWD database, Perez model and authors’ simple refinement method in Shanghai, China.</p> Full article ">Figure 11
<p>Correlation of diffuse solar radiation between the Perez model and the authors’ proposed simple refinement method in three months (from May to July).</p> Full article ">Figure 12
<p>Refined monthly cloud ratios of the CSWD database in three months from May to July in Shanghai, China.</p> Full article ">Figure 13
<p>Impact of cloudless index (<span class="html-italic">Cle</span>) on air-conditioning heat load of buildings when the index of <span class="html-italic">Cle</span> varied from 1.0 to 0.8.</p> Full article ">
18 pages, 8446 KiB  
Article
Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds
by Jacques Piazzola, William Bruch, Christelle Desnues, Philippe Parent, Christophe Yohia and Elisa Canepa
Atmosphere 2021, 12(4), 523; https://doi.org/10.3390/atmos12040523 - 20 Apr 2021
Cited by 15 | Viewed by 3699
Abstract
Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols—and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)—on the spread of the epidemic remains an open and [...] Read more.
Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols—and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)—on the spread of the epidemic remains an open and still debated question. This work aims to study whether or not the meteorological conditions related to the different aerosol properties in continental and coastal urbanized areas might influence the atmospheric transport of the SARS-Cov-2 virus. Our analysis focuses on the lockdown period to reduce the differences in the social behavior and highlight those of the weather conditions. As an example, we investigated the contamination cases during March 2020 in two specific French areas located in both continental and coastal areas with regard to the meteorological conditions and the corresponding aerosol properties, the optical depth (AOD) and the Angstrom exponent provided by the AERONET network. The results show that the analysis of aerosol ground-based data can be of interest to assess a virus survey. We found that moderate to strong onshore winds occurring in coastal regions and inducing humid environment and large sea-spray production episodes coincides with smaller COVID-19 contamination rates. We assume that the coagulation of SARS-Cov-2 viral particles with hygroscopic salty sea-spray aerosols might tend to inhibit its viral infectivity via possible reaction with NaCl, especially in high relative humidity environments typical of maritime sites. Full article
(This article belongs to the Special Issue Air Quality and Health in the Mediterranean)
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<p>(<b>Left</b>): Number of deaths per 100000 residents by French departments. The circles denote the capital cities of the regions investigated: 1 = Nantes, 2 = Paris. (<b>Right</b>): population density of France. Images modified from <a href="https://en.wikipedia.org/wiki/COVID-19_pandemic_in_France" target="_blank">https://en.wikipedia.org/wiki/COVID-19_pandemic_in_France</a> (accessed on 19 February 2021) and <a href="https://en.wikipedia.org/wiki/Demographics_of_France" target="_blank">https://en.wikipedia.org/wiki/Demographics_of_France</a> (accessed on 19 February 2021).</p> Full article ">Figure 2
<p>Number of deaths for COVID-19 per 100,000 residents by department in Paris (red line) and Loire-Atlantique (blue line). See <a href="https://www.gouvernement.fr/info-coronavirus/carte-et-donnees" target="_blank">https://www.gouvernement.fr/info-coronavirus/carte-et-donnees</a> (accessed on 19 February 2021).</p> Full article ">Figure 3
<p>Wind rose recorded in Le Croisic in March April 2020. The wind speed intervals encountered during the campaign are reported above the graphic.</p> Full article ">Figure 4
<p>Time series of both the wind speed (the blue line) and direction (the red line) over March, and April 2020 in (<b>a</b>) the SEM-REV station located in Le Croisic near Nantes and (<b>b</b>) at the station of the Montsouris park in Paris. Above the <a href="#atmosphere-12-00523-f004" class="html-fig">Figure 4</a>a is reported the onshore and offshore wind episodes that occur in the coastal site.</p> Full article ">Figure 5
<p>Calculated air mass back-trajectories in Nantes (French Atlantic shoreline) for March 2020.</p> Full article ">Figure 5 Cont.
<p>Calculated air mass back-trajectories in Nantes (French Atlantic shoreline) for March 2020.</p> Full article ">Figure 6
<p>Calculated air mass back-trajectories in Paris for March 2020.</p> Full article ">Figure 6 Cont.
<p>Calculated air mass back-trajectories in Paris for March 2020.</p> Full article ">Figure 7
<p>Temporal survey of the Angstrom coefficient (<b>left</b>) and the AOD (<b>right</b>) in Le Croisic (Nantes region) in March 2020. The arrow indicates the date of the occurrence of offshore wind conditions.</p> Full article ">Figure 8
<p>Temporal survey of the Angstrom coefficient (<b>left</b>) and the AOD (<b>right</b>) in Paris in March 2020.</p> Full article ">Figure 9
<p>An example of aerosol size distributions typical of the coastal zone issued from measurements conducted on the island of Porquerolles by Van Eijk et al. (2011). As shown, the aerosol concentration <span class="html-italic">dN/dr</span> can be fitted by the sum of five lognormal functions centered on radii of 0.01, 0.03, 0.24, 2 and 10 µm. (the black line). The dashed lines indicate the size intervals dealing with the different aerosol sources found in the coastal zone. The arrows indicate the expected size of SARS-CoV2 (around 100 nm).</p> Full article ">Figure 10
<p>SEM image of a mixing sea-spray-soot as sampled on the Mediterranean coast using a Dekati impactor. The red square denotes a salt crystal, while the black circles show soot. The photograph is issued from aerosol samples acquired on the island of Porquerolles during the MATRAC experiments (Piazzola et al., 2020) [<a href="#B61-atmosphere-12-00523" class="html-bibr">61</a>].</p> Full article ">Figure 11
<p>Variation of the relative humidity versus wind speed for an onshore wind direction (marine air mass episode). The data were recorded on the island of Porquerolles by Piazzola (personal communication). The black line fits the data.</p> Full article ">
20 pages, 6132 KiB  
Article
A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
by Xia Sun, Lian Xie, Shahil Umeshkumar Shah and Xipeng Shen
Atmosphere 2021, 12(4), 522; https://doi.org/10.3390/atmos12040522 - 20 Apr 2021
Cited by 10 | Viewed by 3713
Abstract
In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the [...] Read more.
In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth System Science)
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<p>Three regions for forecast: Gulf of Mexico (bounded by the Gulf coast of the United States, from the southern tip of Florida to Texas; on the southwest and south by Mexico; and on the southeast by Cuba), Caribbean Sea (bordered by the Yucatan Peninsula and the central America on the west and southwest; on the south by Venezuela; and the West Indies); the whole Atlantic Basin is composed of the Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea.</p> Full article ">Figure 2
<p>(<b>a</b>) Differences of MSE between climatology and each model F for the forecast of tropical cyclones in the Atlantic Basin for the SWCV or LOOCV method. The nondimensional percentage of MSE change, computed as (MSE<sub>Clim</sub> − MSE<sub>F</sub>)/MSE<sub>Clim</sub>, is given in the curly brackets. (<b>b</b>) H values averaged among all the verification years for SWCV and LOOCV methods. Positive values denote the superior skill of model F with Lasso applied, compared to the climatology.</p> Full article ">Figure 3
<p>Mean H values with SWCV applied between Lasso and clustering models by response: (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH. Positive values denote the superior skill of model F with clustering analysis, compared to the models with Lasso applied.</p> Full article ">Figure 4
<p>Percentage of times a variable is selected across all 39 windows (unit: %) in the model F with Lasso and SWCV per response: (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH. NINO variables are highlighted in bold lines.</p> Full article ">Figure 5
<p>Same as <a href="#atmosphere-12-00522-f004" class="html-fig">Figure 4</a>, but for models with clustering algorithm. (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH.</p> Full article ">Figure 6
<p>Mean H values between model F with Lasso and SWCV applied, and the climatology by response: (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH. Positive values denote the significant improvement of model F, compared to the climatology.</p> Full article ">Figure 7
<p>Mean H values between ensemble models of top three models using different optimizations with SWCV and the climatology by response: (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH. Positive values denote superior skill of the ensemble model over climatology. Lr represents the ensemble model with optimization using linear regression. Avg represents the average of all selected models and Gd_1 represents the ensemble model with gradient descent optimization with a learning rate of 0.001. Gd_2 represents the ensemble model with gradient descent optimization with a learning rate of 0.0001. Gd_3 represents the ensemble model with gradient descent optimization with a learning rate of 0.00001.</p> Full article ">Figure 8
<p>Same as <a href="#atmosphere-12-00522-f007" class="html-fig">Figure 7</a>, but for the ensemble model of all nine models. (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH.</p> Full article ">Figure 8 Cont.
<p>Same as <a href="#atmosphere-12-00522-f007" class="html-fig">Figure 7</a>, but for the ensemble model of all nine models. (<b>a</b>) ATTC, (<b>b</b>) ATHU, (<b>c</b>) ATMH, (<b>d</b>) CATC, (<b>e</b>) CAHU, (<b>f</b>) CAMH, (<b>g</b>) GUTC, (<b>h</b>) GUHU, and (<b>i</b>) GUMH.</p> Full article ">Figure 9
<p>Comparisons between average ensemble with top three models and that with all nine models.</p> Full article ">Figure 10
<p>Comparisons between average ensemble of top three models with best optimized top three ensemble models obtained using the methods described in <a href="#sec2dot2dot2-atmosphere-12-00522" class="html-sec">Section 2.2.2</a>. Gd_3 represents the model with gradient descent optimization having learning rate 0.00001. Lasso represents the model with Lasso optimization. Gd_2 represents the model with gradient descent optimization having learning rate 0.0001. Lr represents the model with linear regression optimization.</p> Full article ">Figure 11
<p>Predictions for (<b>a</b>) ATTC counts and (<b>b</b>) ATHU counts by different models for Atlantic hurricanes over the years. Avg.Acc.-3 refers to the average ensemble of top three models obtained using the methodology described in <a href="#sec2dot2dot2-atmosphere-12-00522" class="html-sec">Section 2.2.2</a>. F<sub>3N</sub>, F<sub>3B</sub>, and F<sub>3L</sub> are the actual top three models whose average ensemble is formed.</p> Full article ">
23 pages, 4035 KiB  
Article
Study of Urban Heat Islands Using Different Urban Canopy Models and Identification Methods
by Rui Silva, Ana Cristina Carvalho, David Carvalho and Alfredo Rocha
Atmosphere 2021, 12(4), 521; https://doi.org/10.3390/atmos12040521 - 20 Apr 2021
Cited by 12 | Viewed by 4587
Abstract
This work aims to compare the performance of the single?(SLUCM) and multilayer (BEP-Building effect parameterization) urban canopy models (UCMs) coupled with the Weather Research and Forecasting model (WRF), along with the application of two urban heat island (UHI) identification methods. The identification methods [...] Read more.
This work aims to compare the performance of the single?(SLUCM) and multilayer (BEP-Building effect parameterization) urban canopy models (UCMs) coupled with the Weather Research and Forecasting model (WRF), along with the application of two urban heat island (UHI) identification methods. The identification methods are: (1) the “classic method”, based on the temperature difference between urban and rural areas; (2) the “local method” based on the temperature difference at each urban location when the model land use is considered urban, and when it is replaced by the dominant rural land use category of the urban surroundings. The study is performed as a case study for the city of Lisbon, Portugal, during the record-breaking August 2003 heatwave event. Two main differences were found in the UHI intensity (UHII) and spatial distribution between the identification methods: a reduction by half in the UHII during nighttime when using the local method; and a dipole signal in the daytime and nighttime UHI spatial pattern when using the classic method, associated with the sheltering effect provided by the high topography in the northern part of the city, that reduces the advective cooling in the lower areas under prevalent northern wind conditions. These results highlight the importance of using the local method in UHI modeling studies to fully isolate urban canopy and regional geographic contributions to the UHII and distribution. Considerable improvements were obtained in the near?surface temperature representation by coupling WRF with the UCMs but better with SLUCM. The nighttime UHII over the most densely urbanized areas is lower in BEP, which can be linked to its larger nocturnal turbulent kinetic energy (TKE) near the surface and negative sensible heat (SH) fluxes. The latter may be associated with the lower surface skin temperature found in BEP, possibly owing to larger turbulent SH fluxes near the surface. Due to its higher urban TKE, BEP significantly overestimates the planetary boundary layer height compared with SLUCM and observations from soundings. The comparison with a previous study for the city of Lisbon shows that BEP model simulation results heavily rely on the number and distribution of vertical levels within the urban canopy. Full article
(This article belongs to the Special Issue Modeling of Surface-Atmosphere Interactions)
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Figure 1

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<p>Domains’ configuration used in the WRF model simulations.</p> Full article ">Figure 2
<p>Model land use/land cover representation in D‑5 with the location of the nine meteorological stations used to validate the WRF model simulations (1. Lisbon—Alvalade; 2. Amadora; 3. Lisbon—Baixa; 4. Lisbon—Benfica; 5. Barreiro—city station; 6. Cacém; 7. Lisbon—Estefânia; 8. Lisbon—Airport; 9. Lisbon—Geofísico).</p> Full article ">Figure 3
<p>Representation of WRF model topography in the domain D‑5.</p> Full article ">Figure 4
<p>Synoptic chart of the mean sea level pressure in hPa (contours), 850 hPa wind speed in m s<sup>−1</sup> (vectors), and 850 hPa temperature in ˚C (shading), for 1 August 2003 at 1800 UTC.</p> Full article ">Figure 5
<p>Heatwave averaged mean diurnal cycle of the Lisbon near-surface UHI: using Method 1 (M1—dashed lines) and Method 2 (M2—solid lines), for SLUCM (blue lines) and BEP (green lines) UCMs.</p> Full article ">Figure 6
<p>Heatwave averaged daytime and nighttime fields of the near‑surface UHI identified using Method 1 and Method 2 and for SLUCM and BEP urban canopy models.</p> Full article ">Figure 7
<p>Heatwave averaged mean diurnal cycle of the surface fluxes of sensible heat (SH), latent heat (LH), ground (GRD), and their net balance (NET), averaged over the urban grid points.</p> Full article ">Figure 8
<p>Heatwave averaged mean diurnal cycle decomposition of the differences between SLUCM and NURB_SLUCM and BEP and NURB_BEP surface heat fluxes into the different urban classes.</p> Full article ">Figure 9
<p>Heatwave averaged daytime and nighttime zonal means of the parameterized TKE in urban grid points (first 150 m of the PBL), for SLUCM and NURB_SLUCM (left column); BEP and NURB_BEP (right column) simulations.</p> Full article ">Figure 10
<p>Heatwave averaged mean diurnal cycle of skin surface temperature (TSK), first layer soil temperature (SOILT), and T2m, averaged over the urban grid points.</p> Full article ">Figure 11
<p>Time series of the modeled planetary boundary layer height (PBLH) averaged over urban grid points locations, for SLUCM, BEP, NURB_SLUCM, and NURB_BEP simulations.</p> Full article ">
19 pages, 5803 KiB  
Article
Snow Processes and Climate Sensitivity in an Arid Mountain Region, Northern Chile
by Francisco Jara, Miguel Lagos-Zúñiga, Rodrigo Fuster, Cristian Mattar and James McPhee
Atmosphere 2021, 12(4), 520; https://doi.org/10.3390/atmos12040520 - 20 Apr 2021
Cited by 9 | Viewed by 4546
Abstract
Seasonal snow and glaciers in arid mountain regions are essential in sustaining human populations, economic activity, and ecosystems, especially in their role as reservoirs. However, they are threatened by global atmospheric changes, in particular by variations in air temperature and their effects on [...] Read more.
Seasonal snow and glaciers in arid mountain regions are essential in sustaining human populations, economic activity, and ecosystems, especially in their role as reservoirs. However, they are threatened by global atmospheric changes, in particular by variations in air temperature and their effects on precipitation phase, snow dynamics and mass balance. In arid environments, small variations in snow mass and energy balance can produce large changes in the amount of available water. This paper provides insights into the impact of global warming on the mass balance of the seasonal snowpack in the mountainous Copiapó river basin in northern Chile. A dataset from an experimental station was combined with reanalysis data to run a physically based snow model at site and catchment scales. The basin received an average annual precipitation of approximately 130 mm from 2001 to 2016, with sublimation losses higher than 70% of the snowpack. Blowing snow sublimation presented an orographic gradient resultant from the decreasing air temperature and windy environment in higher elevations. Under warmer climates, the snowpack will remain insensitive in high elevations (>4000 m a.s.l.), but liquid precipitation will increase at lower heights. Full article
(This article belongs to the Special Issue Modeling and Measuring Snow Processes across Scales)
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<p>Location of the Upper Copiapó river basin and its three tributaries; from north to south, Jorquera river basin, Pulido river basin and Manflas river basin.</p> Full article ">Figure 2
<p>La Ollita meteorological station and the snow scale, on 15 December, 2015 (photo by Miguel Lagos).</p> Full article ">Figure 3
<p>Flowchart of the interaction of state variables and fluxes calculated by physically based hydrological process modules using CRHM. All the above modules are currently available in the CRHM platform.</p> Full article ">Figure 4
<p>Main physiographic characteristics: elevation (<b>a</b>), slope (<b>b</b>) and aspect (<b>c</b>) derived from Shuttle Radar Topography Mission’s digital elevation model, spatially averaged for each hydrological response unit (HRU) at UCRB.</p> Full article ">Figure 5
<p>Measured wind speed and air temperature at La Ollita station (<b>a</b>), observed and simulated snow water equivalent (SWE) measured precipitation in the rain gauge (<b>b</b>), simulated cumulative components of the snow water balance (<b>c</b>) hourly between 1 April and 1 July 2016.</p> Full article ">Figure 6
<p>Comparison between measured (gray line) and reanalysis ERA-Interim (blue line) hourly meteorological data at La Ollita station during water year 2016.</p> Full article ">Figure 7
<p>Monthly average snow water balance components at the Upper Copiapó basin between 2001 and 2015. Average monthly snow mass components (<b>top</b>) and percent of the ablation (<b>bottom</b>). B.S: blowing snow.</p> Full article ">Figure 8
<p>Daily rates of the snow components (mm/day) compared to the average wind speed and air temperature between 2001 and 2016. Colored by altitudinal bands of 1 km derived from the HRU in UCRB. BSS: blowing snow sublimation and E: surface sublimation. Vertical lines indicate the average May–December of wind speed and air temperature in each band.</p> Full article ">Figure 9
<p>Annual average amount (<b>a</b>) and volume (amount times the area) (<b>b</b>) of the snow mass balance component by HRU ranges of elevation in the Copiapó basin, between 1 April 2000 and 1 April 2016. The volume is portrayed as snow water equivalent (<b>b</b>) and as percentage of snowfall (<b>c</b>). BSS: blowing snow sublimation and E: surface sublimation.</p> Full article ">Figure 10
<p>Sensitivity analysis on the UCRB’s mass balance components with the variation of temperature per one degree Celsius added.</p> Full article ">Figure 11
<p>Sensitivity analysis to warmer conditions of the monthly fractional snow cover area (<b>a</b>), annual snowpack cover duration (SCD, panel (<b>b</b>)) and maximum annual snow water equivalent (SWE) (<b>c</b>). The increment in temperature was in respect to the reference period between April 2001 and 1 April 2016. For the panel b and c, the slope was estimated using the average duration (“diamond symbols”, −6.2 d °C<sup>−1</sup>) and average annual max SWE (−3.4 mm C<sup>−1</sup>), respectively. The cross marks (+) represent the outliers of the dataset.</p> Full article ">
19 pages, 2227 KiB  
Article
Measurements of NOx and Development of Land Use Regression Models in an East-African City
by Asmamaw Abera, Ebba Malmqvist, Yumjirmaa Mandakh, Erin Flanagan, Michael Jerrett, Geremew Sahilu Gebrie, Abebe Genetu Bayih, Abraham Aseffa, Christina Isaxon and Kristoffer Mattisson
Atmosphere 2021, 12(4), 519; https://doi.org/10.3390/atmos12040519 - 19 Apr 2021
Cited by 7 | Viewed by 4867
Abstract
Air pollution causes premature mortality and morbidity globally, but these adverse health effects occur over proportionately in low- and middle-income countries. Lack of both air pollution data and knowledge of its spatial distribution in African countries have been suggested to lead to an [...] Read more.
Air pollution causes premature mortality and morbidity globally, but these adverse health effects occur over proportionately in low- and middle-income countries. Lack of both air pollution data and knowledge of its spatial distribution in African countries have been suggested to lead to an underestimation of health effects from air pollution. This study aims to measure nitrogen oxides (NOx), as well as nitrogen dioxide (NO2), to develop Land Use Regression (LUR) models in the city of Adama, Ethiopia. NOx and NO2 was measured at over 40 sites during six days in both the wet and dry seasons. Throughout the city, measured mean levels of NOx and NO2 were 29.0 µg/m3 and 13.1 µg/m3, respectively. The developed LUR models explained 68% of the NOx variances and 75% of the NO2. Both models included similar geographical predictor variables (related to roads, industries, and transportation administration areas) as those included in prior LUR models. The models were validated by using leave-one-out cross-validation and tested for spatial autocorrelation and multicollinearity. The performance of the models was good, and they are feasible to use to predict variance in annual average NOx and NO2 concentrations. The models developed will be used in future epidemiological and health impact assessment studies. Such studies may potentially support mitigation action and improve public health. Full article
(This article belongs to the Special Issue Air Pollution and Human Health: Current Progress, Challenges and Future Prospects)
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<p>The location of Adama on the East African continent, and the air sampling measurement sites and land use classes included in the LUR models.</p> Full article ">Figure 2
<p>Photo of Ogawa badge in its rain protection from one of the NO<sub>x</sub> and NO<sub>2</sub> sampling sites.</p> Full article ">Figure 3
<p>Diurnal trend of NO<sub>2</sub> and NO<sub>x</sub> in Adama City, averaged from 13 days with complete data.</p> Full article ">Figure 4
<p>Yearly concentrations of NO<sub>2</sub> and NO<sub>x</sub> in Adama City.</p> Full article ">Figure 5
<p>Prediction map of the NO<sub>2</sub> concentrations in a 10-m grid over Adama based on the developed LUR model. The measured levels of NO<sub>2</sub> are presented by dots.</p> Full article ">Figure 6
<p>Prediction map of NO<sub>x</sub> concentrations in a 10-m grid over Adama based on the developed LUR model. Measured levels of NO<sub>x</sub> are presented by dots. Grey marks areas were the model predicted zero or below.</p> Full article ">
18 pages, 6192 KiB  
Article
Impact of SARS-CoV-2 on Ambient Air Quality in Northwest China (NWC)
by Shah Zaib, Jianjiang Lu, Muhammad Zeeshaan Shahid, Sunny Ahmar and Imran Shahid
Atmosphere 2021, 12(4), 518; https://doi.org/10.3390/atmos12040518 - 19 Apr 2021
Cited by 4 | Viewed by 3008
Abstract
SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on [...] Read more.
SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020. Full article
(This article belongs to the Section Air Quality)
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<p>Location map of five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), Qinghai (QH)) in Northwest China (NWC).</p> Full article ">Figure 2
<p>Influence of SARS-CoV-2 on the annual variation of six criteria pollutants: PM<sub>2.5</sub> (<b>a</b>), PM<sub>10</sub> (<b>b</b>), SO<sub>2</sub> (<b>c</b>), NO<sub>2</sub> (<b>d</b>), CO (<b>e</b>), and O<sub>3</sub> (<b>f</b>)), AQI (<b>g</b>), and PM2.5/PM<sub>10</sub> (<b>h</b>) in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) during 2019–2020. Descriptions are as follows: blue line (2019), orange line (2020), yellow line (CAAQS Grade I), blue line (CAAQS Grade II), and yellow dotted line (AQI threshold). The abbreviations are as follows: PM<sub>2.5</sub> (fine particulate matter), PM<sub>10</sub> (coarse particulate matter), SO<sub>2.</sub> (Sulfur dioxide), NO<sub>2</sub> (nitrogen dioxide), CO (carbon monoxide), O<sub>3</sub> (ozone), CAAAQS (Chinese Ambient Air Quality Standards).</p> Full article ">Figure 3
<p>Spatial distribution of PM<sub>2.5</sub> (<b>a</b>,<b>b</b>), PM<sub>10</sub> (<b>c</b>,<b>d</b>), SO<sub>2</sub> (<b>e</b>,<b>f</b>), NO<sub>2</sub> (<b>g</b>,<b>h</b>), CO (<b>i</b>,<b>j</b>), and O<sub>3</sub> (<b>k</b>,<b>i</b>) in northwest China (NWC) during 2019 and 2020. Colors represent the different pollution levels e.g., green (good), yellow (moderate), orange (unhealthy for the sensitive group), red (unhealthy for all), purple (very unhealthy), and maroon (hazardous). The abbreviations are as follows: PM<sub>2.5</sub> (fine particulate matter), PM<sub>10</sub> (coarse particulate matter), SO<sub>2.</sub> (Sulfur dioxide), NO<sub>2</sub> (nitrogen dioxide), CO (carbon monoxide), and O<sub>3</sub> (ozone).</p> Full article ">Figure 4
<p>Influence of SARS-CoV-2 on seasonal variation of six pollutants: PM<sub>2.5</sub> (<b>a</b>), PM<sub>10</sub> (<b>b</b>), SO<sub>2</sub> (<b>c</b>), NO<sub>2</sub> (<b>d</b>), CO (<b>e</b>), O<sub>3</sub> (<b>f</b>); AQI (<b>g</b>), and PM<sub>2.5</sub>/PM<sub>10</sub> ratio (<b>h</b>) in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) during 2019–2020. Descriptions are as follows: blue line (winter), orange line (spring), gray line (summer), yellow line (autumn), blue dotted line (CAAQS Grade II), and yellow dotted line (AQI threshold). The abbreviations are as follows: PM<sub>2.5</sub> (fine particulate matter), PM<sub>10</sub> (coarse particulate matter), SO<sub>2.</sub> (Sulfur dioxide), NO<sub>2</sub> (nitrogen dioxide), CO (carbon monoxide), O<sub>3</sub> (ozone), PM<sub>2.5</sub>/PM<sub>10</sub> (ratio of PM<sub>2.5</sub> with PM<sub>10</sub>), and AQI (air quality index).</p> Full article ">Figure 5
<p>Annual (<b>a</b>,<b>b</b>) and seasonal (spring (<b>c</b>,<b>d</b>), summer (<b>e</b>,<b>f</b>), autumn (<b>g</b>,<b>h</b>), winter (<b>i</b>,<b>j</b>)) variation of PM<sub>2.5</sub>/PM<sub>10</sub> ratio in northwest China (NWC) during 2019 and 2020. Colors represent the different pollution levels e.g., green (good), yellow (moderate), orange (unhealthy for the sensitive group), red (unhealthy for all), and purple (very unhealthy).</p> Full article ">Figure 6
<p>Annual (<b>a</b>,<b>b</b>) and seasonal (spring (<b>c</b>,<b>d</b>), summer (<b>e</b>,<b>f</b>), autumn (<b>g</b>,<b>h</b>), winter (<b>i</b>,<b>j</b>)) variation of AQI in northwest China (NWC) during 2019 and 2020. Colors represent the different classes of air quality index e.g., green (0–50, good, Class I), yellow (51–100, moderate, Class II), orange (101–150, unhealthy for the sensitive group, Class III), red (151–200, unhealthy for all, Class IV), purple (201–300, very unhealthy, Class V), and maroon (300+, hazardous, Class VI).</p> Full article ">Figure 7
<p>Annual and seasonal (spring, summer, autumn, winter) proportion of different air quality index (AQI) classes e.g., Class I (0—50, good, green), Class II (51—100, moderate, yellow), Class III (101—150, unhealthy for the sensitive group, orange), Class IV (151—200, unhealthy for all, red), Class V (201—300, very unhealthy, purple), and Class VI (300+, hazardous, maroon) in northwest China (NWC) during 2019 and 2020.</p> Full article ">
19 pages, 8058 KiB  
Article
Climatic Effects of Spring Mesoscale Oceanic Eddies in the North Pacific: A Regional Modeling Study
by Zhiying Cai, Haiming Xu, Jing Ma and Jiechun Deng
Atmosphere 2021, 12(4), 517; https://doi.org/10.3390/atmos12040517 - 19 Apr 2021
Cited by 2 | Viewed by 3035
Abstract
A high-resolution atmospheric model of the Weather Research and Forecast (WRF) is used to investigate the climatic effects of mesoscale oceanic eddies (OEs) in the North Pacific (NPac) in spring and the respective effects of OEs in the northern NPac associated with the [...] Read more.
A high-resolution atmospheric model of the Weather Research and Forecast (WRF) is used to investigate the climatic effects of mesoscale oceanic eddies (OEs) in the North Pacific (NPac) in spring and the respective effects of OEs in the northern NPac associated with the Kuroshio Extension (KE) and of OEs in the southern NPac related to the subtropical countercurrent. Results show that mesoscale OEs in the NPac can strengthen the upper-level ridge (trough) in the central (eastern) subtropical NPac, together with markedly weakened (strengthened) westerly winds to its south. The mesoscale OEs in the whole NPac act to weaken the upper-level storm track and strengthen lower-level storm activities in the NPac. However, atmospheric responses to the northern and southern NPac OEs are more prominent. The northern NPac OEs can induce tropospheric barotropic responses with a tripole geopotential height (GPH) anomaly pattern to the north of 30° N, while the OEs in both the northern and southern NPac can enhance the upper-level ridge (trough) in the central (eastern) subtropical NPac. Additionally, the northern NPac OEs can shrink the lower-level subtropical high and weaken the easterly trade winds at the low latitudes, while the southern NPac OEs result in a southward shift of the lower-level subtropical high and an eastward shift of the upper-level westerly jet stream. The southern and northern NPac OEs have similar effects on the storm track, leading to an enhanced lower-level storm track over the KE via moistening the atmospheric boundary layer; and they can also exert significant remote influences on lower- and upper-level storm activities over the Northeast Pacific off the west coast of North America. When the intensities of OEs are doubled in the model, the spatial distribution of atmospheric responses is robust, with a larger and more significant magnitude. Additionally, although OEs are part of the mesoscale oceanic processes, the springtime OEs play an opposite role in mesoscale sea-surface temperature anomalies. These findings point to the potential of improving the forecasts of extratropical springtime storm systems and the projections of their responses to future climate change, by improving the representation of ocean eddy-atmosphere interaction in forecast and climate models. Full article
(This article belongs to the Section Climatology)
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<p>Climatological MAM-mean of high-pass (40–300 day) filtered eddy kinetic energy (EKE) in the ocean (shading; units: m<sup>2</sup> s<sup>−2</sup>) in the North Pacific (NPac) from 2003 to 2008 based on AVISO data. The dashed red line denotes the latitude of 25° N.</p> Full article ">Figure 2
<p>Spatial distributions of (<b>a</b>) sea surface temperature (SST) (units: K) in the smoothed-SST (SmthSST) run and (<b>b</b>–<b>d</b>) sea surface temperature anomalies (SSTAs) (units: K) associated with the mesoscale oceanic eddies (OEs) in the (<b>b</b>) EddySSTA, (<b>c</b>) EddySSTA+N25N, and (<b>d</b>) EddySSTA+S25N runs at 00 UTC on 1 March 2004.</p> Full article ">Figure 3
<p>MAM-mean (<b>a</b>) latent heat flux (LHF) (units: W m<sup>−2</sup>); (<b>c</b>) sensible heat flux (SHF) (units: W m<sup>−2</sup>); (<b>e</b>) 300-hPa zonal wind (units: m s<sup>−1</sup>), and (<b>g</b>) 300-hPa EKE in the atmosphere (units: m<sup>2</sup> s<sup>−2</sup>) in CFSR products during 2003–2008. (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) are the same as (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>), respectively, except from the control experiment (CTL).</p> Full article ">Figure 4
<p>The 2003 March-mean SSTAs (contours from −1.5 °C to 1.5 °C at an interval of 0.3; solid and dashed contours are for positive and negative anomalies, respectively, and the zero contour is omitted for clarity) and 10-m wind anomalies (shading; units: m s<sup>−1</sup>) from (<b>a</b>) the OISSTV2 and QuickSCAT datasets and (<b>c</b>) the CTL run. (<b>b</b>) The scatter plot between SSTAs and 10-m wind anomalies in the NPac region (155°–175° E, 35°–45° N; outlined by black dashed lines in the left panels) from (<b>b</b>) the OISSTV2 and QuickSCAT datasets and (<b>d</b>) the CTL run. Each blue dot denotes a model grid box. The red solid line represents the linear regression between the two variables, and its regression coefficient (the slope) is given at the lower-right corner.</p> Full article ">Figure 5
<p>MAM-mean differences of (<b>a</b>,<b>b</b>) geopotential heights (GPH) (shading; units: m); (<b>c</b>,<b>d</b>) zonal winds (shading; units: m s<sup>−1</sup>), and (<b>e</b>,<b>f</b>) storm track at 300 hPa (left) and 850 hPa (right) between EddySSTA and SmthSST runs (EddySSTA minus SmthSST). The contours represent the respective climatological mean from 2003 to 2008 in the CTL run. Hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the Empirical Monte Carlo (EMC) significance test. Note that the storm tracks at 300 and 850 hPa are defined by the EKE in the atmosphere (units: m<sup>2</sup> s<sup>−2</sup>) and the poleward heat flux <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">v</mi> <mo>′</mo> </msup> <msup> <mi mathvariant="normal">T</mi> <mo>′</mo> </msup> </mrow> </semantics></math> (units: K m s<sup>−1</sup>), respectively.</p> Full article ">Figure 6
<p>MAM-mean differences of (<b>a</b>) 300-hPa and (<b>c</b>) 850-hPa GPH (shading; units: m) between EddySSTA + N25N and SmthSST runs (EddySSTA + N25N minus SmthSST). The contours represent the corresponding climatological mean from 2003 to 2008 in the CTL run. Hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the EMC test. (<b>b</b>,<b>d</b>) are the same as (<b>a</b>,<b>c</b>), respectively, except for the differences between EddySSTA + S25N and SmthSST runs (EddySSTA + S25N minus SmthSST).</p> Full article ">Figure 7
<p>Same as <a href="#atmosphere-12-00517-f006" class="html-fig">Figure 6</a>, except for the differences of (<b>a</b>,<b>b</b>) 300-hPa and (<b>c</b>,<b>d</b>) 850-hPa zonal winds.</p> Full article ">Figure 8
<p>Vertical cross sections of MAM-mean differences of zonal winds averaged over 135° E–135° W (shading; units: m s<sup>−1</sup>) between EddySSTA + N25N and SmthSST runs (<b>a</b>) and between EddySSTA + S25N and SmthSST runs (<b>b</b>). The contours represent the climatological mean zonal winds from 2003 to 2008 in the CTL run. Hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the EMC test.</p> Full article ">Figure 9
<p>Differences of mean GPH at 850 hPa (Z850; contour; m; blue solid and red dashed contours are for positive and negative anomalies, respectively, and the zero contour is omitted for clarity) and at 300 hPa (Z300; shading; m) between EddySSTA and SmthSST runs in (<b>a</b>) March; (<b>b</b>) April, and (<b>c</b>) May. Hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the EMC test.</p> Full article ">Figure 10
<p>MAM-mean differences of (<b>a</b>,<b>b</b>) 300-hPa EKE in the atmosphere (shading; units: m<sup>2</sup> s<sup>−2</sup>) and (<b>c</b>,<b>d</b>) 850-hPa transient eddy heat transport (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">v</mi> <mo>′</mo> </msup> <msup> <mi mathvariant="normal">T</mi> <mo>′</mo> </msup> </mrow> </semantics></math>; shading; units: K m s<sup>−1</sup>) between EddySSTA + N25N and SmthSST runs (<b>a</b>,<b>c</b>) and between EddySSTA + S25N and SmthSST runs (<b>b</b>,<b>d</b>). The contours represent the corresponding climatological mean from 2003 to 2008 in the CTL run. The hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the EMC test.</p> Full article ">Figure 11
<p>MAM-mean differences of water vapor vertically averaged from 1000 to 850 hPa (shading; units: g kg<sup>−1</sup>) between EddySSTA + N25N and SmthSST runs (<b>a</b>) and between EddySSTA + S25N and SmthSST runs (<b>b</b>). Hatching denotes that the difference is statistically significant at the 95% confidence level on the basis of the EMC test. The green solid line denotes the lower-level storm track core at 40° N from 150° E to 180° E in the CTL run.</p> Full article ">Figure 12
<p>April-mean differences of (<b>a</b>,<b>b</b>) GPH at 850 hPa (Z850; contour; m; blue solid and red dashed contours are for positive and negative anomalies, respectively, and the zero contour is omitted for clarity) and at 300 hPa (Z300; shading; m); (<b>c</b>,<b>d</b>) 300-hPa EKE in the atmosphere (shading; units: m<sup>2</sup> s<sup>−2</sup>), and (<b>e</b>,<b>f</b>) 850-hPa transient eddy heat transport (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">v</mi> <mo>′</mo> </msup> <msup> <mi mathvariant="normal">T</mi> <mo>′</mo> </msup> </mrow> </semantics></math>; shading; units: K m s<sup>−1</sup>) between EddySSTA + N25N and SmthSST runs (<b>a</b>,<b>c</b>,<b>e</b>) and between EddySSTA + S25N and SmthSST runs (<b>b</b>,<b>d</b>,<b>f</b>), respectively. The hatching denotes the difference is statistically significant at the 95% confidence level on the basis of the EMC test.</p> Full article ">
23 pages, 7500 KiB  
Article
Associations between Exposure to Industrial Air Pollution and Prevalence of Asthma and Atopic Diseases in Haifa Bay Area
by Raanan Raz, Yuval, Ruth Lev Bar-Or, Jeremy D. Kark, Ronit Sinnreich, David M. Broday, Ruthie Harari-Kremer, Lea Bentur, Alex Gileles-Hillel, Lital Keinan-Boker, Andrey Lyubarsky, Dorit Tsur, Arnon Afek, Noam Levin, Estela Derazne and Gilad Twig
Atmosphere 2021, 12(4), 516; https://doi.org/10.3390/atmos12040516 - 19 Apr 2021
Cited by 4 | Viewed by 3899
Abstract
Haifa Bay Area (HBA) contains Israel’s principal industrial area, and there are substantial public concerns about health effects from its emissions. We aimed to examine associations between exposure to air pollution from HBA industrial area with prevalent asthma and other atopic diseases at [...] Read more.
Haifa Bay Area (HBA) contains Israel’s principal industrial area, and there are substantial public concerns about health effects from its emissions. We aimed to examine associations between exposure to air pollution from HBA industrial area with prevalent asthma and other atopic diseases at age 17. This is a cross-sectional study. The study population included all adolescents born in Israel and whose medical status was evaluated for mandatory military recruitment by the Israeli medical corps during 1967–2017. We analyzed prevalent asthma, allergic rhinitis, atopic dermatitis, and rhinoconjunctivitis. We estimated exposure to industrial air pollution by a kriging interpolation of historical SO2 observations and adjusted the associations to the year of birth, SES, school orientation, and traffic pollution. The study population included n = 2,523,745 adolescents, among which 5.9% had prevalent asthma and 4.6% had allergic rhinitis. Residency in HBA was associated with a higher adjusted risk of asthma, compared with non-HBA residency. Still, this association was limited to the three lowest exposure categories, while the highest exposure group had the lowest adjusted risk. Sensitivity analyses and other atopic diseases presented similar results. These results do not provide support for causal relationships between HBA industry-related emissions and prevalent atopic diseases. Full article
(This article belongs to the Special Issue Air Pollution and Human Exposures in Israel)
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<p>A map of the Haifa Bay Area industrial air pollution (HBA-IAP) exposure model, with color-coded exposure categories 1–4.</p> Full article ">Figure 2
<p>Crude prevalence of asthma by HBA-IAP categories. 0 = reference category (non-HBA residents), 1 = lowest HBA-IAP exposure category.</p> Full article ">Figure 3
<p>Association between HBA-IAP and asthma, adjusted for SES, year of birth, school orientation and NO<sub>x</sub>. <span class="html-italic">n</span> = 2,311,240, <span class="html-italic">n</span> cases = 140,166. 0 = reference category (non-HBA residents), 1 = lowest HBA-IAP exposure category.</p> Full article ">Figure 4
<p>Association between HBA-IAP and other atopic diseases, adjusted for SES, year of birth, school orientation, and NO<sub>x</sub>. 0 = reference category (non-HBA residents), 1 = lowest HBA-IAP exposure category.</p> Full article ">Figure A1
<p>Kriging map of SO<sub>2</sub> concentrations in the study area using data from 2002 to 2004. The black circles denote the power plant’s geographical centers and the refineries compound (west and east, respectively). (<b>a</b>) Continuous color-coded concentration map with the observed concentrations shown in their monitoring locations. The color-coding is tailored to the concentrations of the map and the observations. (<b>b</b>) Continuous color-coded concentration map with color-coding tailored to the map values. (<b>c</b>) The same as (<b>b</b>) but with the map values assigned to 12 concentrations ranges. (<b>d</b>) The same as (<b>b</b>) but the concentrations values mapped into the (0–1) range.</p> Full article ">Figure A2
<p>Similar to <a href="#atmosphere-12-00516-f0A1" class="html-fig">Figure A1</a>, but using data from 2001 to 2006. (<b>a</b>) Continuous color-coded concentration map with the observed concentrations shown in their monitoring locations. The color-coding is tailored to the concentrations of the map and the observations. (<b>b</b>) Continuous color-coded concentration map with color-coding tailored to the map values. (<b>c</b>) The same as (<b>b</b>) but with the map values assigned to 12 concentrations ranges. (<b>d</b>) The same as (<b>b</b>) but the concentrations values mapped into the (0–1) range.</p> Full article ">Figure A3
<p>Kriging map of SO<sub>2</sub> concentrations in the study area using data from 2002 to 2004. The black circles denote the power plant’s geographical centers and the refineries compound (west and east, respectively). (<b>a</b>) Using only observations between 06:00 and 13:30. (<b>b</b>) Using only observations between 14:00 and 21:30. (<b>c</b>) Using only observations between 22:00 and 05:30. (<b>d</b>) Using only observations from 20 µg/m<sup>3</sup> and up (all hours of the day).</p> Full article ">Figure A4
<p>Wind roses of wind observed in the Afeq station in the years (<b>a</b>) 2003–2006; (<b>b</b>) 2007–2010; (<b>c</b>) 2011–2014; (<b>d</b>) 2015–2016.</p> Full article ">Figure A5
<p>Wind roses of wind observed in the Bet Dagan meteorological station in the years (<b>a</b>) 1970–1979; (<b>b</b>) 1980–1989; (<b>c</b>) 1990–1999; (<b>d</b>) 2000–2009.</p> Full article ">
1 pages, 153 KiB  
Erratum
Erratum: Hussein et al. Indoor Particle Concentrations, Size Distributions, and Exposures in Middle Eastern Microenvironments. Atmosphere 2020, 11, 41
by Tareq Hussein, Ali Alameer, Omar Jaghbeir, Kolthoum Albeitshaweesh, Mazen Malkawi, Brandon E. Boor, Antti Joonas Koivisto, Jakob Löndahl, Osama Alrifai and Afnan Al-Hunaiti
Atmosphere 2021, 12(4), 515; https://doi.org/10.3390/atmos12040515 - 19 Apr 2021
Cited by 3 | Viewed by 2235
Abstract
The authors would like to correct the published article [...] Full article
(This article belongs to the Collection Indoor Air Quality: From Sampling to Risk Assessment in the Light of New Legislations)
15 pages, 6858 KiB  
Article
Initial Results of Long-term Continuous Observation of Lightning Discharges by FALMA in Chinese Inland Plateau Region
by Panliang Gao, Ting Wu and Daohong Wang
Atmosphere 2021, 12(4), 514; https://doi.org/10.3390/atmos12040514 - 18 Apr 2021
Cited by 5 | Viewed by 2447
Abstract
We started a long-term continuous observation of lightning discharges in the Chinese inland plateau region using a fast antenna lightning mapping array (FALMA). During the first year of observation, 2019, we recorded lightning discharges on 25 days in Yinchuan city, the capital of [...] Read more.
We started a long-term continuous observation of lightning discharges in the Chinese inland plateau region using a fast antenna lightning mapping array (FALMA). During the first year of observation, 2019, we recorded lightning discharges on 25 days in Yinchuan city, the capital of Ningxia. Most of the lightning discharges appeared to occur in the afternoons of individual thunderstorm days in August. We studied the cloud-to-ground (CG) flash percentages, lightning discharge source spatiotemporal distributions, and preliminary breakdown (PB) process characteristics for the two thunderstorm cases that produced the most frequent lightning flashes in 2019 over a wide area. It was found that (1) CG flashes in these two thunderstorms accounted for 28.4% and 32.5% of total lightning flashes, respectively; (2) most lightning discharge sources in these two thunderstorms occurred at temperatures between 5 and ?30 °C, with a peak at around ?10 °C; and (3) more than 90% of well-mapped PB processes of intracloud (IC) flashes propagated downward. By overlapping the altitudes and the progression directions of the PB processes on the lightning source spatiotemporal distributions, we inferred that the main negative charge of the two storms observed in Ningxia, China, was at a height of around ?15 to ?25 °C (7 to 9 km) and the main positive charge was at a height of around 5 to 0 °C (2 to 4 km). Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>Geographical distribution of 25 FALMA sites in Ningxia. The latitude and longitude of the site at (0, 0) is (38.43° N, 106.17° E).</p> Full article ">Figure 2
<p>The number of lightning sources for different thunderstorm days in 2019.</p> Full article ">Figure 3
<p>The spatial distribution of lightning sources.</p> Full article ">Figure 4
<p>Diurnal distribution of lightning sources.</p> Full article ">Figure 5
<p>The temporal and spatial evolutions of lightning sources in the thunderstorm on August 2nd. Source distributions of total lightning flashes (<b>a</b>), CG lightning flashes (<b>b</b>), and IC lightning flashes (<b>c</b>). The temporal evolutions of various types of lightning flashes are shown in (<b>d</b>).</p> Full article ">Figure 6
<p>The temporal and spatial evolutions of lightning sources in the thunderstorm on August 5th. Source distributions of total lightning flashes (<b>a</b>), CG lightning flashes (<b>b</b>), and IC lightning flashes (<b>c</b>). The temporal evolutions of various types of lightning flashes are shown in (<b>d</b>).</p> Full article ">Figure 7
<p>3D mapping results for an inverted-polarity IC flash on August 2nd starting from 20:29:42. (<b>a</b>) E-change waveform following the atmospheric electricity sign convention; (<b>b</b>) source height versus time; (<b>c</b>) west–east vertical view; (<b>d</b>) source distribution along the height; (<b>e</b>) plan view; (<b>f</b>) south–north vertical view.</p> Full article ">Figure 8
<p>3D mapping results for a normal IC flash on August 5th starting from 10:07:01. (<b>a</b>) E-change waveform following the atmospheric electricity sign convention; (<b>b</b>) source height versus time; (<b>c</b>) west–east vertical view; (<b>d</b>) source distribution along the height; (<b>e</b>) plan view; (<b>f</b>) south–north vertical view.</p> Full article ">Figure 9
<p>3D mapping results for a positive CG flash on August 2nd starting from 21:26:36. Two return strokes are denoted with dark triangles. (<b>a</b>) E-change waveform following the atmospheric electricity sign convention; (<b>b</b>) source height versus time; (<b>c</b>) west–east vertical view; (<b>d</b>) source distribution along the height; (<b>e</b>) plan view; (<b>f</b>) south–north vertical view.</p> Full article ">Figure 10
<p>3D mapping results for a simple negative CG flash on July 28th starting from 19:46:10.1. The return stroke is denoted with a green triangle. (<b>a</b>) E-change waveform following the atmospheric electricity sign convention; (<b>b</b>) source height versus time; (<b>c</b>) west–east vertical view; (<b>d</b>) source distribution along the height; (<b>e</b>) plan view; (<b>f</b>) south–north vertical view.</p> Full article ">Figure 11
<p>Spatiotemporal distributions of PB processes in lightning flashes on 2 August 2019. (<b>a</b>) Lightning source density as a function of altitude and time. The shaded area represents the source number counted in 2 min intervals and with a 0.4 km height bin, as shown by the bottom color bar. Black and pink curves indicate the upper and lower altitudes of PB sources in each flash. In particular, the initiation heights of upward PB processes are marked by green triangles. (<b>b</b>) Distribution of lightning sources with height.</p> Full article ">Figure 12
<p>Spatiotemporal distributions of PB processes in lightning flashes on 5 August 2019. (<b>a</b>) The shaded area represents the source number counted in 2 min intervals and with a 0.4 km height bin, as shown by the bottom color bar. Black and pink curves indicate the upper and lower altitudes of PB sources in each flash. In particular, the initiation heights of upward PB processes are marked by green triangles. (<b>b</b>) Distribution of lightning sources with height.</p> Full article ">Figure 13
<p>Spatiotemporal distributions of PB processes in lightning flashes in a thunderstorm that occurred in Gifu, Japan, on 13 July 2017. (<b>a</b>) The shaded area represents the source number counted in 2 min intervals and with a 0.4 km height bin, as shown by the bottom color bar. Black and pink curves indicate the upper and lower altitudes of PB sources in each flash. In particular, the initiation heights of upward PB processes are marked by green triangles. (<b>b</b>) Distribution of lightning sources with height.</p> Full article ">
12 pages, 911 KiB  
Article
Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities
by Xerxes Seposo, Chris Fook Sheng Ng and Lina Madaniyazi
Atmosphere 2021, 12(4), 513; https://doi.org/10.3390/atmos12040513 - 18 Apr 2021
Cited by 3 | Viewed by 3298
Abstract
The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with [...] Read more.
The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius; °C), dew point (degrees Celsius; °C), relative humidity (percent; %), air pressure (kilopascal; kPa), windspeed (meters per second; m/s) and visibility (kilometer; km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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<p>Matrix of correlation coefficients of exposure variables and R<sub>t</sub>.</p> Full article ">Figure 2
<p>Lagged associations of air pressure and RT-PCR tests on R<sub>t</sub>.</p> Full article ">
18 pages, 4816 KiB  
Article
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models
by Fabio Di Nunno, Francesco Granata, Rudy Gargano and Giovanni de Marinis
Atmosphere 2021, 12(4), 512; https://doi.org/10.3390/atmos12040512 - 17 Apr 2021
Cited by 34 | Viewed by 4574
Abstract
The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values of the tide have a considerable practical interest is the [...] Read more.
The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values of the tide have a considerable practical interest is the city of Venice. The MOSE (MOdulo Sperimentale Elettromeccanico) system was created to protect Venice from flooding caused by the highest tides. Proper operation of the protection system requires an adequate forecast model of the highest tides, which is able to provide reliable forecasts even some days in advance. Nonlinear Autoregressive Exogenous (NARX) neural networks are particularly effective in predicting time series of hydrological quantities. In this work, the effectiveness of two distinct NARX-based models was demonstrated in predicting the extreme values of high tides in Venice. The first model requires as input values the astronomical tide, barometric pressure, wind speed, and direction, as well as previously observed sea level values. The second model instead takes, as input values, the astronomical tide and the previously observed sea level values, which implicitly take into account the weather conditions. Both models proved capable of predicting the extreme values of high tides with great accuracy, even greater than that of the models currently used. Full article
(This article belongs to the Special Issue Machine Learning for Extreme Events)
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<p>Location of the <span class="html-italic">Punta della Salute</span> tide gauge (<span class="html-fig-inline" id="atmosphere-12-00512-i001"> <img alt="Atmosphere 12 00512 i001" src="/atmosphere/atmosphere-12-00512/article_deploy/html/images/atmosphere-12-00512-i001.png"/></span>) and <span class="html-italic">Piattaforma CNR</span> weather station (<span class="html-fig-inline" id="atmosphere-12-00512-i002"> <img alt="Atmosphere 12 00512 i002" src="/atmosphere/atmosphere-12-00512/article_deploy/html/images/atmosphere-12-00512-i002.png"/></span>), with a thematic map of the Venice Lagoon [<a href="#B27-atmosphere-12-00512" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>Sketch of the NARX model architecture.</p> Full article ">Figure 3
<p>Comparison between measured and predicted storm tide.</p> Full article ">Figure 4
<p>Extreme storm tide events forecasting in the period 11–14 November 2019, comparison between measured time series and predicted values: Model A—<span class="html-italic">t<sub>a</sub></span> = 12 h (<b>a</b>); Model B—<span class="html-italic">t<sub>a</sub></span> = 12 h (<b>b</b>); Model A—<span class="html-italic">t<sub>a</sub></span> = 24 h (<b>c</b>); Model B—<span class="html-italic">t<sub>a</sub></span> = 24 h (<b>d</b>); Model A—<span class="html-italic">t<sub>a</sub></span> = 48 h (<b>e</b>); Model B—<span class="html-italic">t<sub>a</sub></span> = 48 h (<b>f</b>); Model A—<span class="html-italic">t<sub>a</sub></span> = 72 h (<b>g</b>); Model B—<span class="html-italic">t<sub>a</sub></span> = 72 h (<b>h</b>).</p> Full article ">Figure 5
<p>Comparison between measured and predicted storm tide—Sensitivity analysis to the training time series length.</p> Full article ">Figure 6
<p>Event 1, extreme storm tide event forecasting for the period 10–14 November 2012, comparison between measured time series and predicted values: Model B, <span class="html-italic">t<sub>a</sub></span> = 12 h (on the <b>left</b>), <span class="html-italic">t<sub>a</sub></span> = 72 h (on the <b>right</b>).</p> Full article ">Figure 7
<p>Event 2, extreme storm tide event forecasting for the period 27 October–3 November 2018, comparison between measured time series and predicted values: Model B, <span class="html-italic">t<sub>a</sub></span> = 12 h (on the <b>left</b>), <span class="html-italic">t<sub>a</sub></span> = 72 h (on the <b>right</b>).</p> Full article ">Figure 8
<p>Event 3, extreme storm tide event forecasting for the period 20–29 December 2019, comparison between measured time series and predicted values: Model B, <span class="html-italic">t<sub>a</sub></span> = 12 h (on the <b>left</b>), <span class="html-italic">t<sub>a</sub></span> = 72 h (on the <b>right</b>).</p> Full article ">Figure A1
<p>Sketch of the ensemble model.</p> Full article ">Figure A2
<p>Comparison between measured and predicted storm tide for different lag times—Ensemble model.</p> Full article ">Figure A3
<p>Extreme storm tide event forecasting for the period 11–14 November 2019, comparison between measured time series and predicted values: Model C.</p> Full article ">
23 pages, 10478 KiB  
Article
Effects of Densification on Urban Microclimate—A Case Study for the City of Vienna
by Wolfgang Loibl, Milena Vuckovic, Ghazal Etminan, Matthias Ratheiser, Simon Tschannett and Doris Österreicher
Atmosphere 2021, 12(4), 511; https://doi.org/10.3390/atmos12040511 - 17 Apr 2021
Cited by 25 | Viewed by 6697
Abstract
Climate adaptation, mitigation, and protecting strategies are becoming even more important as climate change is intensifying. The impacts of climate change are especially tangible in dense urban areas due to the inherent characteristics of urban structure and materiality. To assess impacts of densification [...] Read more.
Climate adaptation, mitigation, and protecting strategies are becoming even more important as climate change is intensifying. The impacts of climate change are especially tangible in dense urban areas due to the inherent characteristics of urban structure and materiality. To assess impacts of densification on urban climate and potential adaptation strategies a densely populated Viennese district was modeled as a typical sample area for the city of Vienna. The case study analyzed the large-scale densification potential and its potential effects on microclimate, air flow, comfort, and energy demand by developing 3D models of the area showing the base case and densification scenarios. Three methods were deployed to assess the impact of urban densification: Micro-climate analysis (1) explored urban heat island phenomena, wind pattern analysis (2) investigated ventilation and wind comfort at street level, and energy and indoor climate comfort analysis (3) compared construction types and greening scenarios and analyzed their impact on the energy demand and indoor temperatures. Densification has negative impacts on urban microclimates because of reducing wind speeds and thus weakening ventilation of street canyons, as well as accelerating heat island effects and associated impact on the buildings. However, densification also has daytime cooling effects because of larger shaded areas. On buildings, densification may have negative effects especially in the new upper, sun-exposed floors. Construction material has less impact than glazing area and rooftop greening. Regarding adaptation to climate change, the impacts of street greening, green facades, and green roofs were simulated: The 24-h average mean radiant temperature (MRT) at street level can be reduced by up to 15 K during daytime. At night there is only a slight reduction by a few tenths of 1 K MRT. Green facades have a similar effect on MRT reduction, while green roofs show only a slight reduction by a few tenths of 1 K MRT on street level. The results show that if appropriate measures were applied, negative effects of densification could be reduced, and positive effects could be achieved. Full article
(This article belongs to the Special Issue Urban Design Guidelines for Climate Change)
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<p>3D model of the study area: Base case depicting the current volumes developed from the Land Use and Zoning Plan of the City of Vienna [<a href="#B46-atmosphere-12-00511" class="html-bibr">46</a>].</p> Full article ">Figure 2
<p>3D model of the study area: Densification scenario including the volumes for maximum allowable building height (marked in blue) and the location of a hypothetical high-rise buildings cluster.</p> Full article ">Figure 3
<p>3D model for the microclimate simulations for three subsets of the study area: Large domain for simulating large-scale general densification impacts for base case (<b>a</b>) and densification scenarios (<b>b</b>); sub-domain for simulating impacts of adaptation measures for base case (<b>c</b>) and densification scenarios (<b>d</b>); location for the simulation of the impact of a high-rise cluster for the base case (<b>e</b>) and densification scenarios (<b>f</b>).</p> Full article ">Figure 4
<p>Windrose diagrams: Average wind speeds (color) and directions (bars) observed at the inner city weather station in Vienna for all weather situations (<b>left</b>) and for hours with temperatures &gt; 30 °C (<b>right</b>).</p> Full article ">Figure 5
<p>Number of hours per year (in the map’s legend abbreviated as “h/a” (hours per annum)) with uncomfortable wind speeds.</p> Full article ">Figure 6
<p>Theoretical vertical extensions of the case study buildings with the floor extensions shown in green with flat and slanted roofs [<a href="#B53-atmosphere-12-00511" class="html-bibr">53</a>,<a href="#B54-atmosphere-12-00511" class="html-bibr">54</a>].</p> Full article ">Figure 7
<p>Twenty-four-hour mean radiant temperature (MRT) at street level for large-scale general densification impacts for base case (<b>left</b>) and densification scenario (<b>right</b>).</p> Full article ">Figure 8
<p>Twenty-four-hour MRT at street level for smaller sample area for base case (<b>left</b>) and densification scenario (<b>middle</b>) and 24-h MRT differences at street level between base case and densification scenario.</p> Full article ">Figure 9
<p>Twelve-hour MRT differences at street level between base case and densification scenario for smaller sample area for daytime (<b>left</b>) and nighttime (<b>right</b>).</p> Full article ">Figure 10
<p>Twenty-four-hour MRT at street level for high-rise cluster for base case (top left) and densification scenario (top right) and 12-h MRT differences at street level between base case and densification scenario for smaller sample area for daytime (<b>left</b>) and nighttime (<b>right</b>).</p> Full article ">Figure 11
<p>The 3D model of the smaller sample area for the microclimate simulations: for the initial densification scenario (<b>a</b>), for the densification scenario with green roofs (<b>b</b>), with additional trees at street level (<b>c</b>), and with green facades in a south-western street block (<b>d</b>).</p> Full article ">Figure 12
<p>Twenty-four-hour MRT at street level for the smaller sample area with densification (<b>left</b>), densification and added trees at street level (<b>middle</b>), and the differences at street level between densification scenario without and with trees (<b>right</b>).</p> Full article ">Figure 13
<p>Twenty-four-hour MRT at street level for the smaller sample area with densification with additional green roofs (<b>left</b>) and green facades (<b>right</b>).</p> Full article ">Figure 14
<p>Changes in wind speed and direction for wind direction west for base case (<b>left</b>) and densification scenario (<b>right</b>) with streets with significant changes marked in yellow.</p> Full article ">Figure 15
<p>Changes in redirecting wind flow reducing wind speed for wind direction south-east for base case (<b>left</b>) and densification scenario (<b>right</b>).</p> Full article ">Figure 16
<p>Changes in wind speed and direction for south-east wind direction for base case (<b>left</b>) and the high-rise buildings densification scenario (<b>right</b>).</p> Full article ">Figure 17
<p>Changes in wind comfort for westerly wind direction for base case (<b>left</b>) and a high-rise buildings densification scenario (<b>right</b>).</p> Full article ">Figure 18
<p>Heating energy demand and indoor room temperature for construction types with glazing equal to existing building (for wall type description see <a href="#atmosphere-12-00511-t004" class="html-table">Table 4</a>) [<a href="#B53-atmosphere-12-00511" class="html-bibr">53</a>].</p> Full article ">Figure 19
<p>Heating energy demand and indoor room temperature for construction types with glazing twice to existing building (for wall type description see <a href="#atmosphere-12-00511-t004" class="html-table">Table 4</a>) [<a href="#B53-atmosphere-12-00511" class="html-bibr">53</a>].</p> Full article ">Figure 20
<p>Heating energy demand and indoor room temperature for green roof construction types [<a href="#B54-atmosphere-12-00511" class="html-bibr">54</a>].</p> Full article ">Figure 21
<p>Indoor temperature for green roof facade construction types [<a href="#B54-atmosphere-12-00511" class="html-bibr">54</a>].</p> Full article ">
16 pages, 3625 KiB  
Article
Seasonal Variations of Carbonyls and Their Contributions to the Ozone Formation in Urban Atmosphere of Taiyuan, China
by Zeqian Liu, Yang Cui, Qiusheng He, Lili Guo, Xueying Gao, Yanli Feng, Yuhang Wang and Xinming Wang
Atmosphere 2021, 12(4), 510; https://doi.org/10.3390/atmos12040510 - 17 Apr 2021
Cited by 15 | Viewed by 3129
Abstract
Ambient carbonyls are critical precursors of ozone (O3) and secondary organic aerosols (SOA). To better understand the pollution characteristics of carbonyls in Taiyuan, field samplings were conducted, and 13 carbonyls were detected in an urban site of Taiyuan for the four [...] Read more.
Ambient carbonyls are critical precursors of ozone (O3) and secondary organic aerosols (SOA). To better understand the pollution characteristics of carbonyls in Taiyuan, field samplings were conducted, and 13 carbonyls were detected in an urban site of Taiyuan for the four seasons. The total concentration of carbonyls in the atmosphere was 19.67 ± 8.56 ?g/m3. Formaldehyde (7.70 ± 4.78 ?g/m3), acetaldehyde (2.95 ± 1.20 ?g/m3) and acetone (5.57 ± 2.41 ?g/m3) were the dominant carbonyl compounds, accounting for more than 85% of the total carbonyls. The highest values for formaldehyde and acetone occurred in summer and autumn, respectively, and the lowest occurred in winter. The variations for acetaldehyde were not distinct in the four seasons. Formaldehyde and acetone levels increased obviously in the daytime and decreased at night, while acetaldehyde did not show significant diurnal variations. Higher temperature and stronger sunlight intensity could facilitate the photochemical reaction of volatile organic compounds (VOCs) and enhance the O3 levels in summer. Formaldehyde and acetaldehyde contributed 70–95% of carbonyls’ ozone formation potential (OFP) caused by carbonyls with the highest totals of 268.62 ?g/m3 and 38.14 ?g/m3, respectively. The highest concentrations of carbonyls from south and southwest winds in summer suggest that the coke industries in the southern Taiyuan Basin should be, firstly, controlled for the alleviation of ozone pollution. Full article
(This article belongs to the Section Air Quality)
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<p>The location of sampling site in Taiyuan and distribution of coking areas in Shanxi province.</p> Full article ">Figure 2
<p>Seasonal variations of formaldehyde, acetaldehyde, acetone and MACR×20 in Taiyuan.</p> Full article ">Figure 3
<p>Wind rose plots for four seasons.</p> Full article ">Figure 4
<p>Diurnal variations of formaldehyde, acetaldehyde and acetone in the four seasons.</p> Full article ">Figure 5
<p>Correlation coefficients in Taiyuan in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>) and winter (<b>d</b>) (formaldehyde: C1, acetaldehyde: C2, acetone: C3, T: temperature).</p> Full article ">Figure 6
<p>Time series of ozone formation potential (OFP) of carbonyls during the sampling period in summer.</p> Full article ">Figure A1
<p>The concentration ratios of formaldehyde/CO (C<sub>1</sub>/CO), acetaldehyde/CO (C<sub>2</sub>/CO) and acetone/CO (C<sub>3</sub>/CO).</p> Full article ">Figure A2
<p>Diurnal variations of wind speed and boundary layer height in four seasons.</p> Full article ">Figure A3
<p>The wind direction weighted carbonyls concentrations in summer.</p> Full article ">
20 pages, 3198 KiB  
Article
Wind Energy Assessment during High-Impact Winter Storms in Southwestern Europe
by Ana Gonçalves, Margarida L. R. Liberato and Raquel Nieto
Atmosphere 2021, 12(4), 509; https://doi.org/10.3390/atmos12040509 - 17 Apr 2021
Cited by 7 | Viewed by 3834
Abstract
The electricity produced through renewable resources is dependent on the variability of weather conditions and, thus, on the availability of the resource, as is the case with wind energy. This study aims to assess the wind resource available and the wind energy potential [...] Read more.
The electricity produced through renewable resources is dependent on the variability of weather conditions and, thus, on the availability of the resource, as is the case with wind energy. This study aims to assess the wind resource available and the wind energy potential (WEP) during the December months for the three years 2017, 2018, and 2019, in southwestern Europe, when several high-impact storms affected the region. Additionally, a comparison of Prandtl’s logarithmic law and Power-law equations for extrapolation of the vertical wind profile is performed for onshore conditions, to evaluate the differences in terms of energy production, with the use of different equations. To assess the effect of the strong winds associated with the storms, 10 m wind components are used, with a 6-hourly temporal resolution, for the December months over the southwestern Europe region (30° N–65° N; 40° W–25° E). Results are compared to the climatology (1981–2010) and show an increase of wind intensity of 1.86 m·s?1 in southwestern Europe during December 2019, and a decrease up to 2.72 m·s?1 in December 2018. WEP is calculated for the selected wind turbine, 4 MW E-126 EP3—ENERCON, as well as the values following the wind resource record, that is, (i) higher values in December 2019 in the offshore and onshore regions, reaching 35 MWh and 20 MWh per day, respectively, and (ii) lower values in December 2018, with 35 MWh and 15 MWh per day for offshore and onshore. Differences in WEP when using the two equations for extrapolation of wind vertical profile reached 60% (40%) in offshore (onshore) regions, except for the Alps, where differences of up to 80% were reached. An additional analysis was made to understand the influence of the coefficients of soil roughness and friction used in each equation (Prandtl’s logarithmic law and Power-law), for the different conditions of onshore and offshore. Finally, it is notable that the highest values of wind energy production occurred on the stormy days affecting southwestern Europe. Therefore, we conclude that these high-impact storms had a positive effect on the wind energy production in this region. Full article
(This article belongs to the Special Issue Selected Papers from the Third International Electronic Conference on Atmospheric Sciences)
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<p>The power curve (in kW) of the selected wind turbine (4 MW E-126 EP3, ENERCON) for wind speeds ranging from 0 to over the cut-out velocity (25 m·s<sup>−1</sup>). Note a rated (maximum) power of 4000 kW, wind-rated speed of 13 m·s<sup>−1</sup>, rotor diameter of 127 m, and cut-in velocity of 3 m·s<sup>−1</sup>. Adapted from [<a href="#B56-atmosphere-12-00509" class="html-bibr">56</a>,<a href="#B57-atmosphere-12-00509" class="html-bibr">57</a>].</p> Full article ">Figure 2
<p>(<b>A</b>) Climatological average of ERA5 wind speed at 10 m (m·s<sup>−1</sup>), for the December months of 1981 to 2010. (<b>B</b>–<b>D</b>) Anomalies of wind speed at 10 m (m·s<sup>−1</sup>) for the December months of 2017, 2018, and 2019, respectively.</p> Full article ">Figure 3
<p>Wind Energy Potential (WEP) (MWh·day<sup>−1</sup>): December 2017 (<b>A</b>,<b>B</b>); December 2018 (<b>D</b>,<b>E</b>); December 2019 (<b>G</b>,<b>H</b>); (<b>A</b>,<b>D</b>,<b>G</b>) calculations made using Equation (1) (LogL, <span class="html-italic">z</span><sub>0</sub> = 0.03 m); (<b>B</b>,<b>E</b>,<b>H</b>) calculations made using Equation (2) (PL, α = 0.20); (<b>C</b>,<b>F</b>,<b>I</b>) percentage difference of WEP between the use of Equations (1) and (2), for the respective months of each year.</p> Full article ">Figure 4
<p>(<b>A</b>,<b>E</b>) Climatological average of the Wind Energy Potential (WEP) (MWh·day<sup>−1</sup>) for the December months of 1981 to 2010. Anomalies of WEP (MWh·day<sup>−1</sup>) for the December months of (<b>B</b>) and (<b>F</b>) 2017; (<b>C</b>,<b>G</b>) 2018; (<b>D</b>,<b>H</b>) 2019. (<b>A</b>–<b>D</b>) calculations made using Equation (1) (the LogL, <span class="html-italic">z</span><sub>0</sub> = 0.03 m); (<b>E</b>–<b>H</b>) calculations made using Equation (2) (the PL, α = 0.20).</p> Full article ">Figure 5
<p>Wind speed at 10 m (m·s<sup>−1</sup>) on 8 December 2018.</p> Full article ">Figure 6
<p>Wind energy potential (WEP) (MWh·day<sup>−1</sup>) on 8 December 2018. (<b>A</b>) Calculations made using Equation (1) (the LogL, <span class="html-italic">z</span><sub>0</sub> = 0.03 m); (<b>B</b>) calculations made using Equation (2) (the PL, α = 0.20).</p> Full article ">
11 pages, 711 KiB  
Article
The Influence of Magnetic Turbulence on the Energetic Particle Transport Upstream of Shock Waves
by Silvia Perri, Giuseppe Prete, Francesco Malara, Francesco Pucci and Gaetano Zimbardo
Atmosphere 2021, 12(4), 508; https://doi.org/10.3390/atmos12040508 - 17 Apr 2021
Cited by 7 | Viewed by 2379
Abstract
Energetic particles are ubiquitous in the interplanetary space and their transport properties are strongly influenced by the interaction with magnetic field fluctuations. Numerical experiments have shown that transport in both the parallel and perpendicular directions with respect to the background magnetic field is [...] Read more.
Energetic particles are ubiquitous in the interplanetary space and their transport properties are strongly influenced by the interaction with magnetic field fluctuations. Numerical experiments have shown that transport in both the parallel and perpendicular directions with respect to the background magnetic field is deeply affected by magnetic turbulence spectral properties. Recently, making use of a numerical model with three dimensional isotropic turbulence, the influence of turbulence intermittency and magnetic fluctuations on the energetic particle transport was investigated in the solar wind context. Stimulated by this previous theoretical work, here we analyze the parallel transport of supra-thermal particles upstream of interplanetary shock waves by using in situ particle flux measurements; the aim was to relate particle transport properties to the degree of intermittency of the magnetic field fluctuations and to their relative amplitude at the energetic particle resonant scale measured in the same regions. We selected five quasi-perpendicular and five quasi-parallel shock crossings by the ACE satellite. The analysis clearly shows a tendency to find parallel superdiffusive transport at quasi-perpendicular shocks, with a significantly higher level of the energetic particle fluxes than those observed in the quasi-parallel shocks. Furthermore, the occurrence of anomalous parallel transport is only weakly related to the presence of magnetic field intermittency. Full article
(This article belongs to the Special Issue Turbulence and Instabilities in Fluids and Plasmas)
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<p>Two satellite shock crossings in quasi–parallel (<b>left panels</b>) and in quasi–perpendicular configuration (<b>right panels</b>). From top to bottom: the magnetic field intensity from the ACE/MAG instrument at a resolution of 1 vec/s; the radial component of the solar wind bulk speed and the plasma temperature from the ACE/SWEPAM experiment at 64 s resolution; and the ion fluxes in four energy channels (as indicated in the legend in the right bottom panel) from the ACE/EPAM instrument at a resolution of 12 s, as a function of the distance from the shock time (vertical dashed lines). Notice that far downstream of the 11 February 2011 event (at about 200 min from the shock), a hot (and low density) portion of the solar wind plasma occurs, also associated to larger fluctuations in <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>B</mi> <mo>|</mo> </mrow> </semantics></math>, though this is not actually related to the shock itself.</p> Full article ">Figure 2
<p>Plot in log–log axes of the ion energy fluxes in four different channels (as indicated in the figure legend) as a function of the distance from the shock time. For the quasi-parallel shock of the 17 June 2011 (<b>left panel</b>) the far upstream decay is well fitted by an exponential function <math display="inline"><semantics> <mrow> <mi>J</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>∝</mo> <mo form="prefix">exp</mo> <mrow> <mo>(</mo> <mo>−</mo> <mi>t</mi> <mo>/</mo> <mi>T</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, while in the quasi-perpendicular shock on 11 February 2011 (<b>right panel</b>), the ion fluxes decay as a power-law in the upstream region suggesting superdiffusive transport. The exponential and power−law best fits are reported in the panels together with their best fit parameters.</p> Full article ">Figure 3
<p>Kurtosis as a function of the time scale <math display="inline"><semantics> <mi>τ</mi> </semantics></math> in the quasi-parallel shock crossing of the 17 June 2011 (<b>left panel</b>) and in the quasi-perpendicular shock crossing on 11 February 2011 (<b>right panel</b>). The Gaussian level of 3 is indicated by the horizontal dashed line and the time scale corresponding to the Larmor radius of energetic protons of 100 keV is shown by the vertical solid line. Error bars are also reported.</p> Full article ">Figure 4
<p>Probability density functions of the 100 keV energetic particles’ scattering times computed upstream of shock crossings with different levels of <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>B</mi> <mo>/</mo> <msub> <mi>B</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and similar intermittency (<b>left panel</b>) and with different intermittency values but a similar <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>B</mi> <mo>/</mo> <msub> <mi>B</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (<b>right panel</b>).</p> Full article ">
22 pages, 10551 KiB  
Article
Multiscale Modeling of Convection and Pollutant Transport Associated with Volcanic Eruption and Lava Flow: Application to the April 2007 Eruption of the Piton de la Fournaise (Reunion Island)
by Jean-Baptiste Filippi, Jonathan Durand, Pierre Tulet and Soline Bielli
Atmosphere 2021, 12(4), 507; https://doi.org/10.3390/atmos12040507 - 17 Apr 2021
Cited by 6 | Viewed by 2975
Abstract
Volcanic eruptions can cause damage to land and people living nearby, generate high concentrations of toxic gases, and also create large plumes that limit observations and the performance of forecasting models that rely on these observations. This study investigates the use of micro- [...] Read more.
Volcanic eruptions can cause damage to land and people living nearby, generate high concentrations of toxic gases, and also create large plumes that limit observations and the performance of forecasting models that rely on these observations. This study investigates the use of micro- to meso-scale simulation to represent and predict the convection, transport, and deposit of volcanic pollutants. The case under study is the 2007 eruption of the Piton de la Fournaise, simulated using a high-resolution, coupled lava/atmospheric approach (derived from wildfire/atmosphere coupled code) to account for the strong, localized heat and gaseous fluxes occurring near the vent, over the lava flow, and at the lava–sea interface. Higher resolution requires fluxes over the lava flow to be explicitly simulated to account for the induced convection over the flow, local mixing, and dilution. Comparisons with air quality values at local stations show that the simulation is in good agreement with observations in terms of sulfur concentration and dynamics, and performs better than lower resolution simulation with parameterized surface fluxes. In particular, the explicit representation of the thermal flows associated with lava allows the associated thermal breezes to be represented. This local modification of the wind flow strongly impacts the organization of the volcanic convection (injection height) and the regional transport of the sulfur dioxide emitted at the vent. These results show that explicitly solving volcanic activity/atmosphere complex interactions provides realistic forecasts of induced pollution. Full article
(This article belongs to the Special Issue Coupled Fire-Atmosphere Simulation)
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<p>Different embedded domains at 2 km, 500 m, and 100 m used for the simulation of the eruption of the Piton de la Fournaise, April 2007.</p> Full article ">Figure 2
<p>Arrival time matrix for lava flow. The lightest orange corresponds to the first lava flows of 2 April and the darkest red to the last flows of 7 April.</p> Full article ">Figure 3
<p><b>Above</b>: Emission of CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> from burning forests, emitted by the ForeFire surface model towards atmospheric model. <b>Bottom</b>: Correspondence of CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> emission peaks with forest areas in green. (In blue, new land created by the lava flow and area of water vapor and HCl injection.)</p> Full article ">Figure 4
<p>Estimation of SO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> emission between 4 and 11 April from [<a href="#B28-atmosphere-12-00507" class="html-bibr">28</a>].</p> Full article ">Figure 5
<p>Emission of magma at the eruptive vent according to the authors of [<a href="#B40-atmosphere-12-00507" class="html-bibr">40</a>] and emission of water vapour from ForeFire.</p> Full article ">Figure 6
<p>Evolution of the quantity of sea water evaporated between 2 and 9 April. The green curve corresponds to the dynamics of the theoretical quantity of water. In red, adjustments in this quantity obtained by a top-down approach to better represent injection heights consistent with the satellite data.</p> Full article ">Figure 7
<p>Evolution of the sensible (<b>left</b>) and latent (<b>centre</b>) heat flux in W m<sup>−2</sup> simulated by coupled MesoNH-ForeFire-LAVA on the 2 (<b>top</b>), 4 (<b>centre</b>), and 6 April (<b>bottom</b>) 2007 (at 12:00 UTC). On the right, impacts on potential surface temperature as simulated. Isolines represent topography (in meters asl).</p> Full article ">Figure 8
<p>Simulated surface winds on 6 April 2007 convergence lines in red.</p> Full article ">Figure 9
<p>(<b>top</b>) Wind fields at the surface (color intensity in <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> <math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math><sup>−1</sup>) and wind arrows simulated by MesoNH on 6 April at 06 UTC. Positive vertical speeds are shown in red, negative vertical speeds in green, and potential temperature in black. (<b>bottom</b>) Vertical section of wind field difference ( <math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> <math display="inline"><semantics> <mi mathvariant="normal">s</mi> </semantics></math><sup>−1</sup>) and temperature (K) between a simulation with lava and a simulation without lava.</p> Full article ">Figure 10
<p>(<b>left</b>) CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> fluxes (in mol<math display="inline"><semantics> <mrow> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>m<math display="inline"><semantics> <mrow> <msup> <mrow/> <mn>2</mn> </msup> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) on 6 April at 06 UTC. (<b>right</b>) Vertical section representing CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> concentration (ppm) and isolines of vertical wind intensity (ascending in red and subsiding in green). Dashed line corresponds to the cross section of <a href="#atmosphere-12-00507-f011" class="html-fig">Figure 11</a>.</p> Full article ">Figure 11
<p>(<b>left</b>) HCl Fluxes (in mol<math display="inline"><semantics> <mrow> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>m<math display="inline"><semantics> <mrow> <msup> <mrow/> <mn>2</mn> </msup> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> </mrow> </semantics></math>s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) due to the entry of lava into the sea simulated by MesoNH on 6 April at 06:00UTC. Axis of the vertical section in red. (<b>right</b>) Vertical section representing the concentration of HCl (ppb) and the projected wind vectors in the axis of the section. Dashed line corresponds to the cross section of <a href="#atmosphere-12-00507-f010" class="html-fig">Figure 10</a>.</p> Full article ">Figure 12
<p>Simulated ground SO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> concentration (ppb) and surface wind field, 4 April 00:00UTC.</p> Full article ">Figure 13
<p>Comparison of the surface SO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> concentration (<math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math><math display="inline"><semantics> <mi mathvariant="normal">g</mi> </semantics></math> <math display="inline"><semantics> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>) observed at the ATMO-Réunion stations in Cambaie and Saint-Louis (Black) with the MesoNH simulation (blue) and previous simulations from in [<a href="#B21-atmosphere-12-00507" class="html-bibr">21</a>] (red) between 2 and 7 April. Dashed lines represent information (300) and health (500) concentration levels.</p> Full article ">
28 pages, 4620 KiB  
Article
Venus Atmospheric Dynamics at Two Altitudes: Akatsuki and Venus Express Cloud Tracking, Ground-Based Doppler Observations and Comparison with Modelling
by Pedro Machado, Thomas Widemann, Javier Peralta, Gabriella Gilli, Daniela Espadinha, José E. Silva, Francisco Brasil, José Ribeiro and Ruben Gonçalves
Atmosphere 2021, 12(4), 506; https://doi.org/10.3390/atmos12040506 - 17 Apr 2021
Cited by 13 | Viewed by 3848
Abstract
We present new results of our studies of zonal and meridional winds in both hemispheres of Venus, using ground- and space-based coordinated observations. The results obtained from telescope observations were retrieved with a Doppler velocimetry method. The wind velocities retrieved from space used [...] Read more.
We present new results of our studies of zonal and meridional winds in both hemispheres of Venus, using ground- and space-based coordinated observations. The results obtained from telescope observations were retrieved with a Doppler velocimetry method. The wind velocities retrieved from space used an improved cloud-tracked technique based on the phase correlation between images. We present evidence that the altitude level sensed by our Doppler velocimetry method is approximately four kilometres higher (~4 km) than that using ground-tracked winds (using 380 or 365 nm). Since we often take advantage of coordinated space and ground observations simultaneously, this altitude difference will be very relevant in order to estimate the vertical wind shear at the related heights in future observation campaigns. We also explored a previous coordinated campaign using Akatsuki observations and its Ultraviolet Imager (UVI) at 283 and 365 nm filters, which showed that cloud-tracked winds showed a difference of about 10–15 ms?1, as in the case of the comparison between the Doppler velocimetry winds and the 365 nm cloudtracked winds. The results’ comparison also strongly suggested that the cloud-tracked winds based on the 283 nm filter’s images were sensing at about the same atmospheric altitude level as the Doppler winds. The observational results were compared with the ground-to-thermosphere 3D model developed at the Laboratoire de Meteorologie Dynamique (IPSL-Venus General Circulation Model (VGCM)) and AFES-Venus General Circulation Model (GCM), at several pressure levels (and related heights). The analysis and results showed the following: (1) additional confirmation of the coherence and complementarity in the results provided by these techniques on both the spatial and temporal time scales of the two methods; (2) we noticed in the following that the results from the two different Akatsuki/UVI filters (283 and 365 nm) showed an average difference of about 10–15 ± 5 ms?1, and we suggest this may be related to SO2 atmospheric fluctuations and the particular conditions in the coordinated observing time window; (3) we present evidence indicating that, in the context of our observations, visible Doppler methods (highly self-consistent) seem to sense wind speeds at a vertical level closer to or within the range sensed by the UVI 283 nm filter images (again, in the context of our observations); (4) modelling predicted wind profiles suggests that the layers of the atmosphere of Venus sensed by the methods referred to in Point 3 differ by approximately four km in altitude (~4 ± 2 km) regarding the cloud-tracked winds retrieved using 365 or 380 nm images. Full article
(This article belongs to the Special Issue Observations of Venus Atmosphere)
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<p>Observations’ strategy with the Canada-France-Hawaii Telescope (CFHT) and the high-resolution spectrograph ESPaDOnS (Echelle SpectroPolarimetric Device for the Observation of Stars).</p> Full article ">Figure 2
<p>Six days of daily zonal wind latitudinal profiles retrieved from Akatsuki/UVI 283 nm images, from 26–31 January 2017. The values presented result from a weighted average at each latitudinal band sensed on each day, with a binning of 5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> in latitude. The solid line represents the mean wind latitudinal profile and its respective error bars in velocity and latitudinal location. CT, cloud tracking.</p> Full article ">Figure 3
<p>Daily meridional wind latitudinal profiles from the Akatsuki/UVI 283 nm filter observations (26–31 January 2017). After grouping the meridional wind measurements into a binning of 5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> in latitude, we performed a weighted average at each latitude band. Positive velocities mean a motion towards north and negative ones towards south.</p> Full article ">Figure 4
<p>Left panel: Mean zonal wind latitudinal profile of the Akatsuki/UVI 283 nm filter results. Right panel: Mean meridional wind latitudinal profile also based on the same instrument and filter observations. Both mean profiles consist of the outcome of the weighted average performed upon the daily profiles shown previously from 26–31 January 2017. Each wind velocity magnitude presented is the result of the mean of each latitude band for all days with a binning of 5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> in latitude.</p> Full article ">Figure 5
<p>(Left panel) Comparison between the mean zonal wind latitudinal profiles retrieved from Akatsuki/UVI 283 nm filter (red colour in the figure) obtained in the framework of this present work and the one retrieved from the 365 nm UVI filter (blue colour in the figure) produced in the context of Gonçalves et al. [<a href="#B7-atmosphere-12-00506" class="html-bibr">7</a>]. (Right panel) The same as in the left panel, but in this case regarding the meridional wind’s latitudinal profile.</p> Full article ">Figure 6
<p>Mean zonal wind latitudinal profile. Here, we present the results from cloud-tracked VEx VIRTIS-M 380 nm images, obtained between 8 and 10 February 2014. The weighted mean zonal winds were binned in a 5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> step latitude interval. For comparison purposes, we also plot the precedent zonal profile obtained by Sánchez-Lavega et al. [<a href="#B19-atmosphere-12-00506" class="html-bibr">19</a>] using the same instrument and wavelength images.</p> Full article ">Figure 7
<p>Comparison between this work’s mean zonal wind latitudinal profile (VEx VIRTIS-M at 380 nm) with other previous space-based long-term precedent results using the same instrument and wavelength images [<a href="#B4-atmosphere-12-00506" class="html-bibr">4</a>,<a href="#B6-atmosphere-12-00506" class="html-bibr">6</a>,<a href="#B19-atmosphere-12-00506" class="html-bibr">19</a>,<a href="#B21-atmosphere-12-00506" class="html-bibr">21</a>], as well as the results from Akatsuki/UVI at 365 nm [<a href="#B1-atmosphere-12-00506" class="html-bibr">1</a>,<a href="#B7-atmosphere-12-00506" class="html-bibr">7</a>].</p> Full article ">Figure 8
<p>(Upper panel) Mean zonal wind latitudinal profile retrieved with Doppler velocimetry techniques using high-resolution spectra from CFHT/ESPaDOnS ground-based observations. The segments in different colours are the profile’s contribution from each day of observations (see figure legend). (Lower panel) Here, we present the mean meridional wind latitudinal profile based on the same dataset as was described for the left panel’s case. Positive velocities mean a poleward meridional wind moving from the equator to the north; negative velocities mean a southern hemisphere meridional motion from equator towards the south.</p> Full article ">Figure 9
<p>The upper panel and the lower panel present, respectively, the global mean zonal and meridional latitudinal wind profiles, regarding all three days of observations.</p> Full article ">Figure 10
<p>Comparison between wind results obtained in the context of the present work and other similar results from our group’s previous works. In the upper panel, we display several latitudinal zonal wind profiles obtained by our group at several coordinated observational projects. Space-based (cloud-tracked winds) observations were performed by VEx VIRTIS-M (380 nm), and our simultaneous (or almost simultaneous) day-side observations were made using the high-resolution spectrograph ESPaDOnS at the CFHT telescope (Doppler velocimetry). The comparison between the zonal wind profiles obtained from high-resolution spectra and Doppler velocimetry techniques and cloud-tracked zonal winds retrieved from Akatsuki/UVI instrument with its 283 nm filter, analysed in the framework of this project, is also presented here. (Lower panel) The same as for the upper panel, but in this case, we compare Doppler velocimetry (ground-based) meridional wind latitudinal profiles with the ones retrieved from cloud-tracked winds (space-based). The set of latitudinal meridional wind profiles shown here were retrieved in the scope of this work and compared with the results from our group’s previous projects. HARPS-N, High-Accuracy Radial velocity Planet Searcher for the Northern Hemisphere.</p> Full article ">Figure 11
<p>Comparison between predicted zonal wind profiles from the IPSL-Venus General Circulation Model (VGCM) and the AFES-Venus GCM and the cloud-tracked mean zonal wind latitudinal profiles (VEx/VIRTIS-M (380 nm) and Akatsuki/UVI (365 nm)) in the context of the present work and from reference atmospheric dynamical studies of Venus. Model profiles that best fit the observations are shown in a light blue-coloured band (IPSL-VGCM) and in a light green plot (AFES-Venus).</p> Full article ">Figure 12
<p>Comparison between a model’s predicted meridional flow latitudinal velocity profile and the profiles retrieved from ground-based observations and Doppler velocimetry techniques applied to the high-resolution spectra obtained with ESPaDONS/CFHT. The modelling profiles result from an average of five days; the observation-based profiles also consist of the mean profile from three to seven days of observations (depending on each observing run’ temporal length).</p> Full article ">Figure 13
<p>Comparison between predicted zonal wind profiles (IPSL-VGCM and AFES-Venus), cloud-tracked mean zonal wind latitudinal profiles at 283 nm (space-based: Akatsuki/UVI (this work)) and Doppler velocimetry zonal wind velocity latitudinal profiles (ground-based: VLT/UVES [<a href="#B11-atmosphere-12-00506" class="html-bibr">11</a>], CFHT/ESPaDOnS (Machado et al. [<a href="#B4-atmosphere-12-00506" class="html-bibr">4</a>], Machado et al. [<a href="#B6-atmosphere-12-00506" class="html-bibr">6</a>] and this work)). Model profiles that best fit the observations are shown in a light red-coloured band (IPSL-VGCM) and in a yellow plot (AFES-Venus).</p> Full article ">
14 pages, 5318 KiB  
Article
Atmospheric Thermal and Dynamic Vertical Structures of Summer Hourly Precipitation in Jiulong of the Tibetan Plateau
by Yonglan Tang, Guirong Xu, Rong Wan, Xiaofang Wang, Junchao Wang and Ping Li
Atmosphere 2021, 12(4), 505; https://doi.org/10.3390/atmos12040505 - 16 Apr 2021
Cited by 3 | Viewed by 2056
Abstract
It is an important to study atmospheric thermal and dynamic vertical structures over the Tibetan Plateau (TP) and their impact on precipitation by using long-term observation at representative stations. This study exhibits the observational facts of summer precipitation variation on subdiurnal scale and [...] Read more.
It is an important to study atmospheric thermal and dynamic vertical structures over the Tibetan Plateau (TP) and their impact on precipitation by using long-term observation at representative stations. This study exhibits the observational facts of summer precipitation variation on subdiurnal scale and its atmospheric thermal and dynamic vertical structures over the TP with hourly precipitation and intensive soundings in Jiulong during 2013–2020. It is found that precipitation amount and frequency are low in the daytime and high in the nighttime, and hourly precipitation greater than 1 mm mostly occurs at nighttime. Weak precipitation during the daytime may be caused by air advection, and strong precipitation at nighttime may be closely related with air convection. Both humidity and wind speed profiles show obvious fluctuation when precipitation occurs, and the greater the precipitation intensity, the larger the fluctuation. Moreover, the fluctuation of wind speed is small in the morning, large at noon and largest at night, presenting a similar diurnal cycle to that of convective activity over the TP, which is conductive to nighttime precipitation. Additionally, the inverse layer is accompanied by the inverse humidity layer, and wind speed presents multi-peaks distribution in its vertical structure. Both of these are closely related with the underlying surface and topography of Jiulong. More studies on physical mechanism and numerical simulation are necessary for better understanding the atmospheric phenomenon over the TP. Full article
(This article belongs to the Section Climatology)
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<p>Location of the Jiulong station and its surrounding terrain.</p> Full article ">Figure 2
<p>Diurnal variations of precipitation (<b>a</b>) amount, (<b>b</b>) frequency and (<b>c</b>) intensity in different grades obtained from summer hourly precipitation in Jiulong during 2013–2020.</p> Full article ">Figure 3
<p>(<b>a</b>) Precipitation probability and (<b>b</b>) precipitation proportion in different grades at launch times in Jiulong from 2013–2020.</p> Full article ">Figure 4
<p>Atmospheric temperature profiles in different precipitation grades at launch times in Jiulong from 2013–2020. (<b>a</b>) 02 LST, (<b>b</b>) 08 LST, (<b>c</b>) 14 LST and (<b>d</b>) 20 LST.</p> Full article ">Figure 5
<p>Atmospheric specific humidity profiles in different precipitation grades at launch times in Jiulong during 2013–2020. (<b>a</b>) 02 LST, (<b>b</b>) 08 LST, (<b>c</b>) 14 LST and (<b>d</b>) 20 LST.</p> Full article ">Figure 6
<p>Atmospheric horizontal wind speed profiles in different precipitation grades at launch times in Jiulong from 2013–2020. (<b>a</b>) 02 LST, (<b>b</b>) 08 LST, (<b>c</b>) 14 LST and (<b>d</b>) 20 LST.</p> Full article ">Figure 7
<p>Atmospheric horizontal wind direction probability below 3 km in different precipitation grades at launch times in Jiulong from 2013–2020. (<b>a</b>) 02 LST, (<b>b</b>) 08 LST, (<b>c</b>) 14 LST and (<b>d</b>) 20 LST.</p> Full article ">
26 pages, 13942 KiB  
Article
Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK
by Said Munir, Gulnur Coskuner, Majeed S. Jassim, Yusuf A. Aina, Asad Ali and Martin Mayfield
Atmosphere 2021, 12(4), 504; https://doi.org/10.3390/atmos12040504 - 16 Apr 2021
Cited by 30 | Viewed by 5628
Abstract
The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed [...] Read more.
The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter. Full article
(This article belongs to the Special Issue Air Pollution Changes and Impacts during Abrupt Shifts in Anthropogenic Activities)
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<p>Locations of the air quality monitoring stations (AQMS) within the study area in the UK (Source: OpenStreetMap, developed in ArcGIS 10.5.1).</p> Full article ">Figure 2
<p>Polar plots of wind speed, wind direction and temperature at Manchester Piccadilly AQMS during pre-lockdown, lockdown, and post-lockdown period, 2020.</p> Full article ">Figure 3
<p>Changes in mobility data (%) in the UK during the lockdown period. The vertical red and blue lines show the start and end of the lockdown period, respectively.</p> Full article ">Figure 4
<p>Weekly cycles of NO, NO<sub>2</sub>, and NOx for pre-lock down, lock-down, and post-lockdown periods at Devonshire Green AQMS in Sheffield.</p> Full article ">Figure 5
<p>Weekly cycles of PM<sub>2.5</sub> and PM<sub>10</sub> for pre-lock down, lock-down, and post-lockdown periods at Devonshire Green AQMS in Sheffield.</p> Full article ">Figure 6
<p>Pollution roses demonstrating how weather conditions changed during the three periods: changes in temperature linked with wind speed and directions (<b>a</b>); changes in PM<sub>2.5</sub> linked with changes in wind speed and directions at the Manchester Piccadilly site (<b>b</b>).</p> Full article ">Figure 6 Cont.
<p>Pollution roses demonstrating how weather conditions changed during the three periods: changes in temperature linked with wind speed and directions (<b>a</b>); changes in PM<sub>2.5</sub> linked with changes in wind speed and directions at the Manchester Piccadilly site (<b>b</b>).</p> Full article ">Figure 7
<p>Hysplit back trajectories centred on the UK for 6 days in April (20 to 25 April) (<b>upper-panel</b>) and 6 days in February (20 to 25 February) 2020 (<b>lower-panel</b>).</p> Full article ">Figure 7 Cont.
<p>Hysplit back trajectories centred on the UK for 6 days in April (20 to 25 April) (<b>upper-panel</b>) and 6 days in February (20 to 25 February) 2020 (<b>lower-panel</b>).</p> Full article ">Figure 8
<p>Comparison of deweathered mean hourly air quality data between lockdown period 2020 with the equivalent period in 2019 at Manchester Piccadilly AQMS. (<b>a</b>) NO; (<b>b</b>) NO<sub>2</sub>; (<b>c</b>) NO<sub>X</sub>; (<b>d</b>) PM<sub>2.5</sub>.</p> Full article ">Figure 8 Cont.
<p>Comparison of deweathered mean hourly air quality data between lockdown period 2020 with the equivalent period in 2019 at Manchester Piccadilly AQMS. (<b>a</b>) NO; (<b>b</b>) NO<sub>2</sub>; (<b>c</b>) NO<sub>X</sub>; (<b>d</b>) PM<sub>2.5</sub>.</p> Full article ">Figure 9
<p>Time series plots between normalised daily mean (%) mobility data and air pollutant concentrations at all AQMS January to June 2020. January 13 is taken as the baseline for normalisation. Vertical red and blue lines show the start and end of the lockdown period, respectively. (<b>a</b>) Sheffield Barnsley; (<b>b</b>) Sheffield Devonshire Green; (<b>c</b>) Leeds Headingley and (<b>d</b>) Manchester.</p> Full article ">Figure 9 Cont.
<p>Time series plots between normalised daily mean (%) mobility data and air pollutant concentrations at all AQMS January to June 2020. January 13 is taken as the baseline for normalisation. Vertical red and blue lines show the start and end of the lockdown period, respectively. (<b>a</b>) Sheffield Barnsley; (<b>b</b>) Sheffield Devonshire Green; (<b>c</b>) Leeds Headingley and (<b>d</b>) Manchester.</p> Full article ">Figure 10
<p>Time series plots between daily mean (%) mobility data and PM<sub>2.5</sub> and PM<sub>10</sub> at all AQMS. January 13 is taken as the baseline for normalisation. Vertical red and blue lines show the start and end of the lockdown period, respectively. (<b>a</b>) Sheffield Barnsley; (<b>b</b>) Sheffield Devonshire Green; (<b>c</b>) Leeds Headingley and (<b>d</b>) Manchester.</p> Full article ">Figure 10 Cont.
<p>Time series plots between daily mean (%) mobility data and PM<sub>2.5</sub> and PM<sub>10</sub> at all AQMS. January 13 is taken as the baseline for normalisation. Vertical red and blue lines show the start and end of the lockdown period, respectively. (<b>a</b>) Sheffield Barnsley; (<b>b</b>) Sheffield Devonshire Green; (<b>c</b>) Leeds Headingley and (<b>d</b>) Manchester.</p> Full article ">
27 pages, 10726 KiB  
Article
Exploring the Thermal Microcosms at the Forest Floor—A Case Study of a Temperate Forest
by Denise Boehnke
Atmosphere 2021, 12(4), 503; https://doi.org/10.3390/atmos12040503 - 16 Apr 2021
Cited by 3 | Viewed by 2905
Abstract
With the expected changes in summer weather due to global warming, knowledge of the microclimatic variability at the forest floor dramatically increased in importance for silviculture, wildfire management and biodiversity issues. Thus, during the warm season in 2014, thermal aspects within a heterogeneous [...] Read more.
With the expected changes in summer weather due to global warming, knowledge of the microclimatic variability at the forest floor dramatically increased in importance for silviculture, wildfire management and biodiversity issues. Thus, during the warm season in 2014, thermal aspects within a heterogeneous forest were recorded at nine sites and compared to data from a nearby weather station. It was found that soil (?5 cm) and near-surface (0–2 cm) temperatures under shaded conditions stayed remarkably cooler than temporarily or fully radiated spots inside and outside the forest; largest differences occurred in maxima (July: 22.5 °C to 53.5 °C). Solar radiation was found to be the main driver for the strong heating of near-surface microhabitats, which could be reinforced by the vegetation type (moss). The weather station widely reflected the average condition on forest floor, but lacks the biological meaningful temperature extremes. The measurement system (internal versus external sensor) resulted in differences of up to 6 K. The findings underline the importance of old or dense stands for maintaining cool microrefugia. However, also the need for careful selection and analysis of microclimatic measurements in forests, representative for specific microhabitats, under consideration of ground vegetation modifications. Full article
(This article belongs to the Special Issue Air Quality Assessment and Management)
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Figure 1

Figure 1
<p>Overview of the locations of the individual measuring spots of the DWD station (blue), micrometeorological stations (MS, SS), the Micro-Loggers of System-1 (white, ML-1 to ML-6) and System-2 (red, ML-7 to ML-10). The graphs give an aerial view (source: Google maps) from the forest (<b>a</b>), the adjoining open land to the north (<b>b</b>) as well as images of four diverse forest measurement spots (<b>c</b>–<b>f</b>).</p> Full article ">Figure 2
<p>Features of the Micro-Loggers and climate stations in the forest (MS, SS) and the weather station of the German Weather Service (= Deutscher Wetterdienst, denoted as “DWD) (<b>a</b>,<b>b</b>); (<b>c</b>) illustrates the general measurement setup of the two Micro-Logger Systems 1 (internal sensor) and 2 (external sensor), which were located under moss (<b>top</b>) or grass (<b>bottom</b>).</p> Full article ">Figure 3
<p>Temperature deviations between S1 and S2 (<b>a</b>) according to sensor system and location and (<b>b</b>) along the season. (<b>a</b>) Temperature differences of the two systems (S2 minus S1) at “wide clearing” (clear, in red) and “tree shadow” (shad, in blue) and differences between the locations looking at S1 (black) or S2 (grey) from April 2 to 5. Sunshine duration considerably decreased in the course of the days (h/day: 10.2, 3.1, 0, 0); (<b>b</b>) the lower graph depicts differences between S1 (ML-1) and S2 (ML-7) at “wide clearance” as hourly mean values from spring to autumn 2014.</p> Full article ">Figure 4
<p>Diurnal radiation and temperature pattern during clear sky conditions over three days in June 2014. MS refers to the Medium Station at the “small clearing”, with Ta = air temperature (50 cm), Ts = soil temperature (−5 cm), Tli = near-surface temperature, solrad = solar radiation, and DWD_Ta air temperature (2 m) of the nearby official weather station.</p> Full article ">Figure 5
<p>Diurnal temperature courses of the Micro-Loggers on a clear (<b>a</b>) and a cloudy (<b>b</b>) day in July 2014. On the clear day (July 18th), S1 and S2 are separately presented. The lower graph also shows the temperature situation recorded at the micrometeorological stations MS/SS and the DWD station to illustrate the low differences on cloudy days.</p> Full article ">Figure 6
<p>Averaged diurnal temperature courses in June and August 2014. Data from Micro-Loggers of type S1, from the Small Station (SS) and DWD station are shown. In 2014, June was warm and sunny, while August was cool and rainy compared to the long-term average.</p> Full article ">Figure 7
<p>Overview of the data distribution at daytime in April, June and August 2014. Again, the colors imply the grade of shading of the loggers and stations (dark green = very shaded/moss, yellow = open land/grass, beige = temporal shading/grass (ML-9 = moss), light green = temporal shading/leaf litter). The ordinate depicts the temperature (°C), please note the different scales.</p> Full article ">Figure 8
<p>Accumulated temperature sum for the study period from April to September 2014. (<b>a</b>) Showing the nearby official weather station (DWD) and forest stations (Ts = soil, Ta = air, Tli = near-surface), (<b>b</b>) the Micro-Loggers (ML) and comparable measures of DWD and Small Station (SS) and (<b>c</b>) other official weather stations comparable to the warmest ML.</p> Full article ">Figure A1
<p>Overview of the data distribution of all Micro-Loggers (ML1 to 10), the air and soil temperature of the Medium and Small Stations and the official weather station (DWD). The colors imply the grade of shading of the loggers and stations (dark green = very shaded/moss, yellow = open land/grass, beige = temporal shading/grass (ML-9 = moss), light green = temporal shading/leaf litter). The graph shows the data distribution of the total timespan (top), only at daytime (middle) and only nighttime (bottom). The ordinate depicts the temperature (°C), please note the different scales.</p> Full article ">Figure A2
<p>Spearman correlation matrix of daily temperature averages including the surface-near measurements of the Micro-Logger (ML1 to ML10) and Medium Station (Tl), air temperature (Ta) of the Medium and Small Stations at “small clearing” and the official weather station (DWD) as well as soil temperature (Ts) in 5 cm depth. The lower triangle shows the coefficient of determination (<span class="html-italic">R</span><sup>2</sup>), the upper triangle the scatterplots and linear regression fit lines (red) and the diagonal the histograms.</p> Full article ">Figure A3
<p>Spearman correlation matrix of hourly temperature averages (MS, SS, DWD) and single records (ML), respectively, including the surface-near measurements of the Micro-Logger (ML1 to ML10) and Medium Station (Tl), air temperature (Ta) of the Medium and Small Stations at “small clearing” and the official weather station (DWD) as well as soil temperature (Ts) in 5 cm depth. The lower triangle shows the coefficient of determination (<span class="html-italic">R</span><sup>2</sup>), the upper triangle the scatterplots and linear regression fit lines (red) and the diagonal the histograms.</p> Full article ">
20 pages, 11582 KiB  
Article
Particle Size Analysis of African Dust Haze over the Last 20 Years: A Focus on the Extreme Event of June 2020
by Lovely Euphrasie-Clotilde, Thomas Plocoste and France-Nor Brute
Atmosphere 2021, 12(4), 502; https://doi.org/10.3390/atmos12040502 - 15 Apr 2021
Cited by 20 | Viewed by 3843
Abstract
Over the last decades, the impact of mineral dust from African deserts on human health and climate has been of great interest to the scientific community. In this paper, the climatological analysis of dusty events of the past 20 years in the Caribbean [...] Read more.
Over the last decades, the impact of mineral dust from African deserts on human health and climate has been of great interest to the scientific community. In this paper, the climatological analysis of dusty events of the past 20 years in the Caribbean area has been performed using a particulate approach. The focus is made on June 2020 extreme event dubbed “Godzilla”. To carry out this study, different types of data were used (ground-based, satellites, model, and soundings) on several sites in the Caribbean islands. First, the magnitude of June 2020 event was clearly highlighted using satellite imagery. During the peak of this event, the value of particulate matter with an aerodynamic diameter of less than 10 ??m (PM10) reached a value 9 times greater than the threshold recommended by the World Health Organization in one day. Thereafter, the PM10, the aerosol optical depth, and the volume particle size distribution analyses exhibited their maximum values for June 2020. We also highlighted the exceptional characteristics of the Saharan air layer in terms of thickness and wind speed for this period. Finally, our results showed that the more the proportion of particulate matter with an aerodynamic diameter of less than 2.5 ??m (PM2.5) in PM10 increases, the more the influence of sea salt aerosols is significant. Full article
(This article belongs to the Special Issue Air Pollution Estimation)
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<p>Overview of the Caribbean area with, respectively, from top to the bottom Puerto Rico (<math display="inline"><semantics> <mrow> <msup> <mn>18.23</mn> <mo>°</mo> </msup> </mrow> </semantics></math> N, <math display="inline"><semantics> <mrow> <mo>−</mo> <msup> <mn>66.50</mn> <mo>°</mo> </msup> </mrow> </semantics></math> W; PR in yellow), Guadeloupe archipelago (<math display="inline"><semantics> <mrow> <msup> <mn>16.25</mn> <mo>°</mo> </msup> </mrow> </semantics></math> N, <math display="inline"><semantics> <mrow> <mo>−</mo> <msup> <mn>61.58</mn> <mo>°</mo> </msup> </mrow> </semantics></math> W; GPE in orange), and Barbados (<math display="inline"><semantics> <mrow> <msup> <mn>13.16</mn> <mo>°</mo> </msup> </mrow> </semantics></math> N, <math display="inline"><semantics> <mrow> <mo>−</mo> <msup> <mn>59.55</mn> <mo>°</mo> </msup> </mrow> </semantics></math> W; BAR in green).</p> Full article ">Figure 2
<p>Seasonal satellite images from MODIS for the North Atlantic area with AOD monthly average between 2000 and 2020 for (<b>a</b>) September to November (SON), (<b>b</b>) December to February (DJF), (<b>c</b>) March to May (MAM), and (<b>d</b>) June to August (JJA). [NASA GIOVANNI (<a href="https://giovanni.gsfc.nasa.gov/giovanni/" target="_blank">https://giovanni.gsfc.nasa.gov/giovanni/</a>) (accessed on 20 January 2021)].</p> Full article ">Figure 3
<p>MODIS satellite image of the North Atlantic area with AOD values for the extreme event of June 2020. (NASA GIOVANNI (<a href="https://giovanni.gsfc.nasa.gov/giovanni/" target="_blank">https://giovanni.gsfc.nasa.gov/giovanni/</a>) (accessed on 20 January 2021)).</p> Full article ">Figure 4
<p>AE (440–870 nm) versus AOD (440 nm) distribution for daily data from 1996 to 2021 for Caribbean sites located in PR, GPE and BAR. N represents the data point number.</p> Full article ">Figure 5
<p>Volume Particles Size Distribution (VPSD) <math display="inline"><semantics> <mrow> <mo>[</mo> <mi>d</mi> <mi>V</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>/</mo> <mi>d</mi> <mi>l</mi> <mi>n</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mspace width="0.166667em"/> <mrow> <mo>(</mo> <mi mathvariant="sans-serif">μ</mi> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mi mathvariant="sans-serif">μ</mi> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </semantics></math> of monthly data for (<b>a</b>) African sites (Capo Verde and Dakar) and (<b>b</b>) Caribbean sites (PR, GPE, and BAR) from 1996 to 2021.</p> Full article ">Figure 6
<p><math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> box-plot with medians and outliers in PR for daily data in June from 2006 to 2020.</p> Full article ">Figure 7
<p>Daily average of <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> and visibility data in GPE for June 2020. Standard deviations are illustrated by the whiskers and the vertical dotted lines indicate the passage of the event.</p> Full article ">Figure 8
<p>Monthly average of <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> concentrations in PR between 2000 and 2020. Standard deviations are illustrated by the whiskers.</p> Full article ">Figure 9
<p>Scatter plot showing the correlation (linear regression) between daily <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> data in PR from 2000 to 2020 for (<b>a</b>) all cases and (<b>b</b>) dusty cases (<math display="inline"><semantics> <mrow> <mo>[</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> <mo>]</mo> <mo>≥</mo> <mn>35</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mo>.</mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) according to the following ratios: A1 = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.2, A2 = 0.2 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.4, B = 0.4 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.6, C = 0.6 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.8 and D = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> ≥ 0.8.</p> Full article ">Figure 9 Cont.
<p>Scatter plot showing the correlation (linear regression) between daily <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> data in PR from 2000 to 2020 for (<b>a</b>) all cases and (<b>b</b>) dusty cases (<math display="inline"><semantics> <mrow> <mo>[</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> <mo>]</mo> <mo>≥</mo> <mn>35</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mo>.</mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) according to the following ratios: A1 = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.2, A2 = 0.2 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.4, B = 0.4 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.6, C = 0.6 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.8 and D = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> ≥ 0.8.</p> Full article ">Figure 10
<p>The Volume Particles Size Distribution (VPSD) in PR from 2000 to 2020 for (<b>a</b>) all cases and (<b>b</b>) dusty cases (<math display="inline"><semantics> <mrow> <mo>[</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> <mo>]</mo> <mo>≥</mo> <mn>35</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mo>.</mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) according to the following ratios: A1 = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.2, A2 = 0.2 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.4, B = 0.4 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.6, C = 0.6 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.8 and D = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> ≥ 0.8.</p> Full article ">Figure 11
<p>AE (440–870 nm) versus AOD (440 nm) distribution in PR from 2000 to 2020 for (<b>a</b>) all cases and (<b>b</b>) dusty cases (<math display="inline"><semantics> <mrow> <mo>[</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> <mo>]</mo> <mo>≥</mo> <mn>35</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> <mo>.</mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>) according to the following ratios: A1 = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.2, A2 = 0.2 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.4, B = 0.4 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.6, C = 0.6 ≤<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> &lt; 0.8 and D = <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>M</mi> <mn>2.5</mn> <mo>/</mo> <mi>P</mi> <mi>M</mi> <mn>10</mn> </mrow> </semantics></math> ≥ 0.8.</p> Full article ">Figure 12
<p>(<b>a</b>) NOAA HYSPLIT 10-day backward trajectories ending at San Juan (PR) and Grantley Adams (BAR) on 23 June 2020. The corresponding soundings from Wyoming are depicted in subfigure (<b>b</b>) for San Juan (PR) and subfigure (<b>c</b>) Grantley Adams (BAR).</p> Full article ">
18 pages, 2658 KiB  
Article
The Influence of Large-Scale Environment on the Extremely Active Tropical Cyclone Activity in November 2019 over the Western North Pacific
by Mengying Shi, Sulei Wang, Xiaoxu Qi, Haikun Zhao and Yu Shu
Atmosphere 2021, 12(4), 501; https://doi.org/10.3390/atmos12040501 - 15 Apr 2021
Viewed by 2869
Abstract
In November 2019, tropical cyclone (TC) frequency over the western North Pacific reached its record high. In this study, the possible causes and formation mechanisms of that record high TC frequency are investigated by analyzing the effect of large-scale environmental factors. A comparison [...] Read more.
In November 2019, tropical cyclone (TC) frequency over the western North Pacific reached its record high. In this study, the possible causes and formation mechanisms of that record high TC frequency are investigated by analyzing the effect of large-scale environmental factors. A comparison between the extremely active TC years and extremely inactive TC years is performed to show the importance of the large-scale environment. The contributions of several dynamic and thermodynamic environmental factors are examined on the basis of two genesis potential indexes and the box difference index that can measure the relative contributions of large-scale environmental factors to the change in TC genesis frequency. Results indicate that dynamical factors played a more important role in TC genesis in November 2019 than thermodynamic factors. The main contributions were from enhanced low-level vorticity and strong upward motion accompanied by positive anomalies in local sea surface temperature, while the minor contribution was from changes in vertical wind shear. Changes in these large-scale environmental factors are possibly related to sea surface temperature anomalies over the Pacific (e.g., strong Pacific meridional mode). Full article
(This article belongs to the Section Climatology)
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Figure 1

Figure 1
<p>November TC frequency over the WNP basin from 1979 to 2019. The data was collected from the JTWC best track dataset. The solid line represents the climatological mean over the 41 years. Dashed lines stand for one standard deviation of TC frequency during 1979–2019.</p> Full article ">Figure 2
<p>The genesis locations of TCs in November in the year 2019 (black), the years with high TC frequency (red), and the years with low TC frequency (blue), with different geographic locations for the WNP basin (equator to 30° N, 120° to 180° E) and the SCS (equator to 22° N, 105° to 120° E).</p> Full article ">Figure 3
<p>Differences in environmental factors in November 2019 compared to the climatological mean: (<b>a</b>) 850 hPa absolute vorticity (unit: 10<sup>−6</sup> s<sup>−1</sup>); (<b>b</b>) 500 hPa vertical velocity (unit: −10<sup>−1</sup> Pa s<sup>−1</sup>); (<b>c</b>) meridional gradient of zonal wind at 500 hPa (unit: s<sup>−1</sup>); (<b>d</b>) vertical wind shear (unit: m s<sup>−1</sup>); (<b>e</b>) 600 hPa relative humidity (%); (<b>f</b>) sea surface temperature (unit: °C).</p> Full article ">Figure 4
<p>Differences in environmental factors in years with high and low TC frequency: (<b>a</b>) 850 hPa absolute vorticity (unit: 10<sup>−6</sup> s<sup>−1</sup>); (<b>b</b>) 500 hPa vertical velocity (unit: −10<sup>−1</sup> Pa s<sup>−1</sup>); (<b>c</b>) meridional gradient of zonal wind at 500 hPa (unit: s<sup>−1</sup>); (<b>d</b>) vertical wind shear (unit: m s<sup>−1</sup>); (<b>e</b>) 600 hPa relative humidity (%); (<b>f</b>) sea surface temperature (unit: °C). The values in black dots are significant at a 95% confidence level.</p> Full article ">Figure 5
<p>Anomalies in the WNP (including SCS) for (<b>a</b>) <span class="html-italic">ENGPI</span> in November 2019 compared to years with low TC frequency; (<b>b</b>) <span class="html-italic">ENGPI</span> in years with high TC frequency compared to the low TC frequency years; (<b>c</b>) <span class="html-italic">DGPI</span> in November 2019 compared to years with low TC frequency; and (<b>d</b>) <span class="html-italic">DGPI</span> in years with high TC frequency compared to the low TC frequency years.</p> Full article ">Figure 6
<p>Differences of the GPI (November 2019 minus the climatological mean) and varying variables over different sea areas: total <span class="html-italic">ENGPI</span> anomaly and the <span class="html-italic">ENGPI</span> anomaly varying absolute vorticity (SVOR), vertical wind shear (VWS), maximum potential intensity (<span class="html-italic">MPI</span>), and 600 hPa relative humidity (RH) in the WNP (including SCS) (<b>a</b>), the WNP (excluding SCS) (<b>b</b>), and the SCS (<b>c</b>); Total <span class="html-italic">DGPI</span> anomaly and the <span class="html-italic">DGPI</span> anomaly varying absolute vorticity (SVOR), vertical wind shear (VWS), meridional gradient of zonal wind (Uy), and vertical velocity (OMEGA) in the WNP (including SCS) (<b>d</b>), the WNP (excluding SCS) (<b>e</b>), and the SCS (<b>f</b>).</p> Full article ">Figure 7
<p>Differences of the GPI (the years with high TC frequency minus the years with low TC frequency) and varying variables over different sea areas: total <span class="html-italic">ENGPI</span> anomaly and the <span class="html-italic">ENGPI</span> anomaly varying absolute vorticity (SVOR), vertical wind shear (VWS), maximum potential intensity (<span class="html-italic">MPI</span>), and 600 hPa relative humidity (RH) in the WNP (including SCS) (<b>a</b>), the WNP (excluding SCS) (<b>b</b>), and the SCS (<b>c</b>); Total <span class="html-italic">DGPI</span> anomaly and the <span class="html-italic">DGPI</span> anomaly varying absolute vorticity (SVOR), vertical wind shear (VWS), meridional gradient of zonal wind (Uy), and vertical velocity (OMEGA) in the WNP (including SCS) (<b>d</b>), the WNP (excluding SCS) (<b>e</b>), and the SCS (<b>f</b>).</p> Full article ">Figure 8
<p>SST anomalies for (<b>a</b>) November 2019 minus the climatological mean and (<b>b</b>) the years with high TC frequency minus the climatological mean.</p> Full article ">Figure 9
<p>Standardized time series of TC frequency and Pacific meridional mode (PMM) index (<b>a</b>), and tropical Indian Ocean Dipole (TIOD) index (<b>b</b>) in the Novembers of 1979–2019. Correlation coefficients are also shown, with the sign “*” indicating a significant value at a 95% confidence level.</p> Full article ">
18 pages, 5999 KiB  
Article
The Challenge in the Management of Historic Trees in Urban Environments during Climate Change: The Case of Corso Trieste (Rome, Italy)
by Elisa Gatto, Riccardo Buccolieri, Leonardo Perronace and Jose Luis Santiago
Atmosphere 2021, 12(4), 500; https://doi.org/10.3390/atmos12040500 - 15 Apr 2021
Cited by 10 | Viewed by 3917
Abstract
This study carries out a quantitative analysis of the impact on microclimate (air temperature and thermal comfort) of a row of 165 historical Pinus pinea L. located in a central neighbourhood of Rome (Italy). The analysis starts from a qualitative general analysis on [...] Read more.
This study carries out a quantitative analysis of the impact on microclimate (air temperature and thermal comfort) of a row of 165 historical Pinus pinea L. located in a central neighbourhood of Rome (Italy). The analysis starts from a qualitative general analysis on the stressful conditions leading to tree decline in the urban environment especially during extreme climate change phenomena. Subsequently, the effects of planting new types of trees are assessed using ENVI-met, a 3D prognostic non-hydrostatic model for the simulation of surface-plant-air interactions. Results, obtained by simulating three different scenarios in which the trees are first removed and then modified, show that a gradual renewal of the existing trees, based on priority criteria of maturity or senescence, vegetative and phytosanitary conditions, efficiency of ecosystem services and safety for citizens, has positive effects on thermal comfort. By integrating current results and scientific literature, the final aim of this work is to provide stakeholders with a strategic and systemic planning methodology, which, based on the innovative integrated use of tree management and modelling tools, may (i) enhance the benefits of greening in a scenario of climate change and (ii) lead to intervention strategies based on complementarity between conservation of existing trees and tree renewal. Full article
(This article belongs to the Special Issue Plant Ecosystems in a Changing World: Monitoring, Modelling and Risk Assessment)
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<p>Scheme of the interaction among the physical climate system, exposure, and vulnerability producing risk. Based on [<a href="#B1-atmosphere-12-00500" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Methodological steps.</p> Full article ">Figure 3
<p>(<b>Left</b>): Position of the city of Rome in central Italy. (<b>Right</b>): Focus on Rome with indication of the Corso Trieste (from Google Earth).</p> Full article ">Figure 4
<p>Corso Trieste in Rome (Italy). Left: 1945 satellite image from Google Earth and a 1933 historical photo (<a href="https://www.romasparita.eu/foto-roma-sparita/70111/corso-trieste-2" target="_blank">https://www.romasparita.eu/foto-roma-sparita/70111/corso-trieste-2</a>, accessed on 31 March 2021). Right: Current satellite images and pictures.</p> Full article ">Figure 5
<p>A picture of Corso Trieste characterised by historical specimens (old) and young specimens (new) of <span class="html-italic">Pinus pinea</span> L.</p> Full article ">Figure 6
<p>SYSTUS interface (<a href="http://www.systus.it" target="_blank">www.systus.it</a>, accessed on 31 March 2021) used to geo-reference <span class="html-italic">Pinus pinea</span> L. along Corso Trieste.</p> Full article ">Figure 7
<p>(<b>Left</b>): AccuPar LP-80 ceptometer. (<b>Right</b>): LAI measurements performed during the campaign.</p> Full article ">Figure 8
<p>Example of steps followed for the creation of a 3D model of <span class="html-italic">Pinus pinea</span> L. with the ENVI-met Albero tool. <b>Left</b>: Photographic survey; <b>middle</b>: Measurement and plant geometry simplification; <b>right</b>: Three-dimensional model of <span class="html-italic">Pinus pinea</span> L. In the green box the characteristics of historical and younger specimens are reported.</p> Full article ">Figure 9
<p>Modelling of the 3D study area in ENVI-met. (<b>Left</b>): No vegetation scenario; (<b>middle</b>): Current vegetation scenario; (<b>right</b>): Modified scenario.</p> Full article ">Figure 10
<p>3D geometry of the new tree model in the modified scenario.</p> Full article ">Figure 11
<p>Time evolution of PMV and MRT at different points (P1, P2, P3) in the street canyon shown in the top at right.</p> Full article ">Figure 12
<p>Spatial distribution maps of the Air Temperature, Predicted Mean Vote (PMV) and Mean Radiant Temperature (MRT) values at 12.00 a.m. at a pedestrian height of 1.4 m. The dotted line indicates the area where the average of the indices was calculated.</p> Full article ">
10 pages, 905 KiB  
Article
Addressing Missing Environmental Data via a Machine Learning Scheme
by Chris G. Tzanis, Anastasios Alimissis and Ioannis Koutsogiannis
Atmosphere 2021, 12(4), 499; https://doi.org/10.3390/atmos12040499 - 15 Apr 2021
Cited by 9 | Viewed by 2511
Abstract
An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, [...] Read more.
An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air-quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air-quality distributions. This study used point-monitoring data from the network of stations that operates in Athens, Greece. A machine-learning scheme was applied as a method to spatially estimate pollutants’ concentrations, and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes. Full article
(This article belongs to the Special Issue Selected Papers from the Third International Electronic Conference on Atmospheric Sciences)
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<p>Spatial distribution of the air-quality-monitoring sites in the area of study.</p> Full article ">Figure 2
<p>A two-layer network with sigmoid hidden neurons and linear output neurons.</p> Full article ">
15 pages, 7452 KiB  
Article
Effect of Air Temperature Increase on Changes in Thermal Regime of the Oder and Neman Rivers Flowing into the Baltic Sea
by Adam Choiński, Mariusz Ptak, Alexander Volchak, Ivan Kirvel, Gintaras Valiuškevičius, Sergey Parfomuk, Pavel Kirvel and Svetlana Sidak
Atmosphere 2021, 12(4), 498; https://doi.org/10.3390/atmos12040498 - 15 Apr 2021
Cited by 3 | Viewed by 2769
Abstract
The paper presents long-term changes in water temperature in two rivers, Oder and Neman, with catchments showing different climatic conditions (with dominance of marine climate in the case of the Oder and continental climate in the case of the Neman River). A statistically [...] Read more.
The paper presents long-term changes in water temperature in two rivers, Oder and Neman, with catchments showing different climatic conditions (with dominance of marine climate in the case of the Oder and continental climate in the case of the Neman River). A statistically significant increase in mean annual water temperature was recorded for four observation stations, ranging from 0.17 to 0.39 °C dec?1. At the seasonal scale, for the winter half-year, water temperature increase varied from 0.17 to 0.26 °C dec?1, and for the summer half-year from 0.17 to 0.50 °C dec?1. In three cases (Odra-Brzeg, Odra-S?ubice, Niemen-Grodno), the recorded changes referred to the scale of changes in air temperature. For the fourth station on Neman (Smalininkai), an increase in water temperature in the river was considerably slower than air temperature increase. It should be associated with the substantial role of local conditions (non-climatic) affecting the thermal regime in that profile. Short-term forecast of changes in water temperature showed its further successive increase, a situation unfavorable for the functioning of these ecosystems. Full article
(This article belongs to the Special Issue The Impact of Climate on the Water Environment)
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<p>Location of the study objects: hydrological stations (1: Oder–Słubice, 2: Oder–Brzeg, 3: Nema–Smalininkai, 4: Neman–Grodno), meteorological stations (<b>A</b>: Gorzow Wlkp, <b>B</b>: Opole, <b>C</b>: Raseiniai, <b>D</b>: Grodno).</p> Full article ">Figure 2
<p>The spatial distribution of the thermal continentalism index according to Conrad (%), (after [<a href="#B21-atmosphere-12-00498" class="html-bibr">21</a>], changed).</p> Full article ">Figure 3
<p>Changes in mean annual water (blue) and air (orange) temperature in the years 1965–2014; statistically significant linear trends at the level 0.05 and their equations (straight dotted lines).</p> Full article ">Figure 4
<p>Mean annual spectra of water and air temperature for the Oder and Neman Rivers: (<b>a</b>,<b>b</b>) Odra (Brzeg)-Opole, (<b>c</b>,<b>d</b>) Odra (Słubice)- Gorzów Wlkp, (<b>e</b>,<b>f</b>) Niemen (Grodno)-Grodno, (<b>g</b>,<b>h</b>) Niemen (Smalininkai)-Raseiniai.</p> Full article ">Figure 5
<p>Monthly spectra of water and air temperature for the Oder and Neman Rivers—example for May: (<b>a</b>,<b>b</b>) Odra (Brzeg)-Opole, (<b>c</b>,<b>d</b>) Odra (Słubice)-Gorzów Wlkp, (<b>e</b>,<b>f</b>) Niemen (Grodno)-Grodno, (<b>g</b>,<b>h</b>) Niemen (Smalininkai)-Raseiniai.</p> Full article ">Figure 6
<p>ACF (<b>a</b>) and PACF (<b>b</b>) of the average annual water temperature for station Oder-Brzeg (Cols: Lag, Correlation, Standard Error, Q and P— parameters).</p> Full article ">Figure 7
<p>Average annual water temperature with the excluded trend (for Oder-Brzeg trend is T = 0.39 × t + 9.671).</p> Full article ">Figure 8
<p>ACF (<b>a</b>) and PACF (<b>b</b>) for mean annual water temperature for station Oder-Brzeg with the excluded trend (Cols: Lag, Correlation, Standard Error, Q and P—parameters).</p> Full article ">Figure 9
<p>Forecast of mean annual water temperature for station Oder-Brzeg for the selected ARIMA model (blue—observed values; red—forecasted values; green—±90% confidence interval).</p> Full article ">Figure 10
<p>Predicted temperatures of water and air temperature for the Oder and Neman Rivers: (<b>a</b>) Odra (Brzeg), (<b>b</b>) Opole, (<b>c</b>) Odra (Słubice), (<b>d</b>) Gorzów Wlkp, (<b>e</b>) Niemen (Grodno), (<b>f</b>) Grodno, (<b>g</b>) Niemen (Smalininkai), (<b>h</b>) Raseiniai (±90% confidence interval).</p> Full article ">
12 pages, 5686 KiB  
Article
Impact Study of FY-3B MWRI Data Assimilation in WRFDA
by Chun Yang, Lijian Zhu and Jinzhong Min
Atmosphere 2021, 12(4), 497; https://doi.org/10.3390/atmos12040497 - 15 Apr 2021
Cited by 2 | Viewed by 2964
Abstract
In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was [...] Read more.
In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was evaluated with WRFDA’s three-dimensional variational (3DVAR) data-assimilation scheme. Compared to a benchmark experiment with no MWRI data, assimilating MWRI radiances improved the analyses of typhoon central sea level pressure (CSLP), warm core structure, and wind speed. Moreover, verified with European Center for Medium-Range Weather Forecasts (ECMWF) analysis data, significant improvements in model variable forecast, such as geopotential height and specific humidity, were produced. Substantial error reductions in track, CSLP, and maximum-wind-speed forecasts with MWRI assimilation was also obtained from analysis time to 48 h forecast. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>The coverage of channel 5’s scanning in one day.</p> Full article ">Figure 2
<p>The weighting function of the MWRI.</p> Full article ">Figure 3
<p>The (<b>a</b>) best track and (<b>b</b>) minimum sea level pressure (MSLP) of Typhoon Son-Tinh.</p> Full article ">Figure 4
<p>The distribution of observations used in the CON experiment at 1200 UTC on 26 October 2012. The numbers of each observation are marked on the right.</p> Full article ">Figure 5
<p>The distribution of channel 5 observations: (<b>a</b>) before quality control; (<b>b</b>) after quality control.</p> Full article ">Figure 6
<p>The analyzed brightness temperatures (K) of MWRI 23.8 GHz-V channel 5 from (<b>a</b>) observation, (<b>b</b>) CTRL, (<b>c</b>) CON, and (<b>d</b>) AMWRI experiments at 1800 UTC on 26 October.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>d</b>) The surface wind vectors and sea level pressure (color-shaded) from the analyses of the ECMWF, CTRL, CON, and AMWRI, respectively; (<b>e</b>–<b>h</b>) the west–east cross-sections of temperature anomalies for the ECMWF, CTRL, CON, and AMWRI, respectively; and (<b>i</b>–<b>l</b>) the horizontal wind speed through the vortex center for the ECMWF, CTRL, CON, and AMWRI, respectively, at 1800 UTC on 26 October. The <span class="html-italic">x</span>-axis of (<b>e</b>–<b>l</b>) is the grid number along the black line in (<b>a</b>).</p> Full article ">Figure 8
<p>The aggregated root-mean-square errors of the (<b>a</b>) 24 h and (<b>b</b>) 48 h forecasts for vector wind, temperature, geopotential height, and specific humidity against the ECMWF analyses. The error statistics were obtained from 12 h and 48 h forecasts.</p> Full article ">Figure 9
<p>(<b>a</b>) Aggregated absolute track errors, (<b>b</b>) mean central sea level pressure errors, and (<b>c</b>) maximum wind speed as a function of forecast range from two experiments. The error statistics were obtained from 12 48 h forecasts.</p> Full article ">
19 pages, 25569 KiB  
Article
PM2.5 Magnetic Properties in Relation to Urban Combustion Sources in Southern West Africa
by Aruã da Silva Leite, Jean-François Léon, Melina Macouin, Sonia Rousse, Ricardo Ivan Ferreira da Trindade, Arnaud Proietti, Loïc Drigo, Paul Yves Jean Antonio, Aristide Barthélémy Akpo, Véronique Yoboué and Cathy Liousse
Atmosphere 2021, 12(4), 496; https://doi.org/10.3390/atmos12040496 - 14 Apr 2021
Cited by 10 | Viewed by 3805
Abstract
The physico-chemical characteristics of particulate matter (PM) in African cities remain poorly known due to scarcity of observation networks. Magnetic parameters of PM are robust proxies for the emissions of Fe-bearing particles. This study reports the first magnetic investigation of PM2.5 (PM with [...] Read more.
The physico-chemical characteristics of particulate matter (PM) in African cities remain poorly known due to scarcity of observation networks. Magnetic parameters of PM are robust proxies for the emissions of Fe-bearing particles. This study reports the first magnetic investigation of PM2.5 (PM with aerodynamic size below 2.5 ?m) in Africa performed on weekly PM2.5 filters collected in Abidjan (Ivory Coast) and Cotonou (Benin) between 2015 and 2017. The magnetic mineralogy is dominated by magnetite-like low coercivity minerals. Mass normalized SIRM are 1.65 × 10?2 A m2 kg?1 and 2.28 × 10?2 A m2 kg?1 for Abidjan and Cotonou respectively. Hard coercivity material (S-ratio = 0.96 and MDF = 33 mT) is observed during the dry dusty season. Wood burning emits less iron oxides by PM2.5 mass when compared to traffic sources. PM2.5 magnetic granulometry has a narrow range regardless of the site or season. The excellent correlation between the site-averaged element carbon concentrations and SIRM suggests that PM2.5 magnetic parameters are linked to primary particulate emission from combustion sources. Full article
(This article belongs to the Special Issue Environmental Magnetism Applied to the Study of Atmospheric Aerosols)
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<p>Localization of Abidjan and Cotonou in West Africa. Insets show the sampling sites (AT: Abidjan traffic, AL: Abidjan Landfill, AF: Abidjan fireplace and CT: Cotonou traffic).</p> Full article ">Figure 2
<p>Weekly mean temperature, cumulative rainfall, mean zonal U (U &gt; 0 indicates wind from the West) and meridional V (V &gt; 0 indicates wind from the South) winds recorded in Abidjan (Felix Houphouet Boigny airport) and Cotonou (Cadjehoun airport) from February 2015 to March 2017.</p> Full article ">Figure 3
<p>(<b>a</b>) Weekly values of SIRM<sub>V</sub> versus PM2.5 concentration for each site. (<b>b</b>) Correlation between SIRM<sub>V</sub> means and EC and PM2.5 concentrations means for the whole data series in each site. Error bars are the standard errors.</p> Full article ">Figure 4
<p>Concentration parameters PM2.5, EC, ARM<sub>V</sub> (anhysteretic remanent magnetization volume normalized) and SIRM<sub>V</sub>. The lines are the monthly running means and points the individual weekly measurements.</p> Full article ">Figure 5
<p>EC concentrations and magnetic content in air volume and PM (SIRM<sub>V</sub> and SIRM<sub>M</sub>, respectively) for the two characteristic weather events: the Harmattan period (dry season) and the monsoon season. Means (horizontal lines in the center of the square) and standard deviations (size of the square) are reported.</p> Full article ">Figure 6
<p>xARM/SIRM versusMDF for selected samples in the four sites (circle-AT, square-CT, diamond-AL, triangle-AF, all filled) and data from Dankers [<a href="#B60-atmosphere-12-00496" class="html-bibr">60</a>], Özden Özdemir and Banerjee [<a href="#B61-atmosphere-12-00496" class="html-bibr">61</a>], Maher [<a href="#B59-atmosphere-12-00496" class="html-bibr">59</a>] and Mitchell and Maher [<a href="#B27-atmosphere-12-00496" class="html-bibr">27</a>] (open inverse triangles in salmon, purple, green and blue respectively). Iron oxides reported in Dankers [<a href="#B60-atmosphere-12-00496" class="html-bibr">60</a>], Özden Özdemir and Banerjee [<a href="#B61-atmosphere-12-00496" class="html-bibr">61</a>] and Maher [<a href="#B59-atmosphere-12-00496" class="html-bibr">59</a>] are all composed of synthetic magnetite or ferrimagnetic magnetite-like iron oxides, with known grain sizes (represented by the numbers above the symbols, in μm). Data from Mitchell and Maher [<a href="#B27-atmosphere-12-00496" class="html-bibr">27</a>] are measurements performed in PM10 filters and leaves (total suspended particles, TSP).</p> Full article ">Figure 7
<p>SEM images for the four sites: (<b>a</b>) Abidjan traffic (AT) site showing five iron oxide agglomerations and individual spherules, with sizes ranging from 50 to 500 nm. (<b>b</b>) A detail from (a) of one of the agglomerations (marked in red in figure a), displaying six spherules with sizes ranging from 50 to 300 nm. (<b>c</b>) EDS spectrum of the central spherule marked in blue in figure (a), showing a composition of Fe, O, Si and C. (<b>d</b>) Cotonou traffic site (CT), showing a particle agglomeration with a central particle composed of Fe and S with low traces of Ca, Al, Na, O. The aggregate surrounding this particle has a composition of C, Ca and Al, and a maximum dimension of 3.22 μm. (<b>e</b>) Abidjan landfill site (AL) with a bright irregular shaped particle at the center, composed of Fe and O, and traces of Pb, Zr, Cl, K, Al, Na with dimension of 1.33 μm. (<b>f</b>) Abidjan domestic fire site (AF), with a central spherule of 0.75 μm in diameter, composed of Fe and O. (<b>g</b>) EDS spectrum for the central particle (marked in blue) from the CT site. (<b>h</b>) EDS spectrum from the central particle (marked in blue) from the AL site. (<b>i</b>) EDS spectrum from the spherule (marked in blue) from the AF site. All spectra have the presence of Si, related to the matrix of the filter.</p> Full article ">
22 pages, 857 KiB  
Review
Future Climate Change Impacts on European Viticulture: A Review on Recent Scientific Advances
by Fotoula Droulia and Ioannis Charalampopoulos
Atmosphere 2021, 12(4), 495; https://doi.org/10.3390/atmos12040495 - 14 Apr 2021
Cited by 116 | Viewed by 14427
Abstract
Climate change is a continuous spatiotemporal reality, possibly endangering the viability of the grapevine (Vitis vinifera L.) in the future. Europe emerges as an especially responsive area where the grapevine is largely recognised as one of the most important crops, playing a [...] Read more.
Climate change is a continuous spatiotemporal reality, possibly endangering the viability of the grapevine (Vitis vinifera L.) in the future. Europe emerges as an especially responsive area where the grapevine is largely recognised as one of the most important crops, playing a key environmental and socio-economic role. The mounting evidence on significant impacts of climate change on viticulture urges the scientific community in investigating the potential evolution of these impacts in the upcoming decades. In this review work, a first attempt for the compilation of selected scientific research on this subject, during a relatively recent time frame (2010–2020), is implemented. For this purpose, a thorough investigation through multiple search queries was conducted and further screened by focusing exclusively on the predicted productivity parameters (phenology timing, product quality and yield) and cultivation area alteration. Main findings on the potential impacts of future climate change are described as changes in grapevine phenological timing, alterations in grape and wine composition, heterogeneous effects on grapevine yield, the expansion into areas that were previously unsuitable for grapevine cultivation and significant geographical displacements in traditional growing areas. These compiled findings may facilitate and delineate the implementation of effective adaptation and mitigation strategies, ultimately potentiating the future sustainability of European viticulture. Full article
(This article belongs to the Special Issue Agricultural Production in Relation to Local and Regional Climate Change)
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<p>The vine regions referred to in this review.</p> Full article ">
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