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Atmosphere
Volume 13
Issue 11
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Atmosphere, Volume 13, Issue 11 (November 2022) – 198 articles

Cover Story (view full-size image): The effect of persistently high levels of anthropogenic aerosols during winter on wheat production, an important winter crop in the eastern Indo-Gangetic Plain (IGP), was studied using the Agricultural Production System Simulator (APSIM) model. The model was applied at several nodes within the eastern IGP (Nepal, India, and Bangladesh) using wheat field trial data from 2015 to 2017. The model revealed anthropogenic aerosols reduced wheat grain yield, biomass yield, and crop evapotranspiration by on average 11.2–13.5%, 21.2–22%, and 13.5–15%, respectively, during the 2015–2017 seasons at the eastern IGP sites. The modeled reduction in wheat yield represents an average reduction of more than 3.2 kg per capita per annum due to anthropogenic aerosols, which is a substantial impact on food security in the eastern IGP. View this paper
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15 pages, 6031 KiB  
Article
Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
by Haijun Liu, Dongxing Lei, Jing Yuan, Guoming Yuan, Chunjie Cui, Yali Wang and Wei Xue
Atmosphere 2022, 13(11), 1939; https://doi.org/10.3390/atmos13111939 - 21 Nov 2022
Cited by 4 | Viewed by 2017
Abstract
The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are [...] Read more.
The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model—an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model’s performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC. Full article
(This article belongs to the Section Upper Atmosphere)
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<p>The information on the location of the eight regions on the map.</p> Full article ">Figure 2
<p>TEC data of eight regions selected in this paper.</p> Full article ">Figure 3
<p>TEC values after first-order difference.</p> Full article ">Figure 4
<p>Schematic diagram of sample making process.</p> Full article ">Figure 5
<p>The entire experimental process.</p> Full article ">Figure 6
<p>Structure of an LSTM unit.</p> Full article ">Figure 7
<p>MA-LSTM model structure for TEC prediction.</p> Full article ">Figure 8
<p>Comparison of (<b>a</b>) RMSE, (<b>b</b>) R-square, (<b>c</b>) MAPE values of different models in all regions.</p> Full article ">Figure 9
<p>Comparison of (<b>a</b>) RMSE, (<b>b</b>) R-square, and (<b>c</b>) MAPE values for various models in different months.</p> Full article ">Figure 10
<p>Histograms of absolute error distribution during (<b>a</b>) magnetic quiet periods and (<b>b</b>) magnetic storm periods.</p> Full article ">Figure 11
<p>Comparison of the predictive performance of a (<b>a</b>) magnetic quiet day and a (<b>b</b>) magnetic storm day.</p> Full article ">
23 pages, 1538 KiB  
Article
Usage of Needle and Branches in the Applications of Bioindicator, Source Apportionment and Risk Assessment of PAHs
by Sevil Caliskan Eleren and Yücel Tasdemir
Atmosphere 2022, 13(11), 1938; https://doi.org/10.3390/atmos13111938 - 21 Nov 2022
Cited by 1 | Viewed by 1915
Abstract
Biomonitoring studies have enormous benefits providing a fruitful and cost-efficient means of measuring environmental exposure to toxic chemicals. This study collected ambient air and pine tree components, including needles and 1-year-old and 2-year-old branches, for one year. Concentrations, potential sources and temporal variations [...] Read more.
Biomonitoring studies have enormous benefits providing a fruitful and cost-efficient means of measuring environmental exposure to toxic chemicals. This study collected ambient air and pine tree components, including needles and 1-year-old and 2-year-old branches, for one year. Concentrations, potential sources and temporal variations of atmospheric polycyclic aromatic hydrocarbons (PAHs) were investigated. In general, lower concentration levels were observed in the warmer months. Ambient PAHs pose a serious public health threat and impose a need for calculating cancer risks. It was also intended to define the best tree component reflecting the ambient air PAHs. The consideration of the representative tree component minimizes the unnecessary laboratory processes and expenses in biomonitoring studies. The coefficient of divergence (COD), diagnostic ratio (DR) and principal component analysis (PCA) were employed to specify the PAH sources. As a result of the DR and PCA evaluations, the effect of the industrial area has emerged, besides the dominance of the pollutants originating from traffic and combustion. The results have shown that pine needles and branches were mainly affected by similar sources, which also influenced air concentrations. Inhalation cancer risk values were also calculated and they varied between 1.64 × 10−6 and 3.02 × 10−5. A potential risk increases in the colder season depending on the ambient air PAH concentrations. Full article
(This article belongs to the Special Issue Biomonitoring - an Effective Tool for Air Pollution Assessment)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The location of the sampled pine tree.</p> Full article ">Figure 2
<p>Correlation coefficient (R<sub>s</sub>) and coefficient of divergence (COD) values of PAHs for the ambient air vs. tree components.</p> Full article ">Figure 3
<p>Correlation coefficient (Rs) and coefficient of divergence (COD) values of PAHs for tree components in all seasons. Note: Ne, B1 and B2 refer to the needle, 1-year-old branch and 2-year-old branch, respectively.</p> Full article ">Figure 4
<p>Phe/Ant ratios against Flt/Pyr ratios for the tree components and ambient air. Note: Pine branch-1 and Pine branch-2 refer to 1-year-old and 2-year-old branches.</p> Full article ">Figure 5
<p>PAH cross-plots for the ratios of Indeno/(Indeno+BghiP) and Flt/(Flt+Py) against BaA/(BaA+Chr) ratios in the studied tree components and ambient air. Note: Pine branch-1 and Pine branch-2 refer to 1-year-old and 2-year-old branches.</p> Full article ">Figure 6
<p>ΣCOMB/ΣPAHs ratios for the tree components and ambient air. Note: Pine branch-1 and Pine branch-2 refer to 1-year-old and 2-year-old branches.</p> Full article ">
15 pages, 3931 KiB  
Article
Seasonal Variations in Concentrations and Chemical Compositions of TSP near a Bulk Material Storage Site for a Steel Plant
by Yen-Yi Lee, Sheng-Lun Lin, Bo-Wun Huang, Justus Kavita Mutuku and Guo-Ping Chang-Chien
Atmosphere 2022, 13(11), 1937; https://doi.org/10.3390/atmos13111937 - 21 Nov 2022
Cited by 2 | Viewed by 1884
Abstract
The concentrations of total suspended particles (TSPs) on four buildings near a steel plant’s bulk material storage site for iron ore, coal, limestone, and sinter were evaluated for summer and winter, where the concentrations were 58 (17–55) μg m−3 and 125 (108–155) [...] Read more.
The concentrations of total suspended particles (TSPs) on four buildings near a steel plant’s bulk material storage site for iron ore, coal, limestone, and sinter were evaluated for summer and winter, where the concentrations were 58 (17–55) μg m−3 and 125 (108–155) μg m−3, respectively. A multivariate regression analysis of meteorological parameters with TSP concentrations indicates that temperature, wind speed, and frequency of rainfall are potential predictors of TSP concentrations, where the respective p values for the model are p = 0.005, p = 0.049, and p = 0.046. The strong correlation between usual co-pollutants, CO, NO2, and NOX with TSP concentrations, as indicated by the Pearson correlation values of 0.87, 0.86, and 0.77, respectively, implies substantial pollution from mobile sources. The weak correlation of SO2 with TSP concentrations rules out a significant pollution contribution from power plants. The speciation of TSPs in winter showed the percentage proportions of water-soluble ions, metal elements, and carbon content in winter as 60%, 15%, and 25%, while in summer, they were 68%, 14%, and 18%, respectively. Water-soluble ions were the most significant composition for both seasons, where the predominant species in summer and winter are SO42− and NO3−, respectively. In the TSP metal elements profile, the proportion of natural origin ones exceeded those from anthropogenic sources. The health risk assessment indicates a significant cancer risk posed by chromium, while that posed by other metal elements including Co, Ni, As, and Pb are insignificant. Additionally, all metal elements’ chronic daily occupational exposure levels were below the reference doses except for Cu and Zn. Insights from this investigation can inform decisions on the design of the TSP-capturing mechanism. Specifically, water sprays to capture the water-soluble portion would substantially reduce the amplified concentrations of TSPs, especially in winter. Full article
(This article belongs to the Special Issue Air Pollution in Industrial Regions)
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<p>Schematic representations of (<b>a</b>) a plan view of the four TSP sampling sites and the surroundings of the raw material storage site and (<b>b</b>) wind rose maps for the TSP sample collection dates.</p> Full article ">Figure 2
<p>The concentrations of TSP in the steel company’s administration building, elementary school A, elementary school B, and the public library for (<b>a</b>) summer and (<b>b</b>) winter.</p> Full article ">Figure 3
<p>Regression curves for (<b>a</b>) temperature and wind speed, (<b>b</b>) frequency of rainfall occurrence.</p> Full article ">Figure 4
<p>Pearson correlation values for (<b>a</b>) CO, (<b>b</b>) SO<sub>2</sub>, (<b>c</b>) NO<sub>2</sub>, and (<b>d</b>) NO<sub>X</sub> concentrations with the average TSP concentrations.</p> Full article ">Figure 5
<p>Percentage of water-soluble ions in TSP for winter and summer and at the steel company’s administration building, elementary school A, elementary school B, and public library.</p> Full article ">Figure 6
<p>Mass of elemental and organic carbon in TSP and the ratios of OC/EC at the steel company administration building, elementary school A, elementary school B and public library during summer and winter.</p> Full article ">
18 pages, 5326 KiB  
Article
Error Decomposition of CRA40-Land and ERA5-Land Reanalysis Precipitation Products over the Yongding River Basin in North China
by Ye Zhang, Yintang Wang, Lingjie Li, Leizhi Wang, Qin Wang, Yong Huang and Liping Li
Atmosphere 2022, 13(11), 1936; https://doi.org/10.3390/atmos13111936 - 21 Nov 2022
Cited by 1 | Viewed by 1647
Abstract
Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was [...] Read more.
Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets. Full article
(This article belongs to the Topic Advanced Research in Precipitation Measurements)
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<p>Geographical location of the study area.</p> Full article ">Figure 2
<p>Technical Scheme.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>f</b>) Systematic and random error components (%) of the ERA5-Land reanalysis precipitation dataset.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>f</b>) Systematic and random error components (%) of the CRA40-Land reanalysis precipitation data.</p> Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Proportions of systematic and random errors over time.</p> Full article ">Figure 6
<p>Correlation of the precipitation intensity with systematic and random error components.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>f</b>) Correlation of the elevation with systematic and random error components.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>l</b>) <span class="html-italic">TB</span>, <span class="html-italic">HB</span>, <span class="html-italic">MP</span>, and <span class="html-italic">FP</span> of ERA5-Land reanalysis precipitation dataset.</p> Full article ">Figure 9
<p>(<b>a</b>–l) <span class="html-italic">TB</span>, <span class="html-italic">HB</span>, <span class="html-italic">MP</span>, and <span class="html-italic">FP</span> of CRA40-Land reanalysis precipitation data.</p> Full article ">Figure 10
<p>(<b>a</b>,<b>b</b>) Temporal variations of the error components, namely, <span class="html-italic">TB</span>, <span class="html-italic">HB</span>, <span class="html-italic">MP</span>, and <span class="html-italic">FP</span>.</p> Full article ">Figure 11
<p>Correlation between precipitation intensity and error components <span class="html-italic">TB</span>, <span class="html-italic">HB</span>, <span class="html-italic">MP</span>, and <span class="html-italic">FP</span> (logarithmic base 10 on the abscissa).</p> Full article ">Figure 12
<p>(<b>a</b>–<b>f</b>) Correlation between elevation and error components, namely, <span class="html-italic">TB</span>, <span class="html-italic">HB</span>, <span class="html-italic">MP</span>, and <span class="html-italic">FP</span>.</p> Full article ">
13 pages, 1124 KiB  
Article
The Relationship between Elemental Carbon and Volatile Organic Compounds in the Air of an Underground Metal Mine
by Andrzej Szczurek, Marcin Przybyła and Monika Maciejewska
Atmosphere 2022, 13(11), 1935; https://doi.org/10.3390/atmos13111935 - 21 Nov 2022
Cited by 2 | Viewed by 1951
Abstract
Elemental carbon (EC) content in air is considered a proxy for the diesel exhaust impact at workplaces. This paper examines the possibility of estimating EC content in mine air on the basis of measurements of volatile organic compounds (VOC). The measurement study was [...] Read more.
Elemental carbon (EC) content in air is considered a proxy for the diesel exhaust impact at workplaces. This paper examines the possibility of estimating EC content in mine air on the basis of measurements of volatile organic compounds (VOC). The measurement study was carried out in an underground metal mine. Gas chromatography with mass spectrometry (GC/MS) was applied for VOC determination, and thermal-optical analysis (TOA) with an FID detector was utilized for EC measurements. A correlation was found between the measurements of EC and total VOC (TVOC) as well as the content of individual hydrocarbons C12–C14 in the air of an extraction zone in the mine. A regression model was developed which predicts EC based on C12, C13, and C14, considered individually, and the remaining VOCs detected with GC/MS taken in total. The model was statistically significant (p = 0.053), and it offered an EC prediction error of RMSE = 4.60 µg/sample. The obtained result confirms the possibility of using VOC measurements for the preliminary estimation of EC concentrations in mine air. This approach is feasible given the availability of portable GC/MS and offers easy and fast measurements providing qualitative and quantitative information. Full article
(This article belongs to the Special Issue Feature Papers in Air Quality)
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<p>(<b>a</b>) Sampler used for passive sampling; (<b>b</b>) Sampler magnified with the fiber visible in the syringe.</p> Full article ">Figure 2
<p>The sampling apparatus applied for dynamic sampling. It includes the air sampling pump and cyclone head for collection of the respirable fraction of dust. Samples collected on quartz filters were analyzed for EC using TOA.</p> Full article ">Figure 3
<p>Relationship between the EC and EC/TC in mine air in the extraction zone (EZ) and in the maintenance shop (MS).</p> Full article ">Figure 4
<p>Scatter plot of EC and TVOC measurement results, which were obtained for the concurrently collected samples. The results for the extraction zone (EZ) and maintenance shop (MS) are distinguished with colors.</p> Full article ">Figure 5
<p>The diagnostic plots for regression models which fit the relationship between EC and VOCs in the air of the extraction zone in mine.</p> Full article ">
16 pages, 2455 KiB  
Article
Imputation of Missing PM2.5 Observations in a Network of Air Quality Monitoring Stations by a New kNN Method
by Idit Belachsen and David M. Broday
Atmosphere 2022, 13(11), 1934; https://doi.org/10.3390/atmos13111934 - 21 Nov 2022
Cited by 9 | Viewed by 2752
Abstract
Statistical analyses often require unbiased and reliable data completion. In this work, we imputed missing fine particulate matter (PM2.5) observations from eight years (2012–2019) of records in 59 air quality monitoring (AQM) stations in Israel, using no auxiliary data but the [...] Read more.
Statistical analyses often require unbiased and reliable data completion. In this work, we imputed missing fine particulate matter (PM2.5) observations from eight years (2012–2019) of records in 59 air quality monitoring (AQM) stations in Israel, using no auxiliary data but the available PM2.5 observations. This was achieved by a new k-Nearest Neighbors multivariate imputation method (wkNNr) that uses the correlations between the AQM stations’ data to weigh the distance between the observations. The model was evaluated against an iterative imputation with an Ensemble of Extremely randomized decision Trees (iiET) on artificially and randomly removed data intervals of various lengths: very short (0.5–3 h, corresponding to 1–6 missing values), short (6–24 h), medium-length (36–72 h), long (10–30 d), and very long (30 d–2 y). The new wkNNr model outperformed the iiET in imputing very short missing-data intervals when the adjacent lagging and leading observations were added as model inputs. For longer missing-data intervals, despite its simplicity and the smaller number of hyperparameters required for tuning, the new model showed an almost comparable performance to the iiET. A parallel Python implementation of the new kNN-based multivariate imputation method is available on github. Full article
(This article belongs to the Collection Measurement of Exposure to Air Pollution)
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<p>Locations of the 59 AQM stations whose data were used (lat., lon.). Station names are provided in <a href="#app1-atmosphere-13-01934" class="html-app">Table S1 in the Supplementary Materials</a>.</p> Full article ">Figure 2
<p>Temporal coverage of PM<sub>2.5</sub> observations at the 59 AQM stations in the years 2012–2019. Grey—missing observations, blue and red—non-missing observations. Blue—the training set, red—the test set used for model evaluation, comprised of randomly selected artificially missing time-windows of different lengths (0.5 h, 1 h, 2 h, 3 h, 6 h, 24 h, 36 h, 72 h, 10 d, and 30 d). Station names are specified in <a href="#app1-atmosphere-13-01934" class="html-app">Table S1 in the Supplementary Materials</a>. The test set (red) was extracted only from the 36 AQM stations with accumulated missing data of ≤4 years.</p> Full article ">Figure 3
<p>A pseudo code of the w<span class="html-italic">k</span>NN<span class="html-italic">r</span> algorithm.</p> Full article ">Figure 4
<p>Model performance in terms of (<b>a</b>) NRMSE, (<b>b</b>) R<sup>2</sup>, and (<b>c</b>) NMAE. Each boxplot contains all the imputation evaluation results in the 36 AQM stations (see <a href="#sec2dot2-atmosphere-13-01934" class="html-sec">Section 2.2</a>), categorized according to the missing-data time-window length: very short (0.5–3 h), short (6–24 h), medium-length (36–72 h), and long (10–30 d), with <span class="html-italic">N</span> = 144, 72, 72, and 72, respectively. White triangles: mean values, black horizontal lines: median values, lower and upper box boundaries mark the 25th and 75th percentiles, respectively (i.e., the inter quartile range, IQR). The upper whisker represents the 75th percentile + 1.5 <math display="inline"><semantics> <mo>×</mo> </semantics></math> IQR, and the lower whisker represents the 25th percentile − 1.5 <math display="inline"><semantics> <mo>×</mo> </semantics></math> IQR. Outliers are not shown to avoid clutter.</p> Full article ">Figure 5
<p>Measured PM<sub>2.5</sub> concentrations (black line) vs. imputed concentrations by the w<span class="html-italic">k</span>NN<span class="html-italic">r</span> (green line), w<span class="html-italic">k</span>NN<span class="html-italic">r</span>_ll<sub>2</sub> (red line), iiET (blue line), and iiET_ll<sub>1</sub> (purple line) models for different missing-data time-window lengths (red shade). (<b>a</b>,<b>b</b>) Very short missing-data time-windows in AQM station #2, (<b>c</b>) a medium-length missing-data time-window in AQM station #15, and (<b>d</b>) a long missing-data time-window in AQM station #26 (the w<span class="html-italic">k</span>NN<span class="html-italic">r</span> model results are not shown to avoid clutter).</p> Full article ">Figure 6
<p>Taylor diagrams of the w<span class="html-italic">k</span>NN<span class="html-italic">r</span>_ll<sub>2</sub> and iiET imputation models (represented by distinct colors) in the different seasons (represented by symbols): winter (DJF), spring (MAM), summer (JJA), and fall (SON). The plots correspond to different missing-data time-windows length categories: (<b>a</b>) very short, (<b>b</b>) short, (<b>c</b>) medium-length, and (<b>d</b>) long. The centered root mean squared error (CRMSE) is normalized by the standard deviation (SD) of the observations (see <a href="#app1-atmosphere-13-01934" class="html-app">Table S2 in the Supplementary Materials</a>).</p> Full article ">
19 pages, 13159 KiB  
Article
Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities
by Grazia Fattoruso, Domenico Toscano, Antonella Cornelio, Saverio De Vito, Fabio Murena, Massimiliano Fabbricino and Girolamo Di Francia
Atmosphere 2022, 13(11), 1933; https://doi.org/10.3390/atmos13111933 - 20 Nov 2022
Cited by 4 | Viewed by 2187
Abstract
Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal [...] Read more.
Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations. Full article
(This article belongs to the Special Issue Feature Papers in Air Quality)
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<p>Map of the City of Portici. The map includes: the weather station (by ENEA RC Portici) and the regulatory air quality monitoring station located in the city (both represented in red stars); the area study (represented in the yellow area) and the urban street network classified as street canyon (red lines) and open street (green lines).</p> Full article ">Figure 2
<p>Map of the selected neighborhood of the City of Portici including footprint of the buildings with their height, the monitoring route—Via Libertà (yellow line), Via da Vinci (blue line), Via Diaz (green line), Corso Garibaldi (red line) —and the locations of the traffic vehicular campaigns.</p> Full article ">Figure 3
<p>Maps of the mobile monitoring campaigns along the selected monitoring route.</p> Full article ">Figure 4
<p>Map of the receptor points and the polygon street segments at which SIRANE associates the estimated pollutant concentrations.</p> Full article ">Figure 5
<p>The vehicle feet by the two traffic monitoring campaigns grouped by vehicle category.</p> Full article ">Figure 6
<p>Comparison between the average measured and estimated concentrations of CO and NO<sub>2</sub> pollutants, by aggregating data spatially distributed along the entire monitoring route and for each monitored hush hour.</p> Full article ">Figure 7
<p>Comparison between the average measured and estimated concentrations of CO and NO<sub>2</sub> pollutants in relation to the street canyon segments.</p> Full article ">Figure 8
<p>Comparison between the average measured and estimated concentrations of CO and NO<sub>2</sub> pollutants in relation to the open street segments.</p> Full article ">Figure 9
<p>Comparison between MONICA (blue line) and SIRANE (orange line), for CO pollutant: (<b>a</b>) at 9 am on 5 June; (<b>b</b>) at 9 am on 21 June; (<b>c</b>) at 1 am on 5 June; (<b>d</b>) at 1 am on 21 June; (<b>e</b>) at 5 pm on 5 June; (<b>f</b>) at 5 pm on 21 June. Triangles are street canyons and circle open roads. The ID receptors from 1 to 16 (red lines) are referred to the road segment Corso Garibaildi; IDs from 17 to 28 (yellow lines) to the road segment Via Libertà; IDs from 29 to 40 (blue lines) to the road segment Via DaVinci; IDs from 41 to 61 (green lines) to the road segment Via Diaz.</p> Full article ">Figure 10
<p>Comparison between MONICA (blue line) and SIRANE (orange line), for NO<sub>2</sub> pollutant: (<b>a</b>) at 9 am on 5 June; (<b>b</b>) at 9 am on 21 June; (<b>c</b>) at 1 am on 5 June; (<b>d</b>) at 1 am on 21 June; (<b>e</b>) at 5 pm on 5 June; (<b>f</b>) at 5 pm on 21 June. Triangles are street canyons and circle open roads. The ID receptors from 1 to 16 (red lines) are referred to the road segment Corso Garibaildi; IDs from 17 to 28 (yellow lines) to the road segment Via Libertà; IDs from 29 to 40 (blue lines) to the road segment Via DaVinci; IDs from 41 to 61 (green lines) to the road segment Via Diaz.</p> Full article ">Figure A1
<p>Maps of the estimated concentrations on 5 June at 9 a.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">Figure A2
<p>Maps of the estimated concentrations on 5 June at 1 a.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">Figure A3
<p>Maps of the estimated concentrations on 5 June at 5 p.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">Figure A4
<p>Maps of the estimated concentrations on 21 June at 9 a.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">Figure A5
<p>Maps of the estimated concentrations on 21 June at 1 a.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">Figure A6
<p>Maps of the estimated concentrations on 21 June at 5 p.m.: (<b>a</b>) CO concentrations; (<b>b</b>) NO<sub>2</sub> concentrations.</p> Full article ">
19 pages, 7391 KiB  
Article
The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance
by Ju-Hye Kim, Pedro A. Jiménez, Manajit Sengupta, Jimy Dudhia, Jaemo Yang and Stefano Alessandrini
Atmosphere 2022, 13(11), 1932; https://doi.org/10.3390/atmos13111932 - 20 Nov 2022
Cited by 3 | Viewed by 1728
Abstract
We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In [...] Read more.
We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>The instantaneous pattern of the stochastic perturbations for the (<b>a</b>) aerosol optical depth (AOD) and (<b>b</b>) turbulent kinetic energy (TKE).</p> Full article ">Figure 2
<p>Time series of observed (black) and predicted GHI from WRF-Solar DET (red) and WRF-Solar EPS (blue) from 15 April to 17 April 2018 at TBL (Table Mountain, Colorado), BON (Bondville, Illinois), and SXF (Sioux Falls, South Dakota) SURFRAD sites. The thin blue line is the forecast result from each ensemble member, and the thick line indicates the mean of 10 ensembles.</p> Full article ">Figure 3
<p>(<b>a</b>) Mean GHI and (<b>b</b>) frequencies of clouds from the NSRDB for 2018.</p> Full article ">Figure 4
<p>(<b>a</b>) GHI bias and (<b>b</b>) RMSE (solid lines) as a function of the forecast lead time from WRF-Solar DET (black), WRF-Solar EPS (blue), SKEBS (green), and WRF-Solar PHYS (red) against the NSRDB for 2018 for the CONUS domain, and (<b>b</b>) ensemble spread (dashed lines) of 10 members from WRF-Solar EPS (blue), SKEBS (green), and WRF-Solar PHYS (red).</p> Full article ">Figure 5
<p>Three-day moving average of the (<b>a</b>) bias, (<b>b</b>) MAE, (<b>c</b>) RMSE, and (<b>d</b>) correlation of the GHI forecasts from WRF-Solar DET (black), WRF-Solar EPS (blue), SKEBS (green), and WRF-Solar PHYS (red) for 2018.</p> Full article ">Figure 6
<p>Bias (first column), MAE (second column), and correlation (third column) of the GHI forecasts for 2018 from WRF-Solar DET (first row), WRF-Solar EPS (second row), SKEBS (third row), and WRF-Solar PHYS (fourth row).</p> Full article ">Figure 7
<p>Forecast improvements in the MAE with respect to the WRF-Solar DET simulation for the forecast experiments (<b>a</b>) WRF-Solar EPS, (<b>b</b>) SKEBS, and (<b>c</b>) WRF-Solar PHYS.</p> Full article ">Figure 8
<p>Rank histograms for the probabilistic prediction of GHI for (<b>a</b>) WRF-Solar EPS, and (<b>b</b>) SKEBS, and (<b>c</b>) WRF-Solar PHYS. The gray bars show the frequency of occurrence of the observation in each rank. The solid black line, which is 1/(n + 1) when n equals 10, represents a perfect uniform probability for an n-member ensemble.</p> Full article ">Figure 9
<p>Binned spread–skill diagram of GHI [W/m<sup>2</sup>] for WRF-Solar EPS (blue), SKEBS (green), and WRF-Solar PHYS (red). The ensemble spread is binned into 13 equally populated class intervals.</p> Full article ">Figure 10
<p>Frequency of cloud occurrences in the NSRDB and WRF-Solar DET, WRF-Solar EPS, SKEBS, and WRF-Solar PHYS for 2018.</p> Full article ">Figure 11
<p>Spatial distribution of the Peirce skill score (PSS) of cloud detection for 2018 in WRF-Solar DET, WRF-Solar EPS, SKEBS, and WRF-Solar PHYS.</p> Full article ">Figure 12
<p>The frequency of clouds with respect to various cloud optical depth (COD) in WRF-Solar DET, WRF-Solar EPS, SKEBS, and WRF-Solar PHYS for 2018.</p> Full article ">Figure 13
<p>Changes in the cloud mixing ratio for an arbitrary atmospheric condition and corresponding GHI values. The default amount of cloud mixing ratio is 0.367 g kg<sup>−1</sup>, and the amount of cloud water was subtracted or added linearly by 20%.</p> Full article ">
18 pages, 5780 KiB  
Article
Effective Moisture Evolution since the Last Glacial Maximum Revealed by a Loess Record from the Westerlies-Dominated Ili Basin, NW China
by Yudong Li, Yue Li, Yougui Song, Haoru Wei, Yanping Wang and Nosir Shukurov
Atmosphere 2022, 13(11), 1931; https://doi.org/10.3390/atmos13111931 - 19 Nov 2022
Cited by 3 | Viewed by 2321
Abstract
Moisture variation is extremely relevant for the stability of ecosystems in Central Asia (CA). Therefore, moisture evolution and its potential driving mechanism over the region are always a hot research topic. Although much effort has been devoted to understanding the processes of moisture [...] Read more.
Moisture variation is extremely relevant for the stability of ecosystems in Central Asia (CA). Therefore, moisture evolution and its potential driving mechanism over the region are always a hot research topic. Although much effort has been devoted to understanding the processes of moisture evolutions in CA during the Quaternary, particularly the Holocene, the associated underlying mechanisms remain in a state of persistent debate. In this study, the granulometry, clay mineral and chroma properties of a loess section (named ZSP section) in the westerlies-dominated Ili Basin, NW China are investigated. With the accelerator mass spectrometry radiocarbon dating (AMS 14C)-based Bayesian age–depth model, we provide a sensitive record of effective moisture evolution since the last glacial maximum (LGM) in the basin, and the results help enhance understanding of the possible driving mechanisms for westerly climate change. Comparisons of clay mineralogy indices shows that the study area is involved in the Northern Hemisphere dust cycle processes as a dust source, and the content of <2 μm grain size fraction in the ZSP section can thereby be used to reflect the westerlies’ intensity. After deducting the complicated influencing factors for lightness changes throughout the section, the calibrated lightness is adopted to indicate the regional effective moisture. Our findings show that effective moisture is relatively abundant during the LGM and the middle–late Holocene, with dry climate conditions during the last deglaciation and early Holocene. We argue that westerlies’ intensity was the main factor for driving the effective moisture evolution in the Ili Basin since the LGM. Local and source evaporation intensity and effective intra-annual control time of the westerlies over the study area exerted a minor influence on the moisture changes. Full article
(This article belongs to the Special Issue Quaternary Westerlies and Monsoon Interaction in Asia)
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<p>(<b>a</b>) Locations of the involved study site with atmosphere circulations. ZSP: Zhaosu poma loess section in the Ili Basin (this study); 1. Xinyuan17 loess section, 2. Lujiaowan loess section, 3. Sagaxi aeolian sediment section, 4. Jingyuan loess section, 5. Guliya ice core, 6. Mediterranean Sea (Core MD95−2043). (<b>b</b>) Topographic map showing the study area and its adjacent region. (<b>c</b>) modern soil moisture, temperature, precipitation rate in the Ili Basin, Hydrometeor data source: <a href="http://www.esrl.noaa.gov/psd/data/gridded/" target="_blank">http://www.esrl.noaa.gov/psd/data/gridded/</a> (accessed on 1 October 2022), (Kalnay et al., 1996) [<a href="#B44-atmosphere-13-01931" class="html-bibr">44</a>]. (<b>d</b>) Photographs of the Zhaosu Poma (ZSP) loess section.</p> Full article ">Figure 2
<p>Bayesian age—depth model of the ZSP section based on previous AMS <sup>14</sup>C dates [<a href="#B45-atmosphere-13-01931" class="html-bibr">45</a>].</p> Full article ">Figure 3
<p>X-ray diffractograms of clay fractions of selected samples (<b>a</b>) and relative contents variations of clay minerals with Illite crystallinity, CII (chemical index of illite), and CIA (chemical index of alteration) (<b>b</b>) from the ZSP section.</p> Full article ">Figure 4
<p>Comparison of clay minerals in different regions of the Northern Hemisphere. (Data sources: Sahara—Svensson et al. (2000) [<a href="#B80-atmosphere-13-01931" class="html-bibr">80</a>], Guerzoni et al. (1997) [<a href="#B81-atmosphere-13-01931" class="html-bibr">81</a>]; East Asia—Peng (2004) [<a href="#B82-atmosphere-13-01931" class="html-bibr">82</a>], Gylesjo and Arnold (2006) [<a href="#B83-atmosphere-13-01931" class="html-bibr">83</a>], Leinen et al. (1994) [<a href="#B84-atmosphere-13-01931" class="html-bibr">84</a>], Svensson et al. (2000) [<a href="#B80-atmosphere-13-01931" class="html-bibr">80</a>]; Greenland—Biscaye et al. (1997) [<a href="#B85-atmosphere-13-01931" class="html-bibr">85</a>]; Europe—Fu et al. (2021) [<a href="#B60-atmosphere-13-01931" class="html-bibr">60</a>], Újvari et al. (2012) [<a href="#B86-atmosphere-13-01931" class="html-bibr">86</a>], Varga et al. (2011) [<a href="#B87-atmosphere-13-01931" class="html-bibr">87</a>], Martinez-Lamas et al. (2020) [<a href="#B88-atmosphere-13-01931" class="html-bibr">88</a>]).</p> Full article ">Figure 5
<p>Comparison of the &lt;2 μm content of the ZSP section [<a href="#B35-atmosphere-13-01931" class="html-bibr">35</a>] (<b>c</b>), the EM2 (11 μm) content of the LJW10 section in the northern Tianshan Mountains [<a href="#B90-atmosphere-13-01931" class="html-bibr">90</a>] (<b>d</b>), the EM1 (8.1 μm) content of the SGX section in the southern Tibetan Plateau [<a href="#B91-atmosphere-13-01931" class="html-bibr">91</a>] (<b>e</b>), mean grain sizes (MGS) of Jingyuan loess in the western Chinese Loess Plateau [<a href="#B98-atmosphere-13-01931" class="html-bibr">98</a>] (<b>b</b>) and the χ<sub>fd</sub> of the ZSP loess [<a href="#B33-atmosphere-13-01931" class="html-bibr">33</a>] (<b>a</b>).</p> Full article ">Figure 6
<p>Variations in calibration lightness (ΔL*), lightness (L*), carbonate content (%), redness (a*) versus depth of the ZSP section. (L*, carbonate content and a* are from reference [<a href="#B102-atmosphere-13-01931" class="html-bibr">102</a>].)</p> Full article ">Figure 7
<p>Mechanism of effective moisture evolution in the Ili Basin ((<b>a</b>) calibration lightness ΔL*; (<b>b</b>) CaCO<sub>3</sub> content (%) from loess section XY17 [<a href="#B108-atmosphere-13-01931" class="html-bibr">108</a>]; (<b>c</b>) the &lt;2 μm grain size fraction content (%) from ZSP section [<a href="#B39-atmosphere-13-01931" class="html-bibr">39</a>]; (<b>d</b>) summer insolation gradient between 35° N and 55° N [<a href="#B110-atmosphere-13-01931" class="html-bibr">110</a>]; (<b>e</b>) mean grain sizes (MGS) of Jingyuan loess in the western Chinese Loess Plateau [<a href="#B98-atmosphere-13-01931" class="html-bibr">98</a>]; (<b>f</b>) Guliya ice core δ<sup>18</sup>O record from the Northwestern Tibetan Plateau [<a href="#B111-atmosphere-13-01931" class="html-bibr">111</a>]; (<b>g</b>) Alboran sea surface temperature (°C) [<a href="#B112-atmosphere-13-01931" class="html-bibr">112</a>]; (<b>h</b>) benthic δ<sup>18</sup>O record [<a href="#B113-atmosphere-13-01931" class="html-bibr">113</a>]).</p> Full article ">
17 pages, 6789 KiB  
Article
Black Carbon Personal Exposure during Commuting in the Metropolis of Karachi
by Javeria Javed, Erum Zahir, Haider Abbas Khwaja, Muhammad Kamran Khan and Saiyada Shadiah Masood
Atmosphere 2022, 13(11), 1930; https://doi.org/10.3390/atmos13111930 - 19 Nov 2022
Cited by 1 | Viewed by 2221
Abstract
Black carbon (BC) exposure and inhalation dose of a commuter using four traffic modes (car, bus, auto-rickshaw, and motorbike) were monitored in Karachi, Pakistan. The real-time exposure concentrations in office-peak and off-peak hours were recorded during the winter season using microAeth® AE51 [...] Read more.
Black carbon (BC) exposure and inhalation dose of a commuter using four traffic modes (car, bus, auto-rickshaw, and motorbike) were monitored in Karachi, Pakistan. The real-time exposure concentrations in office-peak and off-peak hours were recorded during the winter season using microAeth® AE51 BC monitors. Exposure concentrations were higher in peak hours and were reduced to half in the off-peak time. The inclination levels of the inhaled dose were similar, and this trend was observed with all four modes of commute. The motorbike was found to be the most exposed mode of transportation, followed by auto-rickshaws, cars, and buses, respectively. However, the order was reversed when accounting for inhaled doses, e.g., the inhalation dose for auto rickshaws was highest, followed by the bus, motorbike, and car, respectively. Spatiotemporal analysis reveals that driving roads with lower traffic intensity and fewer intersections resulted in lower exposures. Therefore, traffic intensity, road topology, the timing of the trip, and the degree of urbanization were found to be the major influences for in-vehicle BC exposure. Full article
(This article belongs to the Section Air Quality and Health)
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<p>Locator map of the study area (Karachi city).</p> Full article ">Figure 2
<p>Road map of the selected routes. Shaheed-e-Millat Road (P1–P10) and Main University Road (P11–P28).</p> Full article ">Figure 3
<p>Average traffic flow per hour along Shaheed<tt>−</tt>e<tt>−</tt>Millat and University Road.</p> Full article ">Figure 4
<p>BC exposure concentrations in (<b>a</b>) Peak and (<b>b</b>) Off-Peak hours for different modes of commute along the selected routes.</p> Full article ">Figure 5
<p>Comparison of peak and off-peak BC exposure for each mode of commute. e.g., (<b>a</b>) car, (<b>b</b>) bus, (<b>c</b>) auto rickshaw, and (<b>d</b>) motorbike. More peak exposure concentrations for all modes of commute were observed during morning rush hours.</p> Full article ">Figure 6
<p>(<b>A</b>). Spatial and temporal distribution of BC concentrations during traffic peak hours for different modes of commute, e.g., (<b>a</b>) car, (<b>b</b>) bus, (<b>c</b>) auto rickshaw, and (<b>d</b>) motorbike. (<b>B</b>). Spatial and temporal distribution of BC concentrations during off-peak hours for different modes of commute, e.g., (<b>a</b>) car, (<b>b</b>) bus, (<b>c</b>) auto rickshaw, and (<b>d</b>) motorbike.</p> Full article ">Figure 6 Cont.
<p>(<b>A</b>). Spatial and temporal distribution of BC concentrations during traffic peak hours for different modes of commute, e.g., (<b>a</b>) car, (<b>b</b>) bus, (<b>c</b>) auto rickshaw, and (<b>d</b>) motorbike. (<b>B</b>). Spatial and temporal distribution of BC concentrations during off-peak hours for different modes of commute, e.g., (<b>a</b>) car, (<b>b</b>) bus, (<b>c</b>) auto rickshaw, and (<b>d</b>) motorbike.</p> Full article ">Figure 7
<p>Comparison of BC Concentrations observed along roads of Karachi with those reported for various cities of the world during commuting. “*” represents studies conducted in the winter season [<a href="#B8-atmosphere-13-01930" class="html-bibr">8</a>,<a href="#B9-atmosphere-13-01930" class="html-bibr">9</a>,<a href="#B29-atmosphere-13-01930" class="html-bibr">29</a>,<a href="#B30-atmosphere-13-01930" class="html-bibr">30</a>,<a href="#B33-atmosphere-13-01930" class="html-bibr">33</a>,<a href="#B34-atmosphere-13-01930" class="html-bibr">34</a>,<a href="#B35-atmosphere-13-01930" class="html-bibr">35</a>,<a href="#B36-atmosphere-13-01930" class="html-bibr">36</a>,<a href="#B37-atmosphere-13-01930" class="html-bibr">37</a>,<a href="#B38-atmosphere-13-01930" class="html-bibr">38</a>].</p> Full article ">Figure 8
<p>Tukey simultaneous confidence intervals (CIs) for One<tt>−</tt>Way ANOVA. Zero (represented as dotted line) indicates that the group means are equal. When a CI does not contain zero, the difference between that pair of groups is statistically significant.</p> Full article ">
17 pages, 4664 KiB  
Article
Using Neural Network NO2-Predictions to Understand Air Quality Changes in Urban Areas—A Case Study in Hamburg
by Anne-Sophie Jesemann, Volker Matthias, Jürgen Böhner and Benjamin Bechtel
Atmosphere 2022, 13(11), 1929; https://doi.org/10.3390/atmos13111929 - 19 Nov 2022
Cited by 5 | Viewed by 2482
Abstract
Due to the link between air pollutants and human health, reliable model estimates of hourly pollutant concentrations are of particular interest. Artificial neural networks (ANNs) are powerful modeling tools capable of reproducing the observed variations in pollutants with high accuracy. We present a [...] Read more.
Due to the link between air pollutants and human health, reliable model estimates of hourly pollutant concentrations are of particular interest. Artificial neural networks (ANNs) are powerful modeling tools capable of reproducing the observed variations in pollutants with high accuracy. We present a simple ANN for the city of Hamburg that estimated the hourly NO2 concentration. The model was trained with a ten-year dataset (2007–2016), tested for the year 2017, and then applied to assess the efficiency of countermeasures against air pollution implemented since 2018. Using both meteorological data and describing the weekday dependent traffic variabilities as predictors, the model performed accurately and showed high consistency over the test data. This proved to be very efficient in detecting anomalies in the time series. The further the prediction was from the time of the training data, the more the modeled data deviated from the measured data. Using the model, we could detect changes in the time series that did not follow previous trends in the training data. The largest deviation occurred during the COVID-19 lockdown in 2020, when traffic volumes decreased significantly. Concluding our case study, the ANN based approach proved suitable for modeling the NO2 concentrations and allowed for the assessment of the efficiency of policy measures addressing air pollution. Full article
(This article belongs to the Special Issue Air Quality Impacts of Vehicle Emissions)
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<p>Workflow including the steps and respective time period.</p> Full article ">Figure 2
<p>Boxplots of the numerical input variables.</p> Full article ">Figure 3
<p>Observed vs. predicted NO<sub>2</sub> concentrations (µg/m<sup>3</sup>) in each month of 2017.</p> Full article ">Figure 4
<p>Average weekly pattern of the observed and predicted NO<sub>2</sub> values in 2017 with a 95% confidence interval (bootstrapped).</p> Full article ">Figure 5
<p>The average weekly pattern of observed NO<sub>2</sub> and predicted NO<sub>2</sub> values with a bootstrapped 95% confidence interval in (<b>a</b>) summer and (<b>b</b>) winter.</p> Full article ">Figure 6
<p>Prediction of the model vs. observed values for the first week (Monday to Sunday) of each quarter in 2017.</p> Full article ">Figure 7
<p>Moving averages (four-weekly) of the difference between the prediction and observation, each for a model with and without the counter as an input feature.</p> Full article ">Figure 8
<p>Moving averages (four-weekly) of difference between prediction and observation, each for the <span class="html-italic">Stresemannstrasse</span> and <span class="html-italic">Habichtstrasse</span> stations.</p> Full article ">Figure 9
<p>Model residuals of NO<sub>2</sub> concentrations for each month in 2020.</p> Full article ">Figure 10
<p>Comparison of the predicted and observed values of week 13 in 2020 during the COVID lockdown (23 to 30 of March).</p> Full article ">
25 pages, 4036 KiB  
Article
Footprints of COVID-19 on Pollution in Southern Spain
by Eszter Wirth, Manuel Alejandro Betancourt-Odio, Macarena Cabeza-García and Ana Zapatero-González
Atmosphere 2022, 13(11), 1928; https://doi.org/10.3390/atmos13111928 - 19 Nov 2022
Cited by 2 | Viewed by 2696
Abstract
Background: Many annual deaths in Spain could be avoided if pollution levels were reduced. Every year, several municipalities in the Community of Andalusia, located in southern Spain, exceed the acceptable levels of atmospheric pollution. In this sense, the evolution of primary air pollutants [...] Read more.
Background: Many annual deaths in Spain could be avoided if pollution levels were reduced. Every year, several municipalities in the Community of Andalusia, located in southern Spain, exceed the acceptable levels of atmospheric pollution. In this sense, the evolution of primary air pollutants during the March–June 2020 lockdown can be taken as reliable evidence to analyze the effectiveness of potential air quality regulations. Data and Method: Using a multivariate linear regression model, this paper assesses the levels of NO2, O3, and PM10 in Andalusia within the 2017–2020 period, relating these representative indices of air quality with lockdown stages during the pandemic and considering control variables such as climatology, weekends, or the intrusion of Saharan dust. To reveal patterns at a local level between geographic zones, a spatial analysis was performed. Results: The results show that the COVID-19 lockdown had a heterogeneous effect on the analyzed pollutants within Andalusia’s geographical regions. In general terms, NO2 and PM10 concentrations decreased in the main metropolitan areas and the industrial districts of Huelva and the Strait of Gibraltar. At the same time, O3 levels rose in high-temperature regions of Cordoba and Malaga. Full article
(This article belongs to the Special Issue Air Quality in Spain and the Iberian Peninsula)
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<p>(<b>a</b>) Andalusia zoning map and (<b>b</b>) distribution of air quality monitoring stations.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Andalusia zoning map and (<b>b</b>) distribution of air quality monitoring stations.</p> Full article ">Figure 2
<p>The left and right sides contain the estimates of the Saharan dust effect by air monitoring stations in the SE and SW, respectively. (<b>a</b>) Saharan dust intrusion on NO<sub>2</sub> in SE Andalusia; (<b>b</b>) Saharan dust intrusion on NO<sub>2</sub> in SW; (<b>c</b>) Saharan dust intrusion on O<sub>3</sub> in SE Andalusia; (<b>d</b>) Saharan dust intrusion on O<sub>3</sub> in SW Andalusia; (<b>e</b>) Saharan dust intrusion on PM<sub>10</sub> in SE Andalusia; (<b>f</b>) Saharan dust intrusion on PM<sub>10</sub> in SW Andalusia.</p> Full article ">Figure 3
<p>The left and right sides contain the Lockdown effect estimates by air monitoring stations in the SE and SW, respectively. (<b>a</b>) Strict lockdown (phase 0) impact on NO<sub>2</sub> in SE Andalusia; (<b>b</b>) Strict lockdown (phase 0) impact on NO<sub>2</sub> in SW Andalusia; (<b>c</b>) Strict lockdown (phase 0) impact on O<sub>3</sub> in SE Andalusia; (<b>d</b>) Strict lockdown (phase 0) impact on O<sub>3</sub> in SW Andalusia; (<b>e</b>) Strict lockdown (phase 0) impact on PM<sub>10</sub> in SE Andalusia; (<b>f</b>) Strict lockdown (phase 0) impact on PM<sub>10</sub> in SW Andalusia.</p> Full article ">Figure 4
<p>The left and right sides contain the estimates of the Lockdown (Phase 2) effect by air monitoring stations in the SE and SW, respectively. (<b>a</b>) Lockdown (phase 2) impact on NO<sub>2</sub> in SE Andalusia; (<b>b</b>) Lockdown (phase 2) impact on NO<sub>2</sub> in SW Andalusia; (<b>c</b>) Lockdown (phase 2) impact on O<sub>3</sub> in SE Andalusia; (<b>d</b>) Lockdown (phase 2) impact on O<sub>3</sub> in SW Andalusia; (<b>e</b>) Lockdown (phase 2) impact on PM<sub>10</sub> in SE Andalusia; (<b>f</b>) Lockdown (phase 2) impact on PM<sub>10</sub> in SW Andalusia.</p> Full article ">
19 pages, 3637 KiB  
Article
Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya
by Peter K. Musyimi, Ghada Sahbeni, Gábor Timár, Tamás Weidinger and Balázs Székely
Atmosphere 2022, 13(11), 1927; https://doi.org/10.3390/atmos13111927 - 18 Nov 2022
Cited by 5 | Viewed by 3151
Abstract
Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) [...] Read more.
Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using Sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements. Full article
(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)
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<p>Busia County in western Kenya: The geographic location of the sampling points. The AET raster map was retrieved from the WaPOR official website.</p> Full article ">Figure 2
<p>Flow chart of the study.</p> Full article ">Figure 3
<p>Histogram of the data distribution with the normality curve.</p> Full article ">Figure 4
<p>Variables derived from Sentinel-3 SLSTR data: (<b>a</b>) NDVI, (<b>b</b>) FVC, (<b>c</b>) LST, and (<b>d</b>) TCWV.</p> Full article ">Figure 5
<p>Remotely sensed variables derived from Sentinel-1 SAR data: (<b>a</b>) VH, (<b>b</b>) VV, (<b>c</b>) difference (VH−VV), (<b>d</b>) ratio, and (<b>e</b>) RVI.</p> Full article ">Figure 6
<p>(<b>a</b>) Pearson’s correlation between the AET and remotely sensed data and (<b>b</b>) variables’ importance based on the GBM model.</p> Full article ">Figure 7
<p>Variation in the AET(y) with the most significant covariates in Busia County: (<b>a</b>) NDVI, (<b>b</b>) FVC, (<b>c</b>) LST, (<b>d</b>) TCWV, and (<b>e</b>) VH −VV.</p> Full article ">Figure 8
<p>Relationship between the observed and predicted AET values for (<b>a</b>) the training set (80%) and (<b>b</b>) the testing set (20%).</p> Full article ">
13 pages, 4101 KiB  
Article
Towards a Healthy Car: UVC LEDs in an Automobile’s HVAC Demonstrates Effective Disinfection of Cabin Air
by Richard M. Mariita, James H. Davis, Michelle M. Lottridge, Rajul V. Randive, Hauke Witting and Johannes Yu
Atmosphere 2022, 13(11), 1926; https://doi.org/10.3390/atmos13111926 - 18 Nov 2022
Cited by 2 | Viewed by 2204
Abstract
Vehicle Heating, ventilation, and air conditioning (HVAC) systems can accumulate and recirculate highly infectious respiratory diseases via aerosols. Integrating Ultraviolet Subtype C (UVC) light-emitting diodes (LEDs) to complement automobile HVAC systems can protect occupants from developing allergies, experiencing inflammatory problems, or acquiring respiratory [...] Read more.
Vehicle Heating, ventilation, and air conditioning (HVAC) systems can accumulate and recirculate highly infectious respiratory diseases via aerosols. Integrating Ultraviolet Subtype C (UVC) light-emitting diodes (LEDs) to complement automobile HVAC systems can protect occupants from developing allergies, experiencing inflammatory problems, or acquiring respiratory infectious diseases by inactivating pathogenic organisms. UVC can add little to no static pressure with minimal space, unlike mercury lamps which are larger and heavier. Additionally, UVC LEDs are effective at low voltage and have no mercury or glass. While previous experiments have shown UVC LED technology can reduce bacteriophage Phi6 concentrations by 1 log in 5 min (selected as the average time to clean the cabin air), those studies had not positioned LED within the HVAC itself or studied the susceptibility of the surrogate at the specific wavelength. This study aimed to assess the disinfection performance of UVC LEDs in automotive HVAC systems and determine the dose–response curve for bacteriophage Phi6, a SARS-CoV-2 surrogate. To achieve this, UVC LEDs were installed in a car HVAC system. To determine inactivation efficacy, a model chamber of 3.5 m3, replicating the typical volume of a car, containing the modified automobile HVAC system was filled with bacteriophage Phi6, and the HVAC was turned on with and without the UVC LEDs being turned on. The results revealed that HVAC complemented with UVC reduced bacteriophage Phi6 levels significantly more than the HVAC alone and reduced the viral concentration in the cabin by more than 90% viral reduction in less than 5 min. The performance after 5 min is expected to be significantly better against SARS-CoV-2 because of its higher sensitivity to UVC, especially at lower wavelengths (below 270 nm). HVAC alone could not achieve a 90% viral reduction of bacteriophage Phi6 in 15 min. Applying UVC LEDs inside an HVAC system is an effective means of quickly reducing the number of aerosolized viral particles in the chamber, by inactivating microorganisms leading to improved cabin air quality. Full article
(This article belongs to the Special Issue Science and Technology of Indoor and Outdoor Environment)
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<p>A map of the field of prevention of the transmission of airborne pathogens in enclosed spaces with a focus on UVC and weaknesses of competing technologies.</p> Full article ">Figure 2
<p>The model of the disinfection chamber in Zemax Optic Studio showing the triangular extrusion light baffles, PCB placement and reflectors. One LED is shining showing the way light is scattered inside.</p> Full article ">Figure 3
<p>The test chamber with labeled components.</p> Full article ">Figure 4
<p>The disinfection chamber as built includes the non-ideality of the BaSO<sub>4</sub> coating, the placement of the triangular extrusion and the extent of the coating of the LED PCB.</p> Full article ">Figure 5
<p>The dose response curve of bacteriophage Phi6 as measured against array with peak wavelength of 267 nm in aqueous solution in a Petri dish. Experiments were done in triplicates and means used in graphing.</p> Full article ">Figure 6
<p>A heat map of simulated light escaping from the reaction chamber.</p> Full article ">Figure 7
<p>A graph of the log reduction in the chamber vs. time. The HVAC + UVC obtained more than 90% viral reduction faster than HVAC only.</p> Full article ">Figure 8
<p>Curves of surviving fraction of Phi6 vs. the sampling time. Log fits of the data are shown vs. this data as well as the extrapolation, based on these fits, to the disinfection rate for SARS-CoV-2 which required less time due to wavelength sensitivity.</p> Full article ">Figure 9
<p>The spectrum of the UVC LED used for the generation of dose response curve of Bacteriophage Phi6 with peak emission of 267 nm. The representative spectrum has an FWHM of 11 nm and an asymmetric tail.</p> Full article ">
16 pages, 1586 KiB  
Communication
Thoughts about the Thermal Environment and the Development of Human Civilisation
by Ioannis Charalampopoulos and Andreas Matzarakis
Atmosphere 2022, 13(11), 1925; https://doi.org/10.3390/atmos13111925 - 18 Nov 2022
Cited by 4 | Viewed by 2577
Abstract
Thermal conditions are the most challenging factors in studying human biometeorology, indoor and outdoor design, and adaptation to climate change. The thermal environment is always present and shapes everyday life, behaviours, and the natural and artificial environment. In this paper, we analyse some [...] Read more.
Thermal conditions are the most challenging factors in studying human biometeorology, indoor and outdoor design, and adaptation to climate change. The thermal environment is always present and shapes everyday life, behaviours, and the natural and artificial environment. In this paper, we analyse some thoughts that link thermal perception to the roots of human civilisation. Following the narrative thread of mythology and the history of religions, there are direct and indirect references to the thermal environment everywhere. The thermal environment may be a part of the core of human culture. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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<p>Pericles speech on Pnyx rock hill. Painting of Philipp Foltz. Wall painting in Maimileaneum Palace of Munich. From Wikimedia commons: <a href="https://commons.wikimedia.org/wiki/File:Discurso_funebre_pericles.PNG" target="_blank">https://commons.wikimedia.org/wiki/File:Discurso_funebre_pericles.PNG</a> (accessed on 10 October 2022).</p> Full article ">Figure 2
<p>The Elysium or Aeneas finding his father at the Elysian Fields, between 1597 and 1607, from the collection of the Museum of Fine Arts of Lyon in Lyon, France. It depicts a scene from Virgil’s Aeneid where Aeneas meets his father, Anchises in Elysium. From Wikimedia commons <a href="https://commons.wikimedia.org/wiki/File:Enee_meeting_with_his_father_in_the_Elysium-Sebastien_Vrancx-MBA_Lyon_H1153-IMG_0415.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Enee_meeting_with_his_father_in_the_Elysium-Sebastien_Vrancx-MBA_Lyon_H1153-IMG_0415.jpg</a> (accessed on 10 October 2022).</p> Full article ">Figure 3
<p>Sennedjem and Iineferti in the Fields of Iaru. From: <a href="https://www.metmuseum.org/art/collection/search/548354" target="_blank">https://www.metmuseum.org/art/collection/search/548354</a> (open access source) (accessed on 10 October 2022).</p> Full article ">
14 pages, 5319 KiB  
Article
Methodology for Virtual Prediction of Vehicle-Related Particle Emissions and Their Influence on Ambient PM10 in an Urban Environment
by Toni Feißel, Florian Büchner, Miles Kunze, Jonas Rost, Valentin Ivanov, Klaus Augsburg, David Hesse and Sebastian Gramstat
Atmosphere 2022, 13(11), 1924; https://doi.org/10.3390/atmos13111924 - 18 Nov 2022
Cited by 4 | Viewed by 2876
Abstract
As a result of rising environmental awareness, vehicle-related emissions such as particulate matter are subject to increasing criticism. The air pollution in urban areas is especially linked to health risks. The connection between vehicle-related particle emissions and ambient air quality is highly complex. [...] Read more.
As a result of rising environmental awareness, vehicle-related emissions such as particulate matter are subject to increasing criticism. The air pollution in urban areas is especially linked to health risks. The connection between vehicle-related particle emissions and ambient air quality is highly complex. Therefore, a methodology is presented to evaluate the influence of different vehicle-related sources such as exhaust particles, brake wear and tire and road wear particles (TRWP) on ambient particulate matter (PM). In a first step, particle measurements were conducted based on field trials with an instrumented vehicle to determine the main influence parameters for each emission source. Afterwards, a simplified approach for a qualitative prediction of vehicle-related particle emissions is derived. In a next step, a virtual inner-city scenario is set up. This includes a vehicle simulation environment for predicting the local emission hot spots as well as a computational fluid dynamics model (CFD) to account for particle dispersion in the environment. This methodology allows for the investigation of emissions pathways from the point of generation up to the point of their emission potential. Full article
(This article belongs to the Special Issue Non-exhaust particle emissions from vehicles)
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<p>Methodology for particle measurement (creation of a database), virtual emission prediction and particle tracking within the environment.</p> Full article ">Figure 2
<p>Test setup for vehicle-based particle emission measurement: (<b>a</b>) schematic illustration of sampling systems for brake wear (blue) and TRWP particles (red)—(<b>b</b>) measurement setup mounted to the test vehicle [<a href="#B18-atmosphere-13-01924" class="html-bibr">18</a>].</p> Full article ">Figure 3
<p>Simulation environment (Erfurt, Germany): (<b>a</b>) vehicle simulation model (<b>b</b>) CFD model (global wind direction: west/global wind speed 10 km/h).</p> Full article ">Figure 4
<p>Correlation between vehicle dynamics and particle generation: exhaust emissions as a function of (<b>a</b>) vehicle acceleration, (<b>b</b>) engine torque and velocity.</p> Full article ">Figure 5
<p>Correlation between vehicle dynamics and particle generation: brake wear emissions as a function of (<b>a</b>) vehicle deceleration, (<b>b</b>) brake pressure and vehicle speed.</p> Full article ">Figure 6
<p>Correlation between vehicle dynamics and particle generation: TRWP emissions as a function of (<b>a</b>) longitudinal and lateral acceleration and (<b>b</b>) slip velocity.</p> Full article ">Figure 7
<p>Estimated vehicle-related PM<sub>10</sub> contribution within a virtual city environment: (<b>a</b>) brake wear, (<b>b</b>) TRWP (<b>c</b>) exhaust emissions (<b>d</b>) virtual PM<sub>10</sub> and PN emission factors.</p> Full article ">Figure 7 Cont.
<p>Estimated vehicle-related PM<sub>10</sub> contribution within a virtual city environment: (<b>a</b>) brake wear, (<b>b</b>) TRWP (<b>c</b>) exhaust emissions (<b>d</b>) virtual PM<sub>10</sub> and PN emission factors.</p> Full article ">Figure 8
<p>Transfer of a predicted particle emission stream into the flow model considering the particle size distribution: (<b>a</b>) particle size distribution of a TRWP sample (<b>b</b>) dispersion of TRWP particles within the road canyons of the city model (global wind direction: west/global wind speed 10 km/h).</p> Full article ">Figure 9
<p>Correlation between global wind conditions and local PM<sub>10</sub> concentrations (Bergstraße): (<b>a</b>) influence of global wind speed (global wind direction: west)—(<b>b</b>) influence of the global wind direction (height above the road surface: 4 m).</p> Full article ">
17 pages, 7001 KiB  
Article
Analysis of the Spatial–Temporal Distribution Characteristics of NO2 and Their Influencing Factors in the Yangtze River Delta Based on Sentinel-5P Satellite Data
by Xiaohui Guo, Zhen Zhang, Zongcai Cai, Leilei Wang, Zhengnan Gu, Yangyang Xu and Jinbiao Zhao
Atmosphere 2022, 13(11), 1923; https://doi.org/10.3390/atmos13111923 - 18 Nov 2022
Cited by 4 | Viewed by 2113
Abstract
The recent rapid economic development in the Yangtze River Delta (YRD) has led to atmospheric destruction; therefore, it is imperative to solve the issue of atmospheric environmental pollution to ensure stable long-term development. Based on the NO2 column concentration observed by the [...] Read more.
The recent rapid economic development in the Yangtze River Delta (YRD) has led to atmospheric destruction; therefore, it is imperative to solve the issue of atmospheric environmental pollution to ensure stable long-term development. Based on the NO2 column concentration observed by the TROPOMI (a tropospheric monitoring instrument) on the Sentinel-5P, the spatial–temporal distribution characteristics of the NO2 column concentration in the YRD from 2019 to 2020 were analyzed using the Google Earth Engine (GEE) platform, and the Geographical Detector (Geodetector) model was used to determine the driving factors of the NO2 column concentration. The results show that the correlation between the NO2 column concentration and the ground-monitored NO2 concentrations reached 70%. The annual variation trend of the NO2 column concentration exhibited a ‘U’-shaped curve, with the characteristics of ‘high in winter and low in summer, with a transition between spring and autumn’. It exhibited obvious agglomeration characteristics in terms of the spatial distribution, with a high-value agglomeration in the central region of the YRD, followed by the northern region, and a low-value agglomeration in the southern region, with higher altitudes. The change in the NO2 column concentration in the YRD was affected by both physical geographical factors and socio-economic factors; it is clear that the influence of socio-economic factors has increased. Full article
(This article belongs to the Special Issue Feature Papers in Air Quality)
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<p>Overview of the study area (note: we used elevation data as a baseline to create the map of the study area; DEM can realize the digital simulation of ground terrain with limited terrain elevation data and express the topographic conditions of the YRD. In the study area map, we have labeled the provincial and municipal boundaries).</p> Full article ">Figure 2
<p>Flowchart of the research methodology.</p> Full article ">Figure 3
<p>Correlation analysis of the ground-monitored NO<sub>2</sub> concentrations and NO<sub>2</sub> column concentration in the YRD.</p> Full article ">Figure 4
<p>Distribution characteristics of the monthly average of NO<sub>2</sub> column concentration in the YRD from 2019 to 2020 (note: this is a violin plot showing the monthly mean change trend. The rectangular box corresponds to the 25–75% range of the NO<sub>2</sub> column concentration, red and blue diamonds represent outliers).</p> Full article ">Figure 5
<p>Monthly spatial distribution of the NO<sub>2</sub> column concentration in the YRD from 2019 to 2020.</p> Full article ">Figure 6
<p>Characteristics of the NO<sub>2</sub> column concentration spatial agglomeration. (<b>a</b>) LISA agglomeration map of NO<sub>2</sub> column concentration in 2019. (<b>b</b>) LISA agglomeration map of NO<sub>2</sub> column concentration in 2020. (<b>c</b>) Hotspot analysis in 2019. (<b>d</b>) Hotspot analysis in 2020.</p> Full article ">Figure 7
<p>Pearson correlations of the NO<sub>2</sub> column concentration and its potential impact factors of the YRD in 2019 and 2020.</p> Full article ">Figure 8
<p>Interactive enhancement of potential impact factors on NO<sub>2</sub> column concentrations of the YRD in 2019 and 2020 (if q(X1∩X2) &gt; q(X1) + q(X2) represents nonlinear enhancement, it is denoted by the octothorpe (<sup>#</sup>); if q(X1∩X2) &gt; Max(q(X1), q(X2)) represents double-factor enhancement, it is denoted using an asterisk (*)).</p> Full article ">Figure 9
<p>Spatial distribution map of NO<sub>2</sub> column concentration and influencing factors in 2019. (notes: (<b>a</b>) NO<sub>2</sub> concentration distribution; (<b>b</b>) Temp: Temperature distribution; (<b>c</b>) Prec: Precipitation distribution; (<b>d</b>) NDVI: Normalized vegetation index; (<b>e</b>) DEM: Digital Elevation Model; (<b>f</b>) SI: Secondary industry; (<b>g</b>) PGDP: regional gross domestic product; (<b>h</b>) CAR: civilian vehicle ownership.)</p> Full article ">
19 pages, 5369 KiB  
Article
Spatial-Temporal Variations of Extreme Precipitation Characteristics and Its Correlation with El Niño-Southern Oscillation during 1960–2019 in Hubei Province, China
by Weizheng Wang, Huiya Tang, Jinping Li and Yukun Hou
Atmosphere 2022, 13(11), 1922; https://doi.org/10.3390/atmos13111922 - 18 Nov 2022
Cited by 4 | Viewed by 2102
Abstract
Extreme precipitation could result in many disasters, such as floods, drought, and soil erosion, further bringing severe economic loss. Based on the daily precipitation records during 1960–2019 of 26 stations obtained from the National Meteorological Science Data Center of China, 10 extreme precipitation [...] Read more.
Extreme precipitation could result in many disasters, such as floods, drought, and soil erosion, further bringing severe economic loss. Based on the daily precipitation records during 1960–2019 of 26 stations obtained from the National Meteorological Science Data Center of China, 10 extreme precipitation indices (EPIs: annual total precipitation (PRCPTOT), max-1-day precipitation amount (RX1day), max-5-day precipitation amount (RX5day), number of heavy rain days (R10), number of very heavy rain days (R10), simple daily intensity index (SDII), consecutive dry days (CDD), continued wet days (CWD), very wet days (R95p) and extremely wet days (R99p)) were chosen and used to analyze the spatial-temporal variation of extreme precipitation within Hubei province, China, which is an important industrial and agricultural base in China. Finally, the correlation between El Niño-Southern Oscillation and EPIs was analyzed by cross-wavelet analysis. Results showed that the annual EPIs varied obviously during 1960–2019, and CWD decreased significantly (p < 0.05). The chosen EPIs were higher in eastern and southwestern Hubei compared to other regions, and RX1day, RX5day, R95p, and R99p were increased in most regions. The spatial-temporal variations of spring and summer EPIs were more obvious than those on an annual scale. In summer, all EPIs except CDD should increase in the near future. More attention should be paid to Wuhan, Enshi, and Macheng, where the RX1day, RX5day, R95p, and R99p will increase in these regions. Finally, the RX1day and R10 were positively correlated with MEI (p < 0.05), while the RX5day, CDD, CWD, and R99p were negatively correlated with MEI (p < 0.05). The extreme precipitation events within Hubei were affected by the El Niño-Southern Oscillation. The results could provide a possible driving factor for precipitation prediction and natural hazard prevention within Hubei province, China. Full article
(This article belongs to the Section Meteorology)
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<p>Overview of the Hubei province, China.</p> Full article ">Figure 2
<p>Trend and 10a average value of EPIs within Hubei province on the annual scale during 1960–2019.</p> Full article ">Figure 3
<p>Moving T-test for each EPI within Hubei province on the annual scale during 1960–2019.</p> Full article ">Figure 4
<p>Spatial variations of annual average EPIs and the changing trend of annual EPIs for each station within the Hubei province.</p> Full article ">Figure 5
<p>Trend and 10a average value of EPIs within Hubei province in spring during 1960–2019.</p> Full article ">Figure 6
<p>Trend and 10a average value of EPIs within Hubei province in summer during 1960–2019.</p> Full article ">Figure 7
<p>Moving T-test for each EPI within Hubei province in spring during 1960–2019.</p> Full article ">Figure 8
<p>Moving T-test for each EPI within Hubei province in summer during 1960–2019.</p> Full article ">Figure 9
<p>Spatial variations of spring average EPIs and the changing trend of spring EPIs for each station within Hubei province.</p> Full article ">Figure 10
<p>Spatial variations of summer average EPIs and the changing trend of spring EPIs for each station within Hubei province.</p> Full article ">Figure 11
<p>Correlations between the MEI and EPIs on a yearly scale. A bold black contour represents the 95 percent significance threshold against red noise; a thin black line represents the cone of influence (COI) and bold arrows reflect phase change.</p> Full article ">Figure 12
<p>The annual EPIs and El Niño and La Niña years from 1960 to 2019 in Hubei province.</p> Full article ">
19 pages, 10475 KiB  
Article
Extreme Value Analysis of NOx Air Pollution in the Winter Seaport of Varna
by Yordan Garbatov, Petar Georgiev and Ivet Fuchedzhieva
Atmosphere 2022, 13(11), 1921; https://doi.org/10.3390/atmos13111921 - 18 Nov 2022
Cited by 4 | Viewed by 1874
Abstract
The work studies extreme pollution events and their return period in the winter seaport of Varna, providing information for decision-makers, government agencies and civil society on how the intensity of shipping traffic may impact the air pollution in the vicinity of the port. [...] Read more.
The work studies extreme pollution events and their return period in the winter seaport of Varna, providing information for decision-makers, government agencies and civil society on how the intensity of shipping traffic may impact the air pollution in the vicinity of the port. Extreme value analysis employing the Weibull distribution is applied to investigate air pollution and the probability of higher concentrations of oxides of nitrogen (NOx) generated by ships while queuing in the winter seaport. Potential cleaning of the air pollution generated by the anchored ships is introduced to meet the acceptable level of air pollution concentrations in coastal zones. The employed ship pollution cleaning and overall ship service costs are minimised to satisfy cleaner environmental conditions. The developed approach is adopted to analyse the air pollution of a port without a monitoring system to control and prevent pollution and with limited information on ship traffic and air pollution. Full article
(This article belongs to the Special Issue The Role of Maritime Transport, Ports and Shipyards on Air Quality and Other Environmental Components)
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<p>Varna seaports (A-Summer and B-Winter seaports), developed by authors based on Google My Maps, <a href="https://www.google.com/intl/en-GB_ALL/permissions/geoguidelines/" target="_blank">https://www.google.com/intl/en-GB_ALL/permissions/geoguidelines/</a>, accessed on 20 October 2022.</p> Full article ">Figure 2
<p>Types (<b>left</b>) and DW (<b>right</b>) of queuing ships.</p> Full article ">Figure 3
<p>Age (<b>left</b>) and length (<b>right</b>) of queuing ships.</p> Full article ">Figure 4
<p>Average arriving ships (<b>left</b>) and installed engine power (<b>right</b>).</p> Full article ">Figure 5
<p>Average queuing time (<b>left</b>) and wind speed (<b>right</b>).</p> Full article ">Figure 6
<p>Average wind direction (<b>left</b>) and weather stability (<b>right</b>).</p> Full article ">Figure 7
<p>Weibull descriptors of arriving ships (<b>left</b>) and installed engine power (<b>right</b>).</p> Full article ">Figure 8
<p>Weibull descriptors of queuing time (<b>left</b>) and wind speed (<b>right</b>).</p> Full article ">Figure 9
<p>Weibull descriptors of weather stability.</p> Full article ">Figure 10
<p>Return period of arriving ships (<b>left</b>) and installed engine power (<b>right</b>).</p> Full article ">Figure 11
<p>Return period of queuing time (<b>left</b>) and wind speed (<b>right</b>).</p> Full article ">Figure 12
<p>Return period of insolation.</p> Full article ">Figure 13
<p>Monte Carlo air pollution generation for <span class="html-italic">x</span> = 500 m, <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Weibull descriptors and lower and upper confidence level of air pollution concentration, <span class="html-italic">x</span> = 250 m (<b>left</b>) and <span class="html-italic">x</span> = 500 m (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Weibull descriptors and lower and upper confidence level of air pollution concentration, <span class="html-italic">x</span> = 750 m (<b>left</b>) and <span class="html-italic">x</span> = 1000 m (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Weibull descriptors and lower and upper confidence level of air pollution concentration, <span class="html-italic">x</span> = 1500 m (<b>left</b>) and <span class="html-italic">x</span> = 2000 m (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Air pollution concentration return value and period, <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>Probability of exceedance of the limit value of 25 μg/m<sup>3</sup> air pollution concentration of <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 19
<p>Risk of <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math> air pollution as a function of wind direction, conditional on distancing, in a one-year return period.</p> Full article ">Figure 20
<p>Risk of <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math> air pollution, projected to the seaport map (<b>left</b>) as a function of distance (<b>right</b>), conditional to wind direction, in a one-year return period.</p> Full article ">
15 pages, 16099 KiB  
Article
Atmosphere Critical Processes Sensing with ACP
by Sergey Pulinets and Pavel Budnikov
Atmosphere 2022, 13(11), 1920; https://doi.org/10.3390/atmos13111920 - 18 Nov 2022
Cited by 6 | Viewed by 1758
Abstract
This manuscript intends to demonstrate the diagnostic value of the previously discussed integrated parameter called atmospheric chemical potential (ACP) for tracking the atmospheric anomalies before strong earthquakes generated by the chain of processes initiated by air ionization due to radon emanation from the [...] Read more.
This manuscript intends to demonstrate the diagnostic value of the previously discussed integrated parameter called atmospheric chemical potential (ACP) for tracking the atmospheric anomalies before strong earthquakes generated by the chain of processes initiated by air ionization due to radon emanation from the Earth’s crust. For this purpose, we considered several kinds of critical processes in the atmosphere using the ACP as an indicator and diagnostic tool: hurricane dynamics, the effects of radioactive pollution (the Chernobyl NPP accident), volcano eruptions and pre-earthquake atmospheric anomalies. We established that in all cases, some unusual features of the studied critical processes were revealed to be invisible when using certain methods of monitoring. This means that the application of ACP may improve the operative monitoring of the critical processes in atmosphere. In the cases of volcano eruptions and earthquakes, ACP can be used for short-term forecast. Full article
(This article belongs to the Section Meteorology)
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<p>Best track positions for Hurricane Sam, 22 September–5 October 2021. Tracking during the extratropical stage is partially based on analyses from the NOAA Ocean Prediction Center.</p> Full article ">Figure 2
<p>ACP distribution on 23 September 2021 at 09:00 UTC. The red oval shows Hurricane Sam’s position.</p> Full article ">Figure 3
<p><b>Left</b> panel—Hurricane Sam’s position on 1 October 2021 at 15:00 UTC; <b>right</b> panel—Hurricane Sam’s position on 1 October at 21:00 UTC.</p> Full article ">Figure 4
<p><b>Left</b> panel—bead instability observed during the auroral substorm on 21 December 2015 [<a href="#B14-atmosphere-13-01920" class="html-bibr">14</a>]; <b>right</b> panel—bead structure observed in the left part of the ACP distribution within the blue circle, seen as the chain of yellow spots at Hurricane Sam’s periphery on 26 September 2021.</p> Full article ">Figure 5
<p><b>Left</b> panel—temperature distribution inside Hurricane Sam; <b>right</b> panel—cross-section of the temperature distribution through the center of Hurricane Sam at a latitude of 21° S. Red lines show the longitudinal position of the yellow.</p> Full article ">Figure 6
<p><b>Left</b> panel—spatial distribution of cesium-137 on 6 May 1986 at 12:00 UTC; <b>right</b> panel—spatial distribution of ACP on 06 May 1986 at 12:00 UTC. Asterisks show the position of Chernobyl NPP, arrows and ovals show similarities of distributions.</p> Full article ">Figure 7
<p><b>Left</b> panel—radon anomaly (black) and typhoon Teresa (October 1994): intensity (red) of the typhoon (knots) and distance (km) between the typhoon and the Taal volcano (blue) are displayed together with radon concentration in soil gas. Green curve is the theorical radioactive decay of radon starting at the peak of the anomaly; <b>Right</b> panel—ACP variation over the caldera of Kīlauea volcano around the time of volcano eruption on 29 September 2021 marked by red triangle.</p> Full article ">Figure 8
<p><b>Left</b> panel—ACP variations over the volcano Bezymyanny (Kamchatka peninsula, Russia) around the time of volcano eruption 22 October 2020; <b>right</b> panel—relative humidity variations over the volcano Bezymyanny (Kamchatka peninsula, Russia) around the time of the volcano eruption on the 22 October 2020. The moment of eruption is marked by red triangles.</p> Full article ">Figure 9
<p><b>Left</b> panel—spatial ACP distribution over the Bezymyanny volcano on 13 October 2020 at 09:00 UTC; <b>right</b> panel spatial ACP distribution over the Kīlauea volcano on 24 September 2021 at 18:00 UTC. The position of the volcano’s caldera is marked by the yellow star.</p> Full article ">Figure 10
<p>The typical urban heat island profile (credit to NOAA). <a href="http://www.crh.noaa.gov/images/lsx/recent_event/urban.gif" target="_blank">http://www.crh.noaa.gov/images/lsx/recent_event/urban.gif</a> (accessed on 10 October 2022).</p> Full article ">Figure 11
<p><b>Left</b> panel—the ACP distribution over Mexico at 06:00 UTC on 22 February 2022; right panel—the ACP distribution over Mexico at 00:00 UTC on 10 February 2022. The location of Mexico City is marked by asterisks. The areas of the suspected anomalies are encircled by red ovals.</p> Full article ">Figure 12
<p>From top to bottom: ACP, relative humidity, air temperature, AOT and air pressure over the Crete M6 earthquake epicenter at a 250 m altitude. The earthquake moment is marked by the vertical red line.</p> Full article ">Figure 13
<p>ACP distribution on 14 March 2021 at 15:00 UTC. The epicenter position of the M6.6 earthquake off the coast of Kamchatka is marked by the star. White circle—Dobrovolsky earthquake preparation zone. The radius of the preparation zone is determined as R = 10<sup>0.43M</sup> [<a href="#B23-atmosphere-13-01920" class="html-bibr">23</a>].</p> Full article ">Figure 14
<p>Average of near- and intermediate-field of ACP (unfiltered—blue; filtered—green) and shear-traction field (red) in the epicentral area of the 16 March 2022 Fukushima earthquake, Japan (time shown with grey vertical dashed line). The ACP follows the temporal evolution of the shear-traction field before the earthquake, while the spike in ACP occurs at the same time and shear-traction increases.</p> Full article ">
18 pages, 3964 KiB  
Article
Ultra-Fine Particle Emissions Characterization and Reduction Technologies in a NG Heavy Duty Engine
by Pierpaolo Napolitano, Davide Di Domenico, Dario Di Maio, Chiara Guido and Stefano Golini
Atmosphere 2022, 13(11), 1919; https://doi.org/10.3390/atmos13111919 - 18 Nov 2022
Cited by 7 | Viewed by 2632
Abstract
This paper describes some strategies to deal with the arduous challenge of reducing emissions from the transport sector. Two different approaches in particle emissions reduction from natural gas (NG) heavy duty (HD) engines were evaluated. The focus was on reducing the ultra-fine sub [...] Read more.
This paper describes some strategies to deal with the arduous challenge of reducing emissions from the transport sector. Two different approaches in particle emissions reduction from natural gas (NG) heavy duty (HD) engines were evaluated. The focus was on reducing the ultra-fine sub 23 nm particles, a key aspect in the vehicles’ impact on human health and environment. To this end, an experimental research activity was carried out on a NG HD engine that was EURO VI regulation compliant. Lubricant oils characterized by different base compositions and ash contents were compared to provide a preferred path to develop formulations. The performed activity on world harmonized transient cycles (WHTCs) have demonstrated a high reduction potential (≈70%) that is reachable by acting on the lube formulation. A CNG particle filter (CPF), derived from the diesel and gasoline engines technology, was fully characterized in terms of its filtration efficiency. Three different types of tests were carried out: steady state, WHTCs, and several idle-to-load step maneuvers. The CPF was highly efficient in reducing solid particles over 10 nm diameter in all the different tests. During WHTCs, the mean abatement efficiency was about 85%. Both technologies provide interesting insights to make NG HD engines compliant with the upcoming Euro VII regulation. Full article
(This article belongs to the Special Issue Vehicle Emissions: New Challenges and Potential Solutions)
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<p>Engine test bench the experimental layout.</p> Full article ">Figure 2
<p>WHTC test cycle: engine speed and torque profiles.</p> Full article ">Figure 3
<p>Combined mean dimensionless PN and Soot results.</p> Full article ">Figure 4
<p>Mean dimensionless PSDFs provided by the tested engine in the WHTC cycle (solid lines). Ref oil standard deviation during the tests (dotted lines).</p> Full article ">Figure 5
<p>WHTC engine speed profile, PN, and Soot emitted engine-out in a typical test. PN and Soot are dimensionless.</p> Full article ">Figure 6
<p>Combined dimensionless mean PN and Soot values collected in the “TWC” and “TWC + CPF” configurations and their corresponding standard deviations (orange vertical lines).</p> Full article ">Figure 7
<p>The 900 rpm (<b>a</b>) and 1400 rpm (<b>b</b>) tests with torque variation over time. The black high-lighted steps refer to the pre-conditioning sections. The green dashed line divides phase 1 from phase 2.</p> Full article ">Figure 7 Cont.
<p>The 900 rpm (<b>a</b>) and 1400 rpm (<b>b</b>) tests with torque variation over time. The black high-lighted steps refer to the pre-conditioning sections. The green dashed line divides phase 1 from phase 2.</p> Full article ">Figure 8
<p>The 900 and 1400 rpm test dimensionless mean PN emissions for the “TWC” configuration related to the engine torque profile.</p> Full article ">Figure 9
<p>The cumulative profile of the mean PN emissions over step load cycle, with and without the CPF; dimensionless values. At 900 rpm (<b>a</b>) and at 1400 rpm (<b>b</b>).</p> Full article ">Figure 10
<p>Zoom of the typical PN emission profiles recorded during the step loads campaign at different phases.</p> Full article ">
11 pages, 32671 KiB  
Article
Evaluation of Outdoor Environment PM10 Concentration in an Organized Industrial Zone Using Geographical Information System
by Fatma Kunt and Şükran Erdoğan
Atmosphere 2022, 13(11), 1918; https://doi.org/10.3390/atmos13111918 - 17 Nov 2022
Viewed by 1599
Abstract
Air pollution adversely affects human health, visibility distance, materials, plants and animal health. Air pollution’s impact on human health arises from inhaling high amounts of harmful substances in the atmosphere. Notably, our understanding of the damage caused by PM10 pollutants is improving [...] Read more.
Air pollution adversely affects human health, visibility distance, materials, plants and animal health. Air pollution’s impact on human health arises from inhaling high amounts of harmful substances in the atmosphere. Notably, our understanding of the damage caused by PM10 pollutants is improving daily. This study aims to measure and analyze PM10 pollution in the Konya Organized Industrial Zone at certain times and places. Measurements were taken at twenty-four locations in the morning, noon and evening hours. The results were compared with the Turkish Air Quality Assessment and Management Regulation, and pollution maps of the regions were created with Surfer Software and ArcGIS 10.1 programs. With the measurements, it was observed that the times at which the limit was exceeded were mainly the evening hours. While no limit exceedance was recorded in the morning hours, the average concentration value was observed once in those hours, and around noon the maximum value was observed five times. In this study, we correlated the measurement results, the values of the measurement points located in the city center and the average number of vehicles passing through the region. It was observed that the PM10 -induced air pollution in the Konya Organized Industrial Zone was caused by dense traffic during evening hours. To prevent traffic-related pollution in the region, it is recommended to increase the number of entrance and exit gates in the industrial zone and to plant trees in appropriate sections. Full article
(This article belongs to the Special Issue Industrial Air Pollution: Emission, Management and Policy)
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<p>Display of measurement points in the OIZ map (numbering represents sampling points).</p> Full article ">Figure 2
<p>Surfer map of PM<sub>10</sub> values (μg/m<sup>3</sup>) measured in the morning (Concentration, Average, Maximum).</p> Full article ">Figure 3
<p>Surfer map of PM<sub>10</sub> values (μg/m<sup>3</sup>) measured around noon (Concentration, Average, Maximum).</p> Full article ">Figure 4
<p>Surfer map of PM<sub>10</sub> values (μg/m<sup>3</sup>) measured in the evening (Concentration, Average, Maximum).</p> Full article ">Figure 5
<p>PM<sub>10</sub> concentration (μg/m<sup>3</sup>) distribution throughout the day.</p> Full article ">Figure 6
<p>PM<sub>10</sub> average concentration (μg/m<sup>3</sup>) distribution throughout the day.</p> Full article ">Figure 7
<p>PM<sub>10</sub> maximum concentration (μg/m<sup>3</sup>) distribution throughout the day.</p> Full article ">Figure 8
<p>PM<sub>10</sub> Maximum Distribution Throughout.</p> Full article ">Figure 9
<p>PM<sub>10</sub> Average Distribution Throughout.</p> Full article ">Figure 10
<p>PM<sub>10</sub> maximum distribution throughout.</p> Full article ">Figure 11
<p>Points exceeding the PM<sub>10</sub> limit value in Konya Organized Industrial Zone.</p> Full article ">
12 pages, 2688 KiB  
Article
Effects of Outdoor Air Pollutants on Indoor Environment Due to Natural Ventilation
by Ayame Tamuro, Ryoichi Kuwahara and Hyuntae Kim
Atmosphere 2022, 13(11), 1917; https://doi.org/10.3390/atmos13111917 - 17 Nov 2022
Cited by 3 | Viewed by 1753
Abstract
This study measured ventilation volumes and particle concentrations in indoor environments with open windows and doors. In addition, the effect of the airflow mode of the air conditioner on the ventilation volume and indoor particle concentration variations was also measured. The ventilation fan [...] Read more.
This study measured ventilation volumes and particle concentrations in indoor environments with open windows and doors. In addition, the effect of the airflow mode of the air conditioner on the ventilation volume and indoor particle concentration variations was also measured. The ventilation fan could only provide approximately 43% of the ventilation volume during the design phase. The amount of ventilation differed depending on the opening area in windows and doors. The ventilation volume was increased by opening multiple windows or doors, even when the area of the opening was the same. No significant change in the ventilation rate was observed, although the air conditioner was expected to promote the ventilation rate in the room when set on blow mode. It was confirmed that both 0.3 and 1 μm particles could enter through the gaps around the windows and doors. Although most of the 5 μm particles were from the outdoor air, when the air conditioner was operated in airflow mode, the removal of 5 μm particles was performed by the air conditioner filter. The use of medium-performance or HEPA filters is expected to remove smaller particulates. Full article
(This article belongs to the Special Issue Science and Technology of Indoor and Outdoor Environment)
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<p>Floor plan and cross-section of the study room.</p> Full article ">Figure 2
<p>Indoor and outdoor measuring instruments.</p> Full article ">Figure 3
<p>Carbon dioxide concentrations with and without a ventilation fan (conditions 1 and 2).</p> Full article ">Figure 4
<p>Carbon dioxide concentrations with a ventilation fan and natural ventilation (conditions 3–5).</p> Full article ">Figure 5
<p>Carbon dioxide concentrations with use of a ventilation fan, natural ventilation, and an air conditioner (conditions 6–8).</p> Full article ">Figure 6
<p>Particulate I/O ratio with and without ventilation fan (conditions 1 and 2).</p> Full article ">Figure 7
<p>Particulate I/O ratio with a ventilation fan and natural ventilation (conditions 3–5).</p> Full article ">Figure 8
<p>Particulate I/O ratios with a ventilation fan, natural ventilation, and an air conditioner (conditions 6–8).</p> Full article ">
25 pages, 9547 KiB  
Article
Quantifying a Reliable Framework to Estimate Hydro-Climatic Conditions via a Three-Way Interaction between Land Surface Temperature, Evapotranspiration, Soil Moisture
by Mercedeh Taheri, Milad Shamsi Anboohi, Mohsen Nasseri, Mostafa Bigdeli and Abdolmajid Mohammadian
Atmosphere 2022, 13(11), 1916; https://doi.org/10.3390/atmos13111916 - 17 Nov 2022
Cited by 4 | Viewed by 1547
Abstract
Distributed hydrological models can be suitable choices for predicting the spatial distribution of water and energy fluxes if the conceptual relationships between the components are defined appropriately. Therefore, an innovative approach has been developed using a simultaneous formulation of bulk heat transfer theory, [...] Read more.
Distributed hydrological models can be suitable choices for predicting the spatial distribution of water and energy fluxes if the conceptual relationships between the components are defined appropriately. Therefore, an innovative approach has been developed using a simultaneous formulation of bulk heat transfer theory, energy budgeting, and water balance as an integrated hydrological model, i.e., the Monthly Continuous Semi-Distributed Energy Water Balance (MCSD-EWB) model, to estimate land surface hydrological components. The connection between water and energy balances is established by evapotranspiration (ET), which is a function of soil moisture and land surface temperature (LST). Thus, the developed structure is based on a three-way coupling between ET, soil moisture, and LST. The LST is obtained via the direct solution of the energy balance equation, and the spatiotemporal distribution of ET is presented using the computed LST and soil moisture through the bulk transfer method and water balance. In addition to the LST computed using the MCSD-EWB model, the LST products of ERA5-Land and MODIS are also utilized as inputs. The results indicate the adequate performance of the model in simulating LST, ET, streamflow, and groundwater level. Furthermore, the developed model performs better by employing the ERA5-Land LST than by using the MODIS LST in estimating the components. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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<p>The study region with its hydro-climatic stations, alluvial aquifer, and DEM. In more detail, plot <b>A</b> is satellite imagery of the watershed provided by Google Earth, demonstrating different land coverage. Plot <b>B</b> represents the topographical variations in the watershed along with the location of dams, hydrometric stations, aquifers, and rivers. Plot <b>C</b> illustrates a second-level watershed containing the study domain with climatologic stations.</p> Full article ">Figure 2
<p>Schematic structure of MCSD-EWB model and exchanges of its energy and mass fluxes. The watershed has been divided into three parts, labeled 1, 2, and 3. The first part (labeled 1) includes karstic areas, and the climatological water balance and karst models have been implemented in spatially distributed and lumped ways, respectively. In the second part (labeled 2), the groundwater aquifer is located under the surface and unsaturated soil layers. Using the same method as that in the first part, climatological modeling has been conducted via a spatially distributed approach, and the groundwater model is a type of lumped model. The modeling process in the third area (labeled 3), which lacks karstic and alluvial aquifers, has been carried out via a distributed method. <span class="html-italic">r<sub>b</sub></span> and <span class="html-italic">r<sub>d</sub></span> are the boundary and turbulent diffusion resistances, respectively. Other parameters are defined in the next sections. In addition, hollow arrows show the input/output energy flows, and dashed and filled arrows show the direction of moisture in the conceptual model.</p> Full article ">Figure 3
<p>Spatiotemporal patterns of long-term monthly average values of LST (°C) based on MCSD-EWB, MCSD-EWB (ERA5-Land), and MCSD-EWB (MODIS) models.</p> Full article ">Figure 4
<p>Spatial patterns of the MAE metric (°C) between monthly EEBT and LST simulated by: (<b>A</b>) MCSD-EWB (MODIS) (<b>B</b>) MCSD-EWB (ERA5-Land) models.</p> Full article ">Figure 5
<p>Boxplots of the <span class="html-italic">RMSE</span> metric (°C) between monthly EEBT and LST simulated by the MCSD-EWB (ERA5-Land) and MCSD-EWB (MODIS) models.</p> Full article ">Figure 6
<p>Spatiotemporal changes in long-term monthly average of ET (mm) based on MCSD-EWB, MCSD-EWB (ERA5-Land), and MCSD-EWB (MODIS) models.</p> Full article ">Figure 7
<p>Spatial distributions of MAE (mm) for the simulated monthly ET between the MCSD-EWB model and: (<b>A</b>) MCSD-EWB (MODIS) and (<b>B</b>) MCSD-EWB (ERA5-Land) models.</p> Full article ">Figure 8
<p>Boxplots of <span class="html-italic">RMSE</span> (mm) for monthly ET between MCSD-EWB and other models (i.e., MCSD-EWB (MODIS), and MCSD-EWB (ERA5-Land)).</p> Full article ">Figure 9
<p><span class="html-italic">NSE</span>, <span class="html-italic">KGE</span>, and <span class="html-italic">RMSE</span> metrics of streamflow and groundwater levels simulated by the MCSD-EWB, MCSD-EWB (ERA5-Land), and MCSD-EWB (MODIS) models.</p> Full article ">Figure 10
<p>Time series for monthly LST (°C), soil moisture (mm/km<sup>2</sup>), and streamflow (mm/km<sup>2</sup>) simulated by the MCSD-EWB, MCSD-EWB (ERA5-Land), and MCSD-EWB (MODIS) models, along with monthly precipitation and observed streamflow averaged over the watershed from 2001 to 2015.</p> Full article ">Figure 11
<p>Time series of monthly groundwater level values simulated by the MCSD-EWB, MCSD-EWB (ERA5-Land), and MCSD-EWB (MODIS) models versus observational values during 2006–2015.</p> Full article ">
14 pages, 5226 KiB  
Article
On the Successiveness of the Two Extreme Cold Events in China during the 2020/21 Winter According to Cold Air Trajectories
by Leying Zhang, Shuxiu Hou and Zuowei Xie
Atmosphere 2022, 13(11), 1915; https://doi.org/10.3390/atmos13111915 - 17 Nov 2022
Cited by 3 | Viewed by 1825
Abstract
Two extreme cold air events successively hit China during 28–31 December 2020 (the late 2020 event) and during 6–8 January 2021 (the early 2021 event), which caused great losses. These two events have received extensive attention in relation to synoptic weather systems and [...] Read more.
Two extreme cold air events successively hit China during 28–31 December 2020 (the late 2020 event) and during 6–8 January 2021 (the early 2021 event), which caused great losses. These two events have received extensive attention in relation to synoptic weather systems and remote forcing. Although it has been noted that a near-surface cool condition can greatly impact tropospheric circulation, its role in the successiveness of two such extreme cold waves remains unclear. This study focused on cold air pathways from the Lagrangian perspective, and explored the potential influence of cold air over the key region in terms of connecting the two cold events using a piecewise potential vorticity inversion. With the obtained results, three cold air sources with three corresponding air routes were identified in the two cold events. The northern pathway dominated the late 2020 event, in which the cold air intruded from the eastern Laptev Sea and moved southward to China. In contrast, the early 2021 event was mainly associated with the northwestern pathway in which the cold air came from the Ural Mountains and moved clockwise. Notably, cold air traveling along the western route from western Lake Balkhash arrived at the north of the Tianshan Mountains earlier and amplified the positive height anomaly in situ. Moreover, such an enhanced positive height anomaly moved the direction of the cold air from the northern and northwestern routes southward and thus played a key role in the successiveness of the two extreme cold events. Full article
(This article belongs to the Special Issue Characteristics and Attribution of Air Temperature Variability)
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<p>Average temperature drop (shading, unit: °C) in the late 2020 event and the early 2021 event. The average temperature drop is the T2m averaged during the event relative to the T2m on the day before the beginning of the event. The green contours are the minimum T2m on 29 December 2020 and 7 January 2021, respectively. Black dots indicate the starting points for the air trajectory tracking.</p> Full article ">Figure 2
<p>Averaged trajectory density (number of trajectory points associated with each grid point) during the 7 days prior to the final day of the late 2020 event and the early 2021 event starting at 50 hPa. The red and purple lines represent the 5 density contours starting at 10 and 30 hPa, respectively. The black contours indicate the 2000 m terrain height.</p> Full article ">Figure 3
<p>(<b>a</b>) Silhouette coefficients in clusters 2–10 and (<b>b</b>) Silhouette coefficients (red dashed line) and each sample number in three cluster.</p> Full article ">Figure 4
<p>Cold air trajectories (line) and the corresponding potential temperatures (shading, unit: K) during 7 days before the final day of the two events for (<b>a</b>,<b>d</b>,<b>g</b>) cluster 1, (<b>b</b>,<b>e</b>,<b>h</b>) cluster 2, and (<b>c</b>,<b>f</b>,<b>i</b>) cluster 3. Trajectories in (<b>a</b>–<b>c</b>) are all the trajectories in two events, while those in (<b>d</b>–<b>f</b>) and (<b>g</b>–<b>i</b>) are in the 2020 and 2021 events, respectively.</p> Full article ">Figure 5
<p>The geopotential height anomaly (shadings, unit: gpm) and wind stream (arrows, units: m/s) at 850 hPa on (<b>a</b>) 25 December 2020, (<b>b</b>) 27 December 2020, (<b>c</b>) 29 December 2020, (<b>d</b>) 31 December 2020, (<b>e</b>) 2 January 2021, (<b>f</b>) 4 January 2021, (<b>g</b>) 6 January 2021, and (<b>h</b>) 8 January 2021. The dots indicate the location of air mass in the corresponding day. The black, purple and blue dots represent the air masses in cluster 1, 2 and 3, respectively. The green box in (b) is the key region over (42° N–47° N, 80° E–90° E).</p> Full article ">Figure 6
<p>(<b>a</b>) The time–pressure cross-section of air temperature anomaly (contour, unit: °C) and geopotential height anomaly (shading, unit: gpm). (<b>b</b>) Daily sea level pressure anomaly (unit: hPa) averaged over (42° N–47° N, 80° E–90° E) from 25 December 2020 to 8 January 2021.</p> Full article ">Figure 7
<p>Daily 850 hPa geopotential height anomaly (shading, unit: gpm) and wind anomalies (arrows, units: m s<sup>−1</sup>) via the piecewise PV inversion on 850 hPa (<b>a</b>–<b>h</b>) from 28 December 2020 to 4 January 2021. The black box is the key region over (42° N–47° N, 80° E–90° E).</p> Full article ">Figure 8
<p>(<b>a</b>) 850 hPa geopotential height anomaly averaged over (42° N–47° N, 80° E–90° E) inverted from the piecewise PV inversion on each level averaged from 28 December 2020 to 4 January 2021. (<b>b</b>) same as (<b>a</b>) but for daily geopotential height anomaly inverted from the 850 hPa PV.</p> Full article ">
17 pages, 4087 KiB  
Article
Investigation of Waves Generated by Tropical Cyclone Kyarr in the Arabian Sea: An Application of ERA5 Reanalysis Wind Data
by Aliasghar Golshani, Masoud Banan-Dallalian, Mehrdad Shokatian-Beiragh, Majid Samiee-Zenoozian and Shahab Sadeghi-Esfahlani
Atmosphere 2022, 13(11), 1914; https://doi.org/10.3390/atmos13111914 - 17 Nov 2022
Cited by 9 | Viewed by 3710
Abstract
In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of [...] Read more.
In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of 8 days. The MIKE 21 SW model was forced by reanalyzed ERA5 wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with buoy data from the Indian National Centre for Ocean Information Services (INCOIS), which is located at 67.44° E, 18.50° N. In addition, the satellite altimeter data (CryoSat-2, SARAL and Jason-3 satellite altimeter data) was utilized for validation. Three wave parameters are considered for the validation: the significant wave height; the peak wave period; and the mean wave direction. The validation results showed that the significant wave height, the peak wave period, and the mean wave direction could be reasonably predicted by the model with reanalysis wind data as input. The maximum significant wave height reached to 10.7 m (with an associated peak wave period of 12.5 s) on 28 October 2019 at 23:00:00 in the middle of the Arabian Sea. For coastal areas, the significant wave height along the Iran and Pakistan (north Arabian Sea) coasts increased to a range of 1.4–2.8 m when tropical cyclone Kyarr moved northward. This wave height along with elevated sea level may cause severe coastal erosion and nearshore inland flooding. Impacts of cyclones on coastal zones critical facilities and infrastructure can be reduced by timely and suitable action before the event, so coastal managers should understand the effect of cyclones and their destructive consequences. The validated model developed in this study may be utilized as input data of evaluating the risk to life and infrastructure in this area. Full article
(This article belongs to the Special Issue Modeling of Ocean Waves: New Advances in Model Evaluation and Optimization)
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<p>Tropical cyclone Kyarr track in the Arabian Sea (2019).</p> Full article ">Figure 2
<p>Wind rose for the Gulf of Oman in Kharif season (1981–2019).</p> Full article ">Figure 3
<p>Frequency of very severe cyclones (1891–2011).</p> Full article ">Figure 4
<p>Tropical cyclone Kyarr wind field (based on ECMWF data) over the Arabian Sea at different times mentioned in <a href="#atmosphere-13-01914-t001" class="html-table">Table 1</a>; a darker gray color indicates a higher wind speed. (X and Y axis show the longitude and latitude, respectively). (<b>a</b>) 24 October, (<b>b</b>) 26 October, (<b>c</b>) 27 October and (<b>d</b>) 29 October.</p> Full article ">Figure 5
<p>Computational domain and bathymetry.</p> Full article ">Figure 6
<p>Comparison of the wind speed from simulations and meteorological synoptic data during tropical cyclone Kyarr at the Chabahar station.</p> Full article ">Figure 7
<p>Comparison of the (<b>a</b>) wave height and (<b>b</b>) wave period (<b>c</b>) wave direction from simulation and measurement data during tropical cyclone Kyarr.</p> Full article ">Figure 7 Cont.
<p>Comparison of the (<b>a</b>) wave height and (<b>b</b>) wave period (<b>c</b>) wave direction from simulation and measurement data during tropical cyclone Kyarr.</p> Full article ">Figure 8
<p>Location of satellite altimeter measurements of wave height during tropical Cyclone Kyarr.</p> Full article ">Figure 9
<p>Satellite altimeter wave data against Mike 21 SW model results during tropical cyclone Kyarr (the blue line is x = y and redline is trendline).</p> Full article ">Figure 10
<p>Optional points along tropical cyclone Kyarr.</p> Full article ">Figure 11
<p>Wave parameters pattern in the computational domain (<b>a</b>) significant wave height, (<b>b</b>) maximum wave height, and (<b>c</b>) peak wave period.</p> Full article ">Figure 11 Cont.
<p>Wave parameters pattern in the computational domain (<b>a</b>) significant wave height, (<b>b</b>) maximum wave height, and (<b>c</b>) peak wave period.</p> Full article ">Figure 12
<p>Wave height at optional points along tropical cyclone Kyarr track.</p> Full article ">Figure 13
<p>Maximum wave height in the study area during tropical cyclone Kyarr.</p> Full article ">Figure 14
<p>Significant wave heights along the Iranian and Pakistani coastlines during tropical cyclone Kyarr.</p> Full article ">
17 pages, 3917 KiB  
Article
Direct Detection of Severe Biomass Burning Aerosols from Satellite Data
by Makiko Nakata, Sonoyo Mukai and Toshiyuki Fujito
Atmosphere 2022, 13(11), 1913; https://doi.org/10.3390/atmos13111913 - 17 Nov 2022
Cited by 6 | Viewed by 1757
Abstract
The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in [...] Read more.
The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere)
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Figure 1
<p>Data sampling areas from GCOM-C/SGLI data from 2018 to 2021. Coastlines are represented using the equal-latitude and equal-longitude projection method from the World Data Bank (<a href="https://www.evl.uic.edu/pape/data/WDB/" target="_blank">https://www.evl.uic.edu/pape/data/WDB/</a>, accessed on 1 September 2022).</p> Full article ">Figure 2
<p>(<b>a</b>) AAI as defined by Equation (1) versus AOT (500) from JAXA/SGLI/L2/ver-2 for BBAs. Gray areas indicate AOT &gt; 5.0 without SGLI/L2/ver-2 products. (<b>b</b>) Frequency histograms of AAI for BBAs. Histograms of AAI divided into three parts (AOT ≤ 0.3, 0.3 &lt; AOT ≤ 2, 2 &lt; AOT ≤ 5) are presented in (<b>b1</b>–<b>b3</b>) for BBAs, where N, m and σ denote the total number of data items, mean value and standard deviation, respectively; (<b>c</b>) same as (<b>a</b>) but for dust; (<b>d</b>) same as (<b>b</b>) but for dust. This is denoted by the arrows at both ends drawn in (<b>a</b>,<b>c</b>). The dashed and dashed-dotted lines represent AOT (500) = 0.3 and AOT (500) = 2, respectively. The asterisk in (<b>d3</b>) represents the scale of the vertical axis on the right side. The scale of the vertical axis on the left side is used in (<b>d1</b>,<b>d2</b>). The red dots indicate the average value of the AAI for every 0.001 of the AOT (500).</p> Full article ">Figure 3
<p>(<b>a</b>) PRI as defined by Equation (2) versus AOT (500) from JAXA/SGLI/L2/ver-2 for BBAs. Gray areas indicate AOT &gt; 5.0 without SGLI/L2/ver-2 products. (<b>b</b>) Frequency histograms of AAI for BBAs. Histograms of AAI divided into three parts (AOT ≤ 0.3, 0.3 &lt; AOT ≤ 2, 2 &lt; AOT ≤ 5) are presented in (<b>b1</b>–<b>b3</b>) for BBAs, where N, m, and σ denote the total number of data items, mean value, and standard deviation, respectively; (<b>c</b>) same as (<b>a</b>) but for dust; (<b>d</b>) same as (<b>b</b>) but for dust. The asterisk in (<b>d3</b>) represents the scale of the vertical axis on the right side. The red dots indicate the average value of the PRI for every 0.001 of the AOT (500).</p> Full article ">Figure 4
<p>Scatterplots of AAI and PRI at each pixel; γ represents the correlation coefficient. (<b>a</b>) BBAs, (<b>b</b>) dust, and (<b>c</b>) BBAs + dust, where the green color represents the overlapping cases.</p> Full article ">Figure 5
<p>SGLI observation results in western North America on 12 September 2020. (<b>a</b>) SGLI color composite image with MODIS hot spots from Terra/MODIS/MOD14 [<a href="#B36-atmosphere-13-01913" class="html-bibr">36</a>]. Orange and red dots represent the AERONET/PNNL site and MODIS/hot spot on 11 and 12 September, (<b>b</b>) SGLI AOT (500) from SGLI/L2/ver.2 with NASA/AERONET sites [<a href="#B37-atmosphere-13-01913" class="html-bibr">37</a>], (<b>c</b>) the candidate areas for the existence of SBBAs.</p> Full article ">Figure 6
<p>(<b>a</b>) AAI over whole land area, (<b>b</b>) AAI over candidate area for SBBAs, (<b>c</b>) PRI over whole land area, (<b>d</b>) PRI over candidate area for SBBAs, (<b>e</b>) COT, and (<b>f</b>) AOT and COT derived from GCOM-C/SGLI over the same scene as <a href="#atmosphere-13-01913-f005" class="html-fig">Figure 5</a>b.</p> Full article ">Figure 7
<p>(<b>a</b>) PR (674 nm), (<b>b</b>) PR (869 nm), (<b>c</b>) R (674 nm), and (<b>d</b>) R (869 nm) observed by the SGLI; (<b>e</b>) wind behavior at 500 hPa and (<b>f</b>) wind behavior at 10 m above the ground simulated by numerical regional model SCALE over the same scene as <a href="#atmosphere-13-01913-f005" class="html-fig">Figure 5</a>a on 12 September 2020. The magnitude of the wind speed is presented below the figure. The small black square and orange dots represent the AERONET/PNNL site and MODIS/hot spots on 11 and 12 September, respectively.</p> Full article ">Figure 8
<p>Numerical results of the reflectance from the finite atmosphere consist of the basic BBA model in terms of the vector radiative transfer method. The polarized radiance (PR) in (<b>a</b>) and the radiance (R) in (<b>b</b>) at a wavelength of 674 nm and 869 nm are represented by a dashed curve and dotted one, respectively, against AOT (500 nm). The solid curve in (<b>a</b>) denotes the PRI defined in Equation (2).</p> Full article ">Figure 9
<p>Sample data measured at NASA/AERONET/PNNL station on 12 September 2020 [<a href="#B31-atmosphere-13-01913" class="html-bibr">31</a>]. (<b>a</b>) Directional information from SGLI and observed data. (<b>b</b>) Spectral AOT by AERONET.</p> Full article ">Figure 10
<p>Acquisition of multidirectional observation data from SGLI [<a href="#B52-atmosphere-13-01913" class="html-bibr">52</a>].</p> Full article ">
4 pages, 188 KiB  
Editorial
Atmospheric and Ocean Optics: Atmospheric Physics III
by Oleg A. Romanovskii and Olga V. Kharchenko
Atmosphere 2022, 13(11), 1912; https://doi.org/10.3390/atmos13111912 - 17 Nov 2022
Cited by 3 | Viewed by 1316
Abstract
This Special Issue aimed to collect novel papers presented at the 27th International Conference on “Atmospheric and Ocean Optics: Atmospheric Physics” (AOO—21) held from 5 to 9 July 2021 in Moscow, Russia [...] Full article
(This article belongs to the Special Issue Atmospheric and Ocean Optics: Atmospheric Physics III)
19 pages, 3602 KiB  
Article
Seasonal Characteristics of Atmospheric PM2.5 in an Urban Area of Vietnam and the Influence of Regional Fire Activities
by Quang Trung Bui, Duc Luong Nguyen and Thi Hieu Bui
Atmosphere 2022, 13(11), 1911; https://doi.org/10.3390/atmos13111911 - 16 Nov 2022
Cited by 1 | Viewed by 2736
Abstract
This study investigated the seasonal variation and chemical characteristics of atmospheric PM2.5 at an urban site in Hanoi City of Vietnam in summer (July 2020) and winter (January 2021) periods. The study results showed that the average value of daily PM2.5 [...] Read more.
This study investigated the seasonal variation and chemical characteristics of atmospheric PM2.5 at an urban site in Hanoi City of Vietnam in summer (July 2020) and winter (January 2021) periods. The study results showed that the average value of daily PM2.5 concentrations observed for the winter period was about 3 times higher than the counterpart for the summer period. The concentrations of major species in atmospheric PM2.5 (SO42−, NH4+, K+, OC and EC) measured during the winter period were also significantly higher than those during the summer period. The contribution of secondary sources to the measured OC (the largest contributor to PM2.5) was larger than that of primary sources during the winter period, compared to those in the summer period. The correlation analysis among anions and cations in PM2.5 suggested that different sources and atmospheric processes could influence the seasonal variations of PM2.5 species. The unfavorable meteorological conditions (lower wind speed and lower boundary layer height) in the winter period were identified as one of the key factors contributing to the high PM2.5 pollution in this period. With the predominance of north and northeast winds during the winter period, the long-range transport of air pollutants which emitted from the highly industrialized areas and the intensive fire regions in the southern part of China and Southeast Asia region were likely other important sources for the highly elevated concentrations of PM2.5 and its chemical species in the study area. Full article
(This article belongs to the Special Issue Wildland Fire under Changing Climate)
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<p>Study area and sampling site for PM<sub>2.5</sub> measurement in Hanoi, Vietnam.</p> Full article ">Figure 2
<p>PM<sub>2.5</sub> daily mean concentration and meteorological conditions for the summer and winter periods.</p> Full article ">Figure 3
<p>Daily PM<sub>2.5</sub> chemical composition measured at the sampling site for the summer and winter periods.</p> Full article ">Figure 4
<p>Daily variation of EC, OC, POC, and SOC concentration for the summer and winter periods.</p> Full article ">Figure 5
<p>Daily variation of NO<sub>3</sub><sup>−</sup>/SO<sub>4</sub><sup>2−</sup> and OC/EC mass ratio for the summer and winter periods.</p> Full article ">Figure 6
<p>Spatial pattern of concentration of key chemical compositions in PM<sub>2.5</sub> in relation to wind direction during: (<b>a</b>) summer period; (<b>b</b>) winter period (for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).</p> Full article ">Figure 7
<p>MODIS cumulative fire radiative power over the Southeast Asian region and three-day air mass backward trajectories (indicated by the black curves) arrived at the sampling site in Hanoi during the summer period (July 2020) and winter period (January 2021).</p> Full article ">
12 pages, 2281 KiB  
Article
Network Analysis Measuring the Impact of Volcanic Eruptions
by Yu Sun, Yuelong Zhang, Jun Meng and Jingfang Fan
Atmosphere 2022, 13(11), 1910; https://doi.org/10.3390/atmos13111910 - 16 Nov 2022
Viewed by 2114
Abstract
Volcanoes can be extremely damaging to the environment, human society, and also impact climate change. During volcanic eruption, massive amounts of gases and dust particles are thrown into the atmosphere and propagated instantaneously by the stratospheric circulation, resulting in a huge impact on [...] Read more.
Volcanoes can be extremely damaging to the environment, human society, and also impact climate change. During volcanic eruption, massive amounts of gases and dust particles are thrown into the atmosphere and propagated instantaneously by the stratospheric circulation, resulting in a huge impact on the interactive pattern of the atmosphere. Here, we develop a climate network-based framework to study the temporal evolution of lower stratospheric atmosphere conditions in relation to a volcanic eruption, the Hunga Tonga-Hunga Ha’apai (HTHH) volcano, which erupted on 20 December 2021. Various spatial-temporal topological features of the climate network are introduced to analyze the nature of the HTHH. We show that our framework has the potential to identify the dominant eruption events of the HTHH and reveal the impact of the HTHH eruption. We find that during the eruption periods of the HTHH, the correlation behaviors in the lower stratosphere became much stronger than during normal periods. Both the degree and clustering coefficients increased significantly during the dominant eruption periods, and could be used as indications for the eruption of HTHH. The underlying mechanism for the observed cooperative mode is related to the impact of a volcanic eruption on global mass circulations. The study on the network topology of the atmospheric structure during a volcanic eruption provides a fresh perspective to investigate the impact of volcanic eruptions. It can also reveal how the interactive patterns of the atmosphere respond to volcanic eruptions and improve our understanding regarding the global impacts of volcanic eruptions. Full article
(This article belongs to the Special Issue New Approaches to Complex Climate Systems)
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<p>The location of the Hunga Tonga-Hunga Ha’api volcano (HTHH, 20.54<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> S, 175.38<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> W).</p> Full article ">Figure 2
<p>The average temperature series at (<b>a</b>) 200 hPa and (<b>b</b>) 2 m around the HTHH. The averaged region is centered at the eruption location (20.54<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> S, 175.38<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> W) and extends from 177.88<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> W to 172.88<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> W and from 23.04<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> S to 18.04<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> S. The start dates (20 December 2021 and 13 January 2022) of the two dominant eruptions of the HTHH are highlighted by two dashed vertical red lines.</p> Full article ">Figure 3
<p>(<b>a</b>) The probability density distribution function of link strength <span class="html-italic">r</span> for all climate networks (with different periods). The distribution for the dominant eruptions of the HTHH are remarked by red and black solid lines, respectively. The distribution for the other periods are shown in dashed lines. (<b>b</b>) The average link strength <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>r</mi> <mo>〉</mo> </mrow> </semantics></math> as a function of time is presented by the blue line. Here, the start dates of the two dominant eruptions correspond to the middle day of the two periods.</p> Full article ">Figure 4
<p>(<b>a</b>) The average degree <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>k</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>〉</mo> </mrow> </semantics></math> and (<b>b</b>) the global clustering coefficient <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> for each evolving climate network are presented.</p> Full article ">Figure 5
<p>Network degree fields over the period (<b>a</b>) before the eruption, (<b>b</b>) the first dominant eruption, (<b>c</b>) the second dominant eruption, and (<b>d</b>) after the eruption of HTHH are presented. The corresponding periods are marked at the top of each subplot. The degree of nodes in the extratropics increases greatly during the eruption periods, which may be related to the impacts of volcanic eruption on global mass circulation.</p> Full article ">Figure 6
<p>Network local clustering coefficient fields over the period (<b>a</b>) before eruption, (<b>b</b>) the first dominant eruption, (<b>c</b>) the second dominant eruption, and (<b>d</b>) after the eruption of the HTHH. The corresponding periods are marked at the top of each subplot.</p> Full article ">
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