Atmosphere

26 pages, 19193 KiB

Open AccessEditor’s ChoiceArticle

Evaluating CMIP6 Historical Mean Precipitation over Africa and the Arabian Peninsula against Satellite-Based Observation

by Isaac Kwesi Nooni, Faustin Katchele Ogou, Abdoul Aziz Saidou Chaibou, Francis Mawuli Nakoty, Gnim Tchalim Gnitou and Jiao Lu

Atmosphere 2023, 14(3), 607; https://doi.org/10.3390/atmos14030607 - 22 Mar 2023

Cited by 7 | Viewed by 2485

Abstract

This study evaluated the historical precipitation simulations of 49 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing annual and seasonal precipitation climatology, linear trends, and their spatial correlation with global SST across Africa and the Arabian [...] Read more.

This study evaluated the historical precipitation simulations of 49 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing annual and seasonal precipitation climatology, linear trends, and their spatial correlation with global SST across Africa and the Arabian Peninsula during the period of 1980–2014, using Global Precipitation Climatology Centre (GPCP) data as a reference. Taylor’s diagram was used to quantify the strengths and weaknesses of the models in simulating precipitation. The CMIP6 multi-mean ensemble (MME) and the majority of the GCMs replicated the dominant features of the spatial and temporal variations reasonably well. The CMIP6 MME outperformed the majority of the individual models. The spatial variation of the CMIP6 MME closely matched the observation. The results showed that at annual and seasonal scales, the GPCP and CMIP6 MME reproduced a coherent spatial pattern in terms of the magnitude of precipitation. The humid region received >300 mm and the arid region received <50 mm across Africa and the Arabian Peninsula. The models from the same modeling centers replicated the precipitation levels across different seasons and regions. The CMIP6 MME and the majority of the individual models overestimate (underestimate) in humid (arid and semi-arid)-climate zones. The annual and pre-monsoon seasons (i.e., DJFMA) were better replicated in the CMIP6 GCMs than in the monsoon-precipitation model (MJJASON). The CMIP6 MME (GPCP) showed stronger wetting (drying) trends in the northern hemisphere. In contrast, a strong drying trend in the CMIP6 MME and a weak wetting trend in the GPCP were shown in the Southern Hemisphere. The CMIP6 MME captures the spatial pattern of linear trends better than individual models across different climate zones and regions. The relationship between precipitation and sea-surface temperature (SST) exhibited a high spatial correlation (−0.80 and 0.80) with large variability across different regions and climate zones. The GPCP (CMIP6 MME) exhibited a heterogenous (homogeneous) spatial pattern, with higher correlation coefficients recorded in the CMIP6 MME than in the GPCP in all cases. Individual models from the same modeling centers showed spatial homogeneity in correlation values. The differences exhibited by the individual GCMs highlight the significance of each model’s unique dynamics and physics; however, model selection should be considered for specific applications. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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19 pages, 2649 KiB

Open AccessEditor’s ChoiceArticle

Tree Traits and Microclimatic Conditions Determine Cooling Benefits of Urban Trees

by Mahmuda Sharmin, Mark G. Tjoelker, Sebastian Pfautsch, Manuel Esperón-Rodriguez, Paul D. Rymer and Sally A. Power

Atmosphere 2023, 14(3), 606; https://doi.org/10.3390/atmos14030606 - 22 Mar 2023

Cited by 6 | Viewed by 2737

Abstract

Trees play a key role in mitigating urban heat by cooling the local environment. This study evaluated the extent to which street trees can reduce sub-canopy air temperature relative to ambient conditions (ΔT), and how ΔT relates to tree traits and microclimatic variables. [...] Read more.

Trees play a key role in mitigating urban heat by cooling the local environment. This study evaluated the extent to which street trees can reduce sub-canopy air temperature relative to ambient conditions (ΔT), and how ΔT relates to tree traits and microclimatic variables. Air temperature under the canopies of 10 species was recorded within residential areas in Western Sydney, Australia, during summer 2019–2020. Tree and canopy traits, namely tree height, specific leaf area, leaf dry matter content, leaf area index, crown width and the Huber value (the ratio of sapwood area to leaf area) were then measured for all species. Species differed significantly in their ΔT values, with peak cooling (maximum ΔT −3.9 °C) observed between 9–10 am and sub-canopy warming (i.e., positive ΔT values) typically occurring during afternoon and overnight. Trees with high LAI and wider canopies were associated with the greatest daytime cooling benefits and lower levels of nighttime warming. ΔT was also negatively related to windspeed and vapor pressure deficit, and positively to solar irradiance. This study provides valuable information on how tree characteristics and microclimate influence potential cooling benefits that may aid planning decisions on the use of trees to mitigate heat in urban landscapes. Full article

(This article belongs to the Special Issue Strategies for Mitigation and Adaptation to Urban Heat)

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11 pages, 277 KiB

Open AccessArticle

Effects of Feeding a Commercial Starch Binding Agent during Heat Stress on Enteric Methane Emission, Rumen Volatile Fatty Acid Contents, and Diet Digestibility of Merino Lambs

by Pragna Prathap, Surinder S. Chauhan, Brian J. Leury, Jeremy J. Cottrell, Aleena Joy, Minghao Zhang and Frank R. Dunshea

Atmosphere 2023, 14(3), 605; https://doi.org/10.3390/atmos14030605 - 22 Mar 2023

Viewed by 1126

Abstract

Twenty-four Merino lambs were allocated to three dietary treatment groups to determine the effects of a dietary starch and protein binding agent and heat stress on methane (CH₄) emissions and rumen parameters. The diets were a wheat-based diet (WD), a 2% [...] Read more.

Twenty-four Merino lambs were allocated to three dietary treatment groups to determine the effects of a dietary starch and protein binding agent and heat stress on methane (CH₄) emissions and rumen parameters. The diets were a wheat-based diet (WD), a 2% Bioprotect™ (Bioprotect™, RealisticAgri, Rutland, UK) treated wheat-based diet (BD), and a maize-based diet (MD) for 3 periods of 1-week duration. During Period 1 (P1) the lambs were maintained under thermoneutral conditions and at a 1.7 × Maintenance (M) level. During P2 and P3, the lambs were maintained under cyclic heat stress conditions and fed at 1.7 × M and 2.0 × M, respectively. Total CH₄ production was lower for the BD diet than the WD diet, which in turn was lower than the MD diet (p < 0.001). Total CH₄ production was lower during P2 than P1 with P3 intermediate (p = 0.04). Rumen total volatile fatty acid (TVFA) concentrations were higher for the WD diet than the MD diet with the BD diet intermediate (p = 0.01). Rumen TVFA concentrations were lower during heat stress than under thermoneutral conditions (p < 0.001). Whole tract starch digestibility was higher for the BD and WD diets than the MD diet (p < 0.001). In conclusion, feeding Merino lambs the BD diet reduces CH₄ emissions without reducing starch digestibility. Full article

(This article belongs to the Special Issue Observations and Management of Livestock Production Emissions)

16 pages, 2353 KiB

Open AccessArticle

Understanding Temporal Patterns and Determinants of Ground-Level Ozone

by Junshun Wang, Jin Dong, Jingxian Guo, Panli Cai, Runkui Li, Xiaoping Zhang, Qun Xu and Xianfeng Song

Atmosphere 2023, 14(3), 604; https://doi.org/10.3390/atmos14030604 - 22 Mar 2023

Viewed by 1606

Abstract

Ground-level ozone pollution causes adverse health effects, and the detailed influences of meteorological factors and precursors on ozone at an hourly scale need to be further understood. We conducted an in-depth analysis of the phase relationships and periods of ground-level ozone in Shunyi [...] Read more.

Ground-level ozone pollution causes adverse health effects, and the detailed influences of meteorological factors and precursors on ozone at an hourly scale need to be further understood. We conducted an in-depth analysis of the phase relationships and periods of ground-level ozone in Shunyi station, Beijing, and contributing factors using wavelet analysis and geographic detectors in 2019. The combined effects of different factors on ozone were also calculated. We found that temperature had the strongest influence on ozone, and they were in phase over time. NO₂ had the greatest explanatory power for the temporal variations in ozone among precursors. The wavelet power spectrum indicated that ozone had a periodic effect on multiple time scales, the most significant being the 22–26 h period. The wavelet coherence spectrum showed that in January–March and October–December, NO₂ and ozone had an antiphase relationship, largely complementary to the in-phase relationship of temperature and ozone. Thus, the main influencing factors varied during the year. The interactions of temperature with NO₂ significantly affected the temporal variations in ozone, and explanatory power surpassed 70%. The findings can deepen understanding of the effects of meteorological factors and precursors on ozone and provide suggestions for mitigating ozone pollution. Full article

(This article belongs to the Special Issue Tropospheric Ozone in China: Current Situation, Formation Mechanism and Control Measures)

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18 pages, 5519 KiB

Open AccessEditor’s ChoiceArticle

Simulation and Analysis of the Influence of Sounding Rocket Outgassing on In-Situ Atmospheric Detection

by Zhiliang Zhang, Yueqiang Sun, Yongping Li, Jiangzhao Ai, Xiaoliang Zheng and Wei Wang

Atmosphere 2023, 14(3), 603; https://doi.org/10.3390/atmos14030603 - 22 Mar 2023

Viewed by 1196

Abstract

The Meridian Project’s sounding rocket mission uses a mass spectrometer to conduct in-situ atmospheric detection. In order to assess the influence of surface material outgassing and the attitude control jet on the spectrometer’s detection, a sounding rocket platform was modeled and simulated. Using [...] Read more.

The Meridian Project’s sounding rocket mission uses a mass spectrometer to conduct in-situ atmospheric detection. In order to assess the influence of surface material outgassing and the attitude control jet on the spectrometer’s detection, a sounding rocket platform was modeled and simulated. Using the physical field simulation software COMSOL and the Monte Carlo method, this study investigated whether the gas molecules from the two cases could enter the in-situ atmospheric mass spectrometer’s sensor sampling port after colliding with the background atmosphere. The simulation results show that the influence of surface material outgassing on the in-situ atmospheric detection is very small, even under the conditions of medium solar activity and medium geomagnetic activity, while the influence of the attitude control jet on the in-situ atmospheric detection is large but can be reduced by reducing the low-altitude attitude control operation and decreasing the transmission probability. Through simulation optimization and according to engineering needs, increasing the nozzle outlet cross-sectional area, increasing the temperature of the gas used for attitude control, increasing the nozzle rotation angle, increasing the nozzle outlet angle, or increasing the nozzle center height can reduce the transmission probability. This model can simulate and analyze the influence of both surface material outgassing and attitude control jets on in-situ atmospheric detection, optimize relevant parameters, and provide new ideas for relevant work. Full article

(This article belongs to the Section Upper Atmosphere)

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14 pages, 2112 KiB

Open AccessArticle

Screening of Absorbents for Viscose Fiber CS₂ Waste Air and Absorption–Desorption Process

by Ruixue Xiao, Kefan Chao, Ju Liu, Muhua Chen, Xinbao Zhu and Bo Fu

Atmosphere 2023, 14(3), 602; https://doi.org/10.3390/atmos14030602 - 22 Mar 2023

Cited by 2 | Viewed by 1324

Abstract

Screening of absorbents is essential for improving the removal rate of carbon disulfide (CS₂) waste air by absorption. In this work, the UNIFAC model in Aspen Plus was utilized to calculate the excess Gibbs function and absorption potential of the binary [...] Read more.

Screening of absorbents is essential for improving the removal rate of carbon disulfide (CS₂) waste air by absorption. In this work, the UNIFAC model in Aspen Plus was utilized to calculate the excess Gibbs function and absorption potential of the binary system of CS₂ with various alcohols, ethers, esters, amines, and aromatic hydrocarbons. The results were used to quantitatively compare the efficiency of each solvent for CS₂ absorption. The theoretical predictions were then verified by absorption experiments in a packed tower. The results showed that the performance of various solvents to CS₂ roughly followed the order of esters < alcohols < amines < heavy aromatics < glycol ethers. Meanwhile, N-methyl-2-pyrrolidone (NMP) is the optimal absorbent for CS₂ waste air treatment. Additionally, the process parameters of absorption and desorption of NMP were optimized. The results illustrated that the average mass removal efficiency of CS₂ by NMP is 95.2% under following conditions: liquid–gas ratio of 3.75 L·m⁻³, a temperature of 20 °C, and inlet concentration lower than 10,000 mg·m⁻³. Under the conditions of 115 °C, 10 kPa, and a desorption time of 45 min, the average desorption rate of CS₂ is 99.6%, and the average water content after desorption is 0.39%. Furthermore, the recycled lean liquid can maintain an excellent CS₂ purification effect during the recycling process. Full article

(This article belongs to the Section Air Pollution Control)

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16 pages, 4882 KiB

Open AccessArticle

Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes

by Munawar Shah, Rasim Shahzad, Muhsan Ehsan, Bushra Ghaffar, Irfan Ullah, Punyawi Jamjareegulgarn and Ahmed M. Hassan

Atmosphere 2023, 14(3), 601; https://doi.org/10.3390/atmos14030601 - 22 Mar 2023

Cited by 9 | Viewed by 2611

Abstract

Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order [...] Read more.

Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order to study possible pre-and post-seismic perturbations during the preparation phase of large-magnitude EQs, statistical and machine learning algorithms are applied to Total Electron Content (TEC) from the Global Positioning System (GPS) and Global Ionosphere Maps (GIMs). We observed TEC perturbation from the Sukkur (27.8° N, 68.9° E) GNSS station near the epicenter of Mw 5.4 Mirpur EQ within 5–10 days before the main shock day by implementing machine learning and statistical analysis. However, no TEC anomaly occurred in GIM-TEC over the Mirpur EQ epicenter. Furthermore, machine learning and statistical techniques are also implemented on GIM TEC data before and after the M_w 7.7 Awaran, where TEC anomalies can be clearly seen within 5–10 days before the seismic day and the subsequent rise in TEC during the 2 days after the main shock. These variations are also evident in GIM maps over the Awaran EQ epicenter. The findings point towards a large emission of EQ energy before and after the main shock during quiet storm days, which aid in the development of lithosphere ionosphere coupling. However, the entire analysis can be expanded to more satellite and ground-based measurements in Pakistan and other countries to reveal the pattern of air ionization from the epicenter through the atmosphere to the ionosphere. Full article

(This article belongs to the Special Issue Structure and Dynamics of Mesosphere and Lower Thermosphere)

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18 pages, 3450 KiB

Open AccessArticle

Improvement of Maximum Air Temperature Forecasts Using a Stacking Ensemble Technique

by Linna Zhao, Shu Lu and Dan Qi

Atmosphere 2023, 14(3), 600; https://doi.org/10.3390/atmos14030600 - 21 Mar 2023

Cited by 2 | Viewed by 1421

Abstract

Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with [...] Read more.

Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with high variance. Here, we proposed a stacking ensemble model named FLT, which consists of a fully connected neural network with embedded layers (ED-FCNN), a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to overcome the high variance of a single neural network and to improve prediction of maximum air temperature. The case study of daily maximum temperature forecast evaluated with observation of almost 2400 weather stations shows substantial improvement over that of single neural network model, ECMWF-IFS and statistical post-processing model. The FLT model can more effectively improve the forecast bias of the ECMWF-IFS model than that of any of the above single neural network model, with the RMSE reduced by 52.36% and the accuracy of temperature forecast increased by 43.12% compared with the ECMWF-IFS model. The average RMSEs of the FLT model decreases by 8.39%, 1.50%, 2.96% and 16.03%, respectively, compared with ED-FCNN, LSTM, TCN and the decaying average method. Full article

(This article belongs to the Section Meteorology)

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15 pages, 5201 KiB

Open AccessArticle

Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

by Zhengyu Zhang, Jiuchun Ren and Yunhua Chang

Atmosphere 2023, 14(3), 599; https://doi.org/10.3390/atmos14030599 - 21 Mar 2023

Cited by 1 | Viewed by 1144

Abstract

Growing evidence links intra-urban gradients in atmospheric fine particles (PM_2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM_2.5 mass [...] Read more.

Growing evidence links intra-urban gradients in atmospheric fine particles (PM_2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM_2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM_2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM_2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM_2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM_2.5 prediction, which has important implications for the intra-urban health assessment of PM_2.5-related pollution exposures. Full article

(This article belongs to the Special Issue Investigate Secondary Aerosol Formation and Source by Stable Isotopes)

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13 pages, 4503 KiB

Open AccessArticle

High Wet Deposition of Black Carbon over the Sichuan Basin of China

by Yu Zhou, Xiaolin Zhang and Yuanzhi Wang

Atmosphere 2023, 14(3), 598; https://doi.org/10.3390/atmos14030598 - 21 Mar 2023

Viewed by 1185

Abstract

The wet deposition flux of black carbon (BC) over the Sichuan Basin is studied on the basis of the MERRA-2 data from 1981 to 2020, aiming at investigating high BC wet deposition flux in China in terms of long-term spatial-temporal trends and influences [...] Read more.

The wet deposition flux of black carbon (BC) over the Sichuan Basin is studied on the basis of the MERRA-2 data from 1981 to 2020, aiming at investigating high BC wet deposition flux in China in terms of long-term spatial-temporal trends and influences of BC column mass density and precipitation. In China, the largest BC wet deposition flux with a regionally-averaged value of 1.00 × 10⁻² μg m⁻² s⁻¹ over the Sichuan Basin is observed, especially in the western and southern regions of the Basin with values as high as 2.20 × 10⁻² μg m⁻² s⁻¹. The seasonality of BC wet deposition flux over the Sichuan Basin depicts maximum levels in autumn, moderate levels in spring and winter, and minimum levels in summer. The monthly mean BC wet deposition flux varies almost twofold, ranging from the lowest average value of 8.05 × 10⁻³ μg m⁻² s⁻¹ in July to the highest 1.28 × 10⁻² μg m⁻² s⁻¹ in October. This study suggests that BC column mass density and precipitation are two significant factors affecting high BC wet deposition flux, whereas BC wet deposition flux is more related to BC column mass density than to precipitation over the Sichuan Basin. Full article

(This article belongs to the Special Issue Carbonaceous Aerosols)

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14 pages, 716 KiB

Open AccessArticle

The Relationship between Ambient Fine Particulate Matter (PM_2.5) Pollution and Depression: An Analysis of Data from 185 Countries

by Ravi Philip Rajkumar

Atmosphere 2023, 14(3), 597; https://doi.org/10.3390/atmos14030597 - 21 Mar 2023

Cited by 4 | Viewed by 1727

Abstract

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM_2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity, and [...] Read more.

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM_2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity, and climatic conditions, and has not been assessed at a cross-national level to date. Moreover, certain studies in this field have yielded negative results, and there are discrepancies between the results obtained in high-income countries and those from low- and middle-income countries. The current study examines cross-sectional and longitudinal associations between the incidence of depression in each country, based on Global Burden of Disease Study data, and the average national level of PM_2.5 based on the World Health Organization’s database, over the past decade (2010–2019). The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that while PM_2.5 levels showed significant cross-sectional associations with the incidence of depression, longitudinal analyses were not suggestive of a direct causal relationship. These findings are discussed in the light of recent contradictory results in this field, and the need to consider the intermediate roles of a number of individual and environmental factors. Full article

(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment)

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26 pages, 6859 KiB

Open AccessArticle

The Effect of Abrupt Changes to Sources of PM₁₀ and PM_2.5 Concentrations in Three Major Agglomerations in Mexico

by Jorge Méndez-Astudillo and Ernesto Caetano

Atmosphere 2023, 14(3), 596; https://doi.org/10.3390/atmos14030596 - 21 Mar 2023

Viewed by 1472

Abstract

In the three major urban agglomerations in Mexico (Mexico City, Monterrey, and Guadalajara), a significant change to anthropogenic sources of air pollution happened in March–May 2020, when policies implemented to stop the spread of the COVID-19 virus in Mexico caused the reduction of [...] Read more.

In the three major urban agglomerations in Mexico (Mexico City, Monterrey, and Guadalajara), a significant change to anthropogenic sources of air pollution happened in March–May 2020, when policies implemented to stop the spread of the COVID-19 virus in Mexico caused the reduction of some anthropogenic sources of air pollution. We study the effect of these significant changes to air pollution sources using satellite-retrieved aerosol optical depth (AOD) and particulate matter (PM₁₀ and PM_2.5) concentrations from ground stations. The Chow test was applied to study trend changes in PM concentrations from 1 January to 30 May 2020. The Mann–Whitney non-parametric test was then used to compare average PM concentrations in April and May pre-lockdown, during lockdown in 2020, and post-lockdown in 2021. The assessment was further performed by evaluating the exceedance of national air quality standard maxima. The trend analysis showed that PM₁₀ concentrations were reduced during lockdown in Mexico City and Monterrey, whereas no change was found for PM₁₀ in Guadalajara and PM_2.5 in the three cities. Further analysis showed that in Mexico City and Guadalajara, average PM₁₀ and PM_2.5 concentrations decreased by 12% in April and May 2020. However, in Monterrey, average PM₁₀ and PM_2.5 concentrations increased by 2.76% and 11.07%, respectively, in April 2021 due to a severe drought that caused dry soils and dust around the city. The results of this research can be used to implement policies for reducing anthropogenic sources to improve the air quality in urban areas. Full article

(This article belongs to the Section Air Pollution Control)

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14 pages, 5684 KiB

Open AccessArticle

Decadal Prediction of the Summer Extreme Precipitation over Southern China

by Huijie Wang, Yanyan Huang, Dapeng Zhang and Huijun Wang

Atmosphere 2023, 14(3), 595; https://doi.org/10.3390/atmos14030595 - 21 Mar 2023

Cited by 2 | Viewed by 1161

Abstract

The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the North Atlantic and spring sea ice concentration (SIC) [...] Read more.

The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the North Atlantic and spring sea ice concentration (SIC) over the East Siberian Sea can significantly affect the EPSC. The summer SST over the North Atlantic influences the low-pressure cyclone in the western Pacific by modulating the SST over the tropical Pacific, thus affecting EPSC. A decrease in the SIC of the East Siberian Sea induces a negative Arctic Oscillation, which induces the increased SST over northwest Pacific and the anomalous cyclone over there, in turn, affecting EPSC. Both predictors have a quasi-period of 10–14 years, which provides useful predictive signals for EPSC. The leading 7-year SST and the leading 5-year SIC are chosen to establish the prediction model based on the decadal increment method, which can well predict the EPSC, especially for the shift in the early 1990s. These results provide a clue to the limited predictability of decadal-scale extreme climate events. Full article

(This article belongs to the Special Issue Long-Term Variability of Atmospheric Precipitation)

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16 pages, 12761 KiB

Open AccessArticle

Temporal Variation of NO₂ and O₃ in Rome (Italy) from Pandora and In Situ Measurements

by Annalisa Di Bernardino, Gabriele Mevi, Anna Maria Iannarelli, Serena Falasca, Alexander Cede, Martin Tiefengraber and Stefano Casadio

Atmosphere 2023, 14(3), 594; https://doi.org/10.3390/atmos14030594 - 21 Mar 2023

Cited by 6 | Viewed by 2058

Abstract

To assess the best measures for the improvement of air quality, it is crucial to investigate in situ and columnar pollution levels. In this study, ground-based measurements of nitrogen dioxide (NO₂) and ozone (O₃) collected in Rome (Italy) between [...] Read more.

To assess the best measures for the improvement of air quality, it is crucial to investigate in situ and columnar pollution levels. In this study, ground-based measurements of nitrogen dioxide (NO₂) and ozone (O₃) collected in Rome (Italy) between 2017 and 2022 are analyzed. Pandora sun-spectrometers provided the time series of the NO₂ vertical column density (VC-NO₂), tropospheric column density (TC-NO₂), near-surface concentration (SC-NO₂), and the O₃ vertical column density (VC-O₃). In situ concentrations of NO₂ and O₃ are provided by an urban background air quality station. The results show a clear reduction of NO₂ over the years, thanks to the recent ecological transition policies, with marked seasonal variability, observable both by columnar and in situ data. Otherwise, O₃ does not show inter-annual variations, although a clear seasonal cycle is detectable. The results suggest that the variation of in situ O₃ is mainly imputable to photochemical reactions while, in the VC-O₃, it is triggered by the predominant contribution of stratospheric O₃. The outcomes highlight the importance of co-located in situ and columnar measurements in urban environments to investigate physical and chemical processes driving air pollution and to design tailored climate change adaptation strategies. Full article

(This article belongs to the Special Issue Challenges in Modelling and Observing Urban Environments: Recent Trends, Current Progress and Future Directions)

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11 pages, 1748 KiB

Open AccessCommunication

Seasonal Characteristics of Fine Particulate Carbonaceous Species in Taiyuan, North China

by Wenbo Li, Xinya Zhao, Fengbo Guo and Kankan Liu

Atmosphere 2023, 14(3), 593; https://doi.org/10.3390/atmos14030593 - 21 Mar 2023

Viewed by 1065

Abstract

To characterize seasonal carbonaceous aerosol pollution in Taiyuan, a typical city in North China that mainly relies heavily on coal, a total of 124 PM_2.5 samples were collected from August 2018 to the next May. The annual mean PM_2.5 concentration was [...] Read more.

To characterize seasonal carbonaceous aerosol pollution in Taiyuan, a typical city in North China that mainly relies heavily on coal, a total of 124 PM_2.5 samples were collected from August 2018 to the next May. The annual mean PM_2.5 concentration was 83.8 ± 48.5 μg m⁻³, with a seasonal rank of winter (117.4 ± 47.6 μg m⁻³) > spring (79.2 ± 34.3 μg m⁻³) > fall (67.3 ± 34.7 μg m⁻³) > summer (31.8 ± 6.5 μg m⁻³), suggesting that fine particulate pollution was still serious in cold seasons. Organic carbon (OC) and elemental carbon (EC) showed similar seasonal patterns with PM_2.5. The mean concentration values of OC in summer, fall, winter, and spring were 5.1 ± 0.9, 11.8 ± 6.4, 22.1 ± 14.9, and 12.2 ± 6.7 μg m⁻³, respectively. The mean concentration values of EC in summer, fall, winter, and spring were 1.5 ± 0.3, 2.5 ± 1.6, 4.4 ± 2.8, and 2.4 ± 1.5 μg m⁻³, respectively. The proportion of total carbon aerosol (TCA) was about 31.7%, 33.8%, 30.0%, and 27.0% in PM_2.5 in summer, fall, winter, and spring, respectively. The good correlation between OC vs. EC and the high value of OC/EC suggests that coal and biomass combustion were the main emissions in cold seasons, aggravated by adverse meteorological conditions and the dustpan-shaped terrain. The mean annual secondary organic carbon (SOC) concentration was 6.1 ± 7.1μg m⁻³, representing 38.7% of the OC content. The present results presented the serious carbonaceous particulate pollution, which might affect haze pollution in cold seasons. Full article

(This article belongs to the Section Aerosols)

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22 pages, 4551 KiB

Open AccessArticle

Apportionment of PM_2.5 Sources across Sites and Time Periods: An Application and Update for Detroit, Michigan

by Zhiyi Yang, Md Kamrul Islam, Tian Xia and Stuart Batterman

Atmosphere 2023, 14(3), 592; https://doi.org/10.3390/atmos14030592 - 20 Mar 2023

Cited by 2 | Viewed by 2238

Abstract

Identifying sources of air pollutants is essential for informing actions to reduce emissions, exposures, and adverse health impacts. This study updates and extends apportionments of particulate matter (PM_2.5) in Detroit, MI, USA, an area with extensive industrial, vehicular, and construction activity [...] Read more.

Identifying sources of air pollutants is essential for informing actions to reduce emissions, exposures, and adverse health impacts. This study updates and extends apportionments of particulate matter (PM_2.5) in Detroit, MI, USA, an area with extensive industrial, vehicular, and construction activity interspersed among vulnerable communities. We demonstrate an approach that uses positive matrix factorization models with combined spatially and temporally diverse datasets to assess source contributions, trend seasonal levels, and examine pandemic-related effects. The approach consolidates measurements from 2016 to 2021 collected at three sites. Most PM_2.5 was due to mobile sources, secondary sulfate, and secondary nitrate; smaller contributions arose from soil/dust, ferrous and non-ferrous metals, and road salt sources. Several sources varied significantly by season and site. Pandemic-related changes were generally modest. Results of the consolidated models were more consistent with respect to trends and known sources, and the larger sample size should improve representativeness and stability. Compared to earlier apportionments, contributions of secondary sulfate and nitrate were lower, and mobile sources now represent the dominant PM_2.5 contributor. We show the growing contribution of mobile sources, the need to update apportionments performed just 5–10 years ago, and that apportionments at a single site may not apply elsewhere in the same urban area, especially for local sources. Full article

(This article belongs to the Special Issue The Michigan-Ontario Ozone Source Experiment (MOOSE))

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17 pages, 2614 KiB

Open AccessArticle

The Source Apportionment of Heavy Metals in Surface Dust in the Main District Bus Stops of Tianshui City Based on the Positive Matrix Factorization Model and Geo-Statistics

by Chunyan Li, Xinmin Wang, Shun Xiao and Hai Wang

Atmosphere 2023, 14(3), 591; https://doi.org/10.3390/atmos14030591 - 20 Mar 2023

Cited by 1 | Viewed by 1717

Abstract

For the pollution assessment and quantitative source apportionment of heavy metals in surface dust, a total of 52 surface dust samples were collected from bus stops in Tianshui City. The geoaccumulation index (I_geo) and potential ecological risk index (RI [...] Read more.

For the pollution assessment and quantitative source apportionment of heavy metals in surface dust, a total of 52 surface dust samples were collected from bus stops in Tianshui City. The geoaccumulation index (I_geo) and potential ecological risk index (RI) were used to analyze the pollution levels caused by heavy metals. The Positive Matrix Factorization (PMF) of the receptor modeling and geo-statistics were employed to analyze the source of the heavy metals. The results were as follows. ① Except for Mn, Co and V, the mean concentrations of other heavy metals have exceeded the local background value of Gansu. The percentage of excessive concentrations of Cu, Zn, Sr, Ba and Pb in the samples was 100%, and that of Cr, Ni and As were 96.15%, 94.23%, and 96.15%, respectively. ② Semivariogram model fitting showed that the block-based coefficients of Cu, Zn, Sr, Ba, Pb, Cr, Ni, and As were between 0.25 and 0.75, indicating that they were mainly affected by human factors. The high values of Pb, Zn, Ni and As were mainly distributed in the eastern part of the study area, and the high values of Cu, Sr, Ba and Cr were distributed in a spot-like pattern in the study area. ③ The I_geo results showed that As, Cu, Zn, and Pb were the main contamination factors, and the optimized RI showed that the heavy metals were the overall ecological risk of intensity, among which Pb, As and Cu were the main ecological factors and should be taken as the priority control objects. ④ Based on the PMF, there are four main sources of eleven heavy metals. V, Mn, and Co were attributed to natural sources, accounting for 18.33%; Cu, Sr, and Ba were from mixed sources of pollution from transportation and industrial alloy manufacturing, accounting for 26.99%; Cr and Ni were from sources of construction waste pollution, accounting for 17.17%, As, Zn and Pb were mainly produced by coal-traffic mixed pollution emissions, accounting for 37.52%. Overall, the study area was dominated by coal-traffic emissions. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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5 pages, 193 KiB

Open AccessEditorial

Bioaerosols: Composition, Meteorological Impact, and Transport

by Salvatore Romano

Atmosphere 2023, 14(3), 590; https://doi.org/10.3390/atmos14030590 - 20 Mar 2023

Cited by 2 | Viewed by 1768

Abstract

The characterization and the main properties of biogenic airborne particles (or bioaerosols) comprising both living and dead microorganisms (such as bacteria, fungi, viruses, pollen, and microbial fragments) are currently of increasing interest in the scientific community [...] Full article

(This article belongs to the Special Issue Bioaerosols: Composition, Meteorological Impact, and Transport)

15 pages, 3771 KiB

Open AccessArticle

Statistical PM_2.5 Prediction in an Urban Area Using Vertical Meteorological Factors

by Jutapas Saiohai, Surat Bualert, Thunyapat Thongyen, Kittichai Duangmal, Parkpoom Choomanee and Wladyslaw W. Szymanski

Atmosphere 2023, 14(3), 589; https://doi.org/10.3390/atmos14030589 - 19 Mar 2023

Cited by 6 | Viewed by 2727

Abstract

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM_2.5 levels and excessive PM_2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show [...] Read more.

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM_2.5 levels and excessive PM_2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM_2.5 levels. The software was trained to predict PM_2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM_2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM_2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM_2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas. Full article

(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)

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11 pages, 1667 KiB

Open AccessArticle

Assessment of Natural Radioactivity and Radon Exhalation in Peruvian Gold Mine Tailings to Produce a Geopolymer Cement

by Rafael Liza, Patrizia Pereyra, Jose Rau, Maribel Guzman, Laszlo Sajo-Bohus and Daniel Palacios

Atmosphere 2023, 14(3), 588; https://doi.org/10.3390/atmos14030588 - 19 Mar 2023

Cited by 5 | Viewed by 2559

Abstract

Mining generates significant amounts of waste that can represent a source of contamination for areas close to the extraction area, generating a negative impact both on the environment and the health of people. This study aims to evaluate the radiological risk derived from [...] Read more.

Mining generates significant amounts of waste that can represent a source of contamination for areas close to the extraction area, generating a negative impact both on the environment and the health of people. This study aims to evaluate the radiological risk derived from exposure to natural radionuclides contained in tailings from Peruvian gold mines and to establish whether the tailings can be used as raw materials in building materials. The mine tailings come from a mining project in the northern highlands of Peru. Radon exhalation was measured using Rad7 in a closed chamber and activity concentration of ²²⁶Ra, ²³²Th, and ⁴⁰K radioisotopes by gamma spectrometry using NaI 3” × 3” detector. Maximum activity concentrations measured for ²²⁶Ra and ²³²Th were 15.38 Bq kg⁻¹ and 11.9 Bq kg⁻¹, respectively; meanwhile, activity concentration for ⁴⁰K ranged from 182.7 Bq kg⁻¹ to 770.8 Bq kg⁻¹. All activity concentrations were below the worldwide average except for ⁴⁰K. The radon exhalation rate varied from 2.8 to 7.2 mBq kg⁻¹ h⁻¹. The gamma index (I_γ), and radiological parameters, including the Radium equivalent activity (Ra_eq), and the external hazard index (H_ex), being below the recommended levels by UNSCEAR, ensure the safe use of these mines tailing to produce a geopolymer cement. Full article

(This article belongs to the Special Issue Radon and NORM: Impact on Air Quality)

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24 pages, 3526 KiB

Open AccessArticle

A Multipurpose Simulation Approach for Hybrid Electric Vehicles to Support the European CO₂ Emissions Framework

by Alessandro Tansini, Georgios Fontaras and Federico Millo

Atmosphere 2023, 14(3), 587; https://doi.org/10.3390/atmos14030587 - 18 Mar 2023

Cited by 2 | Viewed by 2153

Abstract

Hybrid Electric Vehicles (HEVs) are a prominent solution for reducing CO₂ emissions from transport in Europe. They are equipped with at least two propulsion energy converters, an Internal Combustion Engine (ICE) and one or more Electric Machines (EMs), operated in a way [...] Read more.

Hybrid Electric Vehicles (HEVs) are a prominent solution for reducing CO₂ emissions from transport in Europe. They are equipped with at least two propulsion energy converters, an Internal Combustion Engine (ICE) and one or more Electric Machines (EMs), operated in a way to exploit synergies and achieve fuel efficiency. Because of the variety in configurations and strategies, the use of simulation is essential for vehicle development and characterisation of energy consumption. This paper introduces a novel simulation approach to estimate the CO₂ emissions from different hybrid architectures (series, parallel, power-split) and electrification degrees (mild, full, plug-in and range extender) that is relatively simple, flexible and accurate. The approach identifies the optimal power split between the energy converters for any given time in a driving cycle according to three evaluation levels: supervisor, ICE manager and optimiser. The latter relies on the Equivalent Consumption Minimisation Strategy (ECMS) and the limitations imposed by the other two layers. Six light-duty HEVs with different hybrid architectures were tested to support the development of the approach. The results show an indicative accuracy of ±5%, enabling to run assessments of hybrid powertrain solutions and supporting regulatory and consumer information initiatives. Full article

(This article belongs to the Special Issue Vehicle Emissions: New Challenges and Potential Solutions)

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12 pages, 7034 KiB

Open AccessArticle

What Causes the Arabian Gulf Significant Summer Sea Surface Temperature Warming Trend?

by Kamal A. Alawad, Abdullah M. Al-Subhi, Mohammed A. Alsaafani and Turki M. Alraddadi

Atmosphere 2023, 14(3), 586; https://doi.org/10.3390/atmos14030586 - 18 Mar 2023

Cited by 3 | Viewed by 1963

Abstract

The present study investigated the significant sea surface temperature (SST) warming trend during the summer season over the Arabian Gulf (AG) and its links with the large-scale atmospheric driver, namely, the Atlantic multidecadal oscillation (AMO), from 1900 to 2021. The link between the [...] Read more.

The present study investigated the significant sea surface temperature (SST) warming trend during the summer season over the Arabian Gulf (AG) and its links with the large-scale atmospheric driver, namely, the Atlantic multidecadal oscillation (AMO), from 1900 to 2021. The link between the AMO and the AGs oceanic circulations has received little scientific attention. It has been found that there is a significant spatial positive trend, with a maximum of up to 0.6 °C per decade over the far northern end, while the time series trend shows a significant shift after 1995, with an average value of about 0.36 °C per decade. The spatial trend in the AG is eight times and four times higher than the global value from 1980 to 2005 using HadISST and OISST, respectively. The AMOs significant role in the AGs SST significant warming trend has been confirmed by the spatial and temporal correlation coefficient, which is above 0.50 and 0.48, respectively, with statistical significance at the 99% level. The underlying mechanisms that explained the AMO-related AGs SST decadal variability can be explained as follows: when the AMO is in a positive phase, the surface northwesterly wind weakens, leading to (1) less advection of the relatively cold air masses from Southern Europe to the AG and surrounding areas, (2) less evaporation, and thus less surface cooling (3); thus, this enhances the water masses stratification and decreases the upwelling process, and vice versa occurs for the negative phase. For the air temperature, the positive AMO phase coincides with the occurrence of warm air masses covering all of the Arabian Peninsula, North Africa, and Southern Europe. These processes prove that the AMO is a possible candidate for the AGs SST decadal variability, hence enabling a better evaluation of future climate scenarios for this important region. Our results provide initial insights into the AMO-driven spatiotemporal variability in the SST over the AG and prove that the relation is nonstationary over time. Further analyses are required to explore whether the impacts of the AMO are extended to other oceanic variables such as evaporation rate, heat transport, etc. Full article

(This article belongs to the Section Meteorology)

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23 pages, 4694 KiB

Open AccessArticle

N₂O Temporal Variability from the Middle Troposphere to the Middle Stratosphere Based on Airborne and Balloon-Borne Observations during the Period 1987–2018

by Gisèle Krysztofiak, Valéry Catoire, Thierry Dudok de Wit, Douglas E. Kinnison, A. R. Ravishankara, Vanessa Brocchi, Elliot Atlas, Heiko Bozem, Róisín Commane, Francesco D’Amato, Bruce Daube, Glenn S. Diskin, Andreas Engel, Felix Friedl-Vallon, Eric Hintsa, Dale F. Hurst, Peter Hoor, Fabrice Jegou, Kenneth W. Jucks, Armin Kleinböhl, Harry Küllmann, Eric A. Kort, Kathryn McKain, Fred L. Moore, Florian Obersteiner, Yenny Gonzalez Ramos, Tanja Schuck, Geoffrey C. Toon, Silvia Viciani, Gerald Wetzel, Jonathan Williams and Steven C. Wofsy Show full author list Hide full author list

Atmosphere 2023, 14(3), 585; https://doi.org/10.3390/atmos14030585 - 18 Mar 2023

Viewed by 2547

Abstract

Nitrous oxide (N₂O) is the fourth most important greenhouse gas in the atmosphere and is considered the most important current source gas emission for global stratospheric ozone depletion (O₃). It has natural and anthropogenic sources, mainly as an unintended [...] Read more.

Nitrous oxide (N₂O) is the fourth most important greenhouse gas in the atmosphere and is considered the most important current source gas emission for global stratospheric ozone depletion (O₃). It has natural and anthropogenic sources, mainly as an unintended by-product of food production activities. This work examines the identification and quantification of trends in the N₂O concentration from the middle troposphere to the middle stratosphere (MTMS) by in situ and remote sensing observations. The temporal variability of N₂O is addressed using a comprehensive dataset of in situ and remote sensing N₂O concentrations based on aircraft and balloon measurements in the MTMS from 1987 to 2018. We determine N₂O trends in the MTMS, based on observations. This consistent dataset was also used to study the N₂O seasonal cycle to investigate the relationship between abundances and its emission sources through zonal means. The results show a long-term increase in global N₂O concentration in the MTMS with an average of 0.89 ± 0.07 ppb/yr in the troposphere and 0.96 ± 0.15 ppb/yr in the stratosphere, consistent with 0.80 ppb/yr derived from ground-based measurements and 0.799 ± 0.024 ppb/yr ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) satellite measurements. Full article

(This article belongs to the Section Air Quality)

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29 pages, 2175 KiB

Open AccessReview

A Prompt Decarbonization Pathway for Shipping: Green Hydrogen, Ammonia, and Methanol Production and Utilization in Marine Engines

by Jie Shi, Yuanqing Zhu, Yongming Feng, Jun Yang and Chong Xia

Atmosphere 2023, 14(3), 584; https://doi.org/10.3390/atmos14030584 - 17 Mar 2023

Cited by 35 | Viewed by 11076

Abstract

The shipping industry has reached a higher level of maturity in terms of its knowledge and awareness of decarbonization challenges. Carbon-free or carbon-neutralized green fuel, such as green hydrogen, green ammonia, and green methanol, are being widely discussed. However, little attention has paid [...] Read more.

The shipping industry has reached a higher level of maturity in terms of its knowledge and awareness of decarbonization challenges. Carbon-free or carbon-neutralized green fuel, such as green hydrogen, green ammonia, and green methanol, are being widely discussed. However, little attention has paid to the green fuel pathway from renewable energy to shipping. This paper, therefore, provides a review of the production methods for green power (green hydrogen, green ammonia, and green methanol) and analyzes the potential of green fuel for application to shipping. The review shows that the potential production methods for green hydrogen, green ammonia, and green methanol for the shipping industry are (1) hydrogen production from seawater electrolysis using green power; (2) ammonia production from green hydrogen + Haber–Bosch process; and (3) methanol production from CO₂ using green power. While the future of green fuel is bright, in the short term, the costs are expected to be higher than conventional fuel. Our recommendations are therefore as follows: improve green power production technology to reduce the production cost; develop electrochemical fuel production technology to increase the efficiency of green fuel production; and explore new technology. Strengthening the research and development of renewable energy and green fuel production technology and expanding fuel production capacity to ensure an adequate supply of low- and zero-emission marine fuel are important factors to achieve carbon reduction in shipping. Full article

(This article belongs to the Special Issue Shipping Emissions and Air Pollution)

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15 pages, 3016 KiB

Open AccessArticle

A Study on the Wide Range of Relative Humidity in Cirrus Clouds Using Large-Ensemble Parcel Model Simulations

by Miao Zhao and Xiangjun Shi

Atmosphere 2023, 14(3), 583; https://doi.org/10.3390/atmos14030583 - 17 Mar 2023

Cited by 1 | Viewed by 1218

Abstract

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RH_i). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RH_i are investigated through in situ observations [...] Read more.

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RH_i). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RH_i are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RH_i, and ice crystal deposition/sublimation plays a role in mitigating the change in the RH_i. They are the two most important mechanisms involved in controlling the RH_i fluctuation during cirrus evolution and could well explain the wide range of RH_i in wave-related cirrus clouds. Furthermore, a comparison of statistical cloud characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely. Full article

(This article belongs to the Special Issue Properties of Cirrus Cloud by Lidars: Observation and Theory)

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15 pages, 8129 KiB

Open AccessArticle

Application of Radar Radial Velocity Data Assimilation in the Forecasts of Typhoon Linfa Based on Different Horizontal Length Scale Factors

by Huimin Bian, Jinzhong Min and Feifei Shen

Atmosphere 2023, 14(3), 582; https://doi.org/10.3390/atmos14030582 - 17 Mar 2023

Viewed by 1318

Abstract

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of [...] Read more.

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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7 pages, 1503 KiB

Open AccessCommunication

Potential Distribution and Priority Conservation Areas of Pseudotsuga sinensis Forests under Climate Change in Guizhou Province, Southwesten China

by Wangjun Li, Yingqian Yu, Tu Feng, Bin He, Xiaolong Bai and Shun Zou

Atmosphere 2023, 14(3), 581; https://doi.org/10.3390/atmos14030581 - 17 Mar 2023

Viewed by 970

Abstract

Priority conservation areas are the key areas of biodiversity maintenance and ecosystem conservation. Based on a Maxent model, this study predicted the potential distribution of Pseudotsuga sinensis under the current climate and future climate change scenarios in Guizhou province, and then, assessed three [...] Read more.

Priority conservation areas are the key areas of biodiversity maintenance and ecosystem conservation. Based on a Maxent model, this study predicted the potential distribution of Pseudotsuga sinensis under the current climate and future climate change scenarios in Guizhou province, and then, assessed three kinds of priority conservation area under climate change. The results were as follows: (1) The AUC (Area Under the Curve) values showed excellent prediction accuracy of the model. (2) The areas of the potential habitats of P. sinensis forests under the current climate and future climate change scenarios were 22,062.85 km² and 18,395.92 km², respectively. As for their spatial distribution, the potential habitats of P. sinensis forests were distributed in the Bijie, Zunyi, Tongren, Liupanshui and Xingyi regions under the current climate, and in the Kaili region, in addition to the above-mentioned cities, under future climate change scenarios. (3) The total area of priority conservation areas under climate change was 25,350.26 km². The area of the predicted sustainable potential habitats was 15,075.96 km², of the vulnerable potential habitats was 7256.59 km² and of the derivative potential habitats was 3017.71 km². Full article

(This article belongs to the Special Issue Vegetation and Climate Relationships (2nd Edition))

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19 pages, 10748 KiB

Open AccessArticle

Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction

by Qi Zhang and Min Shao

Atmosphere 2023, 14(3), 580; https://doi.org/10.3390/atmos14030580 - 17 Mar 2023

Cited by 1 | Viewed by 1223

Abstract

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these [...] Read more.

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition’s root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume))

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14 pages, 2136 KiB

Open AccessArticle

Emission Source Areas of Fine Particulate Matter (PM_2.5) in Ho Chi Minh City, Vietnam

by Tuyet Nam Thi Nguyen, Nguyen Xuan Du and Nguyen Thi Hoa

Atmosphere 2023, 14(3), 579; https://doi.org/10.3390/atmos14030579 - 17 Mar 2023

Cited by 4 | Viewed by 3225

Abstract

This study aims to determine emission source areas of fine particulate matter (PM_2.5) in Ho Chi Minh (HCM) City, Vietnam, using a conditional bivariate probability function (CBPF) and hybrid receptor models, including three-dimensional potential source contribution function (3D-PSCF) and concentration-weighted trajectory [...] Read more.

This study aims to determine emission source areas of fine particulate matter (PM_2.5) in Ho Chi Minh (HCM) City, Vietnam, using a conditional bivariate probability function (CBPF) and hybrid receptor models, including three-dimensional potential source contribution function (3D-PSCF) and concentration-weighted trajectory (3D-CWT), considering latitudes, longitudes, and height of trajectory segments. Uncertainties of the CBPF and 3D-PSCF/3D-CWT were evaluated based on the 95th confidence intervals and 95% confidence levels, respectively. For the local scale, PM_2.5 in HCM City was primarily emitted from shallow or common ground sources (e.g., vehicle emissions) throughout the year. Regarding non-local source areas, PM_2.5 in HCM City is contributed by those originated from the East Sea (e.g., shipping emissions) and southeastern Vietnam (e.g., Binh Duong and Dong Nai provinces) having several industrial zones with PM_2.5 emission sources, especially in the dry season (December to April of the following year). In the rainy season (May–November), PM_2.5 derived from emission sources in the Mekong Delta (e.g., biomass burning) might be transported to HCM City. However, contribution of the non-local sources to PM_2.5 pollution in HCM City during the rainy season is less important because of PM_2.5 deposition stemmed from the high rainfall amount in this season. Full article

(This article belongs to the Special Issue Air Quality in Metropolitan Areas and Megacities)

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22 pages, 3364 KiB

Open AccessArticle

Assessment of Air Pollution in Different Areas (Urban, Suburban, and Rural) in Slovenia from 2017 to 2021

by Maja Ivanovski, Kris Alatič, Danijela Urbancl, Marjana Simonič, Darko Goričanec and Rudi Vončina

Atmosphere 2023, 14(3), 578; https://doi.org/10.3390/atmos14030578 - 17 Mar 2023

Cited by 3 | Viewed by 2449

Abstract

Air pollution can have a significant effect on human health. The present work is focused on the investigation of daily, monthly, and annual concentration levels of five typical air pollutants (SO₂, NO₂, NO_X, PM₁₀, and [...] Read more.

Air pollution can have a significant effect on human health. The present work is focused on the investigation of daily, monthly, and annual concentration levels of five typical air pollutants (SO₂, NO₂, NO_X, PM₁₀, and PM_2.5) in the Republic of Slovenia (RS) from January 2017 to December 2021. The study was conducted at five different monitoring stations of the following kind: traffic (A), industrial (D), and background (B, C, E). The obtained results showed a decline in the average concentrations for all the studied air pollutants through the years, respectively. The daily average SO₂ concentrations were the lowest in the year 2021 at location B, which is classified as background location, while the highest were detected in the year 2018 at location E, which is also classified as background location. The average daily concentrations of NO₂ and NO_X were the highest at location A in the year 2017, whereas the lowest were detected in the year 2010 and 2021. It is believed that those results are a consequence of measures set by the Slovenian government during the COVID-19 pandemic. The PM₁₀ and PM_2.5 daily average concentrations were the highest at location A in 2017, while the lowest were observed in the year 2019 at location C. Meteorological parameters (temperature, wind speed, and relative humidity) were studied in addition. In general, the high temperatures in ambient air are responsible for the intense concentrations of air pollutants. It was found in the study results for temperature, wind speed, and relative humidity that no significant difference was shown between studied years. Full article

(This article belongs to the Special Issue Feature Papers in Air Pollution, Health Effects Indicators, Exposome, and One Health)

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