Journal Description
Atmosphere
Atmosphere
is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about the Atmosphere.
- Companion journal: Meteorology.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Estimating Concurrent Probabilities of Compound Extremes: An Analysis of Temperature and Rainfall Events in the Limpopo Lowveld Region of South Africa
Atmosphere 2024, 15(5), 557; https://doi.org/10.3390/atmos15050557 (registering DOI) - 30 Apr 2024
Abstract
In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for
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In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans.
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(This article belongs to the Special Issue Extreme Weather Events in a Warming Climate)
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FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm
by
Liangrui Yan, Yuquan Zhou, Yixuan Wu, Miao Cai, Chong Peng, Can Song, Shuoyin Liu and Yubao Liu
Atmosphere 2024, 15(5), 556; https://doi.org/10.3390/atmos15050556 (registering DOI) - 30 Apr 2024
Abstract
The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based
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The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based catalysts and continuously monitoring its evolution for a weather event that occurred on 15 December 2019 in Henan Province, China. Satellite measurements are also used to verify an operational catalyst T&D algorithm. The results show that FY-4A exhibits a remarkable capability of identifying the cloud-seeding tracks and continuously tracing their evolution for a period of over 3 h. About 60 min after the cloud seeding, the cloud crystallization track became clear in the FY-4A tri-channel composite cloud image and lasted for about 218 min. During this time period, the cloud track moved with the cloud system about 153 km downstream (northeast of the operation area). An operational catalyst T&D model was run to simulate the cloud track, and the outputs were extensively compared with the satellite observations. It was found that the forecast cloud track closely agreed with the satellite observations in terms of the track widths, morphology, and movement. Finally, the FY-4A measurements show that there were significant differences in the microphysical properties across the cloud track. The effective cloud radius inside the cloud track was up to 15 μm larger than that of the surrounding clouds; the cloud optical thickness was about 30 μm smaller; and the cloud-top heights inside the cloud track were up to 1 km lower. These features indicate that the cloud-seeding catalysts led to the development of ice-phase processes within the supercooled cloud, with the formation of large ice particles and some precipitation sedimentation.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Evaluating Phoenix Metropolitan Area Ozone Behavior Using Ground-Based Sampling, Modeling, and Satellite Retrievals
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Jason A. Miech, Pierre Herckes, Matthew P. Fraser, Avelino F. Arellano, Mohammad Amin Mirrezaei and Yafang Guo
Atmosphere 2024, 15(5), 555; https://doi.org/10.3390/atmos15050555 (registering DOI) - 30 Apr 2024
Abstract
An oxidizing and harmful pollutant gas, tropospheric ozone is a product of a complex set of photochemical reactions that can make it difficult to enact effective control measures. A better understanding of its precursors including volatile organic compounds (VOCs) and nitrogen oxides (NO
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An oxidizing and harmful pollutant gas, tropospheric ozone is a product of a complex set of photochemical reactions that can make it difficult to enact effective control measures. A better understanding of its precursors including volatile organic compounds (VOCs) and nitrogen oxides (NOx) and their spatial distribution can enable policymakers to focus their control efforts. In this study we used low-cost sensors (LCSs) to increase the spatial resolution of an existing NO2 monitoring network in addition to VOC sampling to better understand summer ozone formation in Maricopa County, Arizona, and observed that afternoon O3 values at the downwind sites were significantly correlated, ~0.27, to the morning NO2 × rate values at the urban sites. Additionally, we looked at the impact of wildfire smoke on ozone exceedances and compared non-smoke days to smoke days. The average O3 on smoke days was approximately 20% higher than on non-smoke days, however, the average NO2 concentration multiplied by estimated photolysis rate (NO2 × rate) values were only 2% higher on smoke days. Finally, we evaluated the ozone sensitivity of the region by calculating HCHO/NO2 ratios using three different datasets: ground, satellite, and model. Although the satellite dataset produced higher HCHO/NO2 ratios than the other datasets, when the proper regime thresholds are applied the three datasets consistently show transition and VOC-limited O3 production regimes over the Phoenix metro area. This suggests a need to implement more VOC emission controls in order to reach O3 attainment in the county.
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(This article belongs to the Special Issue Ozone in Stratosphere and Its Relation to Stratospheric Dynamics)
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Distinguishing Saharan Dust Plume Sources in the Tropical Atlantic Using Elemental Indicators
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Daniel E. Yeager and Vernon R. Morris
Atmosphere 2024, 15(5), 554; https://doi.org/10.3390/atmos15050554 (registering DOI) - 30 Apr 2024
Abstract
The Sahara Desert is the largest contributor of global atmospheric dust aerosols impacting regional climate, health, and ecosystems. The climate effects of these dust aerosols remain uncertain due, in part, to climate model uncertainty of Saharan source region contributions and aerosol microphysical properties.
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The Sahara Desert is the largest contributor of global atmospheric dust aerosols impacting regional climate, health, and ecosystems. The climate effects of these dust aerosols remain uncertain due, in part, to climate model uncertainty of Saharan source region contributions and aerosol microphysical properties. This study distinguishes source region elemental signatures of Saharan dust aerosols sampled during the 2015 Aerosols Ocean Sciences Expedition (AEROSE) in the tropical Atlantic. During the 4-week campaign, cascade impactors size-dependently collected airborne Saharan dust particulate upon glass microfiber filters. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis differentiated metal isotope concentrations within filter samples from various AEROSE dust sampling periods. Back-trajectory analysis and NOAA satellite aerosol optical depth retrievals confirmed source regions of AEROSE ’15 dust samples. Pearson correlational statistics of source region activity and dust isotope concentrations distinguished the elemental signatures of North African potential source areas (PSAs). This study confirmed that elemental indicators of these PSAs remain detectable within dust samples collected far into the marine boundary layer of the tropical Atlantic. Changes detected in dust elemental indicators occurred on sub-weekly timescales across relatively small sampling distances along the 23W parallel of the tropical Atlantic. PSA-2 emissions, covering the western coast of the Sahara, were very strongly correlated (R2 > 0.79) with Ca-44 isotope ratios in AEROSE dust samples; PSA-2.5 emissions, covering eastern Mauritania and western Mali, were very strongly correlated with K-39 ratios; PSA-3 emissions, spanning southwestern Algeria and eastern Mali, were very strongly correlated with Fe-57 and Ti-48 ratios. The abundance of Ca isotopes from PSA-2 was attributed to calcite minerals from dry lakebeds and phosphorous mining activities in Western Sahara, based on source region analysis. The correlation between K isotope ratios and PSA-2.5 was a likely indicator of illite minerals near the El Djouf Desert region, according to corroboration with mineral mapping studies. Fe and Ti ratio correlations with PSA-3 observed in this study were likely indicators of iron and titanium oxides from Sahelian sources still detectable in Atlantic Ocean observations. The rapid changes in isotope chemistry found in AEROSE dust samples provide a unique marker of Saharan source regions and their relative contributions to desert outflows in the Atlantic. These elemental indicators provide source region apportionments of Sahara Desert aerosol flux and deposition into the Atlantic Ocean, as well as a basis for model and satellite validation of Saharan dust emissions for regional climate assessments.
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(This article belongs to the Section Aerosols)
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Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling
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Qian Liu, Bingyan Cui and Zhen Liu
Atmosphere 2024, 15(5), 553; https://doi.org/10.3390/atmos15050553 (registering DOI) - 30 Apr 2024
Abstract
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset
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Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.
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(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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Investigation of BTX Concentrations and Effects of Meteorological Parameters in the Steelpoort Area of Limpopo Province, South Africa
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Collet Maswanganyi, James Tshilongo, Andile Mkhohlakali and Lynwill Martin
Atmosphere 2024, 15(5), 552; https://doi.org/10.3390/atmos15050552 (registering DOI) - 30 Apr 2024
Abstract
It has been demonstrated that benzene, toluene, and xylene are carcinogens. Its combined effects with other contaminants have the potential to harm several ecosystem components. Since most human benzene exposure takes place inside, it is important to understand how outdoor benzene emissions from
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It has been demonstrated that benzene, toluene, and xylene are carcinogens. Its combined effects with other contaminants have the potential to harm several ecosystem components. Since most human benzene exposure takes place inside, it is important to understand how outdoor benzene emissions from traffic and industry affect interior concentrations. However, this area of study has not received enough attention to date. Herein, we examine the outdoor concentrations of benzene, toluene, and xylene (BTX) in a Steelpoort mining area. BTX pollutants were passively sampled on the first seven days of the month, from January to December 2021 using Radiello samplers. The effects of meteorological parameters such as temperature, relative humidity, wind speed, and solar radiation on BTX concentrations were also statistically tested. For all seasons, BTX concentrations were greater in the winter than in the summer with concentrations of 0.69 µg/m3, 2.97 µg/m3 and 0.80 µg/m3 for benzene, toluene and xylene, respectively. In addition, toluene was the most common BTX compound with the highest concentrations when compared to benzene and xylene. Benzene, toluene and xylene, had yearly average concentrations of 0.61 µg/m3, 1.48 µg/m3 and 0.64 µg/m3, respectively. The benzene and xylene concentrations were below international exposure limits (annual, 5 µg/m3 for benzene; weekly, 260 µg/m3 for toluene), as in comparison to the World Health Organization, as well as within South African exceedance limits. Both positive and negative correlations between BTX and meteorological parameters were demonstrated by statistical models. Temperature, wind speed, and relative humidity depicted a weak negative correlation with benzene of 0.003, 0.019 and 0.006, respectively. Toluene showed a positive correlation with wind speed (1.90) and relative humidity (0.041). Overall, the concentration of benzene is of major concern since it is an agent of cancer and it is there in the atmosphere.
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(This article belongs to the Section Air Quality)
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Assessing Satellite Data’s Role in Substituting Ground Measurements for Urban Surfaces Characterization: A Step towards UHI Mitigation
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Davide Parmeggiani, Francesca Despini, Sofia Costanzini, Malvina Silvestri, Federico Rabuffi, Sergio Teggi and Grazia Ghermandi
Atmosphere 2024, 15(5), 551; https://doi.org/10.3390/atmos15050551 (registering DOI) - 29 Apr 2024
Abstract
Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies
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Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts.
Full article
(This article belongs to the Special Issue UHI Analysis and Evaluation with Remote Sensing Data)
Open AccessArticle
Seamless Modeling of Direct and Indirect Aerosol Effects during April 2020 Wildfire Episode in Ukraine
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Mykhailo Savenets, Valeriia Rybchynska, Alexander Mahura, Roman Nuterman, Alexander Baklanov, Markku Kulmala and Tuukka Petäjä
Atmosphere 2024, 15(5), 550; https://doi.org/10.3390/atmos15050550 (registering DOI) - 29 Apr 2024
Abstract
Wildfires frequently occur in Ukraine during agricultural open-burning seasons in spring and autumn. High aerosol concentrations from fire emissions can significantly affect meteorological processes via direct and indirect aerosol effects. To study these impacts, we selected a severe wildfire episode from April 2020
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Wildfires frequently occur in Ukraine during agricultural open-burning seasons in spring and autumn. High aerosol concentrations from fire emissions can significantly affect meteorological processes via direct and indirect aerosol effects. To study these impacts, we selected a severe wildfire episode from April 2020 in the Chornobyl Exclusion Zone (CEZ) and its surrounding area as a case study. We employed the Enviro-HIRLAM modeling system to simulate reference (REF) meteorological conditions, along with direct (DAE), indirect (IDAE), and combined (COMB) aerosol effects. In our simulations, black carbon (BC) and organic carbon (OC) comprised 70–80% of all aerosol mass in the region, represented in two layers of higher concentrations: one near the surface and the other 3–4 km above the surface. Our simulations showed that the inclusion of aerosol effects into the modeling framework led to colder (up to −3 °C) and drier (relative humidity drop up to −20%) conditions near the surface. We also observed localized changes in cloudiness, precipitation (mainly redistribution), and wind speed (up to ±4 m/s), particularly during the movement of atmospheric cold fronts. Larger uncertainties were observed in coarser model simulations when direct aerosol effects were considered. Quantifying the aerosol effects is crucial for predicting and promptly detecting changes that could exacerbate unfavorable weather conditions and wildfires. Such knowledge is essential for improving the effectiveness of emergency response measures.
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(This article belongs to the Section Aerosols)
Open AccessArticle
The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images
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Tianlin Zhang, Zhao Lin, Lei Wang, Wenzheng Zhang, Yazhuo Zhang and Yike Hu
Atmosphere 2024, 15(5), 549; https://doi.org/10.3390/atmos15050549 (registering DOI) - 29 Apr 2024
Abstract
Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being
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Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents' daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors' impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.
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(This article belongs to the Special Issue Impacts of Land Use and Climate Change in Urban Area: Big Data and Machine Learning)
Open AccessArticle
Towards a Model of Snow Accretion for Autonomous Vehicles
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Mateus Carvalho, Sadegh Moradi, Farimah Hosseinnouri, Kiran Keshavan, Eric Villeneuve, Ismail Gultepe, John Komar, Martin Agelin-Chaab and Horia Hangan
Atmosphere 2024, 15(5), 548; https://doi.org/10.3390/atmos15050548 (registering DOI) - 29 Apr 2024
Abstract
Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this
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Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented.
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(This article belongs to the Special Issue Sensitivity of Local Numerical Weather Prediction Models)
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Unsteady and Inhomogeneous Turbulent Fluctuations around Isotropic Equilibrium
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Wouter J. T. Bos
Atmosphere 2024, 15(5), 547; https://doi.org/10.3390/atmos15050547 (registering DOI) - 29 Apr 2024
Abstract
Extracting statistics for turbulent flows directly from the Navier–Stokes equations poses a formidable challenge, particularly when dealing with unsteady or inhomogeneous flows. However, embracing Kolmogorov’s inertial range spectrum for isotropic turbulence as a dynamic equilibrium provides a conceptual starting point for perturbation theory.
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Extracting statistics for turbulent flows directly from the Navier–Stokes equations poses a formidable challenge, particularly when dealing with unsteady or inhomogeneous flows. However, embracing Kolmogorov’s inertial range spectrum for isotropic turbulence as a dynamic equilibrium provides a conceptual starting point for perturbation theory. We review theoretical results, combining perturbation approaches, and phenomenological turbulence closures, which allow us to gain valuable insights into the statistics of unsteady and inhomogeneous turbulence. Additionally, we extend the ideas to the case of the mixing of a passive scalar.
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(This article belongs to the Special Issue Isotropic Turbulence: Recent Advances and Current Challenges)
Open AccessReview
Indoor Air Quality (IAQ) Management in Hong Kong: The Way Forward
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Tsz-Wun Tsang, Kwok-Wai Mui and Ling-Tim Wong
Atmosphere 2024, 15(5), 546; https://doi.org/10.3390/atmos15050546 (registering DOI) - 29 Apr 2024
Abstract
There has been an increasing awareness of indoor air quality (IAQ) management in green building designs, driven by the need to mitigate potential health risks and create sustainable and healthy indoor environments. The COVID-19 pandemic has further highlighted the critical role of ventilation
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There has been an increasing awareness of indoor air quality (IAQ) management in green building designs, driven by the need to mitigate potential health risks and create sustainable and healthy indoor environments. The COVID-19 pandemic has further highlighted the critical role of ventilation and IAQ in reducing the risk of indoor airborne transmission. Governments and organisations worldwide have responded to this growing concern by implementing ventilation requirements and updating IAQ standards and guidelines. In the case of Hong Kong, a developed and densely populated city characterised by high-rise buildings, this study aims to provide a strategic framework for non-governmental agencies to address IAQ issues effectively. A comprehensive review of policies, regulations, and guidelines by international bodies and individual governments, along with an examination of the current IAQ management scheme in Hong Kong, has been conducted. Drawing inspiration from successful IAQ management strategies, the study aims to identify insights and potential pathways for the city’s future development of IAQ management strategies. Overall, this research highlights the importance of proactive IAQ management for buildings and offers a roadmap for Hong Kong’s pursuit of healthier indoor environments.
Full article
(This article belongs to the Special Issue Enhancing Indoor Air Quality: Monitoring, Analysis and Assessment)
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Wind Shear and Aircraft Aborted Landings: A Deep Learning Perspective for Prediction and Analysis
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Afaq Khattak, Jianping Zhang, Pak-wai Chan, Feng Chen, Arshad Hussain and Hamad Almujibah
Atmosphere 2024, 15(5), 545; https://doi.org/10.3390/atmos15050545 (registering DOI) - 29 Apr 2024
Abstract
In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and
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In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Pollution Characteristics and Sources of Ambient Air Dustfall in Urban Area of Beijing
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Yin Zhou, Beibei Li, Yuhu Huang, Yu Zhao, Hongling Yang and Jianping Qin
Atmosphere 2024, 15(5), 544; https://doi.org/10.3390/atmos15050544 (registering DOI) - 29 Apr 2024
Abstract
Since 2016, the Ministry of Ecology and Environment and the Beijing Municipal Government have adjusted the minimum concentration limit for ambient air dustfall several times, indicating that they attach great importance to dustfall. To grasp the pollution characteristics and sources of dustfall, in
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Since 2016, the Ministry of Ecology and Environment and the Beijing Municipal Government have adjusted the minimum concentration limit for ambient air dustfall several times, indicating that they attach great importance to dustfall. To grasp the pollution characteristics and sources of dustfall, in this work, the filtration method was used to determine the insoluble dustfall and water-soluble dustfall in the urban area of Beijing. From our analysis, the influence of the meteorological parameters on dustfall was found, and the chemical components of dustfall were determined. The positive matrix factorization (PMF) model was also utilized to analyze the sources of dustfall. The results indicated that the average amount of dustfall in 2021–2022 was 4.4 t·(km2·30 d)−1, and the proportion of insoluble dustfall deposition was 82.4%. Dustfall was positively correlated with the average wind speed and temperature and negatively correlated with the relative humidity and rain precipitation. The impact of the meteorological parameters on insoluble dustfall and water-soluble dustfall was the opposite. The average proportions of crustal material, ions, organic matter, element carbon, trace elements, and unknown components were 48%, 16%, 14%, 1.4%, 0.20%, and 20%, respectively. The proportions of the crustal material and ions were the highest in spring (57%) and summer (37%). The contribution rates of fugitive dust source, secondary inorganic source, mobile source, coal combustion source, snow melting agent source, and other sources were 42.4%, 19.3%, 8.3%, 3.0%, 2.7%, and 24.3%, respectively. This study supported dustfall pollution control by analysing the pollutant characteristics and sources of dustfall from the standpoint of total chemical components. In order to better control dustfall pollution, control measures and evaluation standards for fugitive dust pollution should be formulated.
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(This article belongs to the Special Issue Characteristics and Source Apportionment of Urban Air Pollution)
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Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data
by
Jieun Wie, Jae-Young Byon and Byung-Kwon Moon
Atmosphere 2024, 15(5), 543; https://doi.org/10.3390/atmos15050543 (registering DOI) - 28 Apr 2024
Abstract
In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and
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In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.
Full article
(This article belongs to the Section Meteorology)
Open AccessArticle
The Relationship between Changes in Hydro-Climate Factors and Maize Crop Production in the Equatorial African Region from 1980 to 2021
by
Isaac Kwesi Nooni, Faustin Katchele Ogou, Daniel Fiifi Tawiah Hagan, Abdoul Aziz Saidou Chaibou, Nana Agyemang Prempeh, Francis Mawuli Nakoty, Zhongfang Jin and Jiao Lu
Atmosphere 2024, 15(5), 542; https://doi.org/10.3390/atmos15050542 (registering DOI) - 28 Apr 2024
Abstract
Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production.
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Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production. The aim of the present work was to contribute to policy and climate adaptation, thus reducing the vulnerability of maize production to climate change over Equatorial Africa. This study firstly examined long-term trends of precipitation (PRE), soil moisture (SM), actual evapotranspiration (E), and potential evapotranspiration (Ep), as well as surface air temperatures, including the minimum (TMIN) and maximum (TMAX). Secondly, the relationship between maize production and these climate variables was quantified for 18 Equatorial African countries (EQCs) over 1980−2021. To assess the linear trends, Mann–Kendall and Sen’s slope tests were used to quantify the magnitude of the hydro-climatic variable trends at the 5% significance level, and Pearson’s correlation coefficient was used to evaluate the relation of these climate parameters with the maize production. The annual mean PRE declined at 0.03 mm day−110a−1. Other climate variables increased at different rates: SM at 0.02 mmday−110a−1, E at 0.03 mm day−110a−1, Ep at 0.02 mm day−1 10a−1, TMIN and TMAX at 0.01 °C day−110a−1. A regional analysis revealed heterogeneous significant wet–dry and warm–cool trends over the EQCs. While, spatially, dry and warm climates were observed in the central to eastern areas, wet and warm conditions dominated the western regions. Generally, the correlations of maize production with the E, Ep, TMAX, and TMIN were strong (r > 0.7) and positive, while moderate (r > 0.45) correlations of maize production with PRE and SM were obvious. These country-wide analyses highlight the significance of climate change policies and offer a scientific basis for designing tailored adaptation strategies in rainfed agricultural regions.
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(This article belongs to the Section Climatology)
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Open AccessArticle
Boundary Layer Height and Trends over the Tarim Basin
by
Akida Salam, Qing He, Alim Abbas, Tongwen Wu, Jie Zhang, Weihua Jie and Junjie Liu
Atmosphere 2024, 15(5), 541; https://doi.org/10.3390/atmos15050541 (registering DOI) - 28 Apr 2024
Abstract
This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test
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This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity.
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(This article belongs to the Topic Advances in Hydro-Geological Research in Arid and Semi-Arid Areas)
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Open AccessReview
Finite Reynolds Number Effect on Small-Scale Statistics of Homogeneous Isotropic Turbulence
by
S. L. Tang, L. Danaila and R. A. Antonia
Atmosphere 2024, 15(5), 540; https://doi.org/10.3390/atmos15050540 (registering DOI) - 28 Apr 2024
Abstract
Since about 1997, the realisation that the finite Reynolds number (FRN) effect needs to be carefully taken into account when assessing the behaviour of small-scale statistics came to the fore. The FRN effect can be analysed either in the real domain or in
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Since about 1997, the realisation that the finite Reynolds number (FRN) effect needs to be carefully taken into account when assessing the behaviour of small-scale statistics came to the fore. The FRN effect can be analysed either in the real domain or in the spectral domain via the scale-by-scale energy budget equation or the transport equation for the energy spectrum. This analysis indicates that the inertial range (IR) is established only when the Taylor microscale Reynolds number is infinitely large, thus raising doubts about published power-law exponents at finite values of , for either the second-order velocity structure function or the energy spectrum. Here, we focus on the transport equation of in decaying grid turbulence, which represents a close approximation to homogeneous isotropic turbulence. Regarding small-scale effects, the large-scale forcing term associated with streamwise advection decreases as increases and finally disappears when is sufficiently large. An approach based on the dual scaling of , i.e., a scaling based on the Kolmogorov scales (when the separation r is small) and another based on the integral scales (when r is large), yields when is infinitely large. This approach also yields when is infinitely large. These results seem to be supported by the trend as increases according to the available experimental data. Overall, the results for decaying turbulence strongly suggest that a tendency towards the predictions of K41 cannot be dismissed at least at Reynolds numbers that are currently beyond the reach of experiments and direct numerical simulations.
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(This article belongs to the Special Issue Isotropic Turbulence: Recent Advances and Current Challenges)
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Exploring Spatio-Temporal Precipitation Variations in Istanbul: Trends and Patterns from Five Stations across Two Continents
by
Yiğitalp Kara, Veli Yavuz, Caner Temiz and Anthony R. Lupo
Atmosphere 2024, 15(5), 539; https://doi.org/10.3390/atmos15050539 (registering DOI) - 28 Apr 2024
Abstract
This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located
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This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located in the European continent and two in the Asian continent, with measurement periods ranging from 72 to 93 years, wet and dry days have been identified, statistics on precipitation conditions during the warm and cold seasons have been generated, categorization based on precipitation intensities has been performed, and analyses have been conducted using extreme precipitation indices. At stations located in the northern part of the city, higher annual total precipitation has been observed compared to those in the south. A similar situation applies to the number of wet days. While during the cold season, the wet and dry day counts are nearly the same across all stations, this condition exhibits significant differences in favor of dry days during the warm season. Apart from dry conditions, “moderate” precipitation is the most frequently observed type across all stations. However, “extreme” events occur significantly more often (6%) during the warm season compared to the cold season (2%). Long-term anomalies in terms of annual precipitation totals have shown similarity between stations in the north and south, which has also been observed in longitudinally close stations. Despite the longer duration of the cold season and stronger temperature gradients, extreme rainfall events are more frequent during the warm season, primarily due to thunderstorm activity. While trend analyses revealed limited significant trends in precipitation intensity categories and extreme indices, the study highlights the importance of comprehensive examination of extreme rainfall events on both station-based and regional levels, shedding light on potential implications for regional climate change. Lastly, during the cold season, the inter-station correlation in terms of annual total precipitation amounts has been considerably higher compared to the warm season.
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(This article belongs to the Section Meteorology)
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Spatiotemporal Dynamics of CO2 Emissions in China Based on Multivariate Spatial Statistics
by
Mengyao Wang, Xiaoyan Dai and Hao Zhang
Atmosphere 2024, 15(5), 538; https://doi.org/10.3390/atmos15050538 (registering DOI) - 28 Apr 2024
Abstract
With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper
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With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are “Low–Low” at the provincial level, and “Low–Low” and “High–High” at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China’s carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies.
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(This article belongs to the Special Issue Carbon Emission and Carbon Neutrality in China)
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