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
Machine Learning to Characterize Biogenic Isoprene Emissions and Atmospheric Formaldehyde with their Environmental Drivers in the Marine Boundary Layer
Atmosphere 2024, 15(6), 679; https://doi.org/10.3390/atmos15060679 (registering DOI) - 31 May 2024
Abstract
Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of
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Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of marine biogenic emissions on MBL formaldehyde (HCHO). The study reveals that HCHO columnar concentrations peaked in summer with 8.25 × 1015 molec/cm2, but the sea–air exchange processes controlled under the wind and sea surface temperature (SST) made marine biogenic emissions represented by isoprene reach their highest levels in winter with 95.93 nmol/m2/day. Analysis was conducted separately for factors influencing marine biogenic emissions and affecting MBL HCHO. It was found that phytoplankton functional types (PFTs) and biological degradation had a significant impact on marine biogenic emissions, with ratio range of 0.07~15.87 and 1.02~5.42 respectively. Machine learning methods were employed to simulate the conversion process of marine biogenic emissions to HCHO in MBL. Based on the SHAP values of the learning model, the importance results indicate that the factors influencing MBL HCHO mainly included NO2, as well as temperature (T) and relative humidity (RH). Specifically, the influence of NO2 on atmospheric HCHO was 1.3 times that of T and 1.6 times that of RH. Wind speed affected HCHO by influencing both marine biogenic emission and the atmospheric physical conditions. Increased marine biogenic emissions in air masses heavily influenced by human activities can reduce HCHO levels to some extent. However, in areas less affected by human activities, marine biogenic emissions can lead to higher levels of HCHO pollution. This research explores the impact of marine biogenic emissions on the HCHO status of the MBL under different atmospheric chemical conditions, offering significant insights into understanding chemical processes in marine atmospheres.
Full article
(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment (2nd Edition))
Open AccessArticle
Variation in and Regulation of Carbon Use Efficiency of Grassland Ecosystem in Northern China
by
Zhuoqun Feng, Li Zhou, Guangsheng Zhou, Yu Wang, Huailin Zhou, Xiaoliang Lv and Liheng Liu
Atmosphere 2024, 15(6), 678; https://doi.org/10.3390/atmos15060678 (registering DOI) - 31 May 2024
Abstract
Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO2 sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate
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Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO2 sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios.
Full article
(This article belongs to the Special Issue Research on the Weather and Climate of the Tibetan Plateau and Its Impact)
Open AccessArticle
Towards Sustainable Mobility: Assessing the Benefits and Implications of Internal Combustion Engine Vehicle Bans and Battery Electric Vehicle Uptake in Qatar
by
Abdulla Alishaq and Daniel Mehlig
Atmosphere 2024, 15(6), 677; https://doi.org/10.3390/atmos15060677 (registering DOI) - 31 May 2024
Abstract
The global shift towards sustainable transportation, primarily through vehicle electrification, is critical in addressing climate change. Qatar presents a knowledge gap with specific challenges and opportunities in this transition. This study calculates the potential reduction in CO2-eq, NOx, and PM2.5 emissions
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The global shift towards sustainable transportation, primarily through vehicle electrification, is critical in addressing climate change. Qatar presents a knowledge gap with specific challenges and opportunities in this transition. This study calculates the potential reduction in CO2-eq, NOx, and PM2.5 emissions resulting from substituting Internal Combustion Engine Vehicles (ICEVs) with Battery Electric Vehicles (BEVs) in Qatar, considering ICEV ban scenarios in 2030, 2035, and 2040, alongside five policy pathways. A Vehicle Stock Model (VSM) simulates Qatar’s fleet evolution from 2022 to 2050, focusing on the vehicle’s operational phase. An ICEV ban in 2030 would result in a 34% cumulative emission reduction in road transport between 2022 and 2050 compared with the Business-as-Usual (BAU) scenario. For NOx and PM2.5, cumulative emissions for the 2030 ICEV ban pathways are approximately 20% and 9% lower, respectively, compared with BAU. This study underscores the necessity of localising environmental strategies to meet Qatar’s specific needs and climate commitments, where results indicate significant emission reductions are possible through BEVs.
Full article
(This article belongs to the Section Air Pollution Control)
Open AccessArticle
Multi-Scale Urban Natural Ventilation Climate Guidance: A Case Study in the Shijiazhuang Metropolitan Area
by
Shuo Zhang, Xiaoyi Fang, Chen Cheng, Jing Chen, Fengxia Guo, Ying Yu and Shanshan Yang
Atmosphere 2024, 15(6), 676; https://doi.org/10.3390/atmos15060676 (registering DOI) - 31 May 2024
Abstract
The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By
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The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By optimizing the urban construction layout to reduce obstruction and leave a passageway for wind to blow in, the natural ventilation environment could be improved. This paper presents a promising approach for natural ventilation planning at both the city and community scales. Based on the assessment of wind environment, heat island intensity, and ventilation potential, the results revealed that winds blowing from the western and northern mountainous area of Shijiazhuang play a natural ventilation inlet role which can provide clean air. The SSHI and SHI were mainly distributed within the Second Ring Road, which has a large proportion of the low ventilation potential level. Thus, six first-class ventilation corridors and thirteen secondary corridors were recommended, which were set to be adapted to the dominant wind direction. Subsequently, an urban climate analysis map (UCAnMap) was developed considering climate sensitivity, and planning recommendations were provided for different climate zones. The relationship between architectural spatial structure and ventilation efficiency was analyzed; the results revealed that increasing the height of the buildings will decrease the proportion of comfortable wind zones, and the overall ventilation efficiency will weaken, so the average building height of a typical block should be controlled within 45 m, which matches ventilation performance requirements. The ventilation efficiency of the block has a certain negative correlation with the building density, and as the building density decreased by more than 10%, the proportion of the comfortable wind zones could increase by 4–5%.
Full article
(This article belongs to the Special Issue Urban Heat Islands and Global Warming (2nd Edition))
Open AccessArticle
IONOLAB-Fusion: Fusion of Radio Occultation into Computerized Ionospheric Tomography
by
Sinem Deniz Yenen and Feza Arikan
Atmosphere 2024, 15(6), 675; https://doi.org/10.3390/atmos15060675 (registering DOI) - 31 May 2024
Abstract
In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only requires the coordinates of the region of interest and range
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In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only requires the coordinates of the region of interest and range with the desired spatio-temporal resolutions. The model ionosphere is formed using spherical voxels in a lexicographical order so that a 4-D ionosphere can be mapped to a 2-D matrix. The model matrix is formed automatically using a background ionospheric model with an optimized retrospective or near-real time manner. The singular value decomposition is applied to extract a subset of significant singular values and corresponding signal subspace basis vectors. The measurement vector is filled automatically with the optimized number of ground-based and space-based paths. The reconstruction is obtained in closed form in the least squares sense. When the performance of IONOLAB-Fusion across Europe was compared with ionosonde profiles, a 26.51% and 32.33% improvement was observed over the background ionospheric model for quiet and disturbed days, respectively. When compared with GIM-TEC, the agreement of IONOLAB-Fusion was 37.89% and 31.58% better than those achieved with the background model for quiet and disturbed days, respectively.
Full article
(This article belongs to the Section Upper Atmosphere)
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Open AccessArticle
The Effects of Upper-Ocean Sea Temperatures and Salinity on the Intensity Change of Tropical Cyclones over the Western North Pacific and the South China Sea: An Observational Study
by
Pak-Wai Chan, Ching-Chi Lam, Tai-Wai Hui, Zhigang Gao, Hongli Fu, Chunjian Sun and Hui Su
Atmosphere 2024, 15(6), 674; https://doi.org/10.3390/atmos15060674 (registering DOI) - 31 May 2024
Abstract
With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl
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With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River.
Full article
(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)
Open AccessArticle
Climate Change Scenarios for Impact Assessment: Lower Zab River Basin (Iraq and Iran)
by
Ruqayah Mohammed and Miklas Scholz
Atmosphere 2024, 15(6), 673; https://doi.org/10.3390/atmos15060673 (registering DOI) - 31 May 2024
Abstract
Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive
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Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios.
Full article
(This article belongs to the Section Biometeorology)
Open AccessArticle
Characterizing Dust and Biomass Burning Events from Sentinel-2 Imagery
by
Simone Lolli, Luciano Alparone, Alberto Arienzo and Andrea Garzelli
Atmosphere 2024, 15(6), 672; https://doi.org/10.3390/atmos15060672 (registering DOI) - 31 May 2024
Abstract
The detection and evaluation of biomass burning and dust events are critical for understanding their impact on air quality, climate, and human health, particularly in the Mediterranean region. This research pioneers an innovative methodology that uses Sentinel-2 multispectral (MS) imagery to meticulously pinpoint
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The detection and evaluation of biomass burning and dust events are critical for understanding their impact on air quality, climate, and human health, particularly in the Mediterranean region. This research pioneers an innovative methodology that uses Sentinel-2 multispectral (MS) imagery to meticulously pinpoint and analyze long-transport dust outbreaks and biomass burning phenomena, originating both locally and transported from remote areas. We developed the dust/biomass burning (DBB) composite normalized differential index, a tool that identifies clear, dusty, and biomass burning scenarios in the selected region. The DBB index jointly employs specific Sentinel-2 bands: B2-B3-B4 for visible light analysis, and B11 and B12 for short-wave infrared (SWIR), exploiting the specificity of each wavelength to assess the presence of different aerosols. A key feature of the DBB index is its normalization by the surface reflectance of the scene, which ensures independence from the underlying texture, such as streets and buildings, for urban areas. The differentiation involves the comparison of the top-of-atmosphere (TOA) reflectance values from aerosol events with those from clear-sky reference images, thereby constituting a sort of calibration. The index is tailored for urban settings, where Sentinel-2 imagery provides a decametric spatial resolution and revisit time of 5 days. The average values of DBB achieve a 96% match with the coarse-mode aerosol optical depths (AOD), measured by a local station of the AERONET network of sun-photometers. In future studies, the map of DBB could be integrated with that achieved from Sentinel-3 images, which offer similar spectral bands, albeit with much less fine spatial resolution, yet benefit from daily coverage.
Full article
(This article belongs to the Special Issue Haze and Related Aerosol Air Pollution in Remote and Urban Areas)
Open AccessArticle
Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020
by
Ce Xu, Gaoliu Huang and Maomao Zhang
Atmosphere 2024, 15(6), 671; https://doi.org/10.3390/atmos15060671 (registering DOI) - 31 May 2024
Abstract
With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors
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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.
Full article
(This article belongs to the Special Issue Impacts of Land Use and Climate Change in Urban Area: Big Data and Machine Learning)
Open AccessArticle
Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model
by
Qiyan Ji, Xiaoyan Jia, Lifang Jiang, Minghong Xie, Ziyin Meng, Yuting Wang and Xiayan Lin
Atmosphere 2024, 15(6), 670; https://doi.org/10.3390/atmos15060670 (registering DOI) - 31 May 2024
Abstract
Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be
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Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies.
Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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Open AccessArticle
Experimental Research on Regulated and Unregulated Emissions from E20-Fuelled Vehicles and Hybrid Electric Vehicles
by
Tao Qiu, Yakun Zhao, Yan Lei, Zexun Chen, Dongdong Guo, Fulu Shi and Tao Wang
Atmosphere 2024, 15(6), 669; https://doi.org/10.3390/atmos15060669 (registering DOI) - 31 May 2024
Abstract
Ethanol as a renewable fuel has been applied in fuel vehicles (FVs), and it is promising in hybrid electric vehicles (HEVs). This work aims to investigate the emission characteristics of ethanol applied in both FVs and plug-in hybrid electric vehicles (PHEVs). The paper
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Ethanol as a renewable fuel has been applied in fuel vehicles (FVs), and it is promising in hybrid electric vehicles (HEVs). This work aims to investigate the emission characteristics of ethanol applied in both FVs and plug-in hybrid electric vehicles (PHEVs). The paper conducted a real-road test of an internal combustion FV and PHEV, respectively, based on the world light vehicle test cycle (WLTC) by using gasoline and regular gasoline under different temperature conditions. The use of E10 and E20 in FVs has been effective in reducing the conventional emissions of the vehicles. At 23 °C, E10 and E20 reduced the conventional emissions including carbon monoxide (CO), total hydrocarbon compound (THC), non-methane hydrocarbon compound (NMHC), particulate matter (PM), and particulate number (PN) by 15.40–31.11% and 11.00–44.13% respectively. At 6 °C, E10 and E20 reduced conventional emissions including THC, CO, and PM by 2.15–8.61% and 11.02–13.34%, respectively. However, nitrogen oxide (NOX) emissions increased to varying degrees. The reduction trend of non-conventional emissions including methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from FVs fueled with E10 and E20 is not significant for vehicles. Overall, the emission reduction effect of E20 is better than that of E10, and the emission reduction effect of ethanol gasoline on vehicle emissions is reduced at low temperatures. Lower ambient temperatures increase vehicle emissions in the low-speed segment but decrease vehicle emissions in the ultra-high-speed segment. HEV emissions of THC, CO, PN, and PM are reduced by 25.28%, 12.72%, 77.34%, and 64.59%, respectively, for E20 compared to gasoline, and the use of E20 in HEVs contributes to the reduction of overall vehicle emissions.
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(This article belongs to the Special Issue Engine Emissions: Assessment and Control)
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Open AccessArticle
Interannual Variability of Water and Heat Fluxes in a Woodland Savanna (Cerrado) in Southeastern Brazil: Effects of Severe Drought and Soil Moisture
by
Lucas F. C. da Conceição, Humberto R. da Rocha, Nelson V. Navarrete, Rafael Rosolem, Osvaldo M. R. Cabral and Helber C. de Freitas
Atmosphere 2024, 15(6), 668; https://doi.org/10.3390/atmos15060668 (registering DOI) - 31 May 2024
Abstract
The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our
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The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m2, the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m2, respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances.
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(This article belongs to the Special Issue Land-Atmosphere Interactions)
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Open AccessArticle
Assessment of Satellite Products in Estimating Tropical Cyclone Remote Precipitation over the Yangtze River Delta Region
by
Xinyue Wu, Yebing Liu, Shulan Liu, Yubing Jin and Huiyan Xu
Atmosphere 2024, 15(6), 667; https://doi.org/10.3390/atmos15060667 - 31 May 2024
Abstract
Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with
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Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with severe typhoon Khanun (2017) and super-typhoon Mangkhut (2018). The satellite products include the CPC MORPHing technique (CMORPH) data, Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement Mission (GPM IMERG). Eight precision evaluation indexes and statistical methods were used to analyze and evaluate the monitoring capabilities of CMORPH, TRMM 3B42, and GPM IMERG satellite precipitation products. The results indicated that the monitoring capability of TRMM satellite precipitation products was superior in capturing the spatial distribution, and GPM products captured the temporal distributions and different category precipitation observed from gauge stations. In contrast, the CMORPH products performed moderately during two heavy rainfall events, often underestimating or overestimating precipitation amounts and inaccurately detecting precipitation peaks. Overall, the three satellite precipitation products showed low POD, high FAR, low TS, and high FBIAS for heavy rainfall events, and the differences in monitoring torrential TRP may be related to satellite retrieval algorithms.
Full article
(This article belongs to the Special Issue Severe Weather: Evolution, Prediction, and Risk Reduction)
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Open AccessArticle
Ocean Surface Warming and Long-Term Variability in Rainfall in Equatorial Pacific Atolls
by
Ian White, Tony Falkland and Farran Redfern
Atmosphere 2024, 15(6), 666; https://doi.org/10.3390/atmos15060666 - 31 May 2024
Abstract
Freshwater availability in Pacific equatorial atolls is highly variable because of the influence of El Niño–Southern Oscillation (ENSO) on rainfall. IPCC projections for the central and western tropical Pacific suggest annual rainfall (Pa) will increase as sea surface temperature (SST) rises.
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Freshwater availability in Pacific equatorial atolls is highly variable because of the influence of El Niño–Southern Oscillation (ENSO) on rainfall. IPCC projections for the central and western tropical Pacific suggest annual rainfall (Pa) will increase as sea surface temperature (SST) rises. Future changes in ENSO frequency and intensity and in hydrological droughts, however, are uncertain. Here, trends in monthly, seasonal, annual, annual maximum, and minimum rainfall in two equatorial atolls in the eastern and central tropical Pacific are compared with trends in the SST of the surrounding Nino regions from 1951 to 2023. Significant increasing trends in the warm season, annual, and annual maximum SST in the Nino1 + 2, Nino3, and Nino4 regions were of order +1.0 °C/100 y. There were no significant trends in the cool season or annual minimum SST. Despite ocean warming, there were no significant trends in atoll Pa, in intra-annual or interannual variability over 7 decades for either SST or Pa, or in the relative strengths of warm/cool and wet/dry seasons. Extreme, large Pa only occurred after 1987, indicative of ocean warming. Extreme, small Pa happened throughout the period, suggesting no change in drought frequency. Correlations between 12-month P and SST were very strong, with historic rates of increases in Pa of around 1200 mm/y/°C, consistent with projections. The results indicate that the recharge of atoll groundwater will increase as oceans warm, but droughts will remain a major challenge.
Full article
(This article belongs to the Special Issue The Impact of Climate Change on Water Resources)
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Open AccessArticle
Emission Factors of Tyre Wear Particles Emitted by Light Road Vehicles in Real Driving Conditions: A New Challenge for Clean Road Transport to Improve Urban Air Quality
by
Salah Khardi
Atmosphere 2024, 15(6), 665; https://doi.org/10.3390/atmos15060665 - 31 May 2024
Abstract
Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted
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Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear.
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(This article belongs to the Special Issue Future Prospects for Air Quality Management in the 21st Century)
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Spatiotemporal Exposure Assessment of PM2.5 Concentration Using a Sensor-Based Air Monitoring System
by
Jihun Shin, Jaemin Woo, Youngtae Choe, Gihong Min, Dongjun Kim, Daehwan Kim, Sanghoon Lee and Wonho Yang
Atmosphere 2024, 15(6), 664; https://doi.org/10.3390/atmos15060664 - 31 May 2024
Abstract
Sensor-based air monitoring instruments (SAMIs) can provide high-resolution air quality data by offering a detailed mapping of areas that air quality monitoring stations (AQMSs) cannot reach. This enhances the precision of estimating PM2.5 concentration levels for areas that have not been directly
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Sensor-based air monitoring instruments (SAMIs) can provide high-resolution air quality data by offering a detailed mapping of areas that air quality monitoring stations (AQMSs) cannot reach. This enhances the precision of estimating PM2.5 concentration levels for areas that have not been directly measured, thereby enabling an accurate assessment of exposure. The study period was from 30 September to 2 October 2019 in the Guro-gu district, Seoul, Republic of Korea. Four models were applied to assess the suitability of the SAMIs and visualize the temporal and spatial distribution of PM2.5. Assuming that the PM2.5 concentrations measured at a SAMI located in the center of the Guro-gu district represent the true values, the PM2.5 concentrations estimated using QGIS spatial interpolation techniques were compared. The SAMIs were used at seven points (S1–S7) according to the distance. Models 3 and 4 accurately estimated the unmeasured points with higher coefficients of determination (R2) than the other models. As the distance from the AQMS increased from S1 to S7, the R2 between the observed and estimated values decreased from 0.89 to 0.29, respectively. The auxiliary installation of SAMIs could resolve regional concentration imbalances, allowing for the accurate estimation of pollutant concentrations and improved risk assessment for the population.
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(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))
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Advanced Understanding of Sea Surface Cooling Off Northeastern Taiwan to Tropical Cyclone by Using Numerical Modeling
by
Hsin-Ju Wu and Zhe-Wen Zheng
Atmosphere 2024, 15(6), 663; https://doi.org/10.3390/atmos15060663 - 31 May 2024
Abstract
From 2001 to 2020, three typhoons with similar moving paths and intensities were found to trigger markedly different cooling off northeastern Taiwan. They were typhoons Utor (2001), Nuri (2008), and Hagupit (2008), which led to maximum sea surface temperature (SST) cooling temperatures of
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From 2001 to 2020, three typhoons with similar moving paths and intensities were found to trigger markedly different cooling off northeastern Taiwan. They were typhoons Utor (2001), Nuri (2008), and Hagupit (2008), which led to maximum sea surface temperature (SST) cooling temperatures of 8.8, 2.7, and 1.4 °C, respectively. The drastic cooling discrepancy implies that the existing understanding of the key mechanism leading to the cooling off northeastern Taiwan could be insufficient. For further exploring the key reason(s) contributing to the marked discrepancy, a regional oceanic modeling system (ROMS) was used to reconstruct the background oceanic environment corresponding to three typhoon passages. Results show that the wide radius of maximum winds of typhoon Utor contributes to the strongest SST cooling by enhancing the Kuroshio intrusion (KI) onto the shelf northeast of Taiwan. Heat budget diagnostics explain why including tidal forcing can substantially promote SST cooling. The process was associated mainly with a stronger vertical advection tied to the influence of de-stratification by tidal mixing. Finally, warmer Taiwan Strait currents, driven by wind forcing the typhoons to pass zonally through the north South China Sea, intruded clockwise into the Longdong coast and accelerated the recovery of sea surface cooling around Longdong.
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(This article belongs to the Special Issue High-Impact Weather Events: Dynamics, Variability and Predictability)
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Assessing the Accuracy of 50 Temperature-Based Models for Estimating Potential Evapotranspiration (PET) in a Mediterranean Mountainous Forest Environment
by
Nikolaos D. Proutsos, Mariangela N. Fotelli, Stefanos P. Stefanidis and Dimitris Tigkas
Atmosphere 2024, 15(6), 662; https://doi.org/10.3390/atmos15060662 - 30 May 2024
Abstract
Potential evapotranspiration (PET) is a crucial parameter for forest development, having an important role in ecological, biometeorological, and hydrological assessments. Accurate estimations of PET using the FAO–56 Penman–Monteith (FAO–56 PM) benchmark method require a wide range of data parameters, which are not typically
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Potential evapotranspiration (PET) is a crucial parameter for forest development, having an important role in ecological, biometeorological, and hydrological assessments. Accurate estimations of PET using the FAO–56 Penman–Monteith (FAO–56 PM) benchmark method require a wide range of data parameters, which are not typically available at meteorological stations installed in forest environments. The aim of this study is to investigate the accuracy of various methods with low data requirements for assessing PET in a Mediterranean forest environment and propose appropriate alternatives for accurate PET estimation. Specifically, 50 temperature-based methods were evaluated against the FAO–56 PM method in a sub-humid forest in northern Greece, using high-quality daily meteorological data. The outcomes indicate that temperature-based methods offer a viable alternative for PET estimation when data availability is limited, with a considerable number of methods (22) presenting low deviations (up to 10%) compared to the benchmark method. Temperature-based models outperformed those incorporating water-related parameters (as relative humidity or precipitation) in Mediterranean forest environments. The top performing methods in the study site, based on several statistical indices, were the equations of Ravazzani et al., proposed in 2012, followed by Hargreaves–Samani in 1985 and Heydari and Heydari in 2014.
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(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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Five Large 13th Century C.E. Volcanic Eruptions Recorded in Antarctica Ice Cores
by
Jihong Cole-Dai, Derek L. Brandis and Dave G. Ferris
Atmosphere 2024, 15(6), 661; https://doi.org/10.3390/atmos15060661 - 30 May 2024
Abstract
Major explosive volcanic eruptions impact the climate by altering the radiative balance of the atmosphere and through feedback mechanisms in the climate system. The extent of the impact depends on the magnitude (aerosol mass loading) and the number or frequency of such eruptions.
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Major explosive volcanic eruptions impact the climate by altering the radiative balance of the atmosphere and through feedback mechanisms in the climate system. The extent of the impact depends on the magnitude (aerosol mass loading) and the number or frequency of such eruptions. Multiple Antarctica ice core records of past volcanic eruptions reveal that the number (5) of major eruptions (volcanic sulfate deposition flux greater than 10 kg km−2) was the highest in the 13th century over the last two millennia. Signals of four of the five eruptions are dated to the second half of the century, indicating consecutive major eruptions capable of causing sustained climate impact via known feedback processes. The fact that signals of four corresponding eruptions have been found in a Greenland ice core indicates that four of the five 13th century eruptions were probably by volcanoes in the low latitudes (between 20° N and 20° S) with substantial aerosol mass loading. These eruptions in the low latitudes likely exerted the strongest volcanic impact on climate in the last two millennia.
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(This article belongs to the Special Issue Impact of Volcanic Eruptions on the Atmosphere)
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Seasonal Cycle of Southern Hemisphere Explosive Growth and Decay of Storms
by
Stacey L. Osbrough and Jorgen S. Frederiksen
Atmosphere 2024, 15(6), 660; https://doi.org/10.3390/atmos15060660 - 30 May 2024
Abstract
The seasonal variability of Southern Hemisphere (SH) synoptic-scale weather systems is analysed for the 20-year timespan 1997 to 2016. The relationships between the SH jet streams and storm tracks based on lower tropospheric circulation anomalies filtered into the high-pass (periods < 4 days)
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The seasonal variability of Southern Hemisphere (SH) synoptic-scale weather systems is analysed for the 20-year timespan 1997 to 2016. The relationships between the SH jet streams and storm tracks based on lower tropospheric circulation anomalies filtered into the high-pass (periods < 4 days) and band-pass (periods between 4 and 8 days) types are examined based on 6-hourly reanalysis data. Leading Empirical Orthogonal Functions (EOFs) and storm tracks based on all (growing and decaying) disturbances are determined. As well, the structure and standard deviations of streamfunction fluctuations are determined separately in three growth rate and three decay rate bins focusing on explosive growth and decay. In all cases, and in each season, the band-pass storm tracks are more zonally symmetric than the high-pass standard deviations and this is also reflected by the EOFs. Leading EOFs in both bands are monopole wavetrains of highs and lows located in the storm tracks with some band-pass disturbances having dipole structures consistent with blocking and northwest cloud bands. EOFs based on the bin with slow-growing fluctuations are structurally similar to the standard EOFs based on all disturbances. EOFs for moderately and explosively growing disturbances are increasingly displaced equatorward with a larger growth rate.
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(This article belongs to the Section Climatology)
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