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
Assessing the Influence of Vehicular Traffic-Associated Atmospheric Pollutants on Pulmonary Function Using Spirometry and Impulse Oscillometry in Healthy Participants: Insights from Bogotá, 2020–2021
Atmosphere 2024, 15(6), 688; https://doi.org/10.3390/atmos15060688 - 4 Jun 2024
Abstract
Air pollution, particularly from particulate matter (PM2.5) and black carbon (eBC), has been implicated in airway pathologies. This study aims to assess the relationship between exposure to these pollutants and respiratory function in various populations, including healthy individuals, while seeking an
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Air pollution, particularly from particulate matter (PM2.5) and black carbon (eBC), has been implicated in airway pathologies. This study aims to assess the relationship between exposure to these pollutants and respiratory function in various populations, including healthy individuals, while seeking an accurate assessment method. A cross-sectional study was conducted in Bogotá, evaluating respiratory function in the users of bicycles, minivans, and buses through spirometry and impulse oscillometry. Measurements were taken along two main avenues, assessing the PM2.5 and eBC concentrations. The results reveal higher pollutant levels on AVE KR 9, correlating with changes in oscillometry values post-travel. Cyclists exhibited differing pre- and post-travel values compared to bus and minivan users, suggesting aerobic exercise mitigates pollutant impacts. However, no statistically significant spirometry or impulse oscillometry variations were observed among routes or modes. Public transport and minivan users showed greater PM2.5 and eBC exposure, yet no significant changes associated with environmental contaminants were found in respiratory function values. These findings underscore the importance of further research on pollutant effects and respiratory health in urban environments, particularly concerning different transport modes.
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(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))
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Impact of High-Resolution Land Cover on Simulation of a Warm-Sector Torrential Rainfall Event in Guangzhou
by
Ning Wang, Yanan Liu, Fan Ping and Jiahua Mao
Atmosphere 2024, 15(6), 687; https://doi.org/10.3390/atmos15060687 - 4 Jun 2024
Abstract
This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data,
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This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall.
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(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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Open AccessArticle
Development of X-Band Geophysical Model Function for Sea Surface Wind Speed Retrieval with ASNARO-2
by
Yuko Takeyama and Shota Kurokawa
Atmosphere 2024, 15(6), 686; https://doi.org/10.3390/atmos15060686 (registering DOI) - 4 Jun 2024
Abstract
In the present study, a new geophysical model function (GMF) is developed for the X-band synthetic aperture radar (SAR) on board the Advanced Satellite with New System Architecture for Observation-2 (ASNARO-2) to retrieve accurate offshore wind speeds. Equivalent neutral wind speeds based on
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In the present study, a new geophysical model function (GMF) is developed for the X-band synthetic aperture radar (SAR) on board the Advanced Satellite with New System Architecture for Observation-2 (ASNARO-2) to retrieve accurate offshore wind speeds. Equivalent neutral wind speeds based on the local forecast model (LFM) are employed as reference wind vectors, and 12,259 matching points from 502 SAR images obtained with horizontal transmitting, horizontal receiving polarization around Japan are collected. To ensure convergence of the calculation, 8129 points are selected from the matching points to determine the basic formula for the GMF and 23 coefficients based on the relationships among the normalized radar cross section, wind speed, incidence angle, and relative wind direction. Compared with the reference wind speeds, the GMF wind speeds showed reproducibility with a bias of −0.10 m/s and an RMSD of 1.37 m/s. Additionally, it can be confirmed that the retrieved wind speed has the bias of 0.03 and the RMSD of 1.68 m/s when compared to the in situ wind speed from the Kuroshio Extension Observatory (KEO) buoy. The accuracy of these retrieved wind speeds is comparable to previous studies, and it is indicated that the developed GMF can be used to retrieve offshore winds from ASNARO-2 images.
Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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Open AccessArticle
Space Weather Effects on Heart Rate Variations: Sex Dependence
by
Maria-Christina Papailiou and Helen Mavromichalaki
Atmosphere 2024, 15(6), 685; https://doi.org/10.3390/atmos15060685 - 3 Jun 2024
Abstract
The effects of solar activity and the accompanying space weather events on human pathological conditions, physiological parameters and other psycho-physiological disturbances have been analyzed in numerous recent investigations. Moreover, many of these studies have particularly focused on the different physical reactions humans have,
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The effects of solar activity and the accompanying space weather events on human pathological conditions, physiological parameters and other psycho-physiological disturbances have been analyzed in numerous recent investigations. Moreover, many of these studies have particularly focused on the different physical reactions humans have, according to their sex, during variations in the physical environment. In the framework of the above, this work analyses heart rate data obtained from volunteers (687 men and 534 women) from three different regions (Athens, Piraeus and Heraklion) of Greece in relation to the geophysical activity and variations of environmental factors. Dst index and Ap index data, along with cosmic ray intensity data derived from the Athens Neutron Monitor Station (A.Ne.Mo.S.), were used. The study expands from April 2011 to January 2018, covering solar cycle 24. The ANalysis Of Variance (ANOVA) and the superimposed epochs methods were used in order to examine heart rate variations depending on sex. Results revealed that women tend to be more sensitive to physical environmental changes. Statistically significant results are related to the geomagnetic activity but were not obtained for cosmic ray variations.
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(This article belongs to the Section Upper Atmosphere)
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Open AccessEditorial
Importance of Heat Health Warnings in Heat Management
by
Andreas Matzarakis
Atmosphere 2024, 15(6), 684; https://doi.org/10.3390/atmos15060684 - 3 Jun 2024
Abstract
During intense heat events, the morbidity and mortality of the population increase [...]
Full article
(This article belongs to the Section Biometeorology)
Open AccessArticle
ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity
by
David Santiago Hernández-Medina, Carlos Alfonso Zafra-Mejía and Hugo Alexander Rondón-Quintana
Atmosphere 2024, 15(6), 683; https://doi.org/10.3390/atmos15060683 - 2 Jun 2024
Abstract
The COVID-19 pandemic precipitated a unique period of social isolation, presenting an unprecedented opportunity to scrutinize the influence of human activities on urban air quality. This study employs ARIMA models to explore the impact of COVID-19 isolation measures on the PM10 and
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The COVID-19 pandemic precipitated a unique period of social isolation, presenting an unprecedented opportunity to scrutinize the influence of human activities on urban air quality. This study employs ARIMA models to explore the impact of COVID-19 isolation measures on the PM10 and PM2.5 concentrations in a high-altitude Latin American megacity (Bogota, Colombia). Three isolation scenarios were examined: strict (5 months), sectorized (1 months), and flexible (2 months). Our findings indicate that strict isolation measures exert a more pronounced effect on the short-term simulated concentrations of PM10 and PM2.5 (PM10: −47.3%; PM2.5: −54%) compared to the long-term effects (PM10: −29.4%; PM2.5: −28.3%). The ARIMA models suggest that strict isolation measures tend to diminish the persistence of the PM10 and PM2.5 concentrations over time, both in the short and long term. In the short term, strict isolation measures appear to augment the variation in the PM10 and PM2.5 concentrations, with a more substantial increase observed for PM2.5. Conversely, in the long term, these measures seem to reduce the variations in the PM concentrations, indicating a more stable behavior that is less susceptible to abrupt peaks. The differences in the reduction in the PM10 and PM2.5 concentrations between the strict and flexible isolation scenarios were 23.8% and 12.8%, respectively. This research provides valuable insights into the potential for strategic isolation measures to improve the air quality in urban environments.
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(This article belongs to the Special Issue Urban Air Pollution, Meteorological Conditions and Human Health)
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Open AccessArticle
Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part II: Classification of Synoptic Circulation Types and Local Meteorological Patterns and Their Relation to Elevated Air Pollution in Spring and Summer
by
Ningbo Jiang, Matthew L. Riley, Merched Azzi, Giovanni Di Virgilio, Hiep Nguyen Duc and Praveen Puppala
Atmosphere 2024, 15(6), 682; https://doi.org/10.3390/atmos15060682 - 1 Jun 2024
Abstract
Abstract: The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air
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Abstract: The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.
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(This article belongs to the Section Aerosols)
Open AccessArticle
Computational Fluid Dynamics Simulation of High-Resolution Spatial Distribution of Sensible Heat Fluxes in Building-Congested Area
by
Jung-Eun Kang, Sang-Hyun Lee, Jin-Kyu Hong and Jae-Jin Kim
Atmosphere 2024, 15(6), 681; https://doi.org/10.3390/atmos15060681 - 1 Jun 2024
Abstract
Urban areas consist of various land cover types, with a high proportion of artificial surfaces among them. This leads to unfavorable thermal environments in urban areas. Continuous research on the thermal environment, specifically on the sensible heat flux (Qh), has
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Urban areas consist of various land cover types, with a high proportion of artificial surfaces among them. This leads to unfavorable thermal environments in urban areas. Continuous research on the thermal environment, specifically on the sensible heat flux (Qh), has been conducted. However, previous research has faced temporal, spatial, and resolution limitations when it comes to detailed analysis of sensible heat flux in urban areas. Therefore, in this study, a computational fluid dynamics (CFD) model combined with the LDAPS and the VUCM was developed to simulate Qh at one-hour intervals over a 1-month period in an urban area with various land cover types. Model validation was performed by comparing it with measurements, confirming the suitability of the model for simulating Qh. The land cover was categorized into five types: building, road, bare land, grassland, and tree areas. Qh exhibited distinct patterns depending on the land cover type. When averaging the Qh distribution over the target period, buildings, roads, and bare land areas showed a predominance of upward Qh values, while grassland and tree areas displayed dominant downward Qh values. Additionally, even within the same land cover types, slight Qh variations were identified based on their surroundings. The averaged Qh value for building areas was the highest at 36.79 W m−2, while that for tree areas was −3.04 W m−2. Moreover, during the target period, the time-averaged Qh showed that building, road, and bare land areas peaked at 14 LST, while grassland and tree areas exhibited very low Qh values. Notably, buildings reached a maximum Qh of 103.30 W m−2 but dropped to a minimum of 1.14 W m−2 at 5 LST.
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(This article belongs to the Section Meteorology)
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Daily Fine Resolution Estimates of the Influence of Wildfires on Fine Particulate Matter in California, 2011–2020
by
Caitlin G. Jones-Ngo, Kathryn C. Conlon, Mohammad Al-Hamdan and Jason Vargo
Atmosphere 2024, 15(6), 680; https://doi.org/10.3390/atmos15060680 - 1 Jun 2024
Abstract
Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5),
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Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM2.5 exposure. Our results show wildfires significantly influence PM2.5 in California and nearly all exceedances of the daily US EPA PM2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks.
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(This article belongs to the Section Air Quality)
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Machine Learning to Characterize Biogenic Isoprene Emissions and Atmospheric Formaldehyde with their Environmental Drivers in the Marine Boundary Layer
by
Tianyu Wang, Shanshan Wang, Ruibin Xue, Yibing Tan, Sanbao Zhang, Chuanqi Gu and Bin Zhou
Atmosphere 2024, 15(6), 679; https://doi.org/10.3390/atmos15060679 - 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.
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(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
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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 - 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.
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(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 - 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.
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(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 - 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 - 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.
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(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 - 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 - 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
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Simone Lolli, Luciano Alparone, Alberto Arienzo and Andrea Garzelli
Atmosphere 2024, 15(6), 672; https://doi.org/10.3390/atmos15060672 - 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 - 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 - 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 - 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.
Full article
(This article belongs to the Special Issue Engine Emissions: Assessment and Control)
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