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
Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network
Atmosphere 2024, 15(6), 647; https://doi.org/10.3390/atmos15060647 (registering DOI) - 28 May 2024
Abstract
Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation
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Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.
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(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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The Design of a Parameterization Scheme for 137Cs Based on the WRF-Chem Model and Its Application in Simulating the Fukushima Nuclear Accident
by
Qun Long, Zengliang Zang, Xiaoyan Ma, Sheng Fang, Yiwen Hu, Yijie Wang, Shuhan Zhuang and Liang Wang
Atmosphere 2024, 15(6), 646; https://doi.org/10.3390/atmos15060646 - 28 May 2024
Abstract
Based on the Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem) atmospheric chemistry model, a parameterization scheme for the radioactive isotope caesium (137Cs), considering processes such as advection, turbulent diffusion, dry deposition, and wet deposition, was constructed, enabling the spatial
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Based on the Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem) atmospheric chemistry model, a parameterization scheme for the radioactive isotope caesium (137Cs), considering processes such as advection, turbulent diffusion, dry deposition, and wet deposition, was constructed, enabling the spatial distribution simulation of the concentration and deposition of 137Cs. The experimental simulation studies were carried out during the high emission period of the Fukushima nuclear accident (from 11 to 17 March 2011). Two sets of comparison experiments, with or without deposition, were designed, the effects of wind field and precipitation on the spatial transport and ground deposition of 137Cs were analyzed, and the influence of wind field and precipitation on 137Cs vertical transport was analyzed in detail. The results indicate that the model can accurately simulate the meteorological and 137Cs variables. On 15 March, 137Cs dispersed towards the Kanto Plain in Japan under the influence of northeastern winds. In comparison to the experiment without deposition, the concentration of 137Cs in the Fukushima area decreased by approximately 286 Bq·m−3 in the deposition experiment. Under the influence of updrafts in the non-deposition experiment, a 137Cs cloud spread upward to a maximum height of 6 km, whereas in the deposition experiment, the highest dispersion of the 137Cs cloud only reach a height of 4 km. Affected by the wind field, dry deposition is mainly distributed in Fukushima, the Kanto Plain, and their eastern ocean areas, with a maximum dry deposition of 5004.5 kBq·m−2. Wet deposition is mainly influenced by the wind field and precipitation, distributed in the surrounding areas of Fukushima, with a maximum wet deposition of 725.3 kBq·m−2. The single-station test results from the deposition experiment were better than those for the non-deposition experiment: the percentage deviations of the Tokyo, Chiba, Maebashi, and Naraha stations decreased by 61%, 69%, 46%, and 51%, respectively, and the percentage root mean square error decreased by 46%, 25%, 38%, and 48%, respectively.
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(This article belongs to the Section Climatology)
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Enhancing CO2 Injection Efficiency: Rock-Breaking Characteristics of Particle Jet Impact in Bottom Hole
by
Yi Wang and Jian Zhao
Atmosphere 2024, 15(6), 645; https://doi.org/10.3390/atmos15060645 - 28 May 2024
Abstract
Storing CO2 in oil and gas reservoirs offers a dual benefit: it reduces atmospheric CO2 concentration while simultaneously enhancing oil displacement efficiency and increasing crude oil production. This is achieved by injecting CO2 into producing oil and gas wells. Employing
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Storing CO2 in oil and gas reservoirs offers a dual benefit: it reduces atmospheric CO2 concentration while simultaneously enhancing oil displacement efficiency and increasing crude oil production. This is achieved by injecting CO2 into producing oil and gas wells. Employing particle jet technology at the bottom of CO2 injection wells significantly expands the bottom hole diameter, thereby improving CO2 injection efficiency and storage safety. To further investigate the rock-breaking characteristics and efficiency, a finite element model for particle jet rock breaking is established by utilizing the smoothed particle hydrodynamics (SPH) method. Specifically, this new model considers the high temperature and confining pressure conditions present at the bottom hole. The dynamic response and fracturing effects of rock subjected to a particle jet are also revealed. The results indicate that particle jet impact rebound significantly influences the size of the impact crater, with the maximum first principal stress primarily concentrated on the crater’s surface. The impact creates a “v”-shaped crater on the rock surface, with both depth and volume increasing proportionally to jet inlet velocity and particle diameter. However, beyond a key particle concentration of 3%, the increase in depth and volume becomes less pronounced. Confining pressure is found to hinder particle impact rock-breaking efficiency, while high temperatures contribute to larger impact depths and breaking volumes. This research can provide theoretical support and parameter guidance for the practical application of particle impact technology in enhancing CO2 injection efficiency at the bottom hole.
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(This article belongs to the Special Issue CO2 Geological Storage and Utilization (2nd Edition))
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Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models
by
Xiangqian Wei, Yi Liu, Jun Guo, Xinyu Chang and Haochuan Li
Atmosphere 2024, 15(6), 644; https://doi.org/10.3390/atmos15060644 - 27 May 2024
Abstract
The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler
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The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.
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(This article belongs to the Special Issue CFD Modeling in Multiphase Flow Transport/Separation Equipment)
Open AccessArticle
Cloud Top Height Retrieval from FY-4A Data: A Residual Module and Genetic Algorithm Approach
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Tao Li, Niantai Chen, Fa Tao, Shuzhen Hu, Jianjun Xue, Rui Han and Di Wu
Atmosphere 2024, 15(6), 643; https://doi.org/10.3390/atmos15060643 - 27 May 2024
Abstract
This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite
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This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Urban–Rural Disparity in Socioeconomic Status, Green Space and Cerebrovascular Disease Mortality
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Wen-Yu Lin, Ping-Yi Lin, Chih-Da Wu, Wen-Miin Liang and Hsien-Wen Kuo
Atmosphere 2024, 15(6), 642; https://doi.org/10.3390/atmos15060642 - 27 May 2024
Abstract
With rapid urbanization in Taiwan, the green space has become a key factor in modifiable cardiovascular disease (CVD) risks. We investigated the relationships between socioeconomic status (SES), green space, and cerebrovascular disease (CBD) at the township level in Taiwan, focusing on urban–rural disparities.
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With rapid urbanization in Taiwan, the green space has become a key factor in modifiable cardiovascular disease (CVD) risks. We investigated the relationships between socioeconomic status (SES), green space, and cerebrovascular disease (CBD) at the township level in Taiwan, focusing on urban–rural disparities. Analyzing data from 358 townships (2011–2020), we examined SES indicators (e.g., low-income households, education levels, median tax payments), green space (Normalized Difference Vegetation Index—NDVI), and CBD mortality rates using the pooled ordinary least squares (OLS) and random-effect models (REM) in panel regression. Additionally, we explored the mediating role of the NDVI in the SES-CBD mortality association. CBD mortality decreased more in urban areas over the decade, with consistent NDVI patterns across regions. Rural areas experienced a decline in low-income households, contrasting with an increase in urban areas. SES variables, NDVI, and time significantly affected CBD mortality in rural areas but not urban ones. Notably, the NDVI had a stronger impact on CBD mortality in rural areas. Mediation analysis revealed the NDVI’s indirect effects, especially in rural areas. Despite overall declines in CBD mortality in Taiwan, urban–rural disparities in SES and green space persist. Addressing these disparities is critical for understanding and developing interventions to mitigate health inequalities.
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(This article belongs to the Section Air Quality and Human Health)
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Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target
by
Yilin Wang, Xianke Hui and Kai Liu
Atmosphere 2024, 15(6), 641; https://doi.org/10.3390/atmos15060641 - 26 May 2024
Abstract
It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon
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It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization.
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(This article belongs to the Section Air Quality)
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Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications
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Mohammad Barooni, Shiva Ghaderpour Taleghani, Masoumeh Bahrami, Parviz Sedigh and Deniz Velioglu Sogut
Atmosphere 2024, 15(6), 640; https://doi.org/10.3390/atmos15060640 - 26 May 2024
Abstract
The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean
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The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.
Full article
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)
Open AccessArticle
Exploring the Effects of Elevated Ozone Concentration on Physiological Processes in Summer Maize in North China Based on Exposure–Response Relationships
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Mansen Wang, Shuyang Xie, Xiaoxiu Lun, Zhouming He, Xin Liu, Wenjun Lv, Luxi Wang, Tian Wang and Junfeng Liu
Atmosphere 2024, 15(6), 639; https://doi.org/10.3390/atmos15060639 - 26 May 2024
Abstract
As the predominant pollutant in North China during the summer months, ozone (O3) exhibits strong oxidizing capabilities. Long-term exposure of crops to ozone will cause a decrease in various physiological indicators, affect crop yields, and pose a serious threat to food
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As the predominant pollutant in North China during the summer months, ozone (O3) exhibits strong oxidizing capabilities. Long-term exposure of crops to ozone will cause a decrease in various physiological indicators, affect crop yields, and pose a serious threat to food security. The North China Plain, the primary region for summer maize production in China, is afflicted by ozone pollution. In order to explore the effects of increasing O3 concentration on the physiological characteristics and photosynthetic characteristics of summer maize, this study took summer-sown maize as the research object and carried out the ozone exposure experiment with open-top chamber (OTCs). The response of maize to O3 exposure was studied by measuring the damage, physiological indexes and photosynthetic indexes in the silking stage (late July to late August) and filling stage (late August to mid-September). The results indicated the following: (1) Prolonged exposure to high O3 concentrations exacerbated leaf chlorosis and damage. (2) The increase in O3 concentration caused lipid peroxidation. The content of malondialdehyde was significantly increased by 32.6%~122.56%. At the same time, chlorophyll was destroyed and decreased by 2.17% to 4.86%. Under ozone exposure, ascorbic acid content was significantly increased by 7.58%~35.69%. The antioxidant indexes of maize were more sensitive during the filling stage. (3) Under O3 exposure, photosynthetic rate, stomatal conductance and intercellular carbon dioxide concentration decreased significantly, indicating that the influence of O3 on maize was mainly due to stomatal limitation. Water use efficiency and transpiration rate decreased significantly. The water use efficiency decreased by 12.84%~35.62%, which led to the weakening of the carbon fixation ability of maize and affected the normal growth and development of maize.
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(This article belongs to the Special Issue Ozone Pollution and Effects in China)
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Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions
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Anton Petrov, Emilia Georgieva and Elena Hristova
Atmosphere 2024, 15(6), 638; https://doi.org/10.3390/atmos15060638 - 25 May 2024
Abstract
The distribution of air pollutants in urban areas is significantly influenced by the presence of various geometric structures, including buildings, bridges, and tunnels. In built-up environments, meteorological conditions may influence the accumulation or dispersion of air pollutants in specific zones. This study examines
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The distribution of air pollutants in urban areas is significantly influenced by the presence of various geometric structures, including buildings, bridges, and tunnels. In built-up environments, meteorological conditions may influence the accumulation or dispersion of air pollutants in specific zones. This study examines the impact of wind and atmospheric stability on the dispersion of air pollutants around an apartment building situated in close proximity to a busy boulevard in a residential district of Sofia, Bulgaria. A series of dispersion simulations were conducted using the Graz Lagrangian Model (GRAL v.22.09) for a range of meteorological conditions, defined as combinations of the direction and velocity of the approaching flow, and of stability conditions within the study area of 1 × 1 km, with a horizontal resolution of 2 m. The resulting spatial distribution revealed the presence of hotspots and strong gradients in the concentration field. A simulation with meteorological data was also conducted, which was aligned with a campaign to monitor vehicular traffic. The sensitivity tests indicate that GRAL is capable of reproducing high-resolution pollutant fields, accounting for building effects at relatively low computational costs. This makes the model potentially attractive for city-wide simulations as well as for air pollution exposure estimation.
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(This article belongs to the Special Issue Urban Air Pollution, Meteorological Conditions and Human Health)
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Heatwaves and Their Impact on Air Quality in Greater Cairo, Egypt
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Amira N. Mostafa, Stéphane C. Alfaro, Sayed. M. Robaa, Ashraf S. Zakey and Mohamed M. Abdel Wahab
Atmosphere 2024, 15(6), 637; https://doi.org/10.3390/atmos15060637 - 25 May 2024
Abstract
Several heatwaves (HWs) have been recorded in Egypt in recent years. Some of these HWs were mild, while others were severe and resulted in mortalities and morbidities. On the other hand, air pollution is considered a health issue in Egypt’s megacities, especially the
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Several heatwaves (HWs) have been recorded in Egypt in recent years. Some of these HWs were mild, while others were severe and resulted in mortalities and morbidities. On the other hand, air pollution is considered a health issue in Egypt’s megacities, especially the capital city, Cairo, and its surroundings, the Greater Cairo (GC) region. In this study, we examine a number of HWs that have hit Egypt in recent years, along with the state of air quality, in terms of PM10, NO2, and O3, during the period of HW incidence, with a focus on the GC region. During the period of study, the frequency, intensity, and duration of HWs have been observed to increase. The total number of recorded HW events was 190, encompassing 376 HW days. The time series of daily mean NO2 showed no correlation with temperature during the months that experienced HWs cases. Conversely, PM10 and O3 concentrations exhibited a similar pattern as that of the daily maximum temperature. This increase in the two pollutant concentrations led to a degradation of the air quality, as demonstrated by the fact that the Air Quality Health Index went from “moderate risk”, on normal days, to “high risk” during the HWs.
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(This article belongs to the Section Air Quality and Human Health)
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Seasonality of Heavy Metal Concentrations in Ambient Particulate Matter in the UK
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David M. Butterfield, Richard J. C. Brown and Andrew S. Brown
Atmosphere 2024, 15(6), 636; https://doi.org/10.3390/atmos15060636 - 24 May 2024
Abstract
The seasonal characteristics of air pollutant concentrations are important for understanding variations in emissions released into the air and in atmospheric chemistry. The patterns seen can be influenced by anthropogenic emissions, meteorological conditions, and the transport of pollutants over long and short distances.
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The seasonal characteristics of air pollutant concentrations are important for understanding variations in emissions released into the air and in atmospheric chemistry. The patterns seen can be influenced by anthropogenic emissions, meteorological conditions, and the transport of pollutants over long and short distances. Whilst seasonality is well understood for some pollutants such as ozone and polycyclic aromatic hydrocarbons, it is poorly understood and under-investigated for heavy metals in particulate matter (PM). This work studies long-term datasets of heavy metals in PM from a relevant UK air quality monitoring network, demonstrating the seasonal characteristics of the concentrations of these metals for the first time. Surprisingly, both ‘high in winter–low in summer’ and ‘low in winter–high in summer’ seasonality was observed, with some metals showing little or no seasonality. The ‘high in winter–low in summer’ seasonality (particularly for As) is attributable to the dominant contribution being from local primary sources, such as burning process producing larger PM sizes. The ‘low in winter–high in summer’ seasonality (particularly for V) is attributable to weak or non-seasonal local sources being dominated by contributions from medium and long-range transport during the summer months, when pollutant transport is more efficient. The findings contribute significantly to our understanding of the seasonality of metals in PM concentrations and the role played by the long-range transport of pollutants. Conclusions are also drawn about the implications for the calculation of annual averages on compliance-based air quality networks if data from a time series of a pollutant that displays seasonal characteristics are missing.
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(This article belongs to the Special Issue Air Quality in the UK (2nd Edition))
Open AccessArticle
The Recovery and Re-Calibration of a 13-Month Aerosol Extinction Profiles Dataset from Searchlight Observations from New Mexico, after the 1963 Agung Eruption
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Juan-Carlos Antuña-Marrero, Graham W. Mann, John Barnes, Abel Calle, Sandip S. Dhomse, Victoria E. Cachorro, Terry Deshler, Zhengyao Li, Nimmi Sharma and Louis Elterman
Atmosphere 2024, 15(6), 635; https://doi.org/10.3390/atmos15060635 - 24 May 2024
Abstract
The recovery and re-calibration of a dataset of vertical aerosol extinction profiles of the 1963/64 stratospheric aerosol layer measured by a searchlight at 32°N in New Mexico, US, is reported. The recovered dataset consists of 105 aerosol extinction profiles at 550 nm that
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The recovery and re-calibration of a dataset of vertical aerosol extinction profiles of the 1963/64 stratospheric aerosol layer measured by a searchlight at 32°N in New Mexico, US, is reported. The recovered dataset consists of 105 aerosol extinction profiles at 550 nm that cover the period from December 1963 to December 1964. It is a unique record of the portion of the aerosol cloud from the March 1963 Agung volcanic eruption that was transported into the Northern Hemisphere subtropics. The data-recovery methodology involved re-digitizing the 105 original aerosol extinction profiles from individual Figures within a research report, followed by the re-calibration. It involves inverting the original equation used to compute the aerosol extinction profile to retrieve the corresponding normalized detector response profile. The re-calibration of the original aerosol extinction profiles used Rayleigh extinction profiles calculated from local soundings. Rayleigh and aerosol slant transmission corrections are applied using the MODTRAN code in transmission mode. Also, a best-estimate aerosol phase function was calculated from observations and applied to the entire column. The tropospheric aerosol phase function from an AERONET station in the vicinity of the searchlight location was applied between 2.76 to 11.7 km. The stratospheric phase function, applied for a 12.2 to 35.2 km altitude range, is calculated from particle-size distributions measured by a high-altitude aircraft in the vicinity of the searchlight in early 1964. The original error estimate was updated considering unaccounted errors. Both the re-calibrated aerosol extinction profiles and the re-calibrated stratospheric aerosol optical depth magnitudes showed higher magnitudes than the original aerosol extinction profiles and the original stratospheric aerosol optical depth, respectively. However, the magnitudes of the re-calibrated variables show a reasonable agreement with other contemporary observations. The re-calibrated stratospheric aerosol optical depth demonstrated its consistency with the tropics-to-pole decreasing trend, associated with the major volcanic eruption stratospheric aerosol pattern when compared to the time-coincident stratospheric aerosol optical depth lidar observations at Lexington at 42° N.
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(This article belongs to the Special Issue Ozone in Stratosphere and Its Relation to Stratospheric Dynamics)
Open AccessArticle
Long-Term and Seasonal Changes in Emission Sources of Atmospheric Particulate-Bound Pyrene and 1-Nitropyrene in Four Selected Cities in the Western Pacific
by
Kazuichi Hayakawa
Atmosphere 2024, 15(6), 634; https://doi.org/10.3390/atmos15060634 - 24 May 2024
Abstract
Abstract: Estimating the source contribution to polycyclic aromatic hydrocarbons (PAHs) and nitropolycyclic aromatic hydrocarbons (NPAHs) in the atmosphere is necessary for developing effective disease control and pollution control measures. The NPAH-PAH combination method (NP method) was used to elucidate the contributions of
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Abstract: Estimating the source contribution to polycyclic aromatic hydrocarbons (PAHs) and nitropolycyclic aromatic hydrocarbons (NPAHs) in the atmosphere is necessary for developing effective disease control and pollution control measures. The NPAH-PAH combination method (NP method) was used to elucidate the contributions of vehicles and coal/biomass combustion to seasonal and long-term urban atmospheric particulate matter (PM)-bound Pyr and 1-NP concentrations in Kanazawa, Kitakyushu, Shenyang and Shanghai in the Western Pacific region from 1997 to 2021. Among the four cities, Kanazawa demonstrated the lowest Pyr concentration. The contribution of vehicles to Pyr before and after 2010 was 35% and 5%, respectively. The 1-NP concentration was reduced by a factor of more than 1/10. These changes can be attributed to the emission control from vehicles. Kitakyushu revealed the second-lowest Pyr and the lowest 1-NP concentrations. Coal combustion was found to be the main contributor to Pyr, while its contribution to 1-NP increased from 9% to 19%. The large contribution of coal combustion is attributed to iron manufacturers. Shenyang demonstrated the highest atmospheric Pyr concentration with its largest seasonal change. Vehicles are the largest contributors to 1-NP. However, coal combustion, including winter coal heating, contributed 97% or more to Pyr and more than 14% to 1-NP. Shanghai revealed the second-highest Pyr and 1-NP concentrations, but the former was substantially lower than that in Shenyang. Coal combustion was the major contributor, but the contribution of vehicles to Pyr was larger before 2010, which was similar to Kanazawa.
Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
Open AccessArticle
Investigating Stagnant Air Conditions in Almaty: A WRF Modeling Approach
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Tatyana Dedova, Larissa Balakay, Edige Zakarin, Kairat Bostanbekov and Galymzhan Abdimanap
Atmosphere 2024, 15(6), 633; https://doi.org/10.3390/atmos15060633 - 24 May 2024
Abstract
This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed
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This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.
Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Open AccessArticle
Estimation and Compensation of the Ionospheric Path Delay Phase in PALSAR-3 and NISAR-L Interferograms
by
Urs Wegmüller, Charles Werner, Othmar Frey and Christophe Magnard
Atmosphere 2024, 15(6), 632; https://doi.org/10.3390/atmos15060632 - 24 May 2024
Abstract
Spatial and temporal variation in the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. In this work, the identification, estimation, and compensation of ionospheric path delay phases in PALSAR-3 and NISAR-L interferograms are discussed. Both of
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Spatial and temporal variation in the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. In this work, the identification, estimation, and compensation of ionospheric path delay phases in PALSAR-3 and NISAR-L interferograms are discussed. Both of these L-band sensors simultaneously acquire SAR data in a main spectral band and in an additional, spectrally separated, narrower second band to support the mitigation of ionospheric path delays. The methods presented permit separating the dispersive and the non-dispersive phase terms based on the double-difference interferogram between the two available spectral bands and the differential interferogram of the main band. The applicability of the proposed methods is demonstrated using PALSAR-3-like data that were simulated based on PALSAR-2 SM1 mode data.
Full article
(This article belongs to the Special Issue Ionospheric Irregularity)
Open AccessArticle
Deep-Learning Correction Methods for Weather Research and Forecasting (WRF) Model Precipitation Forecasting: A Case Study over Zhengzhou, China
by
Jianbin Zhang, Zhiqiu Gao and Yubin Li
Atmosphere 2024, 15(6), 631; https://doi.org/10.3390/atmos15060631 - 24 May 2024
Abstract
Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle the complex features of hourly precipitation, resulting in the
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Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle the complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. In this study, we proposed a deep-learning model called PBT (Population-Based Training)-GRU (Gate Recurrent Unit) based on numerical model NWP gridded forecast data and observation data and employed machine-learning (ML) methods, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gradient-Boosted Decision Tree (GBDT), to correct the WRF hourly precipitation forecasts. To select the evaluation method, we conducted a sample balance experiment and found that when the proportion of positive and negative samples was 1:1, the Threat Score (TS) and accuracy scores were the highest, while the Probability of Detection (POD) score was slightly lower. The results showed that: (1) the overall errors of the PBT-GRU model were relatively smaller, and its root mean square error (RMSE) was only 1.12 mm, which was reduced by 63.04%, 51.72%, 58.36%, 37.43%, and 26.32% compared to the RMSE of WRF, SVM, KNN, GBDT, and RF, respectively; and (2) according to the Taylor diagram, the standard deviation ( ) and correlation coefficient (r) of PBT-GRU were 1.02 and 0.99, respectively, while the and r of RF were 1.12 and 0.98, respectively. Furthermore, the and r of the SVM, GBDT, and KNN models were between those of the above models, with values of 1.24 and 0.95, 1.15 and 0.97, and 1.26 and 0.93, respectively. Based on a comprehensive analysis of the TS, accuracy, RMSE, r and , the PBT-GRU model performed the best, with a significantly better correction effect than that of the ML methods, resulting in an overall performance ranking of PBT-GRU > RF > GBDT > SVM > KNN. This study provides a hint of the possibility that the proposed PBT-GRU model can outperform model precipitation correction based on a small sample of one-station data. Thus, due to its promising performance and excellent robustness, we recommend adopting the proposed PBT-GRU model for precipitation correction in business applications.
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(This article belongs to the Special Issue Deep Learning Algorithms for Weather Forecasting and Climate Prediction)
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Open AccessArticle
Unveiling Trends and Hotspots in Air Pollution Control: A Bibliometric Analysis
by
Jing Chen, Qinghai Chen, Lin Hu, Tingting Yang, Chuangjian Yi and Yingtang Zhou
Atmosphere 2024, 15(6), 630; https://doi.org/10.3390/atmos15060630 - 24 May 2024
Abstract
With the continuous acceleration of urbanization, air pollution has become an increasingly serious threat to public health. Strengthening the detection and control of pollutants has become a focal point in current society. In light of the increasing amount of literature in the field
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With the continuous acceleration of urbanization, air pollution has become an increasingly serious threat to public health. Strengthening the detection and control of pollutants has become a focal point in current society. In light of the increasing amount of literature in the field of air pollution control with every passing year, numerous reviews have been compiled; however, only a limited number employ bibliometric methods to comprehensively review and summarize research trends in this field. Herein, this study utilizes two bibliometric analysis tools, namely, CiteSpace (6.1.R6) and VOSviewer (1.6.20), to conduct a visual and comprehensive analysis of air pollution literature spanning 2000 to 2023. By doing so, it establishes a knowledge framework for research on air pollution control. Simultaneously, collaborative network analysis, reference co-citation network analysis, keyword co-occurrence network analysis, and keyword prominence are employed to undertake an exhaustive and profound visual examination within this domain. Results indicate that, over time, the number of relevant papers has exponentially increased, while interdisciplinary cooperation trends have gradually formed. Additionally, this study describes key areas of current research, including air pollution control residue treatment, regional joint air pollution control, and air pollution control mechanism analysis. Finally, challenges faced by researchers in this field and their different perspectives are discussed. To better integrate research findings on air pollution control, we explore the correlations among data and systematically present their developmental trends. This confirms the interdisciplinary nature of air pollution control research, in the hope of its guiding air pollution control in the future.
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(This article belongs to the Special Issue Air Pollution Control in China: Progress, Challenges, and Perspectives (2nd Edition))
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Open AccessArticle
Characteristics of Atmospheric Ice Nucleation during Spring: A Case Study on Huangshan
by
Kui Chen, Xinhan Chen, Shichao Zhu, Lei Ji and Yan Yin
Atmosphere 2024, 15(6), 629; https://doi.org/10.3390/atmos15060629 - 24 May 2024
Abstract
Atmospheric ice nucleation particles (INPs) play a crucial role in influencing cloud formation and microphysical properties, which in turn impact precipitation and Earth’s radiation budget. However, the influence of anthropogenic activities on the properties and concentrations of INPs remains an area of significant
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Atmospheric ice nucleation particles (INPs) play a crucial role in influencing cloud formation and microphysical properties, which in turn impact precipitation and Earth’s radiation budget. However, the influence of anthropogenic activities on the properties and concentrations of INPs remains an area of significant uncertainty. This study investigated the physical and chemical characteristics of atmospheric ice nucleation particles in Huangshan, China during the May Day labor holiday period (spanning 8 days, from April 27th to May 5th). INP concentrations were measured at temperatures from −17 °C to −26 °C and relative humidities (RHw) from 95% to 101%. Average INP concentrations reached 13.7 L−1 at −26 °C and 101% RH, 137 times higher than at −17 °C and 95% RH. INP concentrations showed exponential increases with decreasing temperature and exponential increases with increasing RH. Concentration fluctuations were observed over time, with a peak of ~30 L−1 (t = −26 °C, RHw = 101%) around the start and end of the holiday period. Aerosol number concentrations were monitored simultaneously. The peak in aerosols larger than 0.5 μm aligned with the peak in INP concentrations, suggesting a link between aerosol levels and INPs. Chemical composition analysis using SEM–EDX revealed the distinct elemental makeup of INPs based on the activation temperature. INPs active at warmer temperatures contained N, Na, and Cl, indicating possible biomass and sea salt origins, while those active at colder temperatures contained crustal elements like Al and Ca.
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(This article belongs to the Special Issue Atmospheric Ice Nucleating Particles, Cloud and Precipitation)
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Open AccessArticle
Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting
by
Tianpeng Zhang, Donghai Wang, Lindong Huang, Yihao Chen and Enguang Li
Atmosphere 2024, 15(6), 628; https://doi.org/10.3390/atmos15060628 - 24 May 2024
Abstract
Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden
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Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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