Atmosphere

10 pages, 3418 KiB

Open AccessArticle

Off-Beam Acoustic Micro-Resonator for QEPAS Sensor with a Custom Quartz Tuning Fork

by Yong Wang, Gang Wang, Jiapeng Wang, Chaofan Feng, Qingyuan Tian, Yifan Chen, Ruyue Cui, Hongpeng Wu and Lei Dong

Atmosphere 2025, 16(3), 352; https://doi.org/10.3390/atmos16030352 - 20 Mar 2025

Viewed by 256

Abstract

Quartz-enhanced photoacoustic spectroscopy (QEPAS) has shown great promise for monitoring greenhouse gases and pollutants with a high measurement accuracy and limit of detection. A QEPAS sensor, which can achieve high photoacoustic signal gain without requiring the laser beam to pass through the two [...] Read more.

Quartz-enhanced photoacoustic spectroscopy (QEPAS) has shown great promise for monitoring greenhouse gases and pollutants with a high measurement accuracy and limit of detection. A QEPAS sensor, which can achieve high photoacoustic signal gain without requiring the laser beam to pass through the two prongs of a quartz tuning fork (QTF), is reported. A custom QTF with a resonant frequency of 7.2 kHz and a quality factor of 8406 was employed as a sound detection element, and the parameters of the acoustic micro-resonator (AmR) in the off-beam QEPAS spectrophone were optimized. A signal-to-noise ratio (SNR) gain of 16 was achieved based on the optimal AmR dimensions compared to the bare custom QTF. Water vapor (H₂O) was detected utilizing the QEPAS sensor equipped with the off-beam spectrophone, achieving a minimum detection limit (MDL) of 4 ppm with a normalized noise equivalent absorption coefficient (NNEA) of 5.7 × 10⁻⁸ cm⁻¹·W·Hz^−1/2 at an integration time of 300 ms. Full article

(This article belongs to the Special Issue New Insights into Photoacoustic Spectroscopy and Its Applications)

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24 pages, 6190 KiB

Open AccessArticle

Calibration of Upper Air Water Vapour Profiles Using the IPRAL Raman Lidar and ERA5 Model Results and Comparison to GRUAN Radiosonde Observations

by Dunya Alraddawi, Philippe Keckhut, Florian Mandija, Alain Sarkissian, Christophe Pietras, Jean-Charles Dupont, Antoine Farah, Alain Hauchecorne and Jacques Porteneuve

Atmosphere 2025, 16(3), 351; https://doi.org/10.3390/atmos16030351 - 20 Mar 2025

Viewed by 283

Abstract

Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant [...] Read more.

Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant altitudes. Raman signals are screened on hourly bases, and a universal calibration method, independent of acquisition mode, is proposed towards operational Lidar water vapour profiles, using co-located ERA5 data. Calibration factors are derived from comparisons between 4 and 6 km, and nightly coefficients determined from hourly factors. Instrumental stability is monitored through the temporal evolution of calibration factors, and stable-period medians are adopted as final values. The uncertainty of calibrated WVMR profiles is assessed by comparison with GRUAN processed Meteomodem M10 radiosondes and ERA5 data. Results show a high agreement (>90%), with IPRAL exhibiting a small negative bias (~10%) below 8 km, reducing to ~5% up to 10.5 km to radiosondes. ERA5 systematically underestimates water vapour at cruise altitudes, with a dry bias increasing from 10% at 9 km to >20% at 11 km. Recent IAGOS corrections to ERA5, improving supersaturation representation, are validated over Paris. This calibrated Lidar data set supports improved atmospheric modelling and contributes to future air traffic management strategies. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing (2nd Edition))

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25 pages, 812 KiB

Open AccessReview

Simulating the Fate of Dimethyl Sulfide (DMS) in the Atmosphere: A Review of Emission and Chemical Parameterizations

by Ernesto Pino-Cortés, Mariela Martínez, Katherine Gómez, Fernando González Taboada, Joshua S. Fu, Golam Sarwar, Rafael P. Fernandez, Sankirna D. Joge, Anoop S. Mahajan and Juan Höfer

Atmosphere 2025, 16(3), 350; https://doi.org/10.3390/atmos16030350 - 20 Mar 2025

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Abstract

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of [...] Read more.

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of the troposphere. Here, we review recent numerical modeling studies that have included DMS emissions, their atmospheric oxidation mechanism, and their subsequent impacts on air quality at regional and global scales. In addition, we discuss the available methods for estimating sea–air DMS fluxes, including parameterizations and climatological datasets, as well as their integration into air quality models. At the regional level, modeling studies focus on the Northern Hemisphere, presenting a large gap in Antarctica, Africa, and the Atlantic coast of South America, whereas at the global scale, modeling studies tend to focus more on polar regions, especially the Arctic. Future studies must consider updated climatologies and parameterizations for more realistic results and the reduction in biases in numerical simulations analysis. Full article

(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment (2nd Edition))

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13 pages, 4154 KiB

Open AccessArticle

A Comparative Study on the Methods of Predictor Extraction from Global Sea Surface Temperature Fields for Statistical Climate Forecast System

by Yawei Cai and Xiangjun Shi

Atmosphere 2025, 16(3), 349; https://doi.org/10.3390/atmos16030349 - 20 Mar 2025

Viewed by 174

Abstract

Statistical climate forecast systems typically do not use preceding global gridded sea surface temperature (SST) data directly; instead, they extract a single predictor (e.g., the Niño3.4 index) or multiple predictors (e.g., time series of several SST spatial modes). In this study, four different [...] Read more.

Statistical climate forecast systems typically do not use preceding global gridded sea surface temperature (SST) data directly; instead, they extract a single predictor (e.g., the Niño3.4 index) or multiple predictors (e.g., time series of several SST spatial modes). In this study, four different SST predictor extracting methods (one single-predictor method and three multiple-predictor methods) are comparatively analyzed within the same climate forecast platform incorporating either the linear regression (LR) model or the neural network (NN) forecast model. Rolling forecast experiments with the LR model show that, compared to a single strong SST predictor, only multiple predictors with more high-quality information (high signal-to-noise ratio) could improve the forecast skill. Sensitivity experiments also show that the influence of multiple-predictor extracting methods on forecast skill from the NN model is much weaker than that from the LR model. Moreover, whether or not multiple SST predictors are orthogonal might also affect the forecast skill. The above analyses provide a reference for establishing statistical climate forecast system based on preceding SST data. Full article

(This article belongs to the Special Issue Extreme Climate Events: Causes, Risk and Adaptation)

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19 pages, 1743 KiB

Open AccessEditor’s ChoiceReview

Some Recent Key Aspects of the DC Global Electric Circuit

by Michael J. Rycroft

Atmosphere 2025, 16(3), 348; https://doi.org/10.3390/atmos16030348 - 20 Mar 2025

Viewed by 539

Abstract

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect [...] Read more.

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect to the Earth’s surface. The circuit is formed by the current I, flowing through the ionosphere all around the world, down through the atmosphere remote from the current sources (J ~2 pA/m² through a resistance R ~250 Ω), through the land and sea surface, and up to the thunderstorms as point discharge currents. This maintains a downward electric field E of magnitude ~130 V/m at the Earth’s surface away from thunderstorms and a charge Q ~−6.10⁵ C on the Earth’s surface. The theoretical modelling of ionospheric currents and the miniscule geomagnetic field perturbations (ΔB ~0.1 nT) which they cause, as derived by Denisenko and colleagues in recent years, are reviewed. The time constant of the GEC, τ = RC, where C is the capacitance of the global circuit capacitor, is estimated via three different methods to be ~7 to 12 min. The influence of stratus clouds in determining the value of τ is shown to be significant. Sudden excitations of the GEC by volcanic lightning in Iceland in 2011 and near the Tonga eruption in 2022 enable τ to be determined, from experimental observations, as ~10 min and 8 min, respectively. It has been suggested that seismic activity, or earthquake precursors, could produce large enough electric fields in the ionosphere to cause detectable effects, either by enhanced radon emission or by enhanced thermal emission from the earthquake region; a review of the quantitative estimates of these mechanisms shows that they are unlikely to produce sufficiently large effects to be detectable. Finally, some possible links between the topics discussed and human health are considered briefly. Full article

(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))

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17 pages, 5745 KiB

Open AccessArticle

The Impact of Climate Change on the Functioning of Drainage Systems in Industrial Areas—A Case Study

by Katarzyna Wartalska, Szymon Szymczewski, Weronika Domalewska, Marcin Wdowikowski, Kornelia Przestrzelska, Andrzej Kotowski and Bartosz Kaźmierczak

Atmosphere 2025, 16(3), 347; https://doi.org/10.3390/atmos16030347 - 20 Mar 2025

Viewed by 306

Abstract

Stormwater drainage from urbanised areas has gained importance due to progressing land surface sealing and climate change. More frequent extreme rainfall events lead to overloaded drainage systems and flash floods, particularly in industrial zones experiencing rapid development. The study analysed the sewage system [...] Read more.

Stormwater drainage from urbanised areas has gained importance due to progressing land surface sealing and climate change. More frequent extreme rainfall events lead to overloaded drainage systems and flash floods, particularly in industrial zones experiencing rapid development. The study analysed the sewage system operation in the Special Economic Zone (SEZ) in Lower Silesia, Poland to assess the impact of climate-induced rainfall changes. Three rainfall scenarios were used: model rainfall using historic rainfall intensities, model rainfall using actual intensities, and real precipitation recorded in June 2022. Findings indicate that climate change has negatively affected the stormwater drainage system, resulting in increased overloads and flooding. Particularly, the II scenario showed a significant rise in rainwater inflow to retention reservoirs by 53.1% for ZR-1 and 44.5% for ZR-2 (compared to the I scenario). To address these issues, adaptations are needed for increased rainwater flows, including additional retention facilities, blue–green infrastructure, or rainwater harvesting for the SEZ needs. Full article

(This article belongs to the Special Issue The Water Cycle and Climate Change (3rd Edition))

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21 pages, 11120 KiB

Open AccessArticle

Spatiotemporal Analysis of NH₃ Emission Sources and Their Relation to Land Use Types in the Eastern German Lowlands

by Christian Saravia and Katja Trachte

Atmosphere 2025, 16(3), 346; https://doi.org/10.3390/atmos16030346 - 20 Mar 2025

Viewed by 312

Abstract

Ammonia (NH₃) emissions, which are key precursors of fine particulate matter, pose significant environmental challenges. This study investigated the spatiotemporal variations in NH₃ emissions across the eastern German lowlands from 2013 to 2022 using IASI-B satellite data. Five major Land [...] Read more.

Ammonia (NH₃) emissions, which are key precursors of fine particulate matter, pose significant environmental challenges. This study investigated the spatiotemporal variations in NH₃ emissions across the eastern German lowlands from 2013 to 2022 using IASI-B satellite data. Five major Land Cover Classes (LCC) –tree, grassland, cropland, built-up areas, and water bodies– were analyzed. The results showed distinct diurnal variations, with nighttime NH₃ concentrations exceeding 2.0 × 10¹⁶ molecules cm⁻² in the peak months. Seasonal patterns indicated significant emissions in March (1.2 × 10¹⁶ molecules cm⁻²), April (1.1 × 10¹⁶ molecules cm⁻²), and August (9.6 × 10¹⁵ molecules cm⁻²), while the lowest concentrations occurred in September (0.6 × 10¹⁵ molecules cm⁻²). Persistent hotspots were identified in the northwestern region, where emissions peaked in spring (1.8 × 10¹⁶ molecules cm⁻²⁾ and summer (1.3 × 10¹⁶ molecules cm⁻²), primarily due to agricultural activities. Over the study period, the annual NH₃ concentration peaked in 2015, 2018, and 2022. Using k-means clustering, three distinct emission zones were identified, with Cluster 3 showing the highest NH₃ emission values, particularly in urban centers, and agricultural zones were identified, covering less than 20% of the study area, where cropland predominates (8%). Meteorological factors significantly influenced NH₃ levels, with negative correlations obtained for precipitation, wind speed, and evaporation, while solar radiation, boundary layer height, and instantaneous moisture fluxes showed positive correlations. A case study from March 2022, employing the HYSPLIT trajectory model, confirmed that agricultural practices are the dominant NH₃ source, with emissions reaching 3.2 × 10¹⁶ molecules cm⁻² in hotspot regions. Full article

(This article belongs to the Special Issue Ammonia Emissions and Particulate Matter (2nd Edition))

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19 pages, 4555 KiB

Open AccessArticle

An Intelligent Decision-Making for Electromagnetic Spectrum Allocation Method Based on the Monte Carlo Counterfactual Regret Minimization Algorithm in Complex Environments

by Guoqin Kang, Ming Tan, Xiaojun Zou, Xuguang Xu, Lixun Han and Hainan Du

Atmosphere 2025, 16(3), 345; https://doi.org/10.3390/atmos16030345 - 20 Mar 2025

Viewed by 263

Abstract

In modern communication, the electromagnetic spectrum serves as the carrier for information transmission, and the only medium enabling information exchange anywhere, anytime. To adapt to the changing dynamics of a complex electromagnetic environment, electromagnetic spectrum allocation algorithms must not only meet the demands [...] Read more.

In modern communication, the electromagnetic spectrum serves as the carrier for information transmission, and the only medium enabling information exchange anywhere, anytime. To adapt to the changing dynamics of a complex electromagnetic environment, electromagnetic spectrum allocation algorithms must not only meet the demands for efficiency and intelligence but also possess anti-jamming capabilities to achieve the best communication effect. Focusing on intelligent wireless communication, this paper proposes a multi-agent hybrid game spectrum allocation method under incomplete information and based on the Monte Carlo counter-factual regret minimization algorithm. Specifically, the method first utilizes frequency usage and interference information from both sides to train agents through extensive simulations using the Monte Carlo Method, allowing the trial values to approach the expected values. Based on the results of each trial, the counterfactual regret minimization algorithm is employed to update the frequency selection strategies for both the user and the interferer. Subsequently, the trained agents from both sides engage in countermeasure communication. Finally, the probabilities of successful communication and successful interference for both sides are statistically analyzed. The results show that under the multi-agent hybrid game spectrum allocation method based on the Monte Carlo counter-factual regret minimization algorithm, the probability of successful interference against the user is 32.5%, while the probability of successful interference by the jammer is 37.3%. The average simulation time per round is 3.06 s. This simulation validates the feasibility and effectiveness of the multi-agent hybrid game spectrum allocation module based on the Monte Carlo counter-factual regret minimization algorithm. Full article

(This article belongs to the Special Issue Intelligent Modeling of the Ionosphere and Troposphere for Radio Application (2nd Edition))

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19 pages, 4122 KiB

Open AccessArticle

Aerodynamic and Dry Deposition Effects of Roadside Trees on NO_x Concentration Changes on Roadways and Sidewalks

by Yeon-Uk Kim, Seung-Bok Lee, Chang Hyeok Kim, Seonyeop Lee and Kyung-Hwan Kwak

Atmosphere 2025, 16(3), 344; https://doi.org/10.3390/atmos16030344 - 19 Mar 2025

Viewed by 254

Abstract

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate [...] Read more.

This study analyzes changes in NO_x concentrations due to the aerodynamic and dry deposition effects of roadside trees in the Jongno area, a central business district of Seoul, Republic of Korea, using a computational fluid dynamics (CFD) model. The simulation results indicate that the on-road NO_x concentration was slightly increased (2.09%) due to the aerodynamic effect of roadside trees. However, the dry deposition effect of roadside trees had a greater impact on reducing NO_x concentrations (−2.77%) along sidewalks. It was observed that the reduction in NO_x concentration due to the dry deposition effect of roadside trees was likely to offset the increase in NO_x concentrations due to the aerodynamic effect of roadside trees, resulting in an overall decrease in NO_x concentrations. Furthermore, sensitivity tests showed that the increase in NO_x concentrations due to the aerodynamic effects of roadside trees was intensified along sidewalks when ambient wind speeds were high, while the decrease in NO_x concentration was proportional to the deposition velocity of roadside trees. Therefore, roadside trees should be planted where aerodynamic effects do not significantly increase NO_x concentrations in order to improve near-road air quality. Full article

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

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16 pages, 4124 KiB

Open AccessArticle

An Explanation of the Poleward Mass Flux in the Stratosphere

by Aarnout J. van Delden

Atmosphere 2025, 16(3), 343; https://doi.org/10.3390/atmos16030343 - 18 Mar 2025

Viewed by 181

Abstract

This paper offers a new perspective on the explanation of the poleward mass flux in the stratosphere. This mass flux represents the upper leg of the so-called Brewer–Dobson circulation. This new perspective is based on the following hypothesis. A positive potential vorticity anomaly, [...] Read more.

This paper offers a new perspective on the explanation of the poleward mass flux in the stratosphere. This mass flux represents the upper leg of the so-called Brewer–Dobson circulation. This new perspective is based on the following hypothesis. A positive potential vorticity anomaly, centered over the North Pole, exists in the stratosphere during the winter half-year. This positive potential vorticity anomaly is associated with a negative isentropic density anomaly, which forms due to cross-isentropic downwelling associated with radiative cooling. Isentropic potential vorticity mixing due to breaking planetary waves weakens this potential vorticity anomaly while zonal-mean thermal wind balance is maintained. This requires a weakening of the negative Polar cap isentropic density anomaly, which in turn requires a poleward isentropic mass flux. Support for this hypothesis is found in a case study of a major Sudden Stratospheric Warming event, as an example of intense potential vorticity mixing. It is shown that the stratosphere, both before and after this event, is very close to zonal-mean thermal wind balance, despite the disruptive potential vorticity mixing, while mass is shifted poleward during this event. Solutions of the potential vorticity-inversion equation, which is an expression of thermal wind balance, for zonal-mean potential vorticity distributions before and after the Sudden Stratospheric Warming, demonstrate that mass must shift poleward to maintain zonal-mean thermal wind balance when the positive potential vorticity anomaly is eliminated by mixing. This perspective on the reasons for the poleward stratospheric mass flux also explains the observed isobaric warming as well as the Polar cap zonal-mean zonal wind reversal during a major Sudden Stratospheric Warming. Full article

(This article belongs to the Special Issue The 15th Anniversary of Atmosphere)

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19 pages, 7516 KiB

Open AccessArticle

An Investigation of Benzene, Toluene, Ethylbenzene, m,p-xylene; Biogenic Volatile Organic Compounds; and Carbonyl Compounds in Chiang Mai’s Atmosphere and Estimation of Their Emission Sources During the Episode Period

by Da-Hyun Baek, Ye-Bin Seo, Jun-Su Gil, Mee-Hye Lee, Ji-Seon Lee, Gang-Woong Lee, Duangduean Thepnuan, In-Young Choi, Sang-Woo Lee, Trieu-Vuong Dinh and Jo-Chun Kim

Atmosphere 2025, 16(3), 342; https://doi.org/10.3390/atmos16030342 - 18 Mar 2025

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Abstract

Air pollution in Chiang Mai during the dry winter season is extremely severe. During this period, high levels of fine particles are primarily generated by open biomass burning in Thailand and neighboring countries. In this study, ambient VOC(Volatile Organic Compounds) samples were collected [...] Read more.

Air pollution in Chiang Mai during the dry winter season is extremely severe. During this period, high levels of fine particles are primarily generated by open biomass burning in Thailand and neighboring countries. In this study, ambient VOC(Volatile Organic Compounds) samples were collected using an adsorbent tube from 13 March to 26 March 2024, with careful consideration of sampling uncertainties to ensure data reliability. Furthermore, while interannual variability exists, the findings reflect atmospheric conditions during this specific period, allowing for an in-depth VOC assessment. A comprehensive approach to VOCs was undertaken, including benzene, toluene, ethylbenzene, m,p-xylene (BTEX); biogenic volatile organic compounds (BVOCs); and carbonyl compounds. Regression analysis was performed to analyze the correlation between isoprene concentrations and wind direction. The results showed a significant variation in isoprene levels, indicating their high concentrations due to biomass burning originating from northern areas of Chiang Mai. The emission sources of BTEX and carbonyl compounds were inferred through their ratio analysis. Additionally, correlation analyses between PM_2.5, BTEX, and carbonyl compounds were conducted to identify common emission pathways. The ratio of BTEX among compounds suggested that long-range pollutant transport contributed more significantly than local traffic emissions. Carbonyl compounds were higher during the episode period, which was likely due to local photochemical reactions and biological contributions. Previous studies in Chiang Mai have primarily focused on PM_2.5, whereas this study examined individual VOC species, their temporal trends, and their interrelationships to identify emission sources. Full article

(This article belongs to the Section Air Quality)

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17 pages, 4458 KiB

Open AccessArticle

Study on the Three-Dimensional Evolution of Ionospheric Disturbances in China During the Geomagnetic Storm on December 1, 2023

by Yifei Yang, Jian Kong, Xiangping Chen, Congcong Ling, Changzeng Tang, Yibin Yao and Zhaorong Zhu

Atmosphere 2025, 16(3), 341; https://doi.org/10.3390/atmos16030341 - 18 Mar 2025

Viewed by 218

Abstract

On 1 December 2023, a strong geomagnetic storm was triggered by an interplanetary shock caused by a coronal mass ejection (CME). This study used data from 193 Global Navigation Satellite System (GNSS) observation stations in China to study the three-dimensional morphological total electron [...] Read more.

On 1 December 2023, a strong geomagnetic storm was triggered by an interplanetary shock caused by a coronal mass ejection (CME). This study used data from 193 Global Navigation Satellite System (GNSS) observation stations in China to study the three-dimensional morphological total electron content (TEC) disturbances during this storm. By analyzing GNSS TEC data from 15 GNSS stations along the magnetic field lines, it was found that TEC disturbances spread from low to high latitudes, confirmed by ionosonde NmF2 data. The TEC disturbance first appeared at the LJHP station, (21.68° N) at 11:30 UT and propagated to the BJFS station (39.60° N) at 13:30 UT with a propagation speed of about 217 m/s and maximum amplitude of ±0.2 m. The TEC disturbance lasted the longest, approximately 4 h, between latitudes 25° N and 32° N. Additionally, this study investigated the ionosphere’s three-dimensional electron density distribution in the Guangxi region using an ionospheric tomography algorithm. Results showed that the TEC disturbances were mainly concentrated between 450 and 580 km in altitude. At 12:00 UT, the maximum change in electron density occurred at a 580 km height at 26° N, 112° E, increasing by 20.54 total electron content unit (TECU). During the main phase of the geomagnetic storm, the electron density expanded from higher to lower layers, while during the recovery phase, it recovered from the lower layers to the higher layers. Full article

(This article belongs to the Section Planetary Atmospheres)

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10 pages, 1337 KiB

Open AccessArticle

Degradation Kinetics of Common Odorants Emitted from WWTPs: A Methodological Approach for Estimating Half-Life Through Reactions with Hydroxyl Radicals

by Marouane Dhia Eddine Bouguerra, Bartłomiej Witkowski, Tomasz Gierczak and Radosław J. Barczak

Atmosphere 2025, 16(3), 340; https://doi.org/10.3390/atmos16030340 - 18 Mar 2025

Viewed by 215

Abstract

In contemporary times, wastewater treatment plants (WWTPs) were recognized as substantial sources of odorous emissions, potentially impacting nearby communities’ sensory experience. This study investigates the half-lives (T½) of odorous compounds emitted from WWTPs and their degradation due to atmospheric hydroxyl radicals (•OH) in [...] Read more.

In contemporary times, wastewater treatment plants (WWTPs) were recognized as substantial sources of odorous emissions, potentially impacting nearby communities’ sensory experience. This study investigates the half-lives (T½) of odorous compounds emitted from WWTPs and their degradation due to atmospheric hydroxyl radicals (•OH) in different environmental settings. The calculated half-lives of specific odorants in rural areas ranged from 31.36 min to 517.33 days, in urban areas from 42.50 min to 1550 days, and in the marine boundary layer from 42.50 min to 129,861 days. These results show that compounds with high reactivity and short T½, such as methanethiol and ethanethiol, degrade rapidly and are less likely to contribute to long-term odor nuisances. In contrast, compounds with longer half-lives, such as carbonyl sulfide and ammonia, persist longer in the atmosphere, with higher potential for sustained odor issues. The findings suggest that •OH plays a significant role in degrading odorous compounds. These insights into odorant–oxidant kinetics may aid in predicting atmospheric half-lives and their contribution to secondary aerosol formation, thus informing regulatory and mitigation strategies to improve air quality. Full article

(This article belongs to the Special Issue Advances in Air Quality Monitoring and Climate Change Mitigation Strategies)

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22 pages, 4257 KiB

Open AccessEditor’s ChoiceArticle

Impacts of Low-Carbon Policies on Air Quality in China’s Metropolitan Areas: Evidence from a Difference-in-Differences Study

by Xuejiao Niu and Ying Liu

Atmosphere 2025, 16(3), 339; https://doi.org/10.3390/atmos16030339 - 17 Mar 2025

Viewed by 233

Abstract

Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon [...] Read more.

Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon policies can effectively achieve significant reductions in air pollutant levels remains uncertain. In China, the implementation of the low-carbon city pilot (LCCP) policy has reduced carbon emissions, but further research is needed to examine its effectiveness regarding achieving air quality co-benefits. Adopting a difference-in-differences model with a 19-year national database of air quality, this study examines whether the LCCP policy improves air quality in China’s metropolitan areas and explores how these policy initiatives address their air pollution challenges. The results indicate that, following the implementation of the LCCP policy, the mean, maximum, and standard deviation of the AQI in pilot cities decreased significantly by 9.3%, 20.8%, and 19.8%, respectively, compared to non-pilot cities. These results suggest that the LCCP policy significantly improves air quality and provide evidence that this improvement is facilitated by advancements in green technology, industrial restructuring, and the optimization of urban planning and landscape design. Full article

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

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21 pages, 33938 KiB

Open AccessArticle

Enhancing Kármán Vortex Street Detection via Auxiliary Networks Incorporating Key Atmospheric Parameters

by Yihan Zhang, Zhi Zhang, Qiao Su, Chaoyue Wu, Yuqi Zhang and Daoyi Chen

Atmosphere 2025, 16(3), 338; https://doi.org/10.3390/atmos16030338 - 17 Mar 2025

Viewed by 205

Abstract

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, [...] Read more.

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, which collectively dictate their formation conditions, spatial arrangement, and dynamic behavior. Although deep learning methodologies have advanced the automated detection of Kármán vortex streets in remote sensing imagery, existing approaches largely emphasize visual feature extraction without adequately incorporating critical atmospheric variables. To overcome this limitation, this study presents an innovative auxiliary network framework that integrates essential atmospheric physical parameters to bolster the detection performance of Kármán vortex streets. Utilizing reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5), representative atmospheric features are extracted and subjected to feature permutation importance (PFI) analysis to quantitatively evaluate the influence of each parameter on the detection task. This analysis identifies five pivotal variables: geopotential, specific humidity, temperature, horizontal wind speed, and vertical air velocity, which are subsequently employed as inputs for the auxiliary task. Building upon the YOLOv8s object detection model, the proposed auxiliary network systematically examines the impact of various atmospheric variable combinations on detection efficacy. Experimental results demonstrate that the integration of horizontal wind speed and vertical air velocity achieves the highest detection metrics (precision of 0.838, recall of 0.797, mAP50 of 0.865, and mAP50-95 of 0.413) in precision-critical scenarios, outperforming traditional image-only detection method (precision of 0.745, recall of 0.745, mAP50 of 0.759, and mAP50-95 of 0.372). The optimized selection of atmospheric parameters markedly improves the detection metrics and reliability of Kármán vortex streets, underscoring the efficacy and practicality of the proposed methodological framework. This advancement paves the way for more robust automated analysis of atmospheric fluid dynamics phenomena. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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30 pages, 7269 KiB

Open AccessArticle

Interaction Between Maximum Entropies of Urban Meteorology and Pollutants: Effects on Relative Humidity and Temperature in the Boundary Layer of a Basin Geomorphology

by Patricio Pacheco, Eduardo Mera, Gustavo Navarro and Steicy Polo

Atmosphere 2025, 16(3), 337; https://doi.org/10.3390/atmos16030337 - 17 Mar 2025

Viewed by 185

Abstract

Using chaos theory, maximum entropies are calculated for 108 time series, each consisting of 28,463 hourly data of urban meteorology and pollutants. The series were measured with standardized and certified instruments (EPA) in six locations at different heights and in three periods (2010/2013, [...] Read more.

Using chaos theory, maximum entropies are calculated for 108 time series, each consisting of 28,463 hourly data of urban meteorology and pollutants. The series were measured with standardized and certified instruments (EPA) in six locations at different heights and in three periods (2010/2013, 2017/2020, and 2019/2022) in a basin geomorphology. Each urban meteorology series corresponds to relative humidity (RH), temperature (T), and wind speed magnitude (WS), and each pollutant series corresponds to 10 µm particulate matter (PM₁₀), 2.5 µm particulate matter (PM_2.5), and carbon monoxide (CO). These pollutants are in the top three places of presence in the studied geomorphology and in incidence in population diseases. From the calculated entropies, a quotient is constructed between the entropies of each of the first two urban meteorology variables (RH and T) and the sum of maximum entropies of the time series of anthropogenic pollutants, demonstrating the gradual decay in time of the quotient that is dominated by the maximum entropies of the pollutants. The latter leads to a more excited and warm boundary layer, due to thermal transfers, which makes it more unpredictable, increasing its capacity to contain water. It is verified that the diffusion is anomalous with alpha < 1 and that the contamination has a high probability, using a heavy-tailed probability function, of causing extreme events by influencing urban meteorology. Full article

(This article belongs to the Section Meteorology)

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12 pages, 4073 KiB

Open AccessArticle

Characteristics of Observed Electromagnetic Wave Ducts in Tropical, Subtropical, and Middle Latitude Locations

by Sandra E. Yuter, McKenzie M. Sevier, Kevin D. Burris and Matthew A. Miller

Atmosphere 2025, 16(3), 336; https://doi.org/10.3390/atmos16030336 - 17 Mar 2025

Viewed by 177

Abstract

Where and at what altitudes electromagnetic wave ducts within the atmosphere are likely to occur is important for a variety of communication and military applications. We examined the modified refractivity profiles and wave duct characteristics derived from nearly 50,000 observed upper air soundings [...] Read more.

Where and at what altitudes electromagnetic wave ducts within the atmosphere are likely to occur is important for a variety of communication and military applications. We examined the modified refractivity profiles and wave duct characteristics derived from nearly 50,000 observed upper air soundings obtained over four years from seven tropical and subtropical islands, as well as middle latitude sites at four US coastal locations, three sites near the Great Lakes, and four US inland sites. Across all location types, elevated ducts were found to be more common than surface-based ducts, and the median duct thicknesses were ~100 m. There was a weak correlation between duct thickness and strength and, essentially, no correlation between the duct strength and duct base height. EM ducts more frequently occurred at the tropical and subtropical island locations (~60%) and middle latitude coastal locations (70%) as compared to the less than 30% of the time that occurred at the Great Lake and US inland sites. The tropical and subtropical island sites were more likely than the other location types to have ducts at altitudes higher than 2 km, which is above the boundary layer height. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling)

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20 pages, 17520 KiB

Open AccessArticle

Border Wars and Climate Change: The Impact on the Evolution of the External Defense System of the Hexi Corridor in the Past 2000 Years

by Xinmin Wang and Bo Tan

Atmosphere 2025, 16(3), 335; https://doi.org/10.3390/atmos16030335 - 16 Mar 2025

Viewed by 171

Abstract

This study establishes a dataset of ancient military defense system sites in the Hexi Corridor area from the Han Dynasty to the Qing Dynasty to analyze the temporal changes and spatial distribution characteristics of the military defense system in different periods. In addition, [...] Read more.

This study establishes a dataset of ancient military defense system sites in the Hexi Corridor area from the Han Dynasty to the Qing Dynasty to analyze the temporal changes and spatial distribution characteristics of the military defense system in different periods. In addition, it compares the climate characteristics of the Hexi Corridor area though the past 2000 years. It also discusses the possible relationship between the construction of the Hexi military defense system and climate change. We found that the Han and Ming Dynasties were the main periods for constructing the regional military defense system. Furthermore, the Wei, Tsin, and Southern and Northern Dynasties expanded the scale based on the previous period. As a result, the spatial distribution was highly concentrated. During this time, multiple cold–dry and warm–humid periods occurred in the region. Moreover, significant climate change coincided with the heyday of building military facilities and the period of frequent warfare. Environmental factors have an impact on the spatial distribution of military sites. Therefore, the northern border war was the direct cause of the construction of the military defense system. However, the transformation of the environment caused by climate change was the fundamental driving force for this process, evolving across different eras. Full article

(This article belongs to the Special Issue Climate Change Over the Tibetan Plateau and Surroundings on Decadal to Sub-Orbital Timescales and Its Driving Mechanisms)

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29 pages, 6449 KiB

Open AccessArticle

Long-Term Spatio-Temporal Analysis, Distribution, and Trends of Dust Events over Iran

by Abbas Ranjbar Saadat Abadi, Nasim Hossein Hamzeh, Dimitris G. Kaskaoutis, Christian Opp and Amin Fazl Kazemi

Atmosphere 2025, 16(3), 334; https://doi.org/10.3390/atmos16030334 - 16 Mar 2025

Cited by 1 | Viewed by 388

Abstract

This study provides a comprehensive evaluation of dust events over Iran, using synoptic data from 286 meteorological stations. The dust events are classified according to synoptic dust codes as suspended dust and others (i.e., blowing dust, dust storms) and based on their intensity [...] Read more.

This study provides a comprehensive evaluation of dust events over Iran, using synoptic data from 286 meteorological stations. The dust events are classified according to synoptic dust codes as suspended dust and others (i.e., blowing dust, dust storms) and based on their intensity with horizontal visibility ≤1, 3, 5, and 10 km. Severe events (visibility ≤ 1 km) of suspended dust (code 06) occurred primarily in the western parts of Iran, while blowing dust events of moderate or severe intensity dominated over the south and eastern Iran, thus revealing a contrasting spatial distribution regarding the type and frequency of dust events. Furthermore, a distinct seasonality is revealed in the number of dust events, since suspended dust maximized in SW Iran from March to July, highly associated with Shamal winds, while blowing dust storms over south and east Iran maximized from April to August. Zabol city, east Iran, and some stations along the coast of the Arabian Sea are highly impacted by this type of dust storm throughout the year. Trend analysis revealed a notable increase in frequency of dust events during the period 1994–2023, particularly in the western part of Iran, mostly attributed to transboundary dust from the Mesopotamian plains. The large increase in dust activity during 1994–2009 was followed by a decrease during the 2010s at many stations, while notable differences were observed in the spatial distribution of the trends in suspended and blowing dust. An inverse correlation between dust events and precipitation anomalies was observed, since years with abnormal precipitation (e.g., 2019; 138% increase) were related to a substantial decrease in dust occurrence. Over an 11-year period, surface dust concentrations exceeded the annual PM₁₀ threshold of 50 µg/m³ on more than 800 days, with maximum concentrations reaching up to 1411 µg/m³. This highlights the urgent need for effective management strategies to mitigate the impacts of dust storms on air quality and public health in Iran. Full article

(This article belongs to the Special Issue Long-Term Dust Transport)

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13 pages, 2749 KiB

Open AccessArticle

CNN-BiLSTM Daily Precipitation Prediction Based on Attention Mechanism

by Longfei Guo, Yunwei Pu and Wenxiang Zhao

Atmosphere 2025, 16(3), 333; https://doi.org/10.3390/atmos16030333 - 15 Mar 2025

Viewed by 344

Abstract

Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear [...] Read more.

Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear and non-stationary characteristics of surface precipitation data, this paper first employs the principal component analysis (PCA) method to extract the principal components of the original data. Given that the convolutional neural network (CNN) is adept at capturing spatial dependencies, bidirectional long short-term memory (Bi-LSTM, a variant of long short-term memory (LSTM)) can capture the long-term dependence of time-series data, and the attention mechanism allows the model to focus on the more important features of the input data. A PCA-CNN-BiLSTM-Attention fusion neural network was constructed. Taking Kunming, China as the study area, the experimental results demonstrate that the Nash efficiency coefficient of the proposed model reaches 0.993, which is 15.3% and 12.6% higher than that of the CNN-LSTM and CNN-BiLSTM models, respectively. This indicates high prediction accuracy and provides an effective and feasible method for daily precipitation prediction. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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17 pages, 10463 KiB

Open AccessArticle

Characteristics of Aerosol Number Concentration Power Spectra and Their Influence on Flux Measurements

by Hao Liu, Renmin Yuan, Bozheng Zhu, Qiang Zhao, Xingyu Zhu, Yuan Liu and Yongchang Li

Atmosphere 2025, 16(3), 332; https://doi.org/10.3390/atmos16030332 - 15 Mar 2025

Viewed by 269

Abstract

In this paper, a water-based aerosol particle counter was used to measure aerosol number concentrations with high temporal resolution at a meteorological tower and on the ground, and the ultrasonic anemometer on the meteorological tower measured the data of the three-dimensional wind speed. [...] Read more.

In this paper, a water-based aerosol particle counter was used to measure aerosol number concentrations with high temporal resolution at a meteorological tower and on the ground, and the ultrasonic anemometer on the meteorological tower measured the data of the three-dimensional wind speed. The power spectrum of the aerosol particle number concentration fluctuation was obtained by using a Fourier transform, and the characteristics of the power spectrum were deeply analyzed. The results show that the aerosol concentration fluctuation power spectrum satisfies the Monin–Obukhov law in the low-frequency (0.02–0.25 Hz) part of the inertial subregion, which is consistent with the characteristics of atmospheric turbulent motion. Significant attenuation occurs in the high-frequency (0.3–5 Hz) range, which is mainly caused by the attenuation of the aerosol concentration by the intake pipe. Using the similarity of the power spectrum in the low-frequency part, using the “−5/3” line as a standard, the characteristic time of the measurement system is obtained by fitting the transfer function. The results show that in the flux measurement experiments in this paper, the characteristic time is usually less than 1 s. Finally, this paper uses the Fourier transform and wavelet transform to correct the high-frequency attenuation in the fluctuation of aerosol concentration and obtains the corrected aerosol flux. The results show that the effect of high-frequency attenuation on the flux is approximately 1–4% in this experiment. Full article

(This article belongs to the Section Aerosols)

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21 pages, 6140 KiB

Open AccessArticle

Wavelet Coherence Analysis of PM₁₀ Variability Due to Changes in Meteorological Factors in the Continental Climate

by Necla Barlik

Atmosphere 2025, 16(3), 331; https://doi.org/10.3390/atmos16030331 - 15 Mar 2025

Viewed by 412

Abstract

The high-altitude region in northeastern Türkiye is known as the Erzurum–Kars Plateau. The Ardahan, Erzurum, and Kars provinces are its most important settlements, established at an altitude of approximately 1800 m on the plateau. In this region, where the continental climate prevails, the [...] Read more.

The high-altitude region in northeastern Türkiye is known as the Erzurum–Kars Plateau. The Ardahan, Erzurum, and Kars provinces are its most important settlements, established at an altitude of approximately 1800 m on the plateau. In this region, where the continental climate prevails, the relationships between the PM₁₀ concentration levels recorded between 2010 and 2022 and meteorological variables were investigated. During the study, the average daily PM₁₀ levels for Ardahan, Erzurum, and Kars in the winter seasons were 73.3, 76.7, and 72.2 µg/m³ respectively. In the same period, the daily average temperature (and humidity) was determined as −6.9 °C (75.0%), −7.1 °C (82.9%), and −6.3 °C (75.7%), respectively, and the average wind speed was determined as 0.9 m/s, 2.2 m/s, and 1.7 m/s, respectively. For these provinces, the highest correlation coefficients between PM₁₀ and temperature (and wind speed) in winter were calculated as −0.47 (−0.36), −0.49 (−0.60), and −0.52 (−0.54), respectively, while the correlation coefficients between PM₁₀ and temperature (and humidity) in summer were calculated as 0.32 (−0.32), 0.39 (−0.35), and 0.55 (−0.48), respectively. In the analysis performed using the wavelet coherence approach, it was possible to determine the relationships between PM₁₀ and meteorological parameters not only in annual cycles, but also in seasonal and even monthly cycles. Full article

(This article belongs to the Section Air Quality)

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20 pages, 2997 KiB

Open AccessEditor’s ChoiceArticle

A Case Study of Ozone Pollution in a Typical Yangtze River Delta City During Typhoon: Identifying Precursors, Assessing Health Risks, and Informing Local Governance

by Mei Wan, Xinglong Pang, Xiaoxia Yang, Kai Xu, Jianting Chen, Yinglong Zhang, Junyue Wu and Yushang Wang

Atmosphere 2025, 16(3), 330; https://doi.org/10.3390/atmos16030330 - 14 Mar 2025

Viewed by 343

Abstract

Ozone (O₃) is a crucial atmospheric component that significantly affects air quality and poses considerable health risks to humans. In the coastal areas of the Yangtze River Delta, typhoons, influenced by the subtropical high-pressure system, can lead to complex ozone pollution [...] Read more.

Ozone (O₃) is a crucial atmospheric component that significantly affects air quality and poses considerable health risks to humans. In the coastal areas of the Yangtze River Delta, typhoons, influenced by the subtropical high-pressure system, can lead to complex ozone pollution situations. This study aimed to explore the causes, sources, and health risks of O₃ pollution during such events. Ground-based data from Jiaxing City’s key ozone precursor (VOCs) composition observations, ERA5 reanalysis data, and models CMAQ-ISAM and PMF were employed. Focusing on the severe ozone pollution event in Jiaxing from 3 to 11 September 2022, the results showed that local ozone production was the main contributor (60.8–81.4%, with an average of 72.3%), while external regional transport was secondary. Concentrations of olefins and aromatic hydrocarbons increased remarkably, playing a vital role in ozone formation. Meteorological conditions, such as reduced cloud cover during typhoon periphery transit, promoted ozone accumulation. By considering the unique respiratory exposure habits of the Chinese population, refined health risk assessments were conducted. Acrolein was found to be the main cause of chronic non-carcinogenic risks (NCRs), with NCR values reaching 1.74 and 2.02 during and after pollution. In lifetime carcinogenic risk (LCR) assessment, the mid-pollution LCR was 1.73 times higher, mainly due to 1,2-dichloroethane and benzene. This study presents a methodology that is readily adaptable to analogous pollution incidents, thereby providing a pragmatic framework to guide actionable local government policy-making aimed at safeguarding public health and mitigating urban ozone pollution. Full article

(This article belongs to the Special Issue Advances in Source Tracing and the Control of Ozone and Its Precursors)

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14 pages, 6009 KiB

Open AccessArticle

The Characteristics of PM_2.5 and O₃ Synergistic Pollution in the Sichuan Basin Urban Agglomeration

by Shaorong Li, Jianhui Guo, Yaqi Wang, Xinyao Lian and Jing Li

Atmosphere 2025, 16(3), 329; https://doi.org/10.3390/atmos16030329 - 13 Mar 2025

Viewed by 348

Abstract

The synergistic pollution of fine particulate matter (PM_2.5) and ozone (O₃) has become one of the major factors affecting ambient air quality. Due to the unique geographical location of the Sichuan Basin, air pollution is more likely to occur. [...] Read more.

The synergistic pollution of fine particulate matter (PM_2.5) and ozone (O₃) has become one of the major factors affecting ambient air quality. Due to the unique geographical location of the Sichuan Basin, air pollution is more likely to occur. To assess the synergistic pollution status of PM_2.5 and O₃ in the Sichuan Basin, this study analyzed time series analysis, correlation analysis, and interaction analysis of PM_2.5 and O₃ based on hourly data from national monitoring stations in the Sichuan Basin from 2015 to 2024. Additionally, the approximate envelope method (AEM) was used to estimate the secondary PM_2.5 concentration. The results showed the following: Chongqing, Zigong, Luzhou, Chengdu, and Deyang experienced severe pollution. From 2015 to 2018, these cities showed high pollution levels. Since 2019, such high levels of pollution have not been observed; during the PM_2.5 pollution period (November to January of the following year), PM_2.5 and O₃-8h exhibited a negative correlation. During the O₃-8 pollution period (May to August), PM_2.5 and O₃-8h showed a positive correlation; secondary PM_2.5 increased with the intensity of photochemical reactions, while the concentration of primary PM_2.5 showed little change compared to secondary PM_2.5. Secondary PM_2.5 concentrations peaked around 8:00–12:00 and reached a trough between 16:00 and 20:00 in all five cities; during the PM_2.5 pollution period, the trend of O₃ in the five cities was consistent. Ozone concentration showed a distinct single-peak daily variation under different PM_2.5 pollution levels. As PM_2.5 concentration increased, the peak O₃ concentration decreased, and the valley concentration became lower. In different seasons, the increase in PM_2.5 concentration can both enhance and suppress the concentration of O₃. The enhanced atmospheric photochemical activity level promotes the formation of secondary components in particles. This achievement can provide a reference for the coordinated control and improvement of air quality in the Sichuan Basin. Full article

(This article belongs to the Section Air Quality)

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18 pages, 7331 KiB

Open AccessArticle

Evaluation of Large Eddy Effects on Land Surface Modeling Based on the FLUXNET Dataset

by Huishan Huang, Lingke Li, Qingche Shi and Shaofeng Liu

Atmosphere 2025, 16(3), 328; https://doi.org/10.3390/atmos16030328 - 13 Mar 2025

Viewed by 320

Abstract

Surface fluxes are vital to understanding land–atmosphere interactions, with similarity theory forming the basis for their parameterization. However, this theory has limitations, particularly due to large eddy effects, which have not been widely considered in Earth system models. A novel scheme was proposed [...] Read more.

Surface fluxes are vital to understanding land–atmosphere interactions, with similarity theory forming the basis for their parameterization. However, this theory has limitations, particularly due to large eddy effects, which have not been widely considered in Earth system models. A novel scheme was proposed to address this, considering large eddy effects under unstable atmospheric conditions. This study systematically evaluates the proposed scheme using the CoLM2014 model, FLUXNET2015 data, and ERA5 data. Based on the analysis of flux parameterization mechanisms, it proposes specific improvements aimed at enhancing the scheme’s performance. Our findings indicate that the proposed and classical schemes yield similar results, partly because they employ the same dimensionless wind speed gradient under near-neutral conditions. Furthermore, the results revealed that friction velocity responded more strongly to large eddies than did heat flux, as friction velocity influenced atmospheric stability and thereby mitigates the large eddy effects on heat flux. Additionally, our analysis reveals that bare soil exhibits the most pronounced changes in surface fluxes and energy partitioning, while grassland-type and forest-type sites display more complex responses. These findings indicate that different land cover types respond distinctly to the influence of large eddies. Overall, this research deepens our understanding of large eddy impacts and improves Earth system modeling by enhancing land–atmosphere interaction parameterization. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

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21 pages, 2607 KiB

Open AccessEditor’s ChoiceArticle

Cross-Examination of Reanalysis Datasets on Elevation-Dependent Climate Change in the Third Pole Region

by Arathi Rameshan, Prashant Singh and Bodo Ahrens

Atmosphere 2025, 16(3), 327; https://doi.org/10.3390/atmos16030327 - 13 Mar 2025

Viewed by 451

Abstract

The scarcity of in situ observation stations and the unreliability of long-term satellite data necessitate the use of reanalysis datasets to study elevation-dependent climate change (EDCC) in the third pole (TP) region. We analyzed elevation-dependent temperature and precipitation patterns over TP using the [...] Read more.

The scarcity of in situ observation stations and the unreliability of long-term satellite data necessitate the use of reanalysis datasets to study elevation-dependent climate change (EDCC) in the third pole (TP) region. We analyzed elevation-dependent temperature and precipitation patterns over TP using the ECMWF Atmospheric Reanalysis Fifth Generation (ERA5), a global reanalysis product with coarse resolution, along with three high-resolution regional reanalysis datasets that cover our study domain: Indian Monsoon Data Assimilation and Analysis (IMDAA), High Asia Refined Analysis—Version 2 (HAR-v2), and Tibetan Plateau Regional Reanalysis (TPRR). Comparing the performance of the four reanalysis datasets in capturing EDCC over TP is crucial, as these datasets provide spatially and temporally consistent data at an optimum resolution that greatly aids EDCC research. Our study results reveal the following: (1) A positive elevation-dependent warming trend is observed across all four datasets in winter and autumn, with varying magnitudes of warming across the datasets. (2) All four datasets exhibit positive elevation-dependent wetting trends in all seasons, except autumn. These are primarily driven by pronounced drying trends at lower elevations and relatively minimal changes in precipitation trends at higher elevations. (3) ERA5 and IMDAA exhibit similar results in capturing elevation-dependent climate change, whereas the TPRR dataset reveals more extreme and unique features in temperature trends compared to the other three datasets. HAR-v2 shows smaller variations in temperature and precipitation trends across different elevations and seasons, in contrast to the other three datasets. While all reanalysis datasets indicate EDCC in the TP, their varying degrees of seasonal and spatial differences underscore the need for a careful evaluation before using them as reference data. Comparison of reanalysis datasets with available observational records, such as in situ measurements and satellite data, over overlapping spatial and temporal domains is essential to assess their quality. This evaluation can help identify the most suitable reanalysis dataset, or combination of datasets, to serve as reliable a reference even in regions or periods without observational data. Full article

(This article belongs to the Section Climatology)

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17 pages, 7826 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Spatial Coverage of Air Quality Monitoring Stations Using Computational Fluid Dynamics

by Giannis Ioannidis, Paul Tremper, Chaofan Li, Till Riedel, Nikolaos Rapkos, Christos Boikos and Leonidas Ntziachristos

Atmosphere 2025, 16(3), 326; https://doi.org/10.3390/atmos16030326 - 12 Mar 2025

Viewed by 500

Abstract

Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant [...] Read more.

Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations. Full article

(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))

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19 pages, 2442 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Impact of Climatic Factors and Air Pollutants on Cardiovascular Mortality in the Eastern Mediterranean Using Machine Learning Models

by Kyriaki Psistaki, Damhan Richardson, Souzana Achilleos, Mark Roantree and Anastasia K. Paschalidou

Atmosphere 2025, 16(3), 325; https://doi.org/10.3390/atmos16030325 - 12 Mar 2025

Cited by 1 | Viewed by 1015

Abstract

Cardiovascular diseases are the most common cause of death worldwide, with atmospheric pollution, and primarily particulate matter, standing out as the most hazardous environmental factor. To explore the exposure–response curves, traditional epidemiological studies rely on generalised additive or linear models and numerous works [...] Read more.

Cardiovascular diseases are the most common cause of death worldwide, with atmospheric pollution, and primarily particulate matter, standing out as the most hazardous environmental factor. To explore the exposure–response curves, traditional epidemiological studies rely on generalised additive or linear models and numerous works have demonstrated the relative risk and the attributable fraction of mortality/morbidity associated with exposure to increased levels of particulate matter. An alternative, probably more effective, procedure to address the above issue is using machine learning models, which are flexible and often outperform traditional methods due to their ability to handle both structured and unstructured data, as well as having the capacity to capture non-linear, complex associations and interactions between multiple variables. This study uses five advanced machine learning techniques to examine the contribution of several climatic factors and air pollutants to cardiovascular mortality in the Eastern Mediterranean region, focusing on Thessaloniki, Greece, and Limassol, Cyprus, covering the periods 1999–2016 and 2005–2019, respectively. Our findings highlight that temperature fluctuations and major air pollutants significantly affect cardiovascular mortality and confirm the higher health impact of temperature and finer particles. The lag analysis performed suggests a delayed effect of temperature and air pollution, showing a temporal delay in health effects following exposure to air pollution and climatic fluctuations, while the seasonal analysis suggests that environmental factors may explain greater variability in cardiovascular mortality during the warm season. Overall, it was concluded that both air quality improvements and adaptive measures to temperature extremes are critical for mitigating cardiovascular risks in the Eastern Mediterranean. Full article

(This article belongs to the Special Issue The Drivers and Impacts of Climate Change Over the Eastern Mediterranean)

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15 pages, 3162 KiB

Open AccessArticle

Elevation Correction of ERA5 Reanalysis Temperature over the Qilian Mountains of China

by Peng Zhao and Lihui Qian

Atmosphere 2025, 16(3), 324; https://doi.org/10.3390/atmos16030324 - 12 Mar 2025

Viewed by 326

Abstract

Air temperature acts as a key indicator of climate change. In regions with high elevations and scarce meteorological stations, reanalysis temperature datasets are vital for estimating temperatures. However, due to the presence of biases in the observational data of these reanalysis datasets, it [...] Read more.

Air temperature acts as a key indicator of climate change. In regions with high elevations and scarce meteorological stations, reanalysis temperature datasets are vital for estimating temperatures. However, due to the presence of biases in the observational data of these reanalysis datasets, it becomes necessary to perform bias correction to augment the accuracy of modeling and prediction. In the present study, a temperature lapse rate model was utilized to correct the ERA5 reanalysis temperature data within the Qilian Mountains (QLMs) in China from 1979 to 2017. The research results show that the constructed temperature lapse rate can effectively reflect the vertical temperature change characteristics in the Qilian Mountains. As the altitude increases, the absolute value of the temperature lapse rate on the northern slope decreases, while the absolute value of the temperature lapse rate on the southern slope increases. The accuracy of the corrected ERA5 temperature data is significantly improved, especially in winter. Among the 17 meteorological stations, 13 stations show a statistically significant improvement in accuracy after correction in winter, accounting for approximately 76.5% of the total stations. This study can provide a reliable data reference for climate research, ecological environment monitoring, and other fields in the Qilian Mountains area. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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21 pages, 8600 KiB

Open AccessArticle

Predictive Model with Machine Learning for Environmental Variables and PM_2.5 in Huachac, Junín, Perú

by Emery Olarte, Jhonatan Gutierrez, Gwayne Roque, Juan J. Soria, Hugo Fernandez, Jackson Edgardo Pérez Carpio and Orlando Poma

Atmosphere 2025, 16(3), 323; https://doi.org/10.3390/atmos16030323 - 12 Mar 2025

Viewed by 464

Abstract

PM_2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM_2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 [...] Read more.

PM_2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM_2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 records from the Huachac, Junin area in Peru were used with regressors of humidity in % and temperature in °C. The focus of this study is PM_2.5AQI and environmental variables. Methods: Exploratory data analysis (EDA) and machine learning predictive models were applied. Results: PM_2.5AQI has high values in winter and spring, with averages of 52.6 and 36.9, respectively, and low values in summer, with a maximum value in September (spring) and a minimum in February (summer). The use of regression models produced precise metrics to choose the best model for the prediction of PM_2.5AQI. Comparison with other research highlights the robustness of the chosen ML models, underlining the potential of ML in PM_2.5AQI. Conclusions: The predictive model found was α = 0.1111111 and a Lambda value λ = 0.150025, represented by PM_2.5AQI = 83.0846522 − 10.302222000 (Humidity) − 0.1268124 (Temperature). The model has an adjusted R² of 0.1483206 and an RMSE of 25.36203, and it allows decision making in the care of the environment. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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