Atmosphere

15 pages, 3006 KiB

Open AccessArticle

A Study of the Relationship Among Radon, Thoron and Radioactive Aerosol Particle Distribution in PM_2.5 Risk Areas in Kanchanaburi Province, Thailand

by Chutima Kranrod, Chanis Rattanapongs, Phachirarat Sola, Arisa Manowan, Ancharee Onjan, Kitkawin Aramrun and Shinji Tokonami

Atmosphere 2024, 15(12), 1439; https://doi.org/10.3390/atmos15121439 (registering DOI) - 29 Nov 2024

Abstract

Tha Maka is the district with the highest incidence of cancer patients in Kanchanaburi province and is classified as a high-risk area for PM_2.5 exposure due to the presence of many sugar factories. Most of the population is in agricultural occupation, leading [...] Read more.

Tha Maka is the district with the highest incidence of cancer patients in Kanchanaburi province and is classified as a high-risk area for PM_2.5 exposure due to the presence of many sugar factories. Most of the population is in agricultural occupation, leading to the annual burning of sugarcane and rice stubble to start new plantings, which is another cause of air pollution. This study aimed to investigate the correlation among radon, thoron, and airborne particles potentially implicated in lung cancer etiology, which focused on monitoring the concentrations of radon, thoron, and their progeny, as well as analyzing the distribution of particle sizes categorized into 10, 2.5, 1, 0.5, and less than 0.5 μm to assess possible health impacts or lung cancer risk factors. The findings indicated that indoor radon concentrations ranged from 13 to 81 Bq m⁻³, with a mean of 26.1 ± 11.9 Bq m⁻³, while indoor thoron concentrations varied from 2 to 52 Bq m⁻³, averaging 15.7 ± 10.8 Bq m⁻³. These levels are below the radiation dose limit recommended by the World Health Organization and the International Commission on Radiological Protection (ICRP). The total annual inhalation dose ranged from 0.44 to 2.02 mSv y⁻¹, which is within the usual limits. The average annual effective doses from attached progeny were 0.83 mSv y⁻¹ for radon and 0.57 mSv y⁻¹ for thoron, both of which are regarded to be low. Consequently, based on all the findings, it may be assumed that radon, thoron, and their progeny may not be the primary contributors to lung cancer in the region. Nonetheless, while the mean value falls below the recommended thresholds established by the ICRP or WHO, it is indisputable that in certain regions, representing roughly 6.6% of the total area, the value surpasses the global average documented by the UNSCEAR. Furthermore, the aerosol particle size predominantly observed was less than 1 μm for radon and 0.5 μm for thoron, which is a significant factor that may influence the incidence of respiratory disorders. Nevertheless, as this study was conducted during the non-burning period, future research must be conducted during the burning season, using supplementary factors to acquire more thorough data. Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

16 pages, 3924 KiB

Open AccessArticle

Assessing the Impacts of Climate Change on Rainfed Maize Production in Burkina Faso, West Africa

by Moussa Waongo, Patrick Laux, Amadou Coulibaly, Souleymane Sy and Harald Kunstmann

Atmosphere 2024, 15(12), 1438; https://doi.org/10.3390/atmos15121438 (registering DOI) - 29 Nov 2024

Abstract

Smallholder rainfed agriculture in West Africa is vital for regional food security and livelihoods, yet it remains highly vulnerable to climate change. Persistently low crop yields, driven by high rainfall variability and frequent climate hazards, highlight the urgent need for evidence-based adaptation strategies. [...] Read more.

Smallholder rainfed agriculture in West Africa is vital for regional food security and livelihoods, yet it remains highly vulnerable to climate change. Persistently low crop yields, driven by high rainfall variability and frequent climate hazards, highlight the urgent need for evidence-based adaptation strategies. This study assesses the impact of climate change on maize yields in Burkina Faso (BF) using a calibrated AquaCrop model and recent climate projections. AquaCrop was calibrated using district-level maize yields from 2009 to 2022 and a genetic optimization technique. Climate change impacts were then simulated using two socioeconomic scenarios (SSP2–4.5 and SSP5–8.5) for the periods 2016–2045 and 2046–2075. Climate projections show that Burkina Faso will experience temperature increases of 0.5–3 °C and decreased precipitation, with the most severe rainfall reductions in the country’s southern half, including the crucial southwestern agricultural zone. Maize yields will predominantly decrease across the country, with projected losses reaching 20% in most regions. The southwestern agricultural zone, critical for national food production, faces substantial yield decreases of up to 40% under the SSP5-8.5 scenario. In light of these findings, future research should employ the calibrated AquaCrop model to evaluate specific combinations of adaptation strategies. These strategies include optimized planting windows, field-level water management practices, and optimal fertilizer application schedules, providing actionable guidance for smallholder farmers in West Africa. Full article

(This article belongs to the Special Issue Climate Change and Agriculture: Impacts and Adaptation)

17 pages, 4574 KiB

Open AccessArticle

Joint Probability Distribution of Extreme Wind Speed and Air Density Based on the Copula Function to Evaluate Basic Wind Pressure

by Lianpeng Zhang, Zeyu Zhang, Chunbing Wu, Xiaodong Ji, Xinyue Xue, Li Jiang and Shihan Yang

Atmosphere 2024, 15(12), 1437; https://doi.org/10.3390/atmos15121437 (registering DOI) - 29 Nov 2024

Abstract

To investigate an appropriate wind load design for buildings considering dynamic air density changes, classical extreme value and copula theories were utilized. Using wind speed, air temperature, and air pressure data from 123 meteorological stations in Shandong Province from 2004 to 2017, a [...] Read more.

To investigate an appropriate wind load design for buildings considering dynamic air density changes, classical extreme value and copula theories were utilized. Using wind speed, air temperature, and air pressure data from 123 meteorological stations in Shandong Province from 2004 to 2017, a joint probability distribution model was established for extreme wind speed and air density. The basic wind pressure was calculated for various conditional return periods. The results indicated that the Gumbel and Gaussian mixture model distributions performed well in extreme wind speed and air density fitting, respectively. The joint extreme wind speed and air density distribution exhibited a distinct bimodal pattern. The higher the wind speed was, the greater the air density for the same return conditional period. For the 10-year return period, the air density surpassed the standard air density, exceeding 1.30 kg/m³. The basic wind pressures under the different conditional return periods were more than 10% greater than those calculated from standard codes. Applying the air density based on the conditional return period in engineering design could enhance structural safety regionally. Full article

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

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34 pages, 3614 KiB

Open AccessArticle

Improving Solar Radiation Prediction in China: A Stacking Model Approach with Categorical Boosting Feature Selection

by Yuehua Ding, Yuhang Wang, Zhe Li, Long Zhao, Yi Shi, Xuguang Xing and Shuangchen Chen

Atmosphere 2024, 15(12), 1436; https://doi.org/10.3390/atmos15121436 (registering DOI) - 29 Nov 2024

Abstract

Solar radiation is an important energy source, and accurately predicting it [daily global and diffuse solar radiation (R_s and R_d)] is essential for research on surface energy exchange, hydrologic systems, and agricultural production. However, R_s and R_d estimation [...] Read more.

Solar radiation is an important energy source, and accurately predicting it [daily global and diffuse solar radiation (R_s and R_d)] is essential for research on surface energy exchange, hydrologic systems, and agricultural production. However, R_s and R_d estimation relies on meteorological data and related model parameters, which leads to inaccuracy in some regions. To improve the estimation accuracy and generalization ability of the R_s and R_d models, 17 representative radiation stations in China were selected. The categorical boosting (CatBoost) feature selection algorithm was utilized to construct a novel stacking model from sample and parameter diversity perspectives. The results revealed that the characteristics related to sunshine duration (n) and ozone (O₃) significantly affect solar radiation prediction. The proposed new ensemble model framework had better accuracy than base models in root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and global performance index (GPI). The solar radiation prediction model is more applicable to coastal areas, such as Shanghai and Guangzhou, than to inland regions of China. The range and mean of RMSE, MAE, and R² for R_s prediction are 1.5737–3.7482 (1.9318), 1.1773–2.6814 (1.4336), and 0.7597–0.9655 (0.9226), respectively; for R_d prediction, they are 1.2589–2.9038 (1.8201), 0.9811–2.1024 (1.3493), and 0.5153–0.9217 (0.7248), respectively. The results of this study can provide a reference for R_s and R_d estimation and related applications in China. Full article

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

27 pages, 7508 KiB

Open AccessArticle

Synergising Machine Learning and Remote Sensing for Urban Heat Island Dynamics: A Comprehensive Modelling Approach

by Guglielmina Mutani, Alessandro Scalise, Xhoana Sufa and Stefania Grasso

Atmosphere 2024, 15(12), 1435; https://doi.org/10.3390/atmos15121435 (registering DOI) - 29 Nov 2024

Abstract

This study evaluates the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI) effects through a place-based approach. Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and [...] Read more.

This study evaluates the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI) effects through a place-based approach. Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and climate data in Turin, Italy. A detailed analysis of the ex-industrial Teksid area revealed a significant reduction in Surface Urban Heat Island Intensity (SUHII), with decreases of −0.94 in summer and −0.54 in winter following regeneration interventions. Using 17 variables in the Random Forest model, key determinants influencing SUHII were identified, including building density, vegetation cover, and surface albedo. This study quantitatively highlights the impact of increasing green spaces and enhancing surface materials to improve solar reflectivity, with findings showing a 19.46% increase in vegetation and a 3.09% rise in albedo after mitigation efforts. Furthermore, the results demonstrate that integrating Local Climate Zones (LCZs) into urban planning, alongside interventions targeting these key variables, can further optimise UHI mitigation and assess changes. This comprehensive approach provides policymakers with a robust tool to enhance urban resilience and guide sustainable planning strategies in response to climate change. Full article

(This article belongs to the Special Issue UHI Analysis and Evaluation with Remote Sensing Data)

22 pages, 6280 KiB

Open AccessArticle

Game Theory-Based Comparison of Disaster Risk Assessment for Two Landfall Typhoons: A Case Study of Jilin Province’s Impact

by Zhennan Dong, Dan Zhu, Yichen Zhang, Jiquan Zhang, Xiufeng Yang and Fanfan Huang

Atmosphere 2024, 15(12), 1434; https://doi.org/10.3390/atmos15121434 (registering DOI) - 29 Nov 2024

Abstract

Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process–Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and [...] Read more.

Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process–Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and comparison of the disaster risk caused by two typhoons, Maysak and Haishen, in Jilin Province. Game theory enhances precision in evaluation beyond conventional approaches, effectively addressing the shortcomings of both subjective and objective weighting methods. Typhoon Maysak and Typhoon Haishen exhibit analogous tracks. They have successively exerted an impact on Jilin Province, and the phenomenon of overlapping rain areas is a crucial factor in triggering disasters. Typhoon Maysak features stronger wind force and greater hourly rainfall intensity, while Typhoon Haishen has a longer duration of rainfall. Additionally, Typhoon Maysak causes more severe disasters in Jilin Province. With regard to the four dimensions of disaster risk, the analysis of hazards reveals that the areas categorized as high risk and above in relation to the two typhoons are mainly located in the central-southern and eastern regions of Jilin Province. Typhoon Maysak has a slightly higher hazard level. During the exposure assessment, it was determined that the high-risk areas occupied 16% of the gross area of Jilin Province. It is mainly concentrated in three economically developed cities, as well as some large agricultural counties. In the context of vulnerability analysis, regions classified as high risk and above constitute 54% of the overall area. The areas classified as having high vulnerability are predominantly located in Yushu, Nong’an, and Songyuan. From the analysis of emergency response and recovery ability, Changchun has strong typhoon disaster prevention and reduction ability. This is proportional to the local level of economic development. The mountainous areas in the east and the regions to the west are comparatively weak. Finally, the comprehensive typhoon disaster risk zoning indicates that the zoning of the two typhoons is relatively comparable. When it comes to high-risk and above areas, Typhoon Maysak accounts for 38% of the total area, while Typhoon Haishen occupies 47%. The regions with low risk are predominantly found in Changchun, across the majority of Baicheng, and at the intersection of Baishan and Jilin. Upon comparing the disasters induced by two typhoons in Jilin Province, it was observed that the disasters caused by Typhoon Maysak were considerably more severe than those caused by Typhoon Haishen. This finding aligns with the intense wind and heavy rainfall brought by Typhoon Maysak. Full article

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

12 pages, 2708 KiB

Open AccessArticle

The Roles of the Eastern Atlantic Niño and Central Atlantic Niño in ENSO Prediction

by Yuzhi Gan, Xingchen Shen, Yishuai Jin, Zhengxiang Rao, Yiqun Pang and Shouyou Huang

Atmosphere 2024, 15(12), 1433; https://doi.org/10.3390/atmos15121433 - 29 Nov 2024

Viewed by 104

Abstract

Recent studies have shown that there are two types of Niño events in the Tropical Atlantic, namely the Eastern Atlantic (EA) Niño and Central Atlantic (CA) Niño modes. However, it remains unknown whether these two types of Niño modes still impact El Niño–Southern [...] Read more.

Recent studies have shown that there are two types of Niño events in the Tropical Atlantic, namely the Eastern Atlantic (EA) Niño and Central Atlantic (CA) Niño modes. However, it remains unknown whether these two types of Niño modes still impact El Niño–Southern Oscillation (ENSO) prediction. This paper investigates the impacts of the EA and CA Niño modes on ENSO predictability with an empirical dynamical model: the Linear Inverse Model (LIM). After selectively including in or excluding from the LIM the EA and CA modes of the Tropical Atlantic, respectively, we discover that the EA mode has a greater significance in ENSO prediction compared to the CA mode. The evolution of the EA and CA mode optimum initial structures also confirms the impact of the EA mode on the Tropical Pacific. Further study shows that the EA mode can improve the Eastern Pacific (EP)-ENSO and Central Pacific (CP)-ENSO predictions, while the CA mode plays a less important role. Despite the significant influence of the EA mode, the CA mode has become increasingly important since the 2000s and the EA mode has been weakened in recent years. Therefore, the role of the CA mode in ENSO prediction after 2000 should be considered in the future. Full article

(This article belongs to the Special Issue Tropical Air-Sea Interactions and Their Impact on East Asian Anomalous Climate)

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25 pages, 8614 KiB

Open AccessArticle

Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM_2.5 Concentrations: A Case Study in Dezhou City, China

by Zhenfang He and Qingchun Guo

Atmosphere 2024, 15(12), 1432; https://doi.org/10.3390/atmos15121432 - 28 Nov 2024

Viewed by 252

Abstract

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM_2.5 concentrations. [...] Read more.

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM_2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM_2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m³. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM_2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control. Full article

(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)

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14 pages, 4608 KiB

Open AccessArticle

Transmission Spectroscopy Along the Transit of Venus: A Proxy for Exoplanets Atmospheric Characterization

by Alexandre Branco, Pedro Machado, Olivier Demangeon, Tomás Azevedo Silva, Sarah A. Jaeggli, Thomas Widemann and Paolo Tanga

Atmosphere 2024, 15(12), 1431; https://doi.org/10.3390/atmos15121431 - 28 Nov 2024

Viewed by 275

Abstract

We present an analysis of high-resolution, near-infrared (NIR) spectra relative to the solar transit of Venus of 5–6 June 2012, as observed with the Facility Infrared Spectropolarimeter (FIRS) at the Dunn Solar Telescope in New Mexico. These observations offer the unique opportunity to [...] Read more.

We present an analysis of high-resolution, near-infrared (NIR) spectra relative to the solar transit of Venus of 5–6 June 2012, as observed with the Facility Infrared Spectropolarimeter (FIRS) at the Dunn Solar Telescope in New Mexico. These observations offer the unique opportunity to probe the upper layers (above ∼84 km in altitude) of a thick, CO₂-dominated atmosphere with the transmission spectroscopy technique—a proxy for future studies of highly-irradiated atmospheres of Earth-sized exoplanets. We were able to directly observe absorption lines from the two most abundant CO₂ isotopologues, and from the main isotopologue of CO in the retrieved spectrum of Venus. Furthermore, we performed a cross-correlation analysis of the transmission spectrum using transmission templates generated with petitRADTRANS. With the cross-correlation technique, it was possible to confirm detections of both CO₂ isotopologues and CO. Additionally, we retrieved a tentative cross-correlation signal for O₃ on Venus. We demonstrate the feasibility of high-resolution, ground-based observations to study the chemical inventory of planetary atmospheres, employing techniques commonly used in exoplanet characterization. Full article

(This article belongs to the Section Planetary Atmospheres)

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11 pages, 4079 KiB

Open AccessCommunication

Study on Ozone and Its Critical Influencing Factors in Key Regions of China

by Zhenhai Wu, Dandan Zhang, Yanqin Ren, Fang Bi, Rui Gao, Xuezhong Wang, Hong Li and Jikang Wang

Atmosphere 2024, 15(12), 1430; https://doi.org/10.3390/atmos15121430 - 27 Nov 2024

Viewed by 219

Abstract

Solar radiation is the fundamental energy source of climate change, which has a significant impact on the generations of secondary fine particulate matter (PM_2.5) and ozone (O₃) in the atmosphere. Additionally, surface solar radiation is also affected by the [...] Read more.

Solar radiation is the fundamental energy source of climate change, which has a significant impact on the generations of secondary fine particulate matter (PM_2.5) and ozone (O₃) in the atmosphere. Additionally, surface solar radiation is also affected by the concentration of PM_2.5, which in turn affects the generation of O₃. To clarify the relationships among the O₃, PM_2.5 and the total radiation intensity, this study analyzes their temporal and spatial variation trends from 2017 to 2019. Meanwhile, as a common precursor of O₃ and PM_2.5, concentration variations in nitrogen dioxide (NO₂) are discussed as well in this study. The results showed the following: (1) There are significant positive correlations between the O₃-8 h concentrations and the total radiation intensities in critical regions, especially in the “2 + 26” cities, Fen-Wei Plain and Yangtze River Delta. (2) The decrease in PM_2.5 concentrations is in good agreement with the trend of NO₂ concentrations, while the response of O₃ concentration to the NO₂ concentration variation differs in different regions, except in the Pearl River Delta. (3) In addition to the meteorological factors, changes in the concentrations and ratios of precursors such as NO₂ and volatile organic compounds (VOCs) likely contribute to the observed fluctuations in O₃ concentrations in recent years. Full article

(This article belongs to the Special Issue Air Pollution in China (3rd Edition))

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24 pages, 2885 KiB

Open AccessArticle

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

by Jiuyu Guo, Bin Chen, Zeke Li, Bijing Liu, Wei Wu and Junjie Yang

Atmosphere 2024, 15(12), 1429; https://doi.org/10.3390/atmos15121429 - 27 Nov 2024

Viewed by 291

Abstract

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy [...] Read more.

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals. Full article

(This article belongs to the Special Issue Renewable Strategies for Emission Reduction: A Multisectoral Approach)

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18 pages, 4767 KiB

Open AccessArticle

Analysis of ENSO Event Intensity Changes and Time–Frequency Characteristic Since 1875

by Yansong Chen, Chengyi Zhao and Hai Zhi

Atmosphere 2024, 15(12), 1428; https://doi.org/10.3390/atmos15121428 - 27 Nov 2024

Viewed by 213

Abstract

This study investigates the characteristics and intensity of El Niño–Southern Oscillation (ENSO) events from January 1875 to December 2023, employing an advanced method for intensity determination based on various ENSO indices defined as a continuous five-month period with temperatures exceeding 0.5 °C for [...] Read more.

This study investigates the characteristics and intensity of El Niño–Southern Oscillation (ENSO) events from January 1875 to December 2023, employing an advanced method for intensity determination based on various ENSO indices defined as a continuous five-month period with temperatures exceeding 0.5 °C for warm events or falling below −0.5 °C for cold events. A total of 40 warm and 41 cold events were identified, with further classification revealing seven extreme warm events and five extreme cold events. The analysis shows a positive skewness in frequency distribution, indicating a predominance of strong warm events. The primary mode of variability is found to be interannual oscillation in the 3–8 year range, with significant decadal oscillations in the 10–16 year range. This study highlights the importance of methodological rigor in evaluating ENSO dynamics, contributing to a more comprehensive understanding of climate variability and offering a reliable framework for future research. Full article

(This article belongs to the Section Climatology)

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18 pages, 6878 KiB

Open AccessArticle

Studying the Impact of LULC Correspondence Between Landsat 8 and Spot 7 Data on Land Surface Temperature Estimation

by Yehia Miky

Atmosphere 2024, 15(12), 1427; https://doi.org/10.3390/atmos15121427 - 27 Nov 2024

Viewed by 196

Abstract

Information about land surface temperature (LST) plays a crucial role in environmental studies, as it provides essential data for understanding climate patterns, assessing ecosystem health, and predicting environmental changes. Understanding the relationship between land cover types and LST is crucial across all disciplines [...] Read more.

Information about land surface temperature (LST) plays a crucial role in environmental studies, as it provides essential data for understanding climate patterns, assessing ecosystem health, and predicting environmental changes. Understanding the relationship between land cover types and LST is crucial across all disciplines that deal with LST data. It helps researchers identify trends in global warming, heatwaves, and cooling effects, which can influence biodiversity, agriculture, and water resources. The accuracy of LST calculations heavily depends on the quality of the data used. However, most satellite thermal data used for LST estimations are in coarse spatial resolution. This study aims to explore the complex interaction between land cover types, considering factors such as proportion and neighboring effects, and LST recalculation by integrating the estimated LST from Landsat thermal band and Spot imagery classification. A machine learning model was employed to quantify the contribution of each Spot pixel to the LST estimated from TIRS data, classifying it as either heating or cooling. The Al Morjan and Al Hamra districts in Jeddah, Saudi Arabia, were used as case studies. The results showed that Spot images achieved a classification accuracy of over 95%, whereas Landsat images did not exceed 77%. The average heating and cooling factors from neighboring pixels were 1.06 and 0.96, respectively. The study demonstrates the improved spatial distribution of LST, with overall temperature increases across all land cover classes. The findings of this study could aid in identifying environmental imbalances and developing effective solutions. Full article

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

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17 pages, 6843 KiB

Open AccessArticle

Examining the Spatial and Temporal Variation of PM_2.5 and Its Linkage with Meteorological Conditions in Dhaka, Bangladesh

by Mizanur Rahman and Lei Meng

Atmosphere 2024, 15(12), 1426; https://doi.org/10.3390/atmos15121426 - 27 Nov 2024

Viewed by 331

Abstract

This study investigates the temporal and spatial variations in PM_2.5 concentrations in Dhaka, Bangladesh, from 2001 to 2023 and evaluates the impact of meteorological factors and the effectiveness of mitigation strategies on air pollution. Using satellite and ground-based data, this study analyzed [...] Read more.

This study investigates the temporal and spatial variations in PM_2.5 concentrations in Dhaka, Bangladesh, from 2001 to 2023 and evaluates the impact of meteorological factors and the effectiveness of mitigation strategies on air pollution. Using satellite and ground-based data, this study analyzed the seasonal trends, daily fluctuations, and the influence of COVID-19 lockdown measures on air quality. Our findings reveal a persistent increase in PM_2.5 levels, particularly during winter, with concentrations frequently exceeding WHO guidelines. Our analysis suggests significant correlations between meteorological conditions and PM_2.5 concentration, highlighting the significant role of meteorological conditions, such as rainfall, humidity, and temperature, in modulating PM_2.5 levels. Our analysis found that PM_2.5 levels exhibited a significant inverse correlation with relative humidity (r = −0.72), rainfall (r = −0.69), and temperatures (r = −0.79), highlighting the role of meteorological conditions in mitigating pollution levels. Additionally, the study underscores the temporary improvements in air quality during lockdown periods, demonstrating the potential benefits of sustained emission control measures. The research emphasizes the need for comprehensive and multi-faceted air quality management strategies, including stringent vehicular and industrial emissions regulations, enhancement of urban green spaces, and public awareness campaigns to mitigate the adverse health impacts of PM_2.5 pollution in Dhaka. Full article

(This article belongs to the Special Issue Land-Atmosphere Interactions)

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19 pages, 3594 KiB

Open AccessArticle

Impact of WRF Model Parameterization Settings on the Quality of Short-Term Weather Forecasts over Poland

by Sebastian Kendzierski

Atmosphere 2024, 15(12), 1425; https://doi.org/10.3390/atmos15121425 - 26 Nov 2024

Viewed by 365

Abstract

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and [...] Read more.

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and atmospheric pressure—to different combinations of physical parameterization schemes. Utilizing data from the Global Forecast System (GFS) spanning 2019 to 2022, a series of model simulations were conducted with support from the Poznań Supercomputing and Networking Center (PCSS). To assess the model’s performance across different weather stations, statistical metrics such as the mean absolute error (MAE) and root mean square error (RMSE) were employed. The findings indicate that the configuration labeled “p2” produced the most accurate forecasts for temperature, wind speed, and atmospheric pressure, achieving MAE values of 1.5 °C, 1.6 m/s, and 2 hPa, respectively. However, forecast inaccuracies were notably higher in mountainous regions, particularly regarding wind speed. These results underscore the importance of selecting appropriate parameterization settings tailored to regional characteristics, as different configurations can significantly impact the forecast accuracy, especially in complex terrains. This study contributes to the understanding of short-term weather forecasting models for Central Europe, offering potential pathways for improving localized forecast accuracy. Full article

(This article belongs to the Section Meteorology)

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25 pages, 88222 KiB

Open AccessArticle

Interactions Between a High-Intensity Wildfire and an Atmospheric Hydraulic Jump in the Case of the 2023 Lahaina Fire

by Clifford Ehrke, Angel Farguell and Adam K. Kochanski

Atmosphere 2024, 15(12), 1424; https://doi.org/10.3390/atmos15121424 - 26 Nov 2024

Viewed by 421

Abstract

On 8 August 2023, a grass fire that started in the city of Lahaina, Hawai’i, grew into the deadliest wildfire in the United States since 1918. This wildfire offers a unique opportunity to explore the impact of high heat output on an atmospheric [...] Read more.

On 8 August 2023, a grass fire that started in the city of Lahaina, Hawai’i, grew into the deadliest wildfire in the United States since 1918. This wildfire offers a unique opportunity to explore the impact of high heat output on an atmospheric hydraulic jump and a downslope wind event. We conducted two WRF-SFIRE simulations to investigate these effects: one incorporating fire–atmosphere feedback and the other without it. Our findings revealed that, in the uncoupled simulation, the hydraulic jump moved inland significantly earlier than in the coupled simulation. This altered the wind pattern near the fire front in the uncoupled simulation, accelerating its lateral spread. The results suggest that fire–atmosphere interactions and their influence on near-fire circulation may be more intricate than previously understood. Specifically, while fire-induced wind acceleration is often linked to enhanced fire spread, this study highlights that, in cases where the lateral fire spread is dominant, fire-induced circulation may reduce cross-flank flow and inhibit the fire growth. Full article

(This article belongs to the Special Issue High-Impact Weather Events: Dynamics, Variability and Predictability)

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21 pages, 4527 KiB

Open AccessArticle

Examining the Effectiveness of a Pilot Waste Classification Policy in Facilitating the Low-Carbon Transition Regarding Solid Waste in China

by Yanshuang Yang and Huimin Li

Atmosphere 2024, 15(12), 1423; https://doi.org/10.3390/atmos15121423 - 26 Nov 2024

Viewed by 199

Abstract

The pilot waste classification policy is pivotal in tackling the challenges associated with the decarbonization of solid waste disposal in China; nevertheless, the efficacy of these pilot policies continues to be a topic of ongoing debate. This study presents a novel methodology utilizing [...] Read more.

The pilot waste classification policy is pivotal in tackling the challenges associated with the decarbonization of solid waste disposal in China; nevertheless, the efficacy of these pilot policies continues to be a topic of ongoing debate. This study presents a novel methodology utilizing an advanced difference-in-differences model, drawing on panel data from 297 cities for the period of 2016 to 2020, encompassing various types of municipal solid wastes and their corresponding carbon emissions. By integrating the waste classification performance as an intermediary variable, this research distinctly investigates how these policies facilitate the transition towards a low-carbon economy. The key findings indicate the following: (1) The implementation of pilot waste classification policies significantly accelerates the low-carbon transition of municipal solid waste disposal, with results substantiated through rigorous empirical testing. (2) The mechanistic analysis reveals a marked efficiency enhancement in waste classification within megacities, resulting in a compensatory effect, while analogous policies do not yield performance improvements in small- and medium-sized cities. (3) The effects of waste classification policies exhibit variability across cities of differing scales, with scale particularly influencing the performance of waste classification, thereby emphasizing the necessity for city-specific strategies in policy execution. The contributions of this study are rooted in its methodological advancements and its sophisticated analysis of the varying effects of waste classification policies, providing critical insights for policymakers seeking to enhance the effects of low-carbon strategies within urban environments. However, it is important to note that the scope of this study is limited to China, and the findings may be most applicable to countries with similar waste management challenges. Full article

(This article belongs to the Special Issue Urban Air Pollution Control and Low-Carbon Development)

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5 pages, 174 KiB

Open AccessEditorial

Advances in Air–Sea Interactions, Climate Variability, and Predictability

by Wei Zhang, Yulong Yao, Duo Chan and Jie Feng

Atmosphere 2024, 15(12), 1422; https://doi.org/10.3390/atmos15121422 - 26 Nov 2024

Viewed by 217

Abstract

Air–sea interaction remains one of the most dynamic and influential components of the Earth’s climate system, significantly shaping the variability and predictability of both weather and climate [...] Full article

(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability)

23 pages, 4305 KiB

Open AccessArticle

The Study of Radioactive Fallout Source of Low-Equivalent Nuclear Bursts Based on Nuclear Cloud Simulation Using the CFD-DPM

by Yangchao Li, Qiang Liu, Wei Liu, Wenshuang Xian, Feifei Li and Kai Zhang

Atmosphere 2024, 15(12), 1421; https://doi.org/10.3390/atmos15121421 - 26 Nov 2024

Viewed by 244

Abstract

The activity-height distribution of radioactive particles in the stabilization cloud of a nuclear burst plays a crucial role in the radioactive fallout prediction model, serving as the source for transport, diffusion, and dose rate calculation modules. A gas-particle multiphase flow solver was developed [...] Read more.

The activity-height distribution of radioactive particles in the stabilization cloud of a nuclear burst plays a crucial role in the radioactive fallout prediction model, serving as the source for transport, diffusion, and dose rate calculation modules. A gas-particle multiphase flow solver was developed using the OpenFOAM Computational Fluid Dynamics (CFD) library and discrete phase method (DPM) library under a two-way coupling regime to simulate the U.S. standard atmosphere of 1976 with good stability. The accuracy of the numerical model was verified through low-equivalent nuclear weapons tests, including RANGER-Able and BUSTER-JANGLE-Sugar, depicting reasonable spatio-temporal changes in cloud profiles. The initialization module of the Defense Land Fallout Interpretative Code (DELFIC) and activity-size distribution, which considered fractionation, were employed for nuclear fireball and radioactive particle initialization. Simulations indicated that the activity-height distribution of the stabilization cloud mainly concentrated on the lower third of air burst cloud caps, while settling near the burst center for surface or near-surface bursts. This study has confirmed the effectiveness of the gas-particle flow solver based on the CFD-DPM method in simulating low-equivalent nuclear clouds and enriching research on radioactive fallout prediction models. Full article

(This article belongs to the Special Issue Numerical Simulation of Aerosol Microphysical Processes (2nd Edition))

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20 pages, 6817 KiB

Open AccessArticle

Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM_2.5) from an Open-Cast Coal Mining Area

by Claudia Galeano-Páez, Hugo Brango, Karina Pastor-Sierra, Andrés Coneo-Pretelt, Gean Arteaga-Arroyo, Ana Peñata-Taborda, Pedro Espitia-Pérez, Dina Ricardo-Caldera, Alicia Humanez-Álvarez, Elizabeth Londoño-Velasco, Roger Espinosa-Sáez, Basilio Diaz-Ponguta, Juliana da Silva, Dione Silva Corrêa and Lyda Espitia-Pérez

Atmosphere 2024, 15(12), 1420; https://doi.org/10.3390/atmos15121420 - 26 Nov 2024

Viewed by 246

Abstract

This study evaluates the cytotoxic and genotoxic effects of PM_2.5 collected from an open-cast coal mining area in northern Colombia. Cyclohexane (CH), dichloromethane (DCM), and acetone (ACE) extracts were obtained using Soxhlet extraction to isolate compounds of different polarities. Human lymphocytes were [...] Read more.

This study evaluates the cytotoxic and genotoxic effects of PM_2.5 collected from an open-cast coal mining area in northern Colombia. Cyclohexane (CH), dichloromethane (DCM), and acetone (ACE) extracts were obtained using Soxhlet extraction to isolate compounds of different polarities. Human lymphocytes were exposed to the extracted compounds, and cytotoxicity and genotoxicity were assessed using the cytokinesis block micronucleus (CBMN) and comet assays, incorporating FPG and ENDO III enzymes to detect oxidative DNA damage. Chemical analysis revealed that the organic fractions consisted mainly of modified hydrocarbons and volatile organic compounds. The CBMN assay showed a significant increase in micronuclei in binucleated (MNBN) and mononucleated (MNMONO) cells and nucleoplasmic bridges (NPB) in exposed lymphocytes. The comet assay revealed substantial oxidative DNA damage, particularly with the ACE extract, which significantly increased oxidized purines and pyrimidines. DCM induced similar effects, while CH showed moderate effects. CREST immunostaining revealed aneugenic activity, particularly in cells exposed to ACE and DCM extracts. These results suggest that polar fractions of PM_2.5, likely containing metals and modified PAHs, contribute to DNA damage and chromosomal instability. The study highlights the need to monitor the composition of PM_2.5 in mining regions to implement stricter environmental policies to reduce exposure and health risks. Full article

(This article belongs to the Special Issue Characteristics and Control of Particulate Matter)

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23 pages, 5624 KiB

Open AccessArticle

Investigation on the Impact of the 2022 Luding M6.8 Earthquake on Regional Low-Frequency Time Code Signals in Northern China

by Fan Zhao, Ping Feng, Zhen Qi, Langlang Cheng, Xin Wang, Luxi Huang, Qiang Liu, Yingming Chen, Xiaoqian Ren and Yu Hua

Atmosphere 2024, 15(12), 1419; https://doi.org/10.3390/atmos15121419 - 26 Nov 2024

Viewed by 266

Abstract

Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky [...] Read more.

Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction. Full article

(This article belongs to the Section Planetary Atmospheres)

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15 pages, 3702 KiB

Open AccessArticle

Environmental and Atmospheric Influences on Academic Performance: The Role of Green Spaces, Roads, and Wildfires Around Schools and Homes in the Federal District, Brazil

by Weeberb J. Requia and Luciano Moura da Silva

Atmosphere 2024, 15(12), 1418; https://doi.org/10.3390/atmos15121418 - 26 Nov 2024

Viewed by 218

Abstract

Environmental characteristics, such as proximity to green spaces and exposure to roads, can significantly influence atmospheric factors like air quality. For instance, areas with abundant green spaces typically exhibit better air quality, while high road density often correlates with increased air pollution, both [...] Read more.

Environmental characteristics, such as proximity to green spaces and exposure to roads, can significantly influence atmospheric factors like air quality. For instance, areas with abundant green spaces typically exhibit better air quality, while high road density often correlates with increased air pollution, both of which can affect students’ cognitive functioning and academic performance. This study aimed to evaluate the association between the environmental and atmospheric conditions—specifically green spaces (measured by the NDVI and green space area), roads (total road length), and wildfires—around students’ schools and homes in the Federal District (FD), Brazil, and their impact on academic performance. We analyzed data from 344,175 public school students across 256 schools in the FD, covering the years 2017 to 2020. Using a mixed-effects regression model, we investigated how neighborhood characteristics such as green spaces, road density, and wildfire frequency influence individual-level academic performance while controlling for temporal, socioeconomic, and school-specific factors. Our findings indicate that the environmental factors around schools, particularly green spaces and road density, have significant associations with academic outcomes. Specifically, a higher road density around schools was linked to lower academic performance, whereas green space presence had a generally positive impact, especially around schools. Wildfires, while negatively associated with performance around homes, had mixed effects around schools. These results underscore the importance of considering environmental and atmospheric factors in urban planning and education policy to enhance student performance. Full article

(This article belongs to the Section Air Quality and Health)

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18 pages, 3432 KiB

Open AccessReview

A Bibliometric Analysis of Convection-Permitting Model Research

by Xiaozan Lyu, Tianqi Ruan and Xiaojing Cai

Atmosphere 2024, 15(12), 1417; https://doi.org/10.3390/atmos15121417 - 25 Nov 2024

Viewed by 260

Abstract

Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research [...] Read more.

Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research trends in CPM, utilizing data from 3508 articles published between 2000 and 2023. The annual number of publications exhibits a linear increase, rising from fewer than 50 in 2000 to over 250 after 2020, with the majority of research originating from the US, China, the UK, and Germany. The most productive institutes include the National Oceanic Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) in the US, each contributing over 10% of total publications. Title and abstract terms in publications related to keywords such as “scenario”, “climate simulation”, etc., dominate publications from 2018 to 2023, coinciding with advances in computing power. Notably, terms associated with CPM physical processes received the highest citations from 2000 to 2023, underscoring the importance of such these research topics. Given the computational expense of running CPMs and the increasing demand for future predictions using CPMs, novel methods for generating long-term simulations are imperative. Full article

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

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13 pages, 1338 KiB

Open AccessArticle

Investigation of the Measurement Uncertainties in the Measurement of BTEX in the Volatile Organic Compound Group

by Hayri Cihan Sıdal and Andaç Akdemir

Atmosphere 2024, 15(12), 1416; https://doi.org/10.3390/atmos15121416 - 25 Nov 2024

Viewed by 228

Abstract

In this study, repeatability, intermediate precision, and recovery were considered within the Type A uncertainty budget, while measurement uncertainties due to the sampling system used (instrument), VOC mixture standard, internal standard, micropipette, temperature effect, methanol, and carbon disulfide were considered Type B uncertainties. [...] Read more.

In this study, repeatability, intermediate precision, and recovery were considered within the Type A uncertainty budget, while measurement uncertainties due to the sampling system used (instrument), VOC mixture standard, internal standard, micropipette, temperature effect, methanol, and carbon disulfide were considered Type B uncertainties. As a result of the studies on the uncertainty components of the BTEX parameters belonging to the group of volatile organic compounds (VOCs), the highest uncertainty component for benzene was intermediate certainty at 24%. The highest uncertainty component for toluene was sampling at 23%. The highest uncertainty component for ethyl benzene was sampling at 25%. The highest uncertainty component for m,p-xylene and o-xylene was sampling at 25%. As a result, intermediate precision, sampling, and calibration uncertainties were identified as the most significant uncertainty components. Full article

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

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20 pages, 3861 KiB

Open AccessArticle

Sensitivity of Streamflow to Changing Rainfall and Evapotranspiration in Catchments Across the Nile Basin

by Charles Onyutha, Brian Odhiambo Ayugi, Kenny Thiam Choy Lim Kam Sian, Hassen Babaousmail, Wenseslas Arineitwe, Josephine Taata Akobo, Cyrus Chelangat and Ambrose Mubialiwo

Atmosphere 2024, 15(12), 1415; https://doi.org/10.3390/atmos15121415 - 25 Nov 2024

Viewed by 282

Abstract

This research focuses on the complex dynamics governing the sensitivity of streamflow to variations in rainfall and potential evapotranspiration (PET) within the Nile basin. By employing a hydrological model, our study examines the interrelationships between meteorological variables and hydrological responses across six catchments [...] Read more.

This research focuses on the complex dynamics governing the sensitivity of streamflow to variations in rainfall and potential evapotranspiration (PET) within the Nile basin. By employing a hydrological model, our study examines the interrelationships between meteorological variables and hydrological responses across six catchments (Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb) and explores the intricate balance between rainfall, PET, and streamflow. Nash Sutcliffe Efficiency (NSE) for calibration of the hydrological model ranged from 0.636 (Ribb) to 0.831 (El Diem). For validation, NSE ranged from 0.608 (Ribb) to 0.811 (Blue Nile). With rainfall kept constant while PET was increased by 5%, the streamflows of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb decreased by 7.00, 5.08, 2.49, 4.10, 1.84, and 7.67%, respectively. With the original PET data unchanged, increasing rainfall of the Blue Nile, El Diem, Kabalega, Malaba, Mpanga, and Ribb by 5% led to an increase in streamflow by 9.02, 9.87, 5.38, 4.34, 6.58, and 8.32%, respectively. The research reveals that the rate at which a catchment losing water to the atmosphere (determined by PET) substantially influences its drying rate. Utilizing linear models, we demonstrate that the surplus rainfall available for increasing streamflow (represented by model intercepts) amplifies with higher rainfall intensities. This highlights the pivotal role of rainfall in shaping catchment water balance dynamics. Moreover, our study stresses the varied sensitivities of catchments within the basin to changes in PET and rainfall. Catchments with lower PET exhibit heightened responsiveness to increasing rainfall, accentuating the influence of evaporative demand on streamflow patterns. Conversely, regions with higher PET rates necessitate refined management strategies due to their increased sensitivity to changes in evaporative demand. Understanding the intricate interplay between rainfall, PET, and streamflow is paramount for developing adaptive strategies amidst climate variability. By examining these relationships, our research contributes essential knowledge for sustainable water resource management practices at both the catchment and regional scales, especially in regions susceptible to varying sensitivities of catchments to climatic conditions. Full article

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

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24 pages, 1969 KiB

Open AccessArticle

Can Tourists’ Summer Vacations Save Energy and Reduce CO₂ Emissions? Evidence from China

by Puwei Zhang, Xiujiang Li, Meixuan Ren, Rui Li and Xin Gao

Atmosphere 2024, 15(12), 1414; https://doi.org/10.3390/atmos15121414 - 25 Nov 2024

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Abstract

This study develops a methodological framework for measuring energy conservation and CO₂ emission reductions that considers both origins and destinations. The framework encompasses four key aspects: transportation, accommodation, cooking, and housing rehabilitation. Data were collected through a literature review, questionnaire surveys, and [...] Read more.

This study develops a methodological framework for measuring energy conservation and CO₂ emission reductions that considers both origins and destinations. The framework encompasses four key aspects: transportation, accommodation, cooking, and housing rehabilitation. Data were collected through a literature review, questionnaire surveys, and field measurement tracking. Compared to living in the origin, senior tourists from Nanchang visiting Zhongyuan Township in China for summer tourism can save 5.747 MJ of energy and reduce CO₂ emissions by 3.303 kg per capita per day. An in-depth analysis indicated that the research site could further enhance energy conservation and reduce CO₂ emissions by improving public transportation services, optimizing the energy structure of the destination, and diversifying the available recreational offerings. Depending on the characteristics of the destination and the primary origin, summer or winter tourism in various countries or regions can employ the methodological framework to evaluate energy conservation and CO₂ emission reductions after identifying specific parameters. The improved pathways identified through this research can serve as a checklist for other countries or regions aiming to explore energy conservation and CO₂-emission-reduction pathways for summer or winter tourism. Enhancing climate-driven tourism development may offer a new avenue for the tourism industry to contribute to carbon reduction targets. Full article

(This article belongs to the Special Issue Climate Change and Tourism: Impacts and Responses)

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35 pages, 52142 KiB

Open AccessArticle

Dust Content Modulation and Spring Heat Waves in Senegal (2003–2022)

by Semou Diouf, Marie-Jeanne G. Sambou, Abdoulaye Deme, Papa Fall, Dame Gueye, Juliette Mignot and Serge Janicot

Atmosphere 2024, 15(12), 1413; https://doi.org/10.3390/atmos15121413 - 25 Nov 2024

Viewed by 412

Abstract

The population of Senegal faces health challenges related to desert dust and heat waves (HWs). This study aims to (a) update the documentation of HWs in Senegal, expanding on the work of Sambou et al. (2019); (b) investigate the modulation of dust indicators [...] Read more.

The population of Senegal faces health challenges related to desert dust and heat waves (HWs). This study aims to (a) update the documentation of HWs in Senegal, expanding on the work of Sambou et al. (2019); (b) investigate the modulation of dust indicators during HWs; and (c) assess the distinct impacts of dust content on night-time and daytime HWs. We use [i] the daily maximum air temperature (Tx), minimum air temperature (Tn), and apparent temperature (Ta) from 12 stations in the Global Surface Summary of the Day (GSOD) database and [ii] the Dust Aerosol Optical Depth (Dust AOD), particulate matter (PM) concentrations, 925 hPa wind, and Mean Sea Level Pressure (MSLP) from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. HWs are defined for each station in spring as periods when Tx, Tn, or Ta exceeds the 95th percentile for at least three consecutive days. Three homogeneous zones from the Atlantic coast to inland Senegal are identified using hierarchical cluster analysis: Zone 1 (Saint-Louis, Dakar-Yoff, Ziguinchor, and Cap Skirring), Zone 2 (Podor, Linguère, Diourbel, and Kaolack), and Zone 3 (Matam, Tambacounda, Kédougou, and Kolda). Our results show that Zone 1 records the highest number of HWs for Tx, Tn, and Ta, while Zone 3 experiences more HWs in terms of Tn and Ta than Zone 2. The influence of dust is notably stronger for HWs linked to Tn and Ta than for those related to Tx. Analysis of the mechanisms shows that the presence of dust in Senegal and its surrounding regions is detected up to four days before the onset of HWs. These findings suggest that dust conditions associated with spring HWs in Senegal may be better distinguished and predicted. Full article

(This article belongs to the Special Issue Exposure Assessment of Air Pollution (2nd Edition))

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24 pages, 1395 KiB

Open AccessArticle

A 4D-EnKF Method via a Modified Cholesky Decomposition and Line Search Optimization for Non-Linear Data Assimilation

by Elías D. Nino-Ruiz and Jairo Diaz-Rodriguez

Atmosphere 2024, 15(12), 1412; https://doi.org/10.3390/atmos15121412 - 24 Nov 2024

Viewed by 302

Abstract

This paper introduces an efficient approach for implementing the Four-Dimensional Variational Ensemble Kalman Filter (4D-EnKF) for non-linear data assimilation, leveraging a modified Cholesky decomposition (4D-EnKF-MC). In this method, control spaces at observation times are represented by full-rank square root approximations of background error [...] Read more.

This paper introduces an efficient approach for implementing the Four-Dimensional Variational Ensemble Kalman Filter (4D-EnKF) for non-linear data assimilation, leveraging a modified Cholesky decomposition (4D-EnKF-MC). In this method, control spaces at observation times are represented by full-rank square root approximations of background error covariance matrices, derived using the modified Cholesky decomposition. To ensure global convergence, we integrate line-search optimization into the filter formulation. The performance of the 4D-EnKF-MC is evaluated through experimental tests using the Lorenz 96 model, and its accuracy is compared to that of a 4D-Var extension of the Maximum-Likelihood Ensemble Filter (4D-MLEF). Through Root Mean Square Error (RMSE) analysis, we demonstrate that the proposed method outperforms the 4D-MLEF across a range of ensemble sizes and observational network configurations, providing a robust and scalable solution for non-linear data assimilation in complex systems. Full article

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

21 pages, 14579 KiB

Open AccessReview

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

by Chunlai Wu, Siyu Lu, Jiawei Tian, Lirong Yin, Lei Wang and Wenfeng Zheng

Atmosphere 2024, 15(12), 1411; https://doi.org/10.3390/atmos15121411 - 24 Nov 2024

Viewed by 429

Abstract

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature [...] Read more.

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research. Full article

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

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24 pages, 8439 KiB

Open AccessArticle

Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China

by Dayang Wang, Shaobo Liu and Dagang Wang

Atmosphere 2024, 15(12), 1410; https://doi.org/10.3390/atmos15121410 - 24 Nov 2024

Viewed by 291

Abstract

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET [...] Read more.

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion. Full article

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

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