Shanzhou Du, Hao Chen, Xiaohua Ding, Zhouquan Liao, Xiang Lu
Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations.
{"title":"Development of Dust Emission Prediction Model for Open-Pit Mines Based on SHPB Experiment and Image Recognition Method","authors":"Shanzhou Du, Hao Chen, Xiaohua Ding, Zhouquan Liao, Xiang Lu","doi":"10.3390/atmos15091118","DOIUrl":"https://doi.org/10.3390/atmos15091118","url":null,"abstract":"Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In-Ho Song, Hyun-Woong Kim, Jong-Sung Park, Seung-Myung Park, Jae-Yun Lee, Eun-Jung Nam, Yong-Jae Lim, Jung-Min Park, Myung-Soo Yoo, Seog-Yeon Cho, Hye-Jung Shin
In this study, the characteristics of ammonia and their effects on secondary particulate matter (PM) formation were analyzed by region in Korea in 2020. The NH3 concentration was high in GJ (11.4 ppb), a neighboring agricultural area, followed by DJ (9.0 ppb) and SE (8.6 ppb), which are located in urban areas. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, demonstrated a lower concentration than other areas. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that the ammonia in the atmosphere not only changes depending on local emissions, but also based on temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, investigations into the effect of ammonia on secondary PM formation were conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with increasing ammonia or PM2.5 concentration. It can be assumed that the PM2.5 concentration increases as nitrates are formed under the ammonia-sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that a reduction in ammonia would not be effective to restrain nitrate formation.
{"title":"Distribution and Characteristics of Ammonia Concentration by Region in Korea","authors":"In-Ho Song, Hyun-Woong Kim, Jong-Sung Park, Seung-Myung Park, Jae-Yun Lee, Eun-Jung Nam, Yong-Jae Lim, Jung-Min Park, Myung-Soo Yoo, Seog-Yeon Cho, Hye-Jung Shin","doi":"10.3390/atmos15091120","DOIUrl":"https://doi.org/10.3390/atmos15091120","url":null,"abstract":"In this study, the characteristics of ammonia and their effects on secondary particulate matter (PM) formation were analyzed by region in Korea in 2020. The NH3 concentration was high in GJ (11.4 ppb), a neighboring agricultural area, followed by DJ (9.0 ppb) and SE (8.6 ppb), which are located in urban areas. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, demonstrated a lower concentration than other areas. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that the ammonia in the atmosphere not only changes depending on local emissions, but also based on temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, investigations into the effect of ammonia on secondary PM formation were conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with increasing ammonia or PM2.5 concentration. It can be assumed that the PM2.5 concentration increases as nitrates are formed under the ammonia-sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that a reduction in ammonia would not be effective to restrain nitrate formation.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"194 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence temperature, humidity, and other atmospheric variables of the surrounding areas. Heat fluxes occur especially during the nighttime in large urbanized areas, characterized by poor vegetation, and are responsible for the formation of Urban Heat and Dry Island, i.e., higher temperatures and lower humidity compared to rural areas. They can be exacerbated under severe conditions, with dangerous consequences for people living in these urban areas. For these reasons, the need of accurately forecasting these phenomena is particularly felt. The present work represents one of the first attempts of using a very high resolution (about 600 m) in a Numerical Weather Prediction model. Performances of this advanced version of ICON have been investigated over a domain located in southern Italy, including the urban metropolitan area of Naples, considering a week characterized by extremely high temperatures. Results highlight that the activation of TERRA-URB scheme entails a better representation of temperature, relative humidity, and wind speed in urban areas, especially during nighttime, also allowing a proper reproduction of Urban Heat and Dry Island effects. Over rural areas, instead, no significant differences are found in model results when the urban canopy scheme is used.
{"title":"Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area","authors":"Davide Cinquegrana, Myriam Montesarchio, Alessandra Lucia Zollo, Edoardo Bucchignani","doi":"10.3390/atmos15091119","DOIUrl":"https://doi.org/10.3390/atmos15091119","url":null,"abstract":"The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence temperature, humidity, and other atmospheric variables of the surrounding areas. Heat fluxes occur especially during the nighttime in large urbanized areas, characterized by poor vegetation, and are responsible for the formation of Urban Heat and Dry Island, i.e., higher temperatures and lower humidity compared to rural areas. They can be exacerbated under severe conditions, with dangerous consequences for people living in these urban areas. For these reasons, the need of accurately forecasting these phenomena is particularly felt. The present work represents one of the first attempts of using a very high resolution (about 600 m) in a Numerical Weather Prediction model. Performances of this advanced version of ICON have been investigated over a domain located in southern Italy, including the urban metropolitan area of Naples, considering a week characterized by extremely high temperatures. Results highlight that the activation of TERRA-URB scheme entails a better representation of temperature, relative humidity, and wind speed in urban areas, especially during nighttime, also allowing a proper reproduction of Urban Heat and Dry Island effects. Over rural areas, instead, no significant differences are found in model results when the urban canopy scheme is used.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"16 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM2.5 concentration. Four distinct PM2.5 exposure categories were established using the quartile method. To assess the association between PM2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM2.5 annual levels exceeding the 5 µg/m3 limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering.
{"title":"Risk Associations between Air Pollution Exposure and Cardiovascular Diseases: A Residential Retrospective Cohort Study","authors":"Elisa Bustaffa, Cristina Mangia, Liliana Cori, Marco Cervino, Fabrizio Bianchi, Fabrizio Minichilli","doi":"10.3390/atmos15091113","DOIUrl":"https://doi.org/10.3390/atmos15091113","url":null,"abstract":"The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM2.5 concentration. Four distinct PM2.5 exposure categories were established using the quartile method. To assess the association between PM2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM2.5 annual levels exceeding the 5 µg/m3 limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"311 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nandin-Erdene Bayart, Krassi Rumchev, Christopher M. Reid, Sylvester Dodzi Nyadanu, Gavin Pereira
Cardiovascular diseases (CVD) are one of the leading causes of death globally, and a major contributor to CVD mortality is ambient air pollution (AAP). This study aimed to evaluate associations between AAP and mortality from CVD, including ischemic heart diseases (IHD) and strokes. Data on daily mortality records, six criteria AAP and meteorology in the capital city of Mongolia were collected between 1 January 2016 and 31 December 2022. A time-stratified case-crossover design was analysed with distributed lag conditional Poisson regression to estimate the relative risk of CVD mortality. We found that for each interquartile range increase in PM2.5, PM10, SO2 and NO2 pollutants, the risk of CVD mortality increased by 1.5% (RR = 1.015; 95% CI: 1.005, 1.025), 4.4% (RR = 1.044; 95% CI: 1.029, 1.059), 3.1% (RR = 1.033; 95% CI: 1.015, 1.047) and 4.8% (RR = 1.048; 95% CI: 1.013, 1.085) at lag01, respectively. The association between all pollutants, except O3, and CVD mortality was higher in subgroups ≥ 65 years and male, during the cold season and after using a new type of coal briquettes. Despite using the new type of coal briquettes, Ulaanbaatar’s ambient air pollution remained higher than the WHO’s guidelines. Based on our findings, we recommend that efforts should be focused on adopting more efficient strategies to reduce the current pollution level.
{"title":"Association between Short-Term Exposure to Ambient Air Pollution and Mortality from Cardiovascular Diseases in Ulaanbaatar, Mongolia","authors":"Nandin-Erdene Bayart, Krassi Rumchev, Christopher M. Reid, Sylvester Dodzi Nyadanu, Gavin Pereira","doi":"10.3390/atmos15091110","DOIUrl":"https://doi.org/10.3390/atmos15091110","url":null,"abstract":"Cardiovascular diseases (CVD) are one of the leading causes of death globally, and a major contributor to CVD mortality is ambient air pollution (AAP). This study aimed to evaluate associations between AAP and mortality from CVD, including ischemic heart diseases (IHD) and strokes. Data on daily mortality records, six criteria AAP and meteorology in the capital city of Mongolia were collected between 1 January 2016 and 31 December 2022. A time-stratified case-crossover design was analysed with distributed lag conditional Poisson regression to estimate the relative risk of CVD mortality. We found that for each interquartile range increase in PM2.5, PM10, SO2 and NO2 pollutants, the risk of CVD mortality increased by 1.5% (RR = 1.015; 95% CI: 1.005, 1.025), 4.4% (RR = 1.044; 95% CI: 1.029, 1.059), 3.1% (RR = 1.033; 95% CI: 1.015, 1.047) and 4.8% (RR = 1.048; 95% CI: 1.013, 1.085) at lag01, respectively. The association between all pollutants, except O3, and CVD mortality was higher in subgroups ≥ 65 years and male, during the cold season and after using a new type of coal briquettes. Despite using the new type of coal briquettes, Ulaanbaatar’s ambient air pollution remained higher than the WHO’s guidelines. Based on our findings, we recommend that efforts should be focused on adopting more efficient strategies to reduce the current pollution level.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingyue Mo, Yanbo Shen, Bin Yuan, Muyuan Li, Chenchen Ding, Beixi Jia, Dong Ye, Dan Wang
This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions.
{"title":"Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations","authors":"Jingyue Mo, Yanbo Shen, Bin Yuan, Muyuan Li, Chenchen Ding, Beixi Jia, Dong Ye, Dan Wang","doi":"10.3390/atmos15091112","DOIUrl":"https://doi.org/10.3390/atmos15091112","url":null,"abstract":"This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"2011 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature spatiotemporal forecasting. However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). This model consists of three main modules: a feature encoder, a multi-scale spatiotemporal translator, and a feature decoder. The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. The results demonstrate that MSLKSTNet achieves superior performance, with a 35% improvement in the MSE metric compared to SimVP. Ablation studies confirmed the significance of the MSSTA unit for spatiotemporal forecasting tasks. We apply the model to the regional ERA5-Land reanalysis temperature dataset, and the experimental results indicate that the proposed method delivers the best forecasting performance, achieving a 42% improvement in the MSE metric over the widely used ConvLSTM model for temperature prediction. This validates the effectiveness and superiority of MSLKSTNet in temperature forecasting tasks.
{"title":"MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction","authors":"Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu","doi":"10.3390/atmos15091114","DOIUrl":"https://doi.org/10.3390/atmos15091114","url":null,"abstract":"The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature spatiotemporal forecasting. However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). This model consists of three main modules: a feature encoder, a multi-scale spatiotemporal translator, and a feature decoder. The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. The results demonstrate that MSLKSTNet achieves superior performance, with a 35% improvement in the MSE metric compared to SimVP. Ablation studies confirmed the significance of the MSSTA unit for spatiotemporal forecasting tasks. We apply the model to the regional ERA5-Land reanalysis temperature dataset, and the experimental results indicate that the proposed method delivers the best forecasting performance, achieving a 42% improvement in the MSE metric over the widely used ConvLSTM model for temperature prediction. This validates the effectiveness and superiority of MSLKSTNet in temperature forecasting tasks.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.
{"title":"A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary","authors":"Shuoyi Ding, Xiaodan Chen, Xuanwen Zhang, Xiang Zhang, Peiqiang Xu","doi":"10.3390/atmos15091115","DOIUrl":"https://doi.org/10.3390/atmos15091115","url":null,"abstract":"In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dong Li, Pengtao Wang, Jingyun Guan, Xiaoliang Xu, Kaiyu Li
The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior intention (ERBI), with anticipated pride and anticipated guilt serving as mediating factors. An empirical study is conducted in Turpan, Xinjiang, which represents a typical inland arid area in China. The results indicate that: (1) thermal stress does not have a significant direct impact on ERBI, nevertheless, anticipated pride and anticipated guilt play crucial mediating roles between thermal stress and this intention. (2) Furthermore, environmental knowledge positively moderates the relationship between anticipated pride, anticipated guilt, and the ERBI. This research contributes to the understanding of how tourists’ anticipatory emotions affect their ERBI in desert climate regions while deepening our comprehension of the driving mechanisms behind such intentions among tourists. Moreover, it provides theoretical references for promoting environmentally responsible behaviors among tourists visiting desert climate regions.
{"title":"Research on the Mechanism of the Influence of Thermal Stress on Tourists’ Environmental Responsibility Behavior Intention: An Example from a Desert Climate Region, China","authors":"Dong Li, Pengtao Wang, Jingyun Guan, Xiaoliang Xu, Kaiyu Li","doi":"10.3390/atmos15091116","DOIUrl":"https://doi.org/10.3390/atmos15091116","url":null,"abstract":"The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior intention (ERBI), with anticipated pride and anticipated guilt serving as mediating factors. An empirical study is conducted in Turpan, Xinjiang, which represents a typical inland arid area in China. The results indicate that: (1) thermal stress does not have a significant direct impact on ERBI, nevertheless, anticipated pride and anticipated guilt play crucial mediating roles between thermal stress and this intention. (2) Furthermore, environmental knowledge positively moderates the relationship between anticipated pride, anticipated guilt, and the ERBI. This research contributes to the understanding of how tourists’ anticipatory emotions affect their ERBI in desert climate regions while deepening our comprehension of the driving mechanisms behind such intentions among tourists. Moreover, it provides theoretical references for promoting environmentally responsible behaviors among tourists visiting desert climate regions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"33 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series.
{"title":"Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics","authors":"William A. Gough, Zhihui Li","doi":"10.3390/atmos15091111","DOIUrl":"https://doi.org/10.3390/atmos15091111","url":null,"abstract":"The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"73 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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