Da-Som Park, Yongjoo Choi, Young Sunwoo, Chang Hoon Jung
This study presents an improved wet scavenging process for particles in air quality modeling, focusing on the Korean Peninsula. New equations were incorporated into the air quality chemical transport model (CTM) to enhance the simulation of particulate matter (PM) concentrations. The modified air quality CTM module, utilizing size-dependent scavenging formulas, was applied to simulate air quality for April 2018, a month characterized by significant precipitation. Results showed that the modified model produced more accurate predictions of PM10 and PM2.5 concentrations compared to the original air quality CTM model. The maximum monthly average differences were 5.46 µg/m3 for PM10 and 2.87 µg/m3 for PM2.5, with pronounced improvements in high-concentration regions. Time-series analyses for Seoul and Busan demonstrated better agreement between modeled and observed values. Spatial distribution comparisons revealed enhanced accuracy, particularly in metropolitan areas. This study highlights the importance of incorporating region-specific, size-dependent wet scavenging processes in air quality models. The improved model shows promise for more accurate air quality predictions, potentially benefiting environmental management and policy-making in the region. Future research should focus on integrating more empirical data to further refine the wet scavenging process in air quality modeling.
{"title":"Development of Wet Scavenging Process of Particles in Air Quality Modeling","authors":"Da-Som Park, Yongjoo Choi, Young Sunwoo, Chang Hoon Jung","doi":"10.3390/atmos15091070","DOIUrl":"https://doi.org/10.3390/atmos15091070","url":null,"abstract":"This study presents an improved wet scavenging process for particles in air quality modeling, focusing on the Korean Peninsula. New equations were incorporated into the air quality chemical transport model (CTM) to enhance the simulation of particulate matter (PM) concentrations. The modified air quality CTM module, utilizing size-dependent scavenging formulas, was applied to simulate air quality for April 2018, a month characterized by significant precipitation. Results showed that the modified model produced more accurate predictions of PM10 and PM2.5 concentrations compared to the original air quality CTM model. The maximum monthly average differences were 5.46 µg/m3 for PM10 and 2.87 µg/m3 for PM2.5, with pronounced improvements in high-concentration regions. Time-series analyses for Seoul and Busan demonstrated better agreement between modeled and observed values. Spatial distribution comparisons revealed enhanced accuracy, particularly in metropolitan areas. This study highlights the importance of incorporating region-specific, size-dependent wet scavenging processes in air quality models. The improved model shows promise for more accurate air quality predictions, potentially benefiting environmental management and policy-making in the region. Future research should focus on integrating more empirical data to further refine the wet scavenging process in air quality modeling.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"76 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190258","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}
Developing short-term forecasting models for global atmospheric temperature and wind in near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in near space based on the MERRA-2 reanalysis dataset from 2010–2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions on all 72 isobaric surfaces. Specifically, at a higher altitude (on the 1.65 Pa isobaric surface, approximately 70 km above sea level), the ConvGRU model achieved a RMSE of about 2.85 K for temperature predictions, and 5.67 m/s and 5.17 m/s for eastward and northward wind. This finding is significantly meaningful for short-term temperature and wind forecasts in near space and for exploring the physical mechanisms related to temperature and wind variations in this region.
开发近空间全球大气温度和风的短期预报模型对于了解大气动力学和支持该地区的人类活动至关重要。虽然数值模型已得到广泛开发，但深度学习技术最近在提高大气预报精度方面显示出了前景。在本研究中，基于 2010-2022 年 MERRA-2 再分析数据集，应用卷积长短期记忆（ConvLSTM）和卷积门控递归单元（ConvGRU）神经网络建立了近空间大气温度和风的短期全球尺度预报模型。模型结果表明，ConvGRU 模型的短期预报结果优于 ConvLSTM 模型。在所有 72 个等压面上，ConvGRU 模式在前三个小时内的温度预测均方根误差约为 1.8 K，东风和北风预测误差分别为 4.2 m/s 和 3.8 m/s。具体来说，在较高的高度（1.65 Pa 等压面，海拔约 70 公里），ConvGRU 模式的温度预测均方根误差约为 2.85 K，东风和北风预测均方根误差分别为 5.67 m/s 和 5.17 m/s。这一发现对于近空间短期温度和风力预报以及探索与该地区温度和风力变化相关的物理机制具有重要意义。
{"title":"Research on Short-Term Forecasting Model of Global Atmospheric Temperature and Wind in the near Space Based on Deep Learning","authors":"Xingxin Sun, Chen Zhou, Jian Feng, Huiyun Yang, Yuqiang Zhang, Zhou Chen, Tong Xu, Zhongxin Deng, Zhengyu Zhao, Yi Liu, Ting Lan","doi":"10.3390/atmos15091069","DOIUrl":"https://doi.org/10.3390/atmos15091069","url":null,"abstract":"Developing short-term forecasting models for global atmospheric temperature and wind in near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in near space based on the MERRA-2 reanalysis dataset from 2010–2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions on all 72 isobaric surfaces. Specifically, at a higher altitude (on the 1.65 Pa isobaric surface, approximately 70 km above sea level), the ConvGRU model achieved a RMSE of about 2.85 K for temperature predictions, and 5.67 m/s and 5.17 m/s for eastward and northward wind. This finding is significantly meaningful for short-term temperature and wind forecasts in near space and for exploring the physical mechanisms related to temperature and wind variations in this region.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190257","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}
Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change.
{"title":"Spatiotemporal Variation Patterns of Drought in Liaoning Province, China, Based on Copula Theory","authors":"Jiayu Wu, Yao Li, Xudong Zhang, Huanjie Cai","doi":"10.3390/atmos15091063","DOIUrl":"https://doi.org/10.3390/atmos15091063","url":null,"abstract":"Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190225","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}
Iurii Cherniak, Irina Zakharenkova, Scott Gleason, Douglas Hunt
In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density.
{"title":"First Detections of Ionospheric Plasma Density Irregularities from GOES Geostationary GPS Observations during Geomagnetic Storms","authors":"Iurii Cherniak, Irina Zakharenkova, Scott Gleason, Douglas Hunt","doi":"10.3390/atmos15091065","DOIUrl":"https://doi.org/10.3390/atmos15091065","url":null,"abstract":"In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190260","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}
Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.
获得高垂直分辨率的温度和湿度剖面对于描述和预测大气运动，特别是了解中、小尺度天气过程的演变、进行短期和近期天气预报以及实施天气改变（人工降雨、人工消雨等）至关重要。地基微波辐射计可以获取高时间和空间分辨率的垂直对流层大气数据。但是，温度和相对湿度的检索精度仍不及无线电探空仪数据，尤其是在多云条件下。因此，提高观测和检索精度是当前研究的一大挑战。本研究的重点是进一步提高大气温湿度剖面检索的精度，并研究云信息（云基高度和云厚度）对温湿度剖面检索的具体影响。在大气温度和相对湿度剖面的检索过程中纳入了地面多通道微波辐射计和毫米波云雷达的观测数据。检索使用了反向传播神经网络（BPNN）。检索结果使用平均绝对误差（MAE）和均方根误差（RMSE）进行量化。统计结果表明，与相对湿度曲线相比，温度曲线受云层信息的影响较小。云层厚度是影响相对湿度剖面检索的主要因素，有云层信息的检索是最佳的检索方法。与无云层信息的检索剖面相比，加入云层信息后大部分高度层的 MAE 值和 RMSE 值都有不同程度的降低，部分高度层的相对湿度（RH）误差降低了约 50%。在 7.75 千米附近，有云层信息的温度剖面的 RMSE 和 MAE 值最大减少了约 1.0 °C，相对湿度剖面的 RMSE 和 MAE 值最大减少了约 10%，这是在 2 千米附近获得的。
{"title":"Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar","authors":"Longwei Zhang, Yingying Ma, Lianfa Lei, Yujie Wang, Shikuan Jin, Wei Gong","doi":"10.3390/atmos15091064","DOIUrl":"https://doi.org/10.3390/atmos15091064","url":null,"abstract":"Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190259","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}
Shuaiqi Zhang, Chongyi E, Xianba Ji, Ping Li, Qiang Peng, Zhaokang Zhang, Qi Zhang
Alluvial parent material soil is an important soil type found on the Qinghai–Tibet Plateau (QTP) in China. However, due to the limited age data for alluvial soils, the relationship between alluvial geomorphological processes and soil pedogenic processes remains unclear. In this study, three representative alluvial parent material profiles on the Buha River alluvial plain in the Qinghai Lake Basin, northeast QTP, were analyzed using the optical luminescence (OSL) dating method. Combined with physical and chemical analyses of the soil, we further analyzed the pedogenic process of alluvial soil. The alluvial parent material of the Buha alluvial plain predominately yielded ages between 11.9 and 9.1 ka, indicating that the alluvial soil began to form during the early Holocene. The development of the alluvial soil on the first-order terrace presents characteristics of entisol with multiple burial episodes, mainly between 8.5 and 4.0 ka, responding to the warm and humid middle Holocene and high lake levels.
冲积母质土是中国青藏高原的一种重要土壤类型。然而，由于冲积土的年龄数据有限，冲积地貌过程与土壤成土过程之间的关系尚不清楚。本研究采用光学发光（OSL）测年法分析了青海高原东北部青海湖盆地布哈河冲积平原上三个具有代表性的冲积母质剖面。结合土壤的理化分析，进一步分析了冲积土壤的成土过程。布哈冲积平原的冲积母质的年龄主要在 11.9 至 9.1 ka 之间，表明冲积土开始形成于全新世早期。一阶阶地冲积土的发育呈现出 entisol 的特征，主要在 8.5 至 4.0 ka 之间有多次埋藏，与温暖潮湿的全新世中期和高湖水位相呼应。
{"title":"Soil Genesis of Alluvial Parent Material in the Qinghai Lake Basin (NE Qinghai–Tibet Plateau) Revealed Using Optically Stimulated Luminescence Dating","authors":"Shuaiqi Zhang, Chongyi E, Xianba Ji, Ping Li, Qiang Peng, Zhaokang Zhang, Qi Zhang","doi":"10.3390/atmos15091066","DOIUrl":"https://doi.org/10.3390/atmos15091066","url":null,"abstract":"Alluvial parent material soil is an important soil type found on the Qinghai–Tibet Plateau (QTP) in China. However, due to the limited age data for alluvial soils, the relationship between alluvial geomorphological processes and soil pedogenic processes remains unclear. In this study, three representative alluvial parent material profiles on the Buha River alluvial plain in the Qinghai Lake Basin, northeast QTP, were analyzed using the optical luminescence (OSL) dating method. Combined with physical and chemical analyses of the soil, we further analyzed the pedogenic process of alluvial soil. The alluvial parent material of the Buha alluvial plain predominately yielded ages between 11.9 and 9.1 ka, indicating that the alluvial soil began to form during the early Holocene. The development of the alluvial soil on the first-order terrace presents characteristics of entisol with multiple burial episodes, mainly between 8.5 and 4.0 ka, responding to the warm and humid middle Holocene and high lake levels.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190261","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 radon concentration activity in buildings is influenced by various factors, including meteorological elements like temperature, pressure, and precipitation, which are recognized as significant influencers. The fluctuations of indoor radon in premises are related to seasonal change. This study aimed to understand better the effects of environmental parameters on indoor radon concentration levels in the Aksu school. Indoor and outdoor temperature differentials heavily influence diurnal indoor radon patterns. The analysis indicates that the correlation between indoor radon and outdoor temperature, dew point, and air humidity is weak and negligible for atmospheric pressure, wind speed, and precipitation, as determined by the obtained values of R2 and the Chaddock scale. The multiple regression model is characterized by the correlation coefficient rxy = 0.605, which corresponds to a close relationship on the Chaddock scale.
{"title":"Influence of Meteorological Parameters on Indoor Radon Concentration Levels in the Aksu School","authors":"Yerlan Kashkinbayev, Meirat Bakhtin, Polat Kazymbet, Anel Lesbek, Baglan Kazhiyakhmetova, Masaharu Hoshi, Nursulu Altaeva, Yasutaka Omori, Shinji Tokonami, Hitoshi Sato, Danara Ibrayeva","doi":"10.3390/atmos15091067","DOIUrl":"https://doi.org/10.3390/atmos15091067","url":null,"abstract":"The radon concentration activity in buildings is influenced by various factors, including meteorological elements like temperature, pressure, and precipitation, which are recognized as significant influencers. The fluctuations of indoor radon in premises are related to seasonal change. This study aimed to understand better the effects of environmental parameters on indoor radon concentration levels in the Aksu school. Indoor and outdoor temperature differentials heavily influence diurnal indoor radon patterns. The analysis indicates that the correlation between indoor radon and outdoor temperature, dew point, and air humidity is weak and negligible for atmospheric pressure, wind speed, and precipitation, as determined by the obtained values of R2 and the Chaddock scale. The multiple regression model is characterized by the correlation coefficient rxy = 0.605, which corresponds to a close relationship on the Chaddock scale.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190262","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}
Gaseous air pollutants emitted primarily by anthropogenic sources form secondary products through photochemical reactions, complicating the regulatory analysis of anthropogenic emissions in the atmosphere. We used an environmental chassis dynamometer and a photochemical smog chamber to conduct a parameter sensitivity experiment to investigate the formation of secondary products from a gasoline passenger car. To simulate the mitigation of ammonia emissions from gasoline vehicle exhausts assuming future emission controls and to allow photochemical oxidation and aging of the vehicle exhaust, ammonia was selectively removed by a series of five denuders installed between the vehicle and photochemical smog chamber. Overall, there were no differences in the formation of secondary organic aerosols and ozone with or without ammonia mitigation. However, the potential for ammonium nitrate particle formation was significantly reduced with ammonia mitigation. In addition, ammonia mitigation resulted in increased aerosol acidity due to nitric acid in the gas phase not being neutralized by ammonia and condensing onto the liquid particle phase, indicating a potentially important secondary effect associated with ammonia mitigation. Thus, we provide new insights into the effects of ammonia mitigation on secondary emissions from gasoline vehicle exhaust and into a potentially useful experimental approach for determining primary and secondary emissions.
{"title":"Effects of Ammonia Mitigation on Secondary Organic Aerosol and Ammonium Nitrate Particle Formation in Photochemical Reacted Gasoline Vehicle Exhausts","authors":"Hiroyuki Hagino, Risa Uchida","doi":"10.3390/atmos15091061","DOIUrl":"https://doi.org/10.3390/atmos15091061","url":null,"abstract":"Gaseous air pollutants emitted primarily by anthropogenic sources form secondary products through photochemical reactions, complicating the regulatory analysis of anthropogenic emissions in the atmosphere. We used an environmental chassis dynamometer and a photochemical smog chamber to conduct a parameter sensitivity experiment to investigate the formation of secondary products from a gasoline passenger car. To simulate the mitigation of ammonia emissions from gasoline vehicle exhausts assuming future emission controls and to allow photochemical oxidation and aging of the vehicle exhaust, ammonia was selectively removed by a series of five denuders installed between the vehicle and photochemical smog chamber. Overall, there were no differences in the formation of secondary organic aerosols and ozone with or without ammonia mitigation. However, the potential for ammonium nitrate particle formation was significantly reduced with ammonia mitigation. In addition, ammonia mitigation resulted in increased aerosol acidity due to nitric acid in the gas phase not being neutralized by ammonia and condensing onto the liquid particle phase, indicating a potentially important secondary effect associated with ammonia mitigation. Thus, we provide new insights into the effects of ammonia mitigation on secondary emissions from gasoline vehicle exhaust and into a potentially useful experimental approach for determining primary and secondary emissions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190263","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}
Alexis Mariaccia, Philippe Keckhut, Alain Hauchecorne
This study is an additional investigation of stratosphere–troposphere coupling based on the recent stratospheric winter descriptions in five distinct modes: January, February, Double, Dynamical, and Dadiative. These modes, established in a previous study, categorize the main stratospheric winter typologies modulated by the timing of important sudden stratospheric warmings (SSWs) and final stratospheric warmings (FSWs). The novelty of this research is to investigate the Northern Annular Mode, mean sea level pressure (MSLP) anomalies in the Ural and Aleutian regions, and the decomposition of Eliassen–Palm flux into wavenumbers 1 and 2 within each mode. The results show that the January and Double modes exhibit similar pre-warming surface signals, characterized by Ural blocking and Aleutian trough events preceding weak polar vortex events. The January mode displays a positive MSLP anomaly of +395 hPa (−191 hPa) in the Ural (Aleutian) region in December, while the Double mode shows +311 hPa (−89 hPa) in November. These modes are primarily wave-1 driven, generating tropospheric responses via negative Arctic Oscillation patterns. Conversely, the February and Dynamical modes show opposite signals, with Aleutian blocking and Ural trough events preceding strong polar vortex events. In December, the February mode exhibits MSLP anomalies of +119 hPa (Aleutian) and −180 hPa (Ural), while the Dynamical mode shows +77 hPa and −184 hPa, respectively. These modes, along with important SSWs in February and dynamical FSWs, are driven by both wave-1 and wave-2 and do not significantly impact the troposphere. The Radiative mode’s occurrence is strongly related to the Aleutian blocking presence. These findings confirm that SSW timing is influenced by specific dynamical forcing related to surface precursors and underscore its importance in subsequent tropospheric responses. This study establishes a connection between early winter tropospheric conditions and upcoming stratospheric states, potentially improving seasonal forecasts in the northern hemisphere.
{"title":"Impact of Polar Vortex Modes on Winter Weather Patterns in the Northern Hemisphere","authors":"Alexis Mariaccia, Philippe Keckhut, Alain Hauchecorne","doi":"10.3390/atmos15091062","DOIUrl":"https://doi.org/10.3390/atmos15091062","url":null,"abstract":"This study is an additional investigation of stratosphere–troposphere coupling based on the recent stratospheric winter descriptions in five distinct modes: January, February, Double, Dynamical, and Dadiative. These modes, established in a previous study, categorize the main stratospheric winter typologies modulated by the timing of important sudden stratospheric warmings (SSWs) and final stratospheric warmings (FSWs). The novelty of this research is to investigate the Northern Annular Mode, mean sea level pressure (MSLP) anomalies in the Ural and Aleutian regions, and the decomposition of Eliassen–Palm flux into wavenumbers 1 and 2 within each mode. The results show that the January and Double modes exhibit similar pre-warming surface signals, characterized by Ural blocking and Aleutian trough events preceding weak polar vortex events. The January mode displays a positive MSLP anomaly of +395 hPa (−191 hPa) in the Ural (Aleutian) region in December, while the Double mode shows +311 hPa (−89 hPa) in November. These modes are primarily wave-1 driven, generating tropospheric responses via negative Arctic Oscillation patterns. Conversely, the February and Dynamical modes show opposite signals, with Aleutian blocking and Ural trough events preceding strong polar vortex events. In December, the February mode exhibits MSLP anomalies of +119 hPa (Aleutian) and −180 hPa (Ural), while the Dynamical mode shows +77 hPa and −184 hPa, respectively. These modes, along with important SSWs in February and dynamical FSWs, are driven by both wave-1 and wave-2 and do not significantly impact the troposphere. The Radiative mode’s occurrence is strongly related to the Aleutian blocking presence. These findings confirm that SSW timing is influenced by specific dynamical forcing related to surface precursors and underscore its importance in subsequent tropospheric responses. This study establishes a connection between early winter tropospheric conditions and upcoming stratospheric states, potentially improving seasonal forecasts in the northern hemisphere.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190264","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}
This study investigates the dust aerosol optical depth (DAOD) variability over the Arabian Peninsula (AP) in the spring season, a region profoundly affected by dust activity due to its desert terrain. Employing the MERRA-2 DAOD reanalysis dataset for the period 1981–2022, a significant trend in DAOD is noted in the spring season compared to the other seasons. The leading Empirical Orthogonal Function (EOF) explains 67% of the total DAOD variance during the spring season, particularly over the central and northeastern parts of AP. The analysis reveals the strengthening of upper-level divergence over the western Pacific, favoring mid-tropospheric positive geopotential height anomalies over the AP, leading to warm and drier surface conditions and increased DAOD. A statistically significant negative relationship (correlation = −0.32, at 95% confidence level) is noted between DAOD over AP and the El Niño-Southern Oscillation (ENSO), suggesting that La Niña conditions may favor higher dust concentrations over the AP region and vice versa during El Niño phase. The high (low) DAOD over the region corresponds to mid-tropospheric positive (negative) geopotential height anomalies through strengthening (weakening) of the upper-level divergence (convergence) over the western Pacific during the La Niña (El Niño) phase. This study shows that ENSO could be a possible precursor to predicting dust variability on a seasonal time scale.
{"title":"Impact of El Niño-Southern Oscillation on Dust Variability during the Spring Season over the Arabian Peninsula","authors":"Yazeed Alsubhi, Gohar Ali","doi":"10.3390/atmos15091060","DOIUrl":"https://doi.org/10.3390/atmos15091060","url":null,"abstract":"This study investigates the dust aerosol optical depth (DAOD) variability over the Arabian Peninsula (AP) in the spring season, a region profoundly affected by dust activity due to its desert terrain. Employing the MERRA-2 DAOD reanalysis dataset for the period 1981–2022, a significant trend in DAOD is noted in the spring season compared to the other seasons. The leading Empirical Orthogonal Function (EOF) explains 67% of the total DAOD variance during the spring season, particularly over the central and northeastern parts of AP. The analysis reveals the strengthening of upper-level divergence over the western Pacific, favoring mid-tropospheric positive geopotential height anomalies over the AP, leading to warm and drier surface conditions and increased DAOD. A statistically significant negative relationship (correlation = −0.32, at 95% confidence level) is noted between DAOD over AP and the El Niño-Southern Oscillation (ENSO), suggesting that La Niña conditions may favor higher dust concentrations over the AP region and vice versa during El Niño phase. The high (low) DAOD over the region corresponds to mid-tropospheric positive (negative) geopotential height anomalies through strengthening (weakening) of the upper-level divergence (convergence) over the western Pacific during the La Niña (El Niño) phase. This study shows that ENSO could be a possible precursor to predicting dust variability on a seasonal time scale.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190266","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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