Ruiyi Tang, Yuanyue Chu, Xiaoqian Liu, Zhishan Yang, Jian Yao
In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China’s specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China’s agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system.
随着绿色低碳发展要求的不断提高，推进符合中国具体国情的低碳农业迫在眉睫。鉴于这种紧迫性，中国农业系统的非二氧化碳温室气体（GHG）排放核算仍处于不断研究和完善的过程中。因此，本文基于碳排放系数法，对 1995 至 2021 年中国西南地区农业非二氧化碳温室气体排放量进行了核算。此外，我们还利用人口、富裕程度和技术回归随机影响模型（STIRPAT）和 Tapio 模型，探讨了驱动因素的影响程度以及与经济发展的关系。我们观察到西南地区农业产生的非二氧化碳温室气体排放量总体呈先增后减的趋势，中部较高，东部和西部较低。经济、人口、结构和技术水平对不同省份的影响程度不同，有利于各地区制定有针对性的农业规划政策。在研究期间的大部分时间里，经济增长与温室气体排放之间存在或弱或强的脱钩现象。最后，提出了促进西南地区低碳农业发展的建议，为明确农业系统的温室气体贡献提供了数据库和政策支持。
{"title":"Driving Factors and Decoupling Effects of Non-CO2 Greenhouse Gas Emissions from Agriculture in Southwest China","authors":"Ruiyi Tang, Yuanyue Chu, Xiaoqian Liu, Zhishan Yang, Jian Yao","doi":"10.3390/atmos15091084","DOIUrl":"https://doi.org/10.3390/atmos15091084","url":null,"abstract":"In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China’s specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China’s agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190217","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 employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.
{"title":"Performance Evaluation of TGFS Typhoon Track Forecasts over the Western North Pacific with Sensitivity Tests on Cumulus Parameterization","authors":"Yu-Han Chen, Sheng-Hao Sha, Chang-Hung Lin, Ling-Feng Hsiao, Ching-Yuang Huang, Hung-Chi Kuo","doi":"10.3390/atmos15091075","DOIUrl":"https://doi.org/10.3390/atmos15091075","url":null,"abstract":"This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190223","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}
Helen Mavromichalaki, Maria Livada, Argyris Stassinakis, Maria Gerontidou, Maria-Christina Papailiou, Line Drube, Aikaterini Karmi
A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the Long Short-Term Memory (LSTM) model, predicting ap index values for the next 72 h at three-hour intervals. During periods of quiet geomagnetic activity, the LSTM model’s performance is sufficient to yield favorable outcomes. Nevertheless, during geomagnetically disturbed conditions, such as geomagnetic storms of different levels, the model needs to be adapted in order to provide accurate ap index results. In particular, when coronal mass ejections occur, the ap Prediction tool is modulated by inserting predominant features of coronal mass ejections such as the date of the event, the estimated time of arrival and the linear speed. In the present work, this tool is described thoroughly; moreover, results for G2 and G3 geomagnetic storms are presented.
{"title":"The ap Prediction Tool Implemented by the A.Ne.Mo.S./NKUA Group","authors":"Helen Mavromichalaki, Maria Livada, Argyris Stassinakis, Maria Gerontidou, Maria-Christina Papailiou, Line Drube, Aikaterini Karmi","doi":"10.3390/atmos15091073","DOIUrl":"https://doi.org/10.3390/atmos15091073","url":null,"abstract":"A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the Long Short-Term Memory (LSTM) model, predicting ap index values for the next 72 h at three-hour intervals. During periods of quiet geomagnetic activity, the LSTM model’s performance is sufficient to yield favorable outcomes. Nevertheless, during geomagnetically disturbed conditions, such as geomagnetic storms of different levels, the model needs to be adapted in order to provide accurate ap index results. In particular, when coronal mass ejections occur, the ap Prediction tool is modulated by inserting predominant features of coronal mass ejections such as the date of the event, the estimated time of arrival and the linear speed. In the present work, this tool is described thoroughly; moreover, results for G2 and G3 geomagnetic storms are presented.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"35 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190221","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}
Fangyun Long, Yanqin Ren, Yuanyuan Ji, Junling Li, Haijie Zhang, Zhenhai Wu, Rui Gao, Fang Bi, Zhengyang Liu, Hong Li
Phthalic acid esters (PAEs) are a class of common environmental endocrine disruptors (EEDs), capable of causing considerable pollution to water, soil, and air and producing a range of adverse health impacts in humans. Although various studies have investigated the pollution characteristics and health hazards of PAEs in different media, a systematic review of PAEs in the broader environmental context is still lacking. In order to comprehensively explore current issues and suggest prospects, the current status, detection technology, toxicity, and health hazards of PAEs were investigated. The results suggest that PAE pollution is a widespread and complex global phenomenon, transported over long distances. The traditional techniques used for determination include high-performance liquid chromatography–mass spectrometry (HPLC-MS), gas chromatography–mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Various detection techniques offer distinct advantages and disadvantages. Moreover, PAEs can cause differing extents of harm to the nervous and reproductive systems of mammals. In the future, it is imperative to improve the detection of PAEs, establish rapid identification approaches, refine toxicological research methods, and investigate more comprehensive health risk assessment methods. These efforts will provide scientific support for the prevention and management of the resulting contaminants.
邻苯二甲酸酯（PAEs）是一类常见的环境内分泌干扰物（EEDs），能够对水、土壤和空气造成严重污染，并对人类健康产生一系列不良影响。虽然已有多项研究对 PAEs 在不同介质中的污染特征和健康危害进行了调查，但目前仍缺乏在更广泛的环境背景下对 PAEs 进行系统综述的研究。为了全面探讨当前的问题并提出展望，研究人员对 PAEs 的现状、检测技术、毒性和健康危害进行了调查。研究结果表明，PAE 污染是一种广泛而复杂的全球现象，具有远距离迁移的特点。传统的检测技术包括高效液相色谱-质谱法（HPLC-MS）、气相色谱-质谱法（GC-MS）和高效液相色谱法（HPLC）。各种检测技术各有利弊。此外，PAE 对哺乳动物的神经系统和生殖系统会造成不同程度的伤害。未来，改进 PAEs 的检测、建立快速鉴定方法、完善毒理学研究方法以及研究更全面的健康风险评估方法势在必行。这些工作将为预防和管理由此产生的污染物提供科学支持。
{"title":"Pollution Characteristics, Toxicological Properties, and Health Risk Assessment of Phthalic Acid Esters in Water, Soil, and Atmosphere","authors":"Fangyun Long, Yanqin Ren, Yuanyuan Ji, Junling Li, Haijie Zhang, Zhenhai Wu, Rui Gao, Fang Bi, Zhengyang Liu, Hong Li","doi":"10.3390/atmos15091071","DOIUrl":"https://doi.org/10.3390/atmos15091071","url":null,"abstract":"Phthalic acid esters (PAEs) are a class of common environmental endocrine disruptors (EEDs), capable of causing considerable pollution to water, soil, and air and producing a range of adverse health impacts in humans. Although various studies have investigated the pollution characteristics and health hazards of PAEs in different media, a systematic review of PAEs in the broader environmental context is still lacking. In order to comprehensively explore current issues and suggest prospects, the current status, detection technology, toxicity, and health hazards of PAEs were investigated. The results suggest that PAE pollution is a widespread and complex global phenomenon, transported over long distances. The traditional techniques used for determination include high-performance liquid chromatography–mass spectrometry (HPLC-MS), gas chromatography–mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Various detection techniques offer distinct advantages and disadvantages. Moreover, PAEs can cause differing extents of harm to the nervous and reproductive systems of mammals. In the future, it is imperative to improve the detection of PAEs, establish rapid identification approaches, refine toxicological research methods, and investigate more comprehensive health risk assessment methods. These efforts will provide scientific support for the prevention and management of the resulting contaminants.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190220","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 Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF).
{"title":"Let It Snow: Intercomparison of Various Total and Snow Precipitation Data over the Tibetan Plateau","authors":"Christine Kolbe, Boris Thies, Jörg Bendix","doi":"10.3390/atmos15091076","DOIUrl":"https://doi.org/10.3390/atmos15091076","url":null,"abstract":"The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF).","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190224","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}
Ryan W. Hirst, Myra J. Giesen, Maria-Valasia Peppa, Kelly Jobling, Dnyaneshwari Jadhav, S. Ziauddin Ahammad, Anil Namdeo, David W. Graham
The world is becoming increasingly urbanized, with migration rates often exceeding the infra-structural capacity in cities across the developing world. As such, many migrants must reside in informal settlements that lack civil and health protection infrastructure, including air quality monitoring. Here, geospatial inverse distance weighting and archived Central Pollution Control Board (CPCB) air quality data for neighboring stations from 2018 to 2021 were used to estimate air conditions in five informal settlements in Delhi, India, spanning the 2020 pandemic lockdown. The results showed that WHO limits for PM2.5 and NO2 were exceeded regularly, although air quality improved during the pandemic. Air quality was always better during the monsoon season (44.3 ± 3.47 and 26.9 ± 2.35 μg/m3 for PM2.5 and NO2, respectively) and poorest in the post-monsoon season (180 ± 15.5 and 55.2 ± 3.59 μg/m3 for PM2.5 and NO2). Differences in air quality among settlements were explained by the proximity to major roads and places of open burning, with NO2 levels often being greater near roads and PM2.5 levels being elevated near places with open burning. Field monitoring was performed in 2023 at three settlements and local CPCB stations. Air quality at settlements and their closest station were not significantly different (p < 0.01). However, field data showed that on-site factors within settlements, such as cooking, ad hoc burning, or micro-scale industry, impact air quality on local scales, suggesting health risks are greater in informal settlements because of greater unregulated activity. City-scale models can estimate mean air quality concentrations at unmonitored locations, but caution is needed because such models can miss local exposures that may have the greatest impact on local health.
{"title":"Modeling of Air Quality near Indian Informal Settlements Where Limited Local Monitoring Data Exist","authors":"Ryan W. Hirst, Myra J. Giesen, Maria-Valasia Peppa, Kelly Jobling, Dnyaneshwari Jadhav, S. Ziauddin Ahammad, Anil Namdeo, David W. Graham","doi":"10.3390/atmos15091072","DOIUrl":"https://doi.org/10.3390/atmos15091072","url":null,"abstract":"The world is becoming increasingly urbanized, with migration rates often exceeding the infra-structural capacity in cities across the developing world. As such, many migrants must reside in informal settlements that lack civil and health protection infrastructure, including air quality monitoring. Here, geospatial inverse distance weighting and archived Central Pollution Control Board (CPCB) air quality data for neighboring stations from 2018 to 2021 were used to estimate air conditions in five informal settlements in Delhi, India, spanning the 2020 pandemic lockdown. The results showed that WHO limits for PM2.5 and NO2 were exceeded regularly, although air quality improved during the pandemic. Air quality was always better during the monsoon season (44.3 ± 3.47 and 26.9 ± 2.35 μg/m3 for PM2.5 and NO2, respectively) and poorest in the post-monsoon season (180 ± 15.5 and 55.2 ± 3.59 μg/m3 for PM2.5 and NO2). Differences in air quality among settlements were explained by the proximity to major roads and places of open burning, with NO2 levels often being greater near roads and PM2.5 levels being elevated near places with open burning. Field monitoring was performed in 2023 at three settlements and local CPCB stations. Air quality at settlements and their closest station were not significantly different (p < 0.01). However, field data showed that on-site factors within settlements, such as cooking, ad hoc burning, or micro-scale industry, impact air quality on local scales, suggesting health risks are greater in informal settlements because of greater unregulated activity. City-scale models can estimate mean air quality concentrations at unmonitored locations, but caution is needed because such models can miss local exposures that may have the greatest impact on local health.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190219","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}
To assess the impact of air pollution on human health in multiple urban areas in Greece, hourly concentrations of common air pollutants (CO, NO2, O3, SO2, PM10, and PM2.5) from 11 monitoring stations in six major Greek cities (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), were used to implement the U.S. EPA’s Air Quality Index (AQI) during a seven-year period (2016–2022). In Athens, the capital city of Greece, hourly PM10 and PM2.5 concentrations were also studied in relation to the prevailing wind patterns, while major PM10 episodes exceeding the official daily EU limit (50 μg/m3) were analyzed using the Potential Source Contribution Function (PSCF) in terms of the air mass origin. According to the AQI results, PM10 and PM2.5 were by far the most hazardous pollutants associated with moderate and unhealthy conditions in all the studied areas. In addition, in Athens, Thessaloniki, and Patra, where the benzene levels were also studied, a potential inhalation cancer risk (>1.0 × 10−6) was detected. In Athens, Saharan dust intrusions were associated with downgraded air quality, whilst regional transport and the accumulation of local emissions triggered increased PM10 and PM2.5 levels in traffic sites, especially during cold periods. Our study highlights the need for the development of early warning systems and emission abatement strategies for PM pollution in Greece.
{"title":"Air Quality Assessment in Six Major Greek Cities with an Emphasis on the Athens Metropolitan Region","authors":"Konstantinos Dimitriou, Nikolaos Mihalopoulos","doi":"10.3390/atmos15091074","DOIUrl":"https://doi.org/10.3390/atmos15091074","url":null,"abstract":"To assess the impact of air pollution on human health in multiple urban areas in Greece, hourly concentrations of common air pollutants (CO, NO2, O3, SO2, PM10, and PM2.5) from 11 monitoring stations in six major Greek cities (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), were used to implement the U.S. EPA’s Air Quality Index (AQI) during a seven-year period (2016–2022). In Athens, the capital city of Greece, hourly PM10 and PM2.5 concentrations were also studied in relation to the prevailing wind patterns, while major PM10 episodes exceeding the official daily EU limit (50 μg/m3) were analyzed using the Potential Source Contribution Function (PSCF) in terms of the air mass origin. According to the AQI results, PM10 and PM2.5 were by far the most hazardous pollutants associated with moderate and unhealthy conditions in all the studied areas. In addition, in Athens, Thessaloniki, and Patra, where the benzene levels were also studied, a potential inhalation cancer risk (>1.0 × 10−6) was detected. In Athens, Saharan dust intrusions were associated with downgraded air quality, whilst regional transport and the accumulation of local emissions triggered increased PM10 and PM2.5 levels in traffic sites, especially during cold periods. Our study highlights the need for the development of early warning systems and emission abatement strategies for PM pollution in Greece.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"48 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190222","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}
Gervásio Annes Degrazia, Felipe Denardin Costa, Luís Gustavo Nogueira Martins, Luis Fernando Camponogara, Michel Stefanello, Cinara Ewerling da Rosa, Tiziano Tirabassi
The primary focus of this article is to derive a solution to obtain the asymptotic turbulent dispersion parameter provided by the spectral Taylor statistical diffusion model. Unlike previous articles, which employed the Dirac delta function to solve the eddy diffusivity formula, in this study, we used the Dirac delta function properties to obtain directly the asymptotic turbulent dispersion parameter from the particles’ spatial dispersion variance described in terms of the Eulerian turbulence spectrum and of the scale factor defined formally as the ratio between Lagrangian and Eulerian timescales. From the Kolmogorov 1941 theory, a detailed derivation for this scale factor is presented. Furthermore, using high mean wind speed data generated by local topographic features, a magnitude for the Kolmogorov constant for the neutral atmospheric boundary layer is evaluated. Thus, this magnitude when added to other values obtained from the selected studies found in the literature provides an average value for the Kolmogorov constant that agrees with large eddy simulation data results. Therefore, this average value allows to obtain a more reliable description of this scale factor. Finally, employing analytical formulations for the observed neutral turbulent spectra and for the velocity variances as well as turbulent statistical quantities measured in a surface neutral atmospheric boundary layer, a vertical dispersion parameter is derived. This vertical dispersion parameter when utilized in a simple Gaussian diffusion model is able to reproduce well contaminant observed concentrations.The Gaussian simulated concentrations also compare well with those simulated by a Lagrangian stochastic particle dispersion model that uses observed vertical spectral peak frequency values at distinct levels of the neutral surface boundary layer. Therefore, the present study shows that the observational determination of a single vertical spectral peak frequency is sufficient to obtain a realistic vertical dispersion parameter characterizing the dispersive effect in the turbulent environment of the surface neutral atmospheric boundary layer.
{"title":"Investigating the Turbulent Vertical Dispersion in a Strong Shear Dominated Neutral Atmospheric Boundary Layer","authors":"Gervásio Annes Degrazia, Felipe Denardin Costa, Luís Gustavo Nogueira Martins, Luis Fernando Camponogara, Michel Stefanello, Cinara Ewerling da Rosa, Tiziano Tirabassi","doi":"10.3390/atmos15091068","DOIUrl":"https://doi.org/10.3390/atmos15091068","url":null,"abstract":"The primary focus of this article is to derive a solution to obtain the asymptotic turbulent dispersion parameter provided by the spectral Taylor statistical diffusion model. Unlike previous articles, which employed the Dirac delta function to solve the eddy diffusivity formula, in this study, we used the Dirac delta function properties to obtain directly the asymptotic turbulent dispersion parameter from the particles’ spatial dispersion variance described in terms of the Eulerian turbulence spectrum and of the scale factor defined formally as the ratio between Lagrangian and Eulerian timescales. From the Kolmogorov 1941 theory, a detailed derivation for this scale factor is presented. Furthermore, using high mean wind speed data generated by local topographic features, a magnitude for the Kolmogorov constant for the neutral atmospheric boundary layer is evaluated. Thus, this magnitude when added to other values obtained from the selected studies found in the literature provides an average value for the Kolmogorov constant that agrees with large eddy simulation data results. Therefore, this average value allows to obtain a more reliable description of this scale factor. Finally, employing analytical formulations for the observed neutral turbulent spectra and for the velocity variances as well as turbulent statistical quantities measured in a surface neutral atmospheric boundary layer, a vertical dispersion parameter is derived. This vertical dispersion parameter when utilized in a simple Gaussian diffusion model is able to reproduce well contaminant observed concentrations.The Gaussian simulated concentrations also compare well with those simulated by a Lagrangian stochastic particle dispersion model that uses observed vertical spectral peak frequency values at distinct levels of the neutral surface boundary layer. Therefore, the present study shows that the observational determination of a single vertical spectral peak frequency is sufficient to obtain a realistic vertical dispersion parameter characterizing the dispersive effect in the turbulent environment of the surface neutral atmospheric boundary layer.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190226","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}
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}
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