Polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F) emissions during the roasting of green Arabica coffee and coffee formulations containing alcohol, sugar, and honey were investigated in this study. Fast and slow roasting methods, which took 5.62 and 9.65 min to achieve a light roast, respectively, were used to evaluate the emissions. The concentrations in the flue gas during the fast roasting of green coffee (0.0296 ng Nm−3 and 0.00364 ng WHO-TEQ Nm−3) were 13.9% and 70.5% higher than during slow roasting, respectively. However, this was only the trend for some formulations, and no significant differences in concentrations were found between the methods at p = 0.05. Thus, the slow roasting method might not necessarily reduce formation when additives are included. The emission factors were 2.86 ng kg−1 and 0.352 ng WHO-TEQ kg−1, and 4.17 ng kg−1 and 0.176 ng WHO-TEQ kg−1 for the fast and slow roasting of green coffee, respectively. Further investigations are warranted to understand the formation mechanism, assess potential health risks, and explore mitigation strategies. These findings have implications for both coffee processing industries and regulatory bodies, as understanding the impact of roasting methods and additives could inform the development of cleaner production practices and targeted emission reduction policies.
本研究调查了阿拉比卡生咖啡和含酒精、糖和蜂蜜的咖啡配方在烘焙过程中的多氯二苯并对二恶英/多氯二苯并呋喃（PCDD/F）排放情况。采用快速和慢速烘焙法对排放物进行了评估，这两种烘焙法分别需要 5.62 分钟和 9.65 分钟才能达到轻度烘焙的效果。在快速烘焙生咖啡时，烟道气中的浓度（0.0296 ng Nm-3 和 0.00364 ng WHO-TEQ Nm-3）分别比慢速烘焙时高 13.9% 和 70.5%。不过，这只是某些配方的趋势，在 p = 0.05 的条件下，两种方法的浓度没有显著差异。因此，在加入添加剂的情况下，慢焙烧方法不一定会减少形成。快速和慢速烘焙生咖啡的排放因子分别为 2.86 ng kg-1 和 0.352 ng WHO-TEQ kg-1，以及 4.17 ng kg-1 和 0.176 ng WHO-TEQ kg-1。为了解其形成机制、评估潜在的健康风险并探索缓解策略，有必要开展进一步的调查。这些发现对咖啡加工业和监管机构都有影响，因为了解烘焙方法和添加剂的影响可以为制定清洁生产方法和有针对性的减排政策提供信息。
{"title":"Emissions of Polychlorinated Dibenzo-p-Dioxins/Dibenzofurans during Coffee Roasting: Exploring the Influence of Roasting Methods and Formulations","authors":"Li-Man Lin, I-Jen Chen, Bo-Wun Huang, Nicholas Kiprotich Cheruiyot, Guo-Ping Chang-Chien","doi":"10.3390/atmos15091127","DOIUrl":"https://doi.org/10.3390/atmos15091127","url":null,"abstract":"Polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F) emissions during the roasting of green Arabica coffee and coffee formulations containing alcohol, sugar, and honey were investigated in this study. Fast and slow roasting methods, which took 5.62 and 9.65 min to achieve a light roast, respectively, were used to evaluate the emissions. The concentrations in the flue gas during the fast roasting of green coffee (0.0296 ng Nm−3 and 0.00364 ng WHO-TEQ Nm−3) were 13.9% and 70.5% higher than during slow roasting, respectively. However, this was only the trend for some formulations, and no significant differences in concentrations were found between the methods at p = 0.05. Thus, the slow roasting method might not necessarily reduce formation when additives are included. The emission factors were 2.86 ng kg−1 and 0.352 ng WHO-TEQ kg−1, and 4.17 ng kg−1 and 0.176 ng WHO-TEQ kg−1 for the fast and slow roasting of green coffee, respectively. Further investigations are warranted to understand the formation mechanism, assess potential health risks, and explore mitigation strategies. These findings have implications for both coffee processing industries and regulatory bodies, as understanding the impact of roasting methods and additives could inform the development of cleaner production practices and targeted emission reduction policies.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248127","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}
Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.
{"title":"Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices","authors":"Ali Gholinia, Peyman Abbaszadeh","doi":"10.3390/atmos15091129","DOIUrl":"https://doi.org/10.3390/atmos15091129","url":null,"abstract":"Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"17 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248126","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}
Dunia Waked, Mariana Matera Veras, Paulo Hilário Nascimento Saldiva, Ana Paula Cremasco Takano
Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects.
{"title":"A New Method Proposed for the Estimation of Exposure to Atmospheric Pollution through the Analysis of Black Pigments on the Lung Surface","authors":"Dunia Waked, Mariana Matera Veras, Paulo Hilário Nascimento Saldiva, Ana Paula Cremasco Takano","doi":"10.3390/atmos15091126","DOIUrl":"https://doi.org/10.3390/atmos15091126","url":null,"abstract":"Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"16 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248129","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}
An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability.
{"title":"Calibration of Typhoon Track Forecasts Based on Deep Learning Methods","authors":"Chengchen Tao, Zhizu Wang, Yilun Tian, Yaoyao Han, Keke Wang, Qiang Li, Juncheng Zuo","doi":"10.3390/atmos15091125","DOIUrl":"https://doi.org/10.3390/atmos15091125","url":null,"abstract":"An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248124","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}
Hydrocarbon traps in the air intake system (AIS) are a common method for controlling evaporative emissions from the air intake path. Several different systems are available, but there is no standard method for determining their efficiencies. Therefore, a component test rig for hydrocarbon traps was developed. Some optimizations were necessary to achieve emission characteristics observed in engine measurements. Using this setup, several measurements were performed on four different hydrocarbon traps. The results were in reasonable agreement with those from engine measurements. Two different hydrocarbon (HC) traps were selected for further studies. In these studies, the repeatability and the dependency of the emission mass level were investigated. Furthermore, the hydrocarbon concentration in the air filter box was determined using point source flame ionization detector (FID) sampling and a metal oxide semiconductor (MOS) sensor. The data showed a correlation with the emission mass determined in a sealed housing emission determination (SHED) test.
{"title":"Hydrocarbon Traps for the Air Intake System: Component Test Rig and SHED Test Procedure for Determining Their Efficiencies","authors":"Matthias Brunnermeier","doi":"10.3390/atmos15091128","DOIUrl":"https://doi.org/10.3390/atmos15091128","url":null,"abstract":"Hydrocarbon traps in the air intake system (AIS) are a common method for controlling evaporative emissions from the air intake path. Several different systems are available, but there is no standard method for determining their efficiencies. Therefore, a component test rig for hydrocarbon traps was developed. Some optimizations were necessary to achieve emission characteristics observed in engine measurements. Using this setup, several measurements were performed on four different hydrocarbon traps. The results were in reasonable agreement with those from engine measurements. Two different hydrocarbon (HC) traps were selected for further studies. In these studies, the repeatability and the dependency of the emission mass level were investigated. Furthermore, the hydrocarbon concentration in the air filter box was determined using point source flame ionization detector (FID) sampling and a metal oxide semiconductor (MOS) sensor. The data showed a correlation with the emission mass determined in a sealed housing emission determination (SHED) test.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"28 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248125","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 introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.
{"title":"Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains","authors":"George Efthimiou","doi":"10.3390/atmos15091124","DOIUrl":"https://doi.org/10.3390/atmos15091124","url":null,"abstract":"This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"12 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248130","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}
Michelle Simões Reboita, Enrique Vieira Mattos, Bruno César Capucin, Diego Oliveira de Souza, Glauber Willian de Souza Ferreira
Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km−2 in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence.
{"title":"A Multi-Scale Analysis of the Extreme Precipitation in Southern Brazil in April/May 2024","authors":"Michelle Simões Reboita, Enrique Vieira Mattos, Bruno César Capucin, Diego Oliveira de Souza, Glauber Willian de Souza Ferreira","doi":"10.3390/atmos15091123","DOIUrl":"https://doi.org/10.3390/atmos15091123","url":null,"abstract":"Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km−2 in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"194 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248128","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}
Moon-Seok Kang, Da-Som Park, Chan-Byeong Chae, Young Sunwoo, Ki-Ho Hong
The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NOx emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea).
{"title":"Monthly Characteristics and Source–Receptor Relationships of Anthropogenic Total Nitrate in Northeast Asia","authors":"Moon-Seok Kang, Da-Som Park, Chan-Byeong Chae, Young Sunwoo, Ki-Ho Hong","doi":"10.3390/atmos15091121","DOIUrl":"https://doi.org/10.3390/atmos15091121","url":null,"abstract":"The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NOx emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea).","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"65 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248131","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}
Jason A. Miech, Saed Aker, Zhaobo Zhang, Hasan Ozer, Matthew P. Fraser, Pierre Herckes
With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer.
{"title":"Tire Wear Emissions by Highways: Impact of Season and Surface Type","authors":"Jason A. Miech, Saed Aker, Zhaobo Zhang, Hasan Ozer, Matthew P. Fraser, Pierre Herckes","doi":"10.3390/atmos15091122","DOIUrl":"https://doi.org/10.3390/atmos15091122","url":null,"abstract":"With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248084","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}
Elena A. Kukavskaya, Anna V. Bogorodskaya, Ludmila V. Buryak, Olga P. Kalenskaya, Susan G. Conard
Wildfires and logging play an important role in regulating soil carbon fluxes in forest ecosystems. In Siberia, large areas are disturbed by fires and logging annually. Climate change and increasing anthropogenic pressure have resulted in the expansion of disturbed areas in recent decades. However, few studies have focused on the effects of these disturbances on soil CO2 efflux in the vast Siberian areas. The objective of our research was to evaluate differences in CO2 efflux from soils to the atmosphere between undisturbed sites and sites affected by wildfire and logging in Scots pine forests of southern Siberia. We examined 35 plots (undisturbed forest, burned forest, logged plots, and logged and burned plots) on six study sites in the Angara region and four sites in the Zabaikal region. Soil CO2 efflux was measured using an LI-800 infrared gas analyzer. We found that both fire and logging significantly reduced soil efflux in the first years after a disturbance due to a reduction in vegetation biomass and consumption of the forest floor. We found a substantially lower CO2 efflux in forests burned by high-severity fires (74% less compared to undisturbed forests) than in forests burned by moderate-severity (60% less) and low-severity (37% less) fires. Clearcut logging resulted in 6–60% lower soil CO2 efflux at most study sites, while multiple disturbances (logging and fire) had 48–94% lower efflux. The soil efflux rate increased exponentially with increasing soil temperature in undisturbed Scots pine forests (p < 0.001) and on logged plots (p < 0.03), while an inverse relationship to soil temperature was observed in burned forests (p < 0.03). We also found a positive relationship (R = 0.60–0.83, p < 0.001) between ground cover depth and soil CO2 efflux across all the plots studied. Our results demonstrate the importance of disturbance factors in the assessment of regional and global carbon fluxes. The drastic changes in CO2 flux rates following fire and logging should be incorporated into carbon balance models to improve their reliability in a changing environment.
{"title":"Effects of Wildfire and Logging on Soil CO2 Efflux in Scots Pine Forests of Siberia","authors":"Elena A. Kukavskaya, Anna V. Bogorodskaya, Ludmila V. Buryak, Olga P. Kalenskaya, Susan G. Conard","doi":"10.3390/atmos15091117","DOIUrl":"https://doi.org/10.3390/atmos15091117","url":null,"abstract":"Wildfires and logging play an important role in regulating soil carbon fluxes in forest ecosystems. In Siberia, large areas are disturbed by fires and logging annually. Climate change and increasing anthropogenic pressure have resulted in the expansion of disturbed areas in recent decades. However, few studies have focused on the effects of these disturbances on soil CO2 efflux in the vast Siberian areas. The objective of our research was to evaluate differences in CO2 efflux from soils to the atmosphere between undisturbed sites and sites affected by wildfire and logging in Scots pine forests of southern Siberia. We examined 35 plots (undisturbed forest, burned forest, logged plots, and logged and burned plots) on six study sites in the Angara region and four sites in the Zabaikal region. Soil CO2 efflux was measured using an LI-800 infrared gas analyzer. We found that both fire and logging significantly reduced soil efflux in the first years after a disturbance due to a reduction in vegetation biomass and consumption of the forest floor. We found a substantially lower CO2 efflux in forests burned by high-severity fires (74% less compared to undisturbed forests) than in forests burned by moderate-severity (60% less) and low-severity (37% less) fires. Clearcut logging resulted in 6–60% lower soil CO2 efflux at most study sites, while multiple disturbances (logging and fire) had 48–94% lower efflux. The soil efflux rate increased exponentially with increasing soil temperature in undisturbed Scots pine forests (p < 0.001) and on logged plots (p < 0.03), while an inverse relationship to soil temperature was observed in burned forests (p < 0.03). We also found a positive relationship (R = 0.60–0.83, p < 0.001) between ground cover depth and soil CO2 efflux across all the plots studied. Our results demonstrate the importance of disturbance factors in the assessment of regional and global carbon fluxes. The drastic changes in CO2 flux rates following fire and logging should be incorporated into carbon balance models to improve their reliability in a changing environment.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"5 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248083","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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