Pei Xing, Ruozi Yang, Wupeng Du, Ya Gao, Chunyi Xuan, Jiayi Zhang, Jun Wang, Mengxin Bai, Bing Dang, Feilin Xiong
With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.
{"title":"Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing","authors":"Pei Xing, Ruozi Yang, Wupeng Du, Ya Gao, Chunyi Xuan, Jiayi Zhang, Jun Wang, Mengxin Bai, Bing Dang, Feilin Xiong","doi":"10.3390/atmos15091132","DOIUrl":"https://doi.org/10.3390/atmos15091132","url":null,"abstract":"With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248090","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}
Radon concentrations in the tourist part of the Cango cave were measured using 25 strategically placed electret ion chambers. Airflow rates were also measured and found to be less than 1 m/s throughout the cave. An IDW interpolated radon concentration overlay was constructed using QGIS and overlayed on maps of the cave. The maximum radon concentration of 2625 Bq/m3 was measured in the Grand Hall, located in the central part of the cave following a narrow passage. The initial part of the cave near the entrance exhibited normal cave breathing characteristics, with radon concentrations of less than 300 Bq/m3. The deepest section of the cave, however, demonstrated an unexpected decrease in radon levels, temperature, and humidity. The average radon concentration in the Cango cave, measured at 1265 Bq/m3, is relatively low compared to other caves worldwide that need mitigation measures according to the International Commission on Radiological Protection (ICRP).
{"title":"Investigating Radon Concentrations in the Cango Cave, South Africa","authors":"Jacques Bezuidenhout, Rikus le Roux","doi":"10.3390/atmos15091133","DOIUrl":"https://doi.org/10.3390/atmos15091133","url":null,"abstract":"Radon concentrations in the tourist part of the Cango cave were measured using 25 strategically placed electret ion chambers. Airflow rates were also measured and found to be less than 1 m/s throughout the cave. An IDW interpolated radon concentration overlay was constructed using QGIS and overlayed on maps of the cave. The maximum radon concentration of 2625 Bq/m3 was measured in the Grand Hall, located in the central part of the cave following a narrow passage. The initial part of the cave near the entrance exhibited normal cave breathing characteristics, with radon concentrations of less than 300 Bq/m3. The deepest section of the cave, however, demonstrated an unexpected decrease in radon levels, temperature, and humidity. The average radon concentration in the Cango cave, measured at 1265 Bq/m3, is relatively low compared to other caves worldwide that need mitigation measures according to the International Commission on Radiological Protection (ICRP).","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248122","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 equilibrium factor F is one of the parameters that should be considered when assessing the effective dose based on radon activity concentration. Since the equilibrium factor in various environments ranges theoretically from a value close to 0 to 1, it is expected that dose assessment based on one recommended coefficient value may lead to an underestimation or overestimation of the dose. That is why it is essential to measure this quantity if the basis for dose assessment is the radon concentration and not the concentration of radon decay products. The equilibrium factors were determined based on measurements of radon activity concentration and potential alpha energy concentration and varied from 0.15 to 0.94, with an arithmetic mean of 0.55. The average effective dose calculated for the employee taking into account these values was 31 mSv, assuming an annual working time of 1800 h. In turn, the average effective dose calculated for the equilibrium factor of 0.2 as recommended by the International Commission on Radiological Protection (ICRP) was equal to 13 mSv.
{"title":"Radon Equilibrium Factor and the Assessment of the Annual Effective Dose at Underground Workplaces","authors":"Agata Grygier, Krystian Skubacz","doi":"10.3390/atmos15091131","DOIUrl":"https://doi.org/10.3390/atmos15091131","url":null,"abstract":"The equilibrium factor F is one of the parameters that should be considered when assessing the effective dose based on radon activity concentration. Since the equilibrium factor in various environments ranges theoretically from a value close to 0 to 1, it is expected that dose assessment based on one recommended coefficient value may lead to an underestimation or overestimation of the dose. That is why it is essential to measure this quantity if the basis for dose assessment is the radon concentration and not the concentration of radon decay products. The equilibrium factors were determined based on measurements of radon activity concentration and potential alpha energy concentration and varied from 0.15 to 0.94, with an arithmetic mean of 0.55. The average effective dose calculated for the employee taking into account these values was 31 mSv, assuming an annual working time of 1800 h. In turn, the average effective dose calculated for the equilibrium factor of 0.2 as recommended by the International Commission on Radiological Protection (ICRP) was equal to 13 mSv.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"41 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248089","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}
With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.
{"title":"In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California","authors":"Reshmasri Deevi, Mingming Lu","doi":"10.3390/atmos15091130","DOIUrl":"https://doi.org/10.3390/atmos15091130","url":null,"abstract":"With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"36 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248088","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}
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}
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