As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO2 flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO2 flux. Results indicate that throughout the observation period, the range of CO2 flux in the paddy field was −0.1 to −38.4 μmol/(m2·s), with a mean of −12.9 μmol/(m2·s). The highest CO2 flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO2 absorption. Diurnal variation in CO2 flux exhibited a “U”-shaped curve, with flux reaching its peak absorption at 11:30. The CO2 flux was notably higher in the morning than in the afternoon. The nocturnal CO2 flux remained relatively stable, primarily originating from respiratory CO2 emissions. The rice canopy CO2 flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO2 flux. The simulation of CO2 flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO2 flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors.
{"title":"Observation and Simulation of CO2 Fluxes in Rice Paddy Ecosystems Based on the Eddy Covariance Technique","authors":"Jinghan Wang, Jiayan Wang, Hui Zhao, Youfei Zheng","doi":"10.3390/atmos15050517","DOIUrl":"https://doi.org/10.3390/atmos15050517","url":null,"abstract":"As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO2 flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO2 flux. Results indicate that throughout the observation period, the range of CO2 flux in the paddy field was −0.1 to −38.4 μmol/(m2·s), with a mean of −12.9 μmol/(m2·s). The highest CO2 flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO2 absorption. Diurnal variation in CO2 flux exhibited a “U”-shaped curve, with flux reaching its peak absorption at 11:30. The CO2 flux was notably higher in the morning than in the afternoon. The nocturnal CO2 flux remained relatively stable, primarily originating from respiratory CO2 emissions. The rice canopy CO2 flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO2 flux. The simulation of CO2 flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO2 flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"61 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xue Jiang, Xugeng Cheng, Jane Liu, Zhixiong Chen, Hong Wang, Huiying Deng, Jun Hu, Yongcheng Jiang, Mengmiao Yang, Chende Gai, Zhiqiang Cheng
The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O3 variability from diurnal to seasonal scales. Our results show that in comparison with the lowland urban areas (coastal areas), the mountainous forest areas (inland areas) are characterized with less human activates, lower precursor emissions, wetter and colder meteorological conditions, and denser vegetation covers. This can lead to lower chemical O3 production and higher O3 deposition rates in the inland areas. The annual mean of 8-h O3 maximum concentrations (MDA8 O3) in the inland areas are ~15 μg·m−3 (i.e. ~15%) lower than that in the coastal areas. The day-to-day variation in surface O3 in the two types of the areas is rather similar, with a correlation coefficient of 0.75 between them, suggesting similar influences on large scales, such as weather patterns, regional O3 transport, and background O3. Over 2016–2020, O3 concentrations in all the areas shows a trend of “rising and then falling”, with a peak in 2017 and 2018. Daily MDA8 O3 correlates with solar radiation most in the coastal areas, while in the inland areas, it is correlated with relative humidity most. Diurnally, during the morning, O3 concentrations in the inland areas increase faster than in the coastal areas in most seasons, mainly due to a faster increase in temperature and decrease in humidity. While in the evening, O3 concentrations decrease faster in the inland areas than in the coastal areas, mostly attributable to a higher titration effect in the inland areas. Seasonally, both areas share a double-peak variation in O3 concentrations, with two peaks in spring and autumn and two valleys in summer and winter. We found that the valley in summer is related to the summer Asian monsoon that induces large-scale convections bringing local O3 upward but blocking inflow of O3 downward, while the one in winter is due to low O3 production. The coastal areas experienced more exceedance days (~30 days per year) than inland areas (~5-10 days per year), with O3 sources largely from the northeast. Overall, the similarities and differences in O3 concentrations between inland and coastal areas in southeastern China are rather unique, reflecting the collective impact of geographic-related meteorology, O3 precursor emissions, and vegetation on surface O3 concentrations.
{"title":"Comparison of Surface Ozone Variability in Mountainous Forest Areas and Lowland Urban Areas in Southeast China","authors":"Xue Jiang, Xugeng Cheng, Jane Liu, Zhixiong Chen, Hong Wang, Huiying Deng, Jun Hu, Yongcheng Jiang, Mengmiao Yang, Chende Gai, Zhiqiang Cheng","doi":"10.3390/atmos15050519","DOIUrl":"https://doi.org/10.3390/atmos15050519","url":null,"abstract":"The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O3 variability from diurnal to seasonal scales. Our results show that in comparison with the lowland urban areas (coastal areas), the mountainous forest areas (inland areas) are characterized with less human activates, lower precursor emissions, wetter and colder meteorological conditions, and denser vegetation covers. This can lead to lower chemical O3 production and higher O3 deposition rates in the inland areas. The annual mean of 8-h O3 maximum concentrations (MDA8 O3) in the inland areas are ~15 μg·m−3 (i.e. ~15%) lower than that in the coastal areas. The day-to-day variation in surface O3 in the two types of the areas is rather similar, with a correlation coefficient of 0.75 between them, suggesting similar influences on large scales, such as weather patterns, regional O3 transport, and background O3. Over 2016–2020, O3 concentrations in all the areas shows a trend of “rising and then falling”, with a peak in 2017 and 2018. Daily MDA8 O3 correlates with solar radiation most in the coastal areas, while in the inland areas, it is correlated with relative humidity most. Diurnally, during the morning, O3 concentrations in the inland areas increase faster than in the coastal areas in most seasons, mainly due to a faster increase in temperature and decrease in humidity. While in the evening, O3 concentrations decrease faster in the inland areas than in the coastal areas, mostly attributable to a higher titration effect in the inland areas. Seasonally, both areas share a double-peak variation in O3 concentrations, with two peaks in spring and autumn and two valleys in summer and winter. We found that the valley in summer is related to the summer Asian monsoon that induces large-scale convections bringing local O3 upward but blocking inflow of O3 downward, while the one in winter is due to low O3 production. The coastal areas experienced more exceedance days (~30 days per year) than inland areas (~5-10 days per year), with O3 sources largely from the northeast. Overall, the similarities and differences in O3 concentrations between inland and coastal areas in southeastern China are rather unique, reflecting the collective impact of geographic-related meteorology, O3 precursor emissions, and vegetation on surface O3 concentrations.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"43 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Tomazin, Andreja Kukec, Viktor Švigelj, Janez Mulec, Tadeja Matos
Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the low microbial load in the air and the absence of other aeroallergens, but on the other hand, due to the lack of comprehensive air monitoring, there is little information on the pristine and human-influenced aerobiota in such environments. The aim of this study was to assess the anthropogenic effects of speleotherapy on the air microbiota and to investigate its potential impact on human health in Sežana Hospital Cave (Slovenia). From May 2020 to January 2023, air samples were collected in the cave before and after speleotherapeutic activities using two different volumetric air sampling methods—impaction and impingement—to isolate airborne microbiota. Along with sampling, environmental data were measured (CO2, humidity, wind, and temperature) to explore the anthropogenic effects on the aerobiota. While the presence of patients increased microbial concentrations by at least 83.3%, other parameters exhibited a lower impact or were attributed to seasonal changes. The structure and dynamics of the airborne microbiota are similar to those in show caves, indicating anthropization of the cave. Locally, concentrations of culturable microorganisms above 1000 CFU/m3 were detected, which could have negative or unpredictable effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria and fungi typically associated with human microbiota was found in the air and identified by MALDI-TOF MS with a 90.9% identification success rate. Micrococcus luteus, Kocuria rosea, Staphylococcus hominis, and Staphylococcus capitis were identified as reliable indicators of cave anthropization.
{"title":"Effects of Speleotherapy on Aerobiota: A Case Study from the Sežana Hospital Cave, Slovenia","authors":"R. Tomazin, Andreja Kukec, Viktor Švigelj, Janez Mulec, Tadeja Matos","doi":"10.3390/atmos15050518","DOIUrl":"https://doi.org/10.3390/atmos15050518","url":null,"abstract":"Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the low microbial load in the air and the absence of other aeroallergens, but on the other hand, due to the lack of comprehensive air monitoring, there is little information on the pristine and human-influenced aerobiota in such environments. The aim of this study was to assess the anthropogenic effects of speleotherapy on the air microbiota and to investigate its potential impact on human health in Sežana Hospital Cave (Slovenia). From May 2020 to January 2023, air samples were collected in the cave before and after speleotherapeutic activities using two different volumetric air sampling methods—impaction and impingement—to isolate airborne microbiota. Along with sampling, environmental data were measured (CO2, humidity, wind, and temperature) to explore the anthropogenic effects on the aerobiota. While the presence of patients increased microbial concentrations by at least 83.3%, other parameters exhibited a lower impact or were attributed to seasonal changes. The structure and dynamics of the airborne microbiota are similar to those in show caves, indicating anthropization of the cave. Locally, concentrations of culturable microorganisms above 1000 CFU/m3 were detected, which could have negative or unpredictable effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria and fungi typically associated with human microbiota was found in the air and identified by MALDI-TOF MS with a 90.9% identification success rate. Micrococcus luteus, Kocuria rosea, Staphylococcus hominis, and Staphylococcus capitis were identified as reliable indicators of cave anthropization.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reem K. Alshammari, Omer Alrwais, Mehmet Sabih Aksoy
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional network (TCN). This study highlights the effectiveness of LSTM and TCN models in capturing the complex temporal dynamics of dust storms and demonstrates that they outperform traditional methods, as evidenced by their lower mean absolute error (MAE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the LSTM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64, and R2 score of 0.64. In Jeddah, the LSTM model also exhibited an MAE of 2.48 and an RMSE of 2.96. This research shows the potential of using deep learning models to improve the accuracy and reliability of dust storm frequency forecasts.
{"title":"Machine Learning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features","authors":"Reem K. Alshammari, Omer Alrwais, Mehmet Sabih Aksoy","doi":"10.3390/atmos15050520","DOIUrl":"https://doi.org/10.3390/atmos15050520","url":null,"abstract":"Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional network (TCN). This study highlights the effectiveness of LSTM and TCN models in capturing the complex temporal dynamics of dust storms and demonstrates that they outperform traditional methods, as evidenced by their lower mean absolute error (MAE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the LSTM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64, and R2 score of 0.64. In Jeddah, the LSTM model also exhibited an MAE of 2.48 and an RMSE of 2.96. This research shows the potential of using deep learning models to improve the accuracy and reliability of dust storm frequency forecasts.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"46 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qin Liu, Peng Cheng, Meixia Lyu, Xinyang Yan, Qingping Xiao, Xiaoqin Li, Lei Wang, Lili Bao
Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct “single-peak” pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB.
{"title":"Impacts of Climate Change on Runoff in the Heihe River Basin, China","authors":"Qin Liu, Peng Cheng, Meixia Lyu, Xinyang Yan, Qingping Xiao, Xiaoqin Li, Lei Wang, Lili Bao","doi":"10.3390/atmos15050516","DOIUrl":"https://doi.org/10.3390/atmos15050516","url":null,"abstract":"Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct “single-peak” pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"107 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eladio Altamira-Colado, Daniel Cuevas-González, Marco A. Reyna, Juan-Pablo García-Vázquez, R. L. Avitia, Alvaro R Osornio-Vargas
Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.
{"title":"Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review","authors":"Eladio Altamira-Colado, Daniel Cuevas-González, Marco A. Reyna, Juan-Pablo García-Vázquez, R. L. Avitia, Alvaro R Osornio-Vargas","doi":"10.3390/atmos15050515","DOIUrl":"https://doi.org/10.3390/atmos15050515","url":null,"abstract":"Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"56 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140671045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events.
{"title":"An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China","authors":"Yajing Wu, Zhangyan Xu, Liping Xu, Jianxin Wei","doi":"10.3390/atmos15040460","DOIUrl":"https://doi.org/10.3390/atmos15040460","url":null,"abstract":"Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"26 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).
{"title":"Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning","authors":"Xiaoyong Gong, Ying Zhang, Meng Fan, Xinxin Zhang, Shipeng Song, Zhongbin Li","doi":"10.3390/atmos15010118","DOIUrl":"https://doi.org/10.3390/atmos15010118","url":null,"abstract":"Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"46 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xinchen Wei, Ge Liu, S. Nan, Tingting Qian, Ting Zhang, Xin Mao, Yuhan Feng, Yuwei Zhou
This study investigates the interdecadal change in the covariability between the Tibetan Plateau (TP) east–west dipole precipitation and Indian precipitation during summer and primarily explores the modulation of atmospheric circulation anomalies on the covariability. The results reveal that the western TP precipitation (WTPP), eastern TP precipitation (ETPP), and northwestern Indian precipitation (NWIP) have covariability, with an in-phase variation between the WTPP and NWIP and an out-of-phase variation between the WTPP and ETPP. Moreover, this covariability was unclear during 1981–2004 and became significant during 2005–2019, showing a clear interdecadal change. During 2005–2019, a thick geopotential height anomaly, which tilted slightly northward, governed the TP, forming upper- and lower-level coupled circulation anomalies (i.e., anomalous upper-level westerlies over the TP and lower-level southeasterlies and northeasterlies around the southern flank of the TP). As such, the upper- and lower-tropospheric circulation anomalies synergistically modulate the summer WTPP, ETPP, and NWIP, causing the covariability of summer precipitation over the TP and India during 2005–2019. The upper- or lower-level circulation anomalies cannot independently result in significant precipitation covariability. During 1981–2004, the upper- and lower-level circulation anomalies were not strongly coupled, which caused precipitation non-covariability. The sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and tropical Atlantic (TA) may synergistically modulate the upper- and lower-level coupled circulation anomalies, contributing to the covariability of the WTPP, ETPP, and NWIP during 2005–2019. The modulation of the WNP and TA SSTs on the coupled circulation anomalies was weaker during 1981–2004, which was therefore not conducive to this precipitation covariability. This study may provide valuable insights into the characteristics and mechanisms of spatiotemporal variation in summer precipitation over the TP and its adjacent regions, thus offering scientific support for local water resource management, ecological environment protection, and social and economic development.
{"title":"Interdecadal Change in the Covariability of the Tibetan Plateau and Indian Summer Precipitation and Associated Circulation Anomalies","authors":"Xinchen Wei, Ge Liu, S. Nan, Tingting Qian, Ting Zhang, Xin Mao, Yuhan Feng, Yuwei Zhou","doi":"10.3390/atmos15010117","DOIUrl":"https://doi.org/10.3390/atmos15010117","url":null,"abstract":"This study investigates the interdecadal change in the covariability between the Tibetan Plateau (TP) east–west dipole precipitation and Indian precipitation during summer and primarily explores the modulation of atmospheric circulation anomalies on the covariability. The results reveal that the western TP precipitation (WTPP), eastern TP precipitation (ETPP), and northwestern Indian precipitation (NWIP) have covariability, with an in-phase variation between the WTPP and NWIP and an out-of-phase variation between the WTPP and ETPP. Moreover, this covariability was unclear during 1981–2004 and became significant during 2005–2019, showing a clear interdecadal change. During 2005–2019, a thick geopotential height anomaly, which tilted slightly northward, governed the TP, forming upper- and lower-level coupled circulation anomalies (i.e., anomalous upper-level westerlies over the TP and lower-level southeasterlies and northeasterlies around the southern flank of the TP). As such, the upper- and lower-tropospheric circulation anomalies synergistically modulate the summer WTPP, ETPP, and NWIP, causing the covariability of summer precipitation over the TP and India during 2005–2019. The upper- or lower-level circulation anomalies cannot independently result in significant precipitation covariability. During 1981–2004, the upper- and lower-level circulation anomalies were not strongly coupled, which caused precipitation non-covariability. The sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and tropical Atlantic (TA) may synergistically modulate the upper- and lower-level coupled circulation anomalies, contributing to the covariability of the WTPP, ETPP, and NWIP during 2005–2019. The modulation of the WNP and TA SSTs on the coupled circulation anomalies was weaker during 1981–2004, which was therefore not conducive to this precipitation covariability. This study may provide valuable insights into the characteristics and mechanisms of spatiotemporal variation in summer precipitation over the TP and its adjacent regions, thus offering scientific support for local water resource management, ecological environment protection, and social and economic development.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"91 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The near-surface atmospheric electrostatic field plays a pivotal role in comprehending the global atmospheric circuit model and its influence on climate change. Prior to delving into the intricate interplay between solar activities, geological activities, and atmospheric electric field, a comprehensive examination of the diurnal fair atmospheric electric field’s baseline curve within a specific region is essential. Based on the atmospheric electric field network monitoring in Yunnan Province in the year 2022, this study systematically investigated the distribution of the atmospheric electric field under both fair-weather and disturbed weather conditions at a quadrilateral array encompassing Chuxiong Station, Mouding Station, Lufeng Station, and Dali Station. The primary focus was on elucidating the variations in the daily variation curves of fair atmospheric electric fields and conducting a comparative analysis with the Carnegie curves. The possible reasons for the differences among them are also discussed in this study, but more observational evidence is required to confirm the specific causes in the future.
{"title":"Observations and Variability of Near-Surface Atmospheric Electric Fields across Multiple Stations","authors":"Wen Li, Zhibin Sun, Zhaoai Yan, Zhongsong Ma","doi":"10.3390/atmos15010124","DOIUrl":"https://doi.org/10.3390/atmos15010124","url":null,"abstract":"The near-surface atmospheric electrostatic field plays a pivotal role in comprehending the global atmospheric circuit model and its influence on climate change. Prior to delving into the intricate interplay between solar activities, geological activities, and atmospheric electric field, a comprehensive examination of the diurnal fair atmospheric electric field’s baseline curve within a specific region is essential. Based on the atmospheric electric field network monitoring in Yunnan Province in the year 2022, this study systematically investigated the distribution of the atmospheric electric field under both fair-weather and disturbed weather conditions at a quadrilateral array encompassing Chuxiong Station, Mouding Station, Lufeng Station, and Dali Station. The primary focus was on elucidating the variations in the daily variation curves of fair atmospheric electric fields and conducting a comparative analysis with the Carnegie curves. The possible reasons for the differences among them are also discussed in this study, but more observational evidence is required to confirm the specific causes in the future.","PeriodicalId":504666,"journal":{"name":"Atmosphere","volume":"59 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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