In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.
{"title":"Remote Sensing Monitoring and Multidimensional Impact Factor Analysis of Urban Heat Island Effect in Zhengzhou City","authors":"Xiangjun Zhang, Guoqing Li, Haikun Yu, Guangxu Gao, Zhengfang Lou","doi":"10.3390/atmos15091097","DOIUrl":"https://doi.org/10.3390/atmos15091097","url":null,"abstract":"In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190159","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}
Baoqi Li, Yanyu Chi, Hang Zhou, Shaoxiong Zhang, Yao Lu
Extreme climate events are occurring frequently under global warming. Previous studies primarily focused on isolated extreme climate events, whereas research on the synergistic changes between extreme cold (EC) and extreme warm (EW) events remains limited. This study conducted trend, correlation, and dispersion analyses on EC and EW, as well as their synergistic changes, in the Sanjiang Plain from 1960 to 2019, using inverse distance weighting, statistical methods, and the Mann–Kendall test. The results indicated that cold-to-warm (C2W) and warm-to-cold (W2C) events were significantly and positively correlated with elevation, with correlation coefficients (r) of 0.76 and 0.84, respectively. Meanwhile, C2W showed a significant negative correlation with latitude (r = −0.55), while W2C also exhibited a significant negative correlation with latitude (r = −0.71). However, there was a significant positive correlation between (EC) and latitude (r = 0.65). After 1980, both the declining trend of EC and the increasing trend of EW slowed down, and the trends in C2W and W2C changed from decline to increase. The dispersion of EC and EW shows an increasing trend, while the dispersion of C2W and W2C exhibits a decreasing trend. This study provides important references for studying temperature fluctuations and addressing extreme climate changes.
{"title":"The Study of Synergistic Changes in Extreme Cold and Warm Events in the Sanjiang Plain","authors":"Baoqi Li, Yanyu Chi, Hang Zhou, Shaoxiong Zhang, Yao Lu","doi":"10.3390/atmos15091092","DOIUrl":"https://doi.org/10.3390/atmos15091092","url":null,"abstract":"Extreme climate events are occurring frequently under global warming. Previous studies primarily focused on isolated extreme climate events, whereas research on the synergistic changes between extreme cold (EC) and extreme warm (EW) events remains limited. This study conducted trend, correlation, and dispersion analyses on EC and EW, as well as their synergistic changes, in the Sanjiang Plain from 1960 to 2019, using inverse distance weighting, statistical methods, and the Mann–Kendall test. The results indicated that cold-to-warm (C2W) and warm-to-cold (W2C) events were significantly and positively correlated with elevation, with correlation coefficients (r) of 0.76 and 0.84, respectively. Meanwhile, C2W showed a significant negative correlation with latitude (r = −0.55), while W2C also exhibited a significant negative correlation with latitude (r = −0.71). However, there was a significant positive correlation between (EC) and latitude (r = 0.65). After 1980, both the declining trend of EC and the increasing trend of EW slowed down, and the trends in C2W and W2C changed from decline to increase. The dispersion of EC and EW shows an increasing trend, while the dispersion of C2W and W2C exhibits a decreasing trend. This study provides important references for studying temperature fluctuations and addressing extreme climate changes.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190179","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}
Understanding the spatiotemporal dynamics of atmospheric PM2.5 concentration is highly challenging due to its evolution processes have complex and nonlinear patterns. Traditional mode decomposition methods struggle to accurately capture the mode features of PM2.5 concentrations. In this study, we utilized the global linearization capabilities of the Koopman method to analyze the hourly and daily spatiotemporal processes of PM2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region from 2019 to 2021. This approach decomposes the data into the superposition of different spatial modes, revealing their hierarchical spatiotemporal structure and reconstructing the dynamic processes. The results show that PM2.5 concentrations exhibit high-frequency cycles of 12 and 24 h, as well as low-frequency cycles of 124 and 353 days, while also revealing spatiotemporal modes of growth, recession, and oscillation. The superposition of these modes enables the reconstruction of spatiotemporal dynamics with a mean absolute percentage error (MAPE) of only 0.6%. Unlike empirical mode decomposition (EMD), Koopman mode decomposition (KMD) method avoids mode aliasing and provides a clearer identification of global and key modes compared to wavelet analysis. These findings underscore the effectiveness of KMD method in analyzing and reconstructing the spatiotemporal dynamics of PM2.5 concentration, offering new insights into the understanding and reconstruction of other complex spatiotemporal phenomena.
{"title":"Analysis of Global and Key PM2.5 Dynamic Mode Decomposition Based on the Koopman Method","authors":"Yuhan Yu, Dantong Liu, Bin Wang, Feng Zhang","doi":"10.3390/atmos15091091","DOIUrl":"https://doi.org/10.3390/atmos15091091","url":null,"abstract":"Understanding the spatiotemporal dynamics of atmospheric PM2.5 concentration is highly challenging due to its evolution processes have complex and nonlinear patterns. Traditional mode decomposition methods struggle to accurately capture the mode features of PM2.5 concentrations. In this study, we utilized the global linearization capabilities of the Koopman method to analyze the hourly and daily spatiotemporal processes of PM2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region from 2019 to 2021. This approach decomposes the data into the superposition of different spatial modes, revealing their hierarchical spatiotemporal structure and reconstructing the dynamic processes. The results show that PM2.5 concentrations exhibit high-frequency cycles of 12 and 24 h, as well as low-frequency cycles of 124 and 353 days, while also revealing spatiotemporal modes of growth, recession, and oscillation. The superposition of these modes enables the reconstruction of spatiotemporal dynamics with a mean absolute percentage error (MAPE) of only 0.6%. Unlike empirical mode decomposition (EMD), Koopman mode decomposition (KMD) method avoids mode aliasing and provides a clearer identification of global and key modes compared to wavelet analysis. These findings underscore the effectiveness of KMD method in analyzing and reconstructing the spatiotemporal dynamics of PM2.5 concentration, offering new insights into the understanding and reconstruction of other complex spatiotemporal phenomena.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"18 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190162","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}
Zhaopeng Li, Kai Zhao, Xiaoling Yuan, Yinan Zhou, Li Yang, Hanyu Geng
China’s approach to air pollution control has been shown successful in East Asian countries and even elsewhere in the world. The analysis of the evolution and control of air pollution in China over the past 75 years can be used as a reference for developing countries suffering from air pollution resulting from urbanization. Based on the sorting and mining of relevant indicators, data and policy texts from the areas of population, economy, space and social urbanization, the findings suggest that the presence of air pollution and its changing forms indeed have complex interactive relationships with the process of urbanization. Specifically: (1) the feature of air pollution has changed from “single pollutant and pollution source to multiple pollutants and pollution source, local pollution to regional pollution, light pollution to heavy compound pollution” as a result of urbanization, the emphasizing of construction and the neglect of governance, the emphasizing of economics and the neglect of ecology, and the emphasizing of immediate interests over long-term interests; (2) the interactive relationship between air pollution and urbanization has also gone through three stages from being irrelevant each other to “urbanization determines air pollution” and then “air pollution restricts urbanization”; (3) this has forced air pollution control to shift from the traditional “treating symptoms” to “high-quality urbanization”, thus promoting air pollution and urbanization to move “from confrontation to unification”. Therefore, air pollution control is not a simple technical issue; one of the keys lies in exploring how to adjust the urbanization model, so as to achieve the “win–win” of urbanization and air pollution control.
{"title":"Evolution and Control of Air Pollution in China over the Past 75 Years: An Analytical Framework Based on the Multi-Dimensional Urbanization","authors":"Zhaopeng Li, Kai Zhao, Xiaoling Yuan, Yinan Zhou, Li Yang, Hanyu Geng","doi":"10.3390/atmos15091093","DOIUrl":"https://doi.org/10.3390/atmos15091093","url":null,"abstract":"China’s approach to air pollution control has been shown successful in East Asian countries and even elsewhere in the world. The analysis of the evolution and control of air pollution in China over the past 75 years can be used as a reference for developing countries suffering from air pollution resulting from urbanization. Based on the sorting and mining of relevant indicators, data and policy texts from the areas of population, economy, space and social urbanization, the findings suggest that the presence of air pollution and its changing forms indeed have complex interactive relationships with the process of urbanization. Specifically: (1) the feature of air pollution has changed from “single pollutant and pollution source to multiple pollutants and pollution source, local pollution to regional pollution, light pollution to heavy compound pollution” as a result of urbanization, the emphasizing of construction and the neglect of governance, the emphasizing of economics and the neglect of ecology, and the emphasizing of immediate interests over long-term interests; (2) the interactive relationship between air pollution and urbanization has also gone through three stages from being irrelevant each other to “urbanization determines air pollution” and then “air pollution restricts urbanization”; (3) this has forced air pollution control to shift from the traditional “treating symptoms” to “high-quality urbanization”, thus promoting air pollution and urbanization to move “from confrontation to unification”. Therefore, air pollution control is not a simple technical issue; one of the keys lies in exploring how to adjust the urbanization model, so as to achieve the “win–win” of urbanization and air pollution control.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"36 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190180","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}
In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.
{"title":"A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought","authors":"Jingjing Fan, Yue Zhao, Dongnan Wang, Xiong Zhou, Yunyun Li, Wenwei Zhang, Fanfan Xu, Shibo Wei","doi":"10.3390/atmos15091094","DOIUrl":"https://doi.org/10.3390/atmos15091094","url":null,"abstract":"In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"58 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190183","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}
Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.
准确的风速预报是提供精细化专业气象服务（如风能发电和交通运营等）的关键环节。本文利用2022年1月、4月、7月和10月的CMA-MESO模式预报资料和CARAS-SUR_1 km地面真实网格资料，采用随机森林算法，建立并评估了河北省西北部复杂地形区近地面1 km分辨率风速网格预报降尺度校正模型。结果表明，经过降尺度校正后，整个复杂地形研究区网格预报风速的空间分布更加精细，空间分辨率从 3 km 提高到 1 km，反映了精细尺度的地形效应。与原始模型相比，修正后的风速预报精度显著提高，预报误差在时间和空间上都表现出稳定性。平均偏差从 2.25 m/s 降至 0.02 m/s，均方根误差（RMSE）从 3.26 m/s 降至 0.52 m/s。复杂地形、预报准备时间和季节因素造成的预报误差明显减小。就风速类别而言，修正后的预报明显改善了风速低于 8 m/s 的预报，RMSE 从 2.02 m/s 降至 0.59 m/s。对于 8 米/秒以上的风速，校正效果也很好，均方根误差从 2.20 米/秒降至 1.65 米/秒。选择 2022 年 4 月 26 日张家口强风过程进行分析，发现降尺度校正后的预报风速与观测站和地面实况格点的观测风速非常接近。特别是在受强风影响的地区，如巴山高原和山谷，校正效果尤为显著，具有重要的参考价值。
{"title":"Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain","authors":"Xin Liu, Zhimin Li, Yanbo Shen","doi":"10.3390/atmos15091090","DOIUrl":"https://doi.org/10.3390/atmos15091090","url":null,"abstract":"Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190161","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}
Dominic Clements, Matthew Coburn, Simon J. Cox, Florentin M. J. Bulot, Zheng-Tong Xie, Christina Vanderwel
The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a `normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.
{"title":"Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume","authors":"Dominic Clements, Matthew Coburn, Simon J. Cox, Florentin M. J. Bulot, Zheng-Tong Xie, Christina Vanderwel","doi":"10.3390/atmos15091089","DOIUrl":"https://doi.org/10.3390/atmos15091089","url":null,"abstract":"The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a `normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190215","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}
Cláudia Penedos, Guillermo Salamanca, Beatriz Tavares, João Fonseca, Pedro Carreiro-Martins, Rodrigo Rodrigues-Alves, Ángel Moral de Gregorio, Antonio Valero, Manuel Branco Ferreira
Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., >200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.
{"title":"Aerobiology of Olive Pollen (Olea europaea L.) in the Atmosphere of the Iberian Peninsula","authors":"Cláudia Penedos, Guillermo Salamanca, Beatriz Tavares, João Fonseca, Pedro Carreiro-Martins, Rodrigo Rodrigues-Alves, Ángel Moral de Gregorio, Antonio Valero, Manuel Branco Ferreira","doi":"10.3390/atmos15091087","DOIUrl":"https://doi.org/10.3390/atmos15091087","url":null,"abstract":"Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., >200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190184","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}
Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta
We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R2 = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R2 = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons.
{"title":"Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain","authors":"Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta","doi":"10.3390/atmos15091085","DOIUrl":"https://doi.org/10.3390/atmos15091085","url":null,"abstract":"We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R2 = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R2 = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"39 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190181","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}
Beryllium is a lightweight metal that is toxic to humans. The critical health effects related to beryllium exposure are liver toxicity, immune system toxicity, and chronic beryllium disease (CBD). This study investigated the effects of occupational beryllium exposure on liver and lung function and hematologic parameters among beryllium smelter workers. A cross-sectional study was performed by comparing 65 exposed workers and 34 non-exposed workers. Health information was collected through questionnaire surveys and biochemical tests. The concentration of urinary beryllium was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings indicated that the urinary beryllium levels of the exposed workers and the controls were 0.48 (0.115, 1.19) μg/mL and 0.0125 (0.005, 0.005) μg/mL, respectively (p < 0.001). Compared with the controls, the exposed workers showed a significant increase in serum alanine aminotransferase (ALT) level, hemoglobin (HGB) concentration, white blood cell (WBC) count, red blood cell (RBC) count, and systolic and diastolic blood pressure (SBP, DBP) (p < 0.05). Furthermore, the HGB concentration and ALT level were significantly correlated with the concentration of beryllium in urine (p < 0.05). The exposed workers had increased urinary concentrations of beryllium, in contrast to the control subjects. Moreover, the urinary beryllium levels among the exposed workers are much higher than that in the Chinese general population. Beryllium-exposed workers may be at risk of liver and hematologic impairments.
{"title":"Impact of Chronic Beryllium Exposure on Liver and Lung Function and Hematologic Parameters","authors":"Jing Dai, Xinlin Bi, Hui Yuan, Qingyu Meng, Yina Yang, Xueqin Wang, Xiaoying Ma, Chunguang Ding, Fen Wang","doi":"10.3390/atmos15091086","DOIUrl":"https://doi.org/10.3390/atmos15091086","url":null,"abstract":"Beryllium is a lightweight metal that is toxic to humans. The critical health effects related to beryllium exposure are liver toxicity, immune system toxicity, and chronic beryllium disease (CBD). This study investigated the effects of occupational beryllium exposure on liver and lung function and hematologic parameters among beryllium smelter workers. A cross-sectional study was performed by comparing 65 exposed workers and 34 non-exposed workers. Health information was collected through questionnaire surveys and biochemical tests. The concentration of urinary beryllium was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings indicated that the urinary beryllium levels of the exposed workers and the controls were 0.48 (0.115, 1.19) μg/mL and 0.0125 (0.005, 0.005) μg/mL, respectively (p < 0.001). Compared with the controls, the exposed workers showed a significant increase in serum alanine aminotransferase (ALT) level, hemoglobin (HGB) concentration, white blood cell (WBC) count, red blood cell (RBC) count, and systolic and diastolic blood pressure (SBP, DBP) (p < 0.05). Furthermore, the HGB concentration and ALT level were significantly correlated with the concentration of beryllium in urine (p < 0.05). The exposed workers had increased urinary concentrations of beryllium, in contrast to the control subjects. Moreover, the urinary beryllium levels among the exposed workers are much higher than that in the Chinese general population. Beryllium-exposed workers may be at risk of liver and hematologic impairments.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190182","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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