Heavy metals in road dusts can directly pose significant health risks through ingestion, inhalation, and dermal contact. This study investigated the pollution, distribution, and health effect of heavy metals in street dust from industrial, capital city, and peri-urban areas of Bangladesh. Inductively coupled plasma mass spectrometry (ICP-MS) examined eight hazardous heavy metals such as Zn, Cu, Pb, Ni, Mn, Cr, Cd, and Co. Results revealed that industrial areas showed the highest metal concentrations, following the order Mn > Zn > Cr > Pb > Ni > Co > Cd, with an average level of 444.35, 299.25, 238.31, 54.22, 52.78, 45.66, and 2.73 mg/kg, respectively, for fine particles (≤20 μm). Conversely, multivariate statistical analyses were conducted to assess pollution levels and sources. Anthropogenic activities like traffic emissions, construction, and industrial processing were the main pollution sources. A pollution load index revealed that industrial areas had significantly higher pollution (PLI of 2.45), while the capital city and peri-urban areas experienced moderate pollution (PLI of 1.54 and 1.59). Hazard index values were below the safety level of 1, but health risk evaluations revealed increased non-carcinogenic risks for children, especially from Cr, Ni, Cd, and Pb where Cr poses the highest cancer risk via inhalation, with values reaching 1.13 × 10−4–5.96 × 10−4 falling within the threshold level (10−4 to 10−6). These results underline the need for continuous environmental monitoring and pollution control in order to lower health hazards.
{"title":"Sources, Distribution, and Health Implications of Heavy Metals in Street Dust across Industrial, Capital City, and Peri-Urban Areas of Bangladesh","authors":"Md. Sohel Rana, Qingyue Wang, Weiqian Wang, Christian Ebere Enyoh, Md. Rezwanul Islam, Yugo Isobe, Md Humayun Kabir","doi":"10.3390/atmos15091088","DOIUrl":"https://doi.org/10.3390/atmos15091088","url":null,"abstract":"Heavy metals in road dusts can directly pose significant health risks through ingestion, inhalation, and dermal contact. This study investigated the pollution, distribution, and health effect of heavy metals in street dust from industrial, capital city, and peri-urban areas of Bangladesh. Inductively coupled plasma mass spectrometry (ICP-MS) examined eight hazardous heavy metals such as Zn, Cu, Pb, Ni, Mn, Cr, Cd, and Co. Results revealed that industrial areas showed the highest metal concentrations, following the order Mn > Zn > Cr > Pb > Ni > Co > Cd, with an average level of 444.35, 299.25, 238.31, 54.22, 52.78, 45.66, and 2.73 mg/kg, respectively, for fine particles (≤20 μm). Conversely, multivariate statistical analyses were conducted to assess pollution levels and sources. Anthropogenic activities like traffic emissions, construction, and industrial processing were the main pollution sources. A pollution load index revealed that industrial areas had significantly higher pollution (PLI of 2.45), while the capital city and peri-urban areas experienced moderate pollution (PLI of 1.54 and 1.59). Hazard index values were below the safety level of 1, but health risk evaluations revealed increased non-carcinogenic risks for children, especially from Cr, Ni, Cd, and Pb where Cr poses the highest cancer risk via inhalation, with values reaching 1.13 × 10−4–5.96 × 10−4 falling within the threshold level (10−4 to 10−6). These results underline the need for continuous environmental monitoring and pollution control in order to lower health hazards.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"51 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190185","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 open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain ‘hotspots’ with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality.
{"title":"Quantifying Future Annual Fluxes of Polychlorinated Dibenzo-P-Dioxin and Dibenzofuran Emissions from Sugarcane Burning in Indonesia via Grey Model","authors":"Lailatus Siami, Yu-Chun Wang, Lin-Chi Wang","doi":"10.3390/atmos15091078","DOIUrl":"https://doi.org/10.3390/atmos15091078","url":null,"abstract":"The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs or dioxins) emissions using the grey model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. An annual emission inventory at the provincial level was developed using the activity rate of dry crop residue from national agencies and literature, following the guidelines set by the United Nations Environment Programme (UNEP). Emission distributions from 2016 to 2022 were then mapped. The average PCDD/F emission values show significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, regions with intensive sugarcane production, such as Lampung and East Java consistently show high emissions, often exceeding 400 pg/m2. Emissions calculated using the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. Emission predictions using GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected are projected to see a slight decline. This forecast assumes no changes in regional intervention strategies. Most regions in Java Island show a gradual increase in emissions, except for East Java, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions such as Gorontalo and parts of East Java are projected to remain ‘hotspots’ with consistently high emissions, highlighting the need for targeted interventions. To address emission hotspots, this study emphasizes the need for cleaner agricultural practices, enhanced enforcement of environmental regulations, and the integration of advanced monitoring technologies to mitigate the environmental and health impacts of PCDD/F emissions in Indonesia. Future studies should consider developing monthly emissions profiles to better account for local agricultural practices and seasonal conditions. The emission data generated in this study, which include both spatial and temporal distributions, are valuable for air quality modeling studies and can help assess the impact of current and future emissions on ambient air quality.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190186","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}
Xuefeng Peng, Yu Feng, Han Zang, Dan Zhao, Shiqi Zhang, Ziang Cai, Juan Wang, Peihao Peng
The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R2= 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R2 = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.
{"title":"Climate Warming Has Contributed to the Rise of Timberlines on the Eastern Tibetan Plateau but Slowed in Recent Years","authors":"Xuefeng Peng, Yu Feng, Han Zang, Dan Zhao, Shiqi Zhang, Ziang Cai, Juan Wang, Peihao Peng","doi":"10.3390/atmos15091083","DOIUrl":"https://doi.org/10.3390/atmos15091083","url":null,"abstract":"The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R2= 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R2 = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"15 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190218","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}
Fei Meng, Lifan Qi, Hongda Li, Xinyue Yang, Jiantao Liu
Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.
{"title":"Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE","authors":"Fei Meng, Lifan Qi, Hongda Li, Xinyue Yang, Jiantao Liu","doi":"10.3390/atmos15091080","DOIUrl":"https://doi.org/10.3390/atmos15091080","url":null,"abstract":"Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190187","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 precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.
{"title":"CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks","authors":"Isa Ebtehaj, Hossein Bonakdari","doi":"10.3390/atmos15091082","DOIUrl":"https://doi.org/10.3390/atmos15091082","url":null,"abstract":"Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190216","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}
Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.
{"title":"Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022","authors":"Huazhu Xue, Haojie Zhang, Zhanliang Yuan, Qianqian Ma, Hao Wang, Zhi Li","doi":"10.3390/atmos15091081","DOIUrl":"https://doi.org/10.3390/atmos15091081","url":null,"abstract":"Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"161 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190189","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}
Mario Moreira, Bernardo Rocha, Pedro Pinho, Lisa Grifoni, Stefano Loppi, Aldo Winkler
Monitoring atmospheric pollution in urban areas is challenging because pollutant deposition occurs at short distances, requiring a large amount of sampling and analysis to characterize it. Ecological indicators can help overcome this problem, allowing us to select sites with the highest deposition of pollutants from the atmosphere. Nevertheless, a major gap is the temporal characterization of the accumulation rate of magnetic particles in ecological indicators, which is critical to understand if the bioaccumulation process is linear or if saturation occurs. To overcome this problem, Parmotrema perlatum lichens were magnetically and chemically studied in a pollution gradient over space and time. Lichen transplants were exposed over 18 weeks to a high-traffic road. Results show that magnetic properties and element composition reflected both distance from the road (nonlinear decrease of up to 100 m from source) and exposure time (increasingly linearly over the entire study period with eightfold increments), showing that up to 18 weeks, the accumulation rate remained constant over time, with no saturation occurring. Chemical analysis showed a strong linear relationship between the accumulation of zinc (Zn), antimony (Sb), manganese (Mn), copper (Cu) chromium (Cr) and magnetic susceptibility. Magnetization acquisition curves reveal a time-dependent low-coercivity component, interpreted as mainly related to nonexhaust, mostly brake abrasion particle emissions. It is concluded that the magnetic properties of lichen transplants can be used in urban environments to characterize the spatial and temporal patterns of the deposition of pollution metallic particles from the atmosphere.
{"title":"Lichen Transplants for Magnetic and Chemical Biomonitoring of Airborne Particulate Matter: A Spatial and Temporal Study in Lisbon, Portugal","authors":"Mario Moreira, Bernardo Rocha, Pedro Pinho, Lisa Grifoni, Stefano Loppi, Aldo Winkler","doi":"10.3390/atmos15091079","DOIUrl":"https://doi.org/10.3390/atmos15091079","url":null,"abstract":"Monitoring atmospheric pollution in urban areas is challenging because pollutant deposition occurs at short distances, requiring a large amount of sampling and analysis to characterize it. Ecological indicators can help overcome this problem, allowing us to select sites with the highest deposition of pollutants from the atmosphere. Nevertheless, a major gap is the temporal characterization of the accumulation rate of magnetic particles in ecological indicators, which is critical to understand if the bioaccumulation process is linear or if saturation occurs. To overcome this problem, Parmotrema perlatum lichens were magnetically and chemically studied in a pollution gradient over space and time. Lichen transplants were exposed over 18 weeks to a high-traffic road. Results show that magnetic properties and element composition reflected both distance from the road (nonlinear decrease of up to 100 m from source) and exposure time (increasingly linearly over the entire study period with eightfold increments), showing that up to 18 weeks, the accumulation rate remained constant over time, with no saturation occurring. Chemical analysis showed a strong linear relationship between the accumulation of zinc (Zn), antimony (Sb), manganese (Mn), copper (Cu) chromium (Cr) and magnetic susceptibility. Magnetization acquisition curves reveal a time-dependent low-coercivity component, interpreted as mainly related to nonexhaust, mostly brake abrasion particle emissions. It is concluded that the magnetic properties of lichen transplants can be used in urban environments to characterize the spatial and temporal patterns of the deposition of pollution metallic particles from the atmosphere.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190188","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}
Jianhong Gan, Tao Liao, Youming Qu, Aijuan Bai, Peiyang Wei, Yuling Gan, Tongli He
Changes in the jet stream not only affect the persistence of climate change and the frequency of extreme weather but are also closely related to climate change phenomena such as global warming. The manual way of drawing the jet stream axes in meteorological operations suffers from low efficiency and subjectivity issues. Automatic identification algorithms based on wind field analysis have some shortcomings, such as poor generalization ability, and it is difficult to handle merging and splitting. A semi-supervised learning jet stream axis identification method is proposed combining consistency learning and self-training. First, a segmentation model is trained via semi-supervised learning. In semi-supervised learning, two neural networks with the same structure are initialized with different methods, based on which pseudo-labels are obtained. The high-confidence pseudo-labels are selected by adding perturbation into the feature layer, and the selected pseudo-labels are incorporated into the training set for further self-training. Then, the jet stream narrow regions are segmented via the trained segmentation model. Finally, the jet stream axes are obtained with the skeleton extraction method. This paper uses the semi-supervised jet stream axis identification method to learn features from unlabeled data to achieve a small amount of labeled data to effectively train the model and improve the method’s generalization ability in a small number of labeled cases. Experiments on the jet stream axis dataset show that the identification precision of the presented method on the test set exceeds about 78% for SOTA baselines, and the improved method exhibits better performance compared to the correlation network model and the semi-supervised method.
喷流的变化不仅影响气候变化的持续性和极端天气的发生频率，而且与全球变暖等气候变化现象密切相关。气象业务中人工绘制喷流轴的方式存在效率低和主观性强的问题。基于风场分析的自动识别算法也存在一些不足，如泛化能力差、难以处理合并和分裂等。本文提出了一种结合一致性学习和自我训练的半监督学习喷气流轴识别方法。首先，通过半监督学习训练分割模型。在半监督学习中，用不同的方法初始化两个结构相同的神经网络，并在此基础上获得伪标签。通过在特征层中添加扰动来选择高置信度的伪标签，并将所选的伪标签纳入训练集进行进一步的自我训练。然后，通过训练好的分割模型分割喷流狭窄区域。最后，利用骨架提取方法获得喷流轴线。本文采用半监督喷气流轴识别方法，从未标明的数据中学习特征，从而实现少量标注数据有效训练模型，提高方法在少量标注情况下的泛化能力。在喷气流轴数据集上的实验表明，本文提出的方法在测试集上的识别精度超过了 SOTA 基线的约 78%，与相关网络模型和半监督方法相比，改进后的方法表现出更好的性能。
{"title":"An Automatic Jet Stream Axis Identification Method Based on Semi-Supervised Learning","authors":"Jianhong Gan, Tao Liao, Youming Qu, Aijuan Bai, Peiyang Wei, Yuling Gan, Tongli He","doi":"10.3390/atmos15091077","DOIUrl":"https://doi.org/10.3390/atmos15091077","url":null,"abstract":"Changes in the jet stream not only affect the persistence of climate change and the frequency of extreme weather but are also closely related to climate change phenomena such as global warming. The manual way of drawing the jet stream axes in meteorological operations suffers from low efficiency and subjectivity issues. Automatic identification algorithms based on wind field analysis have some shortcomings, such as poor generalization ability, and it is difficult to handle merging and splitting. A semi-supervised learning jet stream axis identification method is proposed combining consistency learning and self-training. First, a segmentation model is trained via semi-supervised learning. In semi-supervised learning, two neural networks with the same structure are initialized with different methods, based on which pseudo-labels are obtained. The high-confidence pseudo-labels are selected by adding perturbation into the feature layer, and the selected pseudo-labels are incorporated into the training set for further self-training. Then, the jet stream narrow regions are segmented via the trained segmentation model. Finally, the jet stream axes are obtained with the skeleton extraction method. This paper uses the semi-supervised jet stream axis identification method to learn features from unlabeled data to achieve a small amount of labeled data to effectively train the model and improve the method’s generalization ability in a small number of labeled cases. Experiments on the jet stream axis dataset show that the identification precision of the presented method on the test set exceeds about 78% for SOTA baselines, and the improved method exhibits better performance compared to the correlation network model and the semi-supervised method.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"51 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190298","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}
Ruiyi Tang, Yuanyue Chu, Xiaoqian Liu, Zhishan Yang, Jian Yao
In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China’s specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China’s agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system.
随着绿色低碳发展要求的不断提高，推进符合中国具体国情的低碳农业迫在眉睫。鉴于这种紧迫性，中国农业系统的非二氧化碳温室气体（GHG）排放核算仍处于不断研究和完善的过程中。因此，本文基于碳排放系数法，对 1995 至 2021 年中国西南地区农业非二氧化碳温室气体排放量进行了核算。此外，我们还利用人口、富裕程度和技术回归随机影响模型（STIRPAT）和 Tapio 模型，探讨了驱动因素的影响程度以及与经济发展的关系。我们观察到西南地区农业产生的非二氧化碳温室气体排放量总体呈先增后减的趋势，中部较高，东部和西部较低。经济、人口、结构和技术水平对不同省份的影响程度不同，有利于各地区制定有针对性的农业规划政策。在研究期间的大部分时间里，经济增长与温室气体排放之间存在或弱或强的脱钩现象。最后，提出了促进西南地区低碳农业发展的建议，为明确农业系统的温室气体贡献提供了数据库和政策支持。
{"title":"Driving Factors and Decoupling Effects of Non-CO2 Greenhouse Gas Emissions from Agriculture in Southwest China","authors":"Ruiyi Tang, Yuanyue Chu, Xiaoqian Liu, Zhishan Yang, Jian Yao","doi":"10.3390/atmos15091084","DOIUrl":"https://doi.org/10.3390/atmos15091084","url":null,"abstract":"In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China’s specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China’s agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"74 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.
{"title":"Performance Evaluation of TGFS Typhoon Track Forecasts over the Western North Pacific with Sensitivity Tests on Cumulus Parameterization","authors":"Yu-Han Chen, Sheng-Hao Sha, Chang-Hung Lin, Ling-Feng Hsiao, Ching-Yuang Huang, Hung-Chi Kuo","doi":"10.3390/atmos15091075","DOIUrl":"https://doi.org/10.3390/atmos15091075","url":null,"abstract":"This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：