H. Park, S. Yang, Y. Yoo, J. Jung, I. Moon, H. Cho, J. Kim
A microbubble scrubber is a hybrid type scrubber that combines the advantages of a general scrubber with the advantages of the microbubble. Microbubble which has generally under 50 μm diameter is one of the effective ways to remove air pollutants, like PM, NOx, and SOx. The low-pressure microbubble (LPMB) scrubber is a low-power, high-efficiency method that uses a blower to draw flue gas into the solution and generate microbubbles in the water by using low-pressure or negative pressure. The objective of this study was to enhance the removal efficiency of air pollutants in an LPMB scrubber by determining its optimal operating conditions for generating a large number of microbubbles. To achieve this, we developed a CFD model based on a pilot-scale LPMB scrubber and conducted case studies under different operating conditions using fluid flow analysis. The case study consisted of 12 cases according to the pressure difference (1,000, 3,000, 5,000, and 7,000 Pa) between the scrubber inlet and outlet and the initial water level (–0.2, 0, and +0.2 m). The simulation results showed that the optimal operating conditions were a pressure difference of 5,000 Pa and an initial water level of –0.2 m. The removal rates of PM, NOx, and SOx were 99.9, 92.6, and 99.0%, respectively when operating under the optimal operating conditions of the LPMB scrubber. The results suggest that the proposed optimal operating conditions can effectively enhance the removal efficiency of the LPMB scrubber.
{"title":"Development and Optimization of A Low-Pressure Microbubble Scrubber for Air Pollutants Removal Using CFD","authors":"H. Park, S. Yang, Y. Yoo, J. Jung, I. Moon, H. Cho, J. Kim","doi":"10.3808/jei.31-40","DOIUrl":"https://doi.org/10.3808/jei.31-40","url":null,"abstract":"A microbubble scrubber is a hybrid type scrubber that combines the advantages of a general scrubber with the advantages of the microbubble. Microbubble which has generally under 50 μm diameter is one of the effective ways to remove air pollutants, like PM, NOx, and SOx. The low-pressure microbubble (LPMB) scrubber is a low-power, high-efficiency method that uses a blower to draw flue gas into the solution and generate microbubbles in the water by using low-pressure or negative pressure. The objective of this study was to enhance the removal efficiency of air pollutants in an LPMB scrubber by determining its optimal operating conditions for generating a large number of microbubbles. To achieve this, we developed a CFD model based on a pilot-scale LPMB scrubber and conducted case studies under different operating conditions using fluid flow analysis. The case study consisted of 12 cases according to the pressure difference (1,000, 3,000, 5,000, and 7,000 Pa) between the scrubber inlet and outlet and the initial water level (–0.2, 0, and +0.2 m). The simulation results showed that the optimal operating conditions were a pressure difference of 5,000 Pa and an initial water level of –0.2 m. The removal rates of PM, NOx, and SOx were 99.9, 92.6, and 99.0%, respectively when operating under the optimal operating conditions of the LPMB scrubber. The results suggest that the proposed optimal operating conditions can effectively enhance the removal efficiency of the LPMB scrubber.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"7 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. L. A, G. Q. Wang, Q. Z. Zhang, P. Z. Wang, B. L. Xue, Z. Y. Gao, Y. B. Peng
The prediction of basin water quality has become an urgent need for water environment management, where water pollution is on the increase. Currently, physical models are primarily used for water quality predictions, but the models are not adaptable for the automatic future prediction of watershed water quality owing to their non-automatic boundary setting. The development of big data, which has led to artificial intelligence (AI) technology, has remedied the deficiency of physical models and has been widely used in water quality prediction. However, the accuracy of AI models depends only on the quantity and quality of dataset, which is applied on specific and discrete sections with enough data and difficult to extend to regions with limited monitoring data. Thus, we constructed migration and distribution gates to express the spatial influence of different variables from different sections on the water quality of a specific and discrete section. The temporal processes were expressed by degradation equation. The migration gate, distribution gate and degradation equations were incorporated into Long Short-Term Memory Network (LSTM) to improve the operation mechanism of the LSTM algorithm to create the Im-LSTM model, which considers both the temporal influence of a specific section and the spatial influence of other sections on a specific section at basin scale. Compared to ANN, LSTM, Im-LSTM showed the best performance for basin water quality prediction, especially for mainstream sections at sudden pollution process. Thus, the proposed Im-LSTM provides a new approach for water environment supervision.
{"title":"Water Quality Prediction Based on an Innovated Physical and Data Driving Hybrid Model at Basin Scale","authors":"Y. L. A, G. Q. Wang, Q. Z. Zhang, P. Z. Wang, B. L. Xue, Z. Y. Gao, Y. B. Peng","doi":"10.3808/jei.202400510","DOIUrl":"https://doi.org/10.3808/jei.202400510","url":null,"abstract":"The prediction of basin water quality has become an urgent need for water environment management, where water pollution is on the increase. Currently, physical models are primarily used for water quality predictions, but the models are not adaptable for the automatic future prediction of watershed water quality owing to their non-automatic boundary setting. The development of big data, which has led to artificial intelligence (AI) technology, has remedied the deficiency of physical models and has been widely used in water quality prediction. However, the accuracy of AI models depends only on the quantity and quality of dataset, which is applied on specific and discrete sections with enough data and difficult to extend to regions with limited monitoring data. Thus, we constructed migration and distribution gates to express the spatial influence of different variables from different sections on the water quality of a specific and discrete section. The temporal processes were expressed by degradation equation. The migration gate, distribution gate and degradation equations were incorporated into Long Short-Term Memory Network (LSTM) to improve the operation mechanism of the LSTM algorithm to create the Im-LSTM model, which considers both the temporal influence of a specific section and the spatial influence of other sections on a specific section at basin scale. Compared to ANN, LSTM, Im-LSTM showed the best performance for basin water quality prediction, especially for mainstream sections at sudden pollution process. Thus, the proposed Im-LSTM provides a new approach for water environment supervision.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"14 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.
{"title":"Optimization Models for Long-Term Planning of Municipal Solid Waste Management Systems: A Review with An Emphasis on Mass Balances","authors":"M. K. Korucu, İ. Kucukoglu","doi":"10.3808/jei.1-15","DOIUrl":"https://doi.org/10.3808/jei.1-15","url":null,"abstract":"The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"67 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.
{"title":"Optimization Models for Long-Term Planning of Municipal Solid Waste Management Systems: A Review with An Emphasis on Mass Balances","authors":"M. K. Korucu, İ. Kucukoglu","doi":"10.3808/jei.202400504","DOIUrl":"https://doi.org/10.3808/jei.202400504","url":null,"abstract":"The vast majority of decision-making approaches used for long-term planning of municipal solid waste management systems (LPMSWMS) are ground on scenario-based structures. However, the scenario-based structures may overlook many real-world possibilities because of their restricted mass balances. This study is the first attempt to review the current state of optimization models, which are used as a decision-making approach for LPMSWMS, by focusing on the mass balances. In line with this purpose, 146 peer-reviewed articles were examined based on a new literature evaluation scheme. According to the findings, it can be stated that a significant majority of the articles offer non-deterministic optimization models dealing with the uncertain nature of the LPMSWMS problems. Considering all optimization models examined in the study, most of the model formulations have linear mathematical forms in terms of objective and constraint functions. However, it is quite interesting that none of the models produced solutions for a management system alternative with an integrated (non-restricted) mass balance. Accordingly, it is very questionable whether the results obtained from the current models have the power to give the most suitable solution for an up-to-date management system. As a result of the review, it is highly recommended that the optimization models to be conducted for the LPMSWMS in the future should search for new mathematical approaches considering the integrated mass balances under certainty and/or uncertainty.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"162 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. J. Chen, C. Z. Huang, R. Feng, P. Zhang, Y. Wu, W. Huang
{"title":"Multifunctional PVDF Membrane Coated with ZnO-Ag Nanocomposites for Wastewater Treatment and Fouling Mitigation: Factorial and Mechanism Analyses","authors":"X. J. Chen, C. Z. Huang, R. Feng, P. Zhang, Y. Wu, W. Huang","doi":"10.3808/jei.202300486","DOIUrl":"https://doi.org/10.3808/jei.202300486","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"8 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88553899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elisavet Amanatidou, G. Samiotis, E. Trikoilidou, L. Tsikritzis, N. Taousanidis
{"title":"Centennial Assessment of Greenhouse Gases Emissions of Young and Old Hydroelectric Reservoir in Mediterranean Mainland","authors":"Elisavet Amanatidou, G. Samiotis, E. Trikoilidou, L. Tsikritzis, N. Taousanidis","doi":"10.3808/jei.202300485","DOIUrl":"https://doi.org/10.3808/jei.202300485","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"113 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79783489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. T. Z. Myo, Y. P. Zhang, Q. H. Song, A. G. Chen, D. X. Yang, L. G. Zhou, Y. X. Lin, Z. Phyo, X. H. Fei, N. S. Liang
Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p < 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p < 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.
{"title":"Assessing Canopy Phenological Variations and Gross Primary Productivity in A Savanna Ecosystem in Yuanjiang, Yunnan Province of Southwest China","authors":"S. T. Z. Myo, Y. P. Zhang, Q. H. Song, A. G. Chen, D. X. Yang, L. G. Zhou, Y. X. Lin, Z. Phyo, X. H. Fei, N. S. Liang","doi":"10.3808/jei.202300499","DOIUrl":"https://doi.org/10.3808/jei.202300499","url":null,"abstract":"Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p < 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p < 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Time-Series Forecasting of Chlorophyll-a in Coastal Areas Using LSTM, GRU and Attention-Based RNN Models","authors":"S. Wu, Z. Du, F. Zhang, Y. Zhou, R. Liu","doi":"10.3808/jei.202300494","DOIUrl":"https://doi.org/10.3808/jei.202300494","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"71 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77012847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Liu, M. Liu, G. Sun, Z. Q. Zhou, D. L. Wang, F. He, J. Li, J. Xie, R. Gettler, E. Brunson, J. Steevens, D. Xu
Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.
{"title":"Assessing Environmental Oil Spill Based on Fluorescence Images of Water Samples and Deep Learning","authors":"D. Liu, M. Liu, G. Sun, Z. Q. Zhou, D. L. Wang, F. He, J. Li, J. Xie, R. Gettler, E. Brunson, J. Steevens, D. Xu","doi":"10.3808/jei.202300491","DOIUrl":"https://doi.org/10.3808/jei.202300491","url":null,"abstract":"Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"33 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75588024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Zhang, W. Gao, D. Shao, W. Nardin, C. Gualtieri, T. Sun
{"title":"The Effects of Intra-Annual Variability of River Discharge on the Spatio-Temporal Dynamics of Saltmarsh Vegetation at River Mouth Bar: Insights from an Ecogeomorphological Model","authors":"S. Zhang, W. Gao, D. Shao, W. Nardin, C. Gualtieri, T. Sun","doi":"10.3808/jei.202300498","DOIUrl":"https://doi.org/10.3808/jei.202300498","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"281 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72695912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","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：