This study proposes an ecological reservoir operation by linking ecological model and optimization model of reservoir. A remote sensing based ecological model was developed in which water temperature and total dissolved solids were simulated in the surface layer of reservoir. Moreover, ecological suitability maps were generated by a fuzzy inference system in which water temperature and total dissolved solids were inputs and normalized suitability was output. An ecological reservoir operation model was developed consistent with the ecological model to minimize ecological suitability loss of the reservoir as well as water supply loss. Moreover, a non-ecological operation was developed to minimize water supply loss only. Reliability and vulnerability indices and average habitat suitability in the reservoir were applied to measure the performance of the optimization model. Results indicated that reliability of water supply is reduced 6% due to using ecological operation model of the reservoir. Furthermore, vulnerability of water supply is increased more than 14% using the ecological operation. In contrast, the average habitat suitability of the reservoir is increased more than 0.35 by ecological operation which means it is remarkably effective on mitigating ecological impacts. Based on case study results, applying ecological operation of the reservoir is necessary to mitigate the ecological impacts of the reservoir on the ecological sustainability of reservoirs as well as downstream. The proposed method is able to balance the habitat requirements and humans’ needs
{"title":"An Environmental Operation of Reservoirs through Linking Ecological Storage Model and Evolutionary Optimization","authors":"M. Sedighkia, B. Datta, P. Saeidipour","doi":"10.3808/jei.202400513","DOIUrl":"https://doi.org/10.3808/jei.202400513","url":null,"abstract":"This study proposes an ecological reservoir operation by linking ecological model and optimization model of reservoir. A remote sensing based ecological model was developed in which water temperature and total dissolved solids were simulated in the surface layer of reservoir. Moreover, ecological suitability maps were generated by a fuzzy inference system in which water temperature and total dissolved solids were inputs and normalized suitability was output. An ecological reservoir operation model was developed consistent with the ecological model to minimize ecological suitability loss of the reservoir as well as water supply loss. Moreover, a non-ecological operation was developed to minimize water supply loss only. Reliability and vulnerability indices and average habitat suitability in the reservoir were applied to measure the performance of the optimization model. Results indicated that reliability of water supply is reduced 6% due to using ecological operation model of the reservoir. Furthermore, vulnerability of water supply is increased more than 14% using the ecological operation. In contrast, the average habitat suitability of the reservoir is increased more than 0.35 by ecological operation which means it is remarkably effective on mitigating ecological impacts. Based on case study results, applying ecological operation of the reservoir is necessary to mitigate the ecological impacts of the reservoir on the ecological sustainability of reservoirs as well as downstream. The proposed method is able to balance the habitat requirements and humans’ needs\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"2014 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140631113","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}
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.202400506","DOIUrl":"https://doi.org/10.3808/jei.202400506","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":"19 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977251","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}
W. Xia, X. Li, M. Cheng, W. P. Xiong, B. Song, Y. Liu, Y. Yang, W. J. Wang, S. Chen, G. M. Zeng, C. Y. Zhou
Recently, graphitic carbon nitride (g-C3N4), a promising visible-light-driven semiconductor material, has received enormous attention for photocatalytic water splitting, organic pollutant degradation, and CO2 reduction. However, the photocatalytic activity of bulk g-C3N4 is restricted due to the insufficient light adsorption, ineffective utilization of photogenerated charge carriers, and low specific surface area. Compared with bulk g-C3N4, the three-dimensional graphitic carbon nitride based materials (3D CNBMs) have outstanding physical and chemical characteristics, such as large specific area, plentiful active sites, and excellent electrical conductivity. This article reviews the latest achievements in 3D CNBMs, and presents the state-of-the-art advances in the synthetic methods of 3D CNBMs. Meanwhile, various applications of 3D CNBMs in photocatalysis, photo-electrochemistry, and electrochemistry are systematically reviewed and discussed. In addition, possible improvements and perspectives of 3D CNBMs are proposed. This review aims to summarize a panorama of the up-to-date processes of 3D CNBMs in environmental and energy applications and provide some innovative thoughts to accelerate the ground-breaking research and development of 3D CNBMs for a sustainable future.
{"title":"Recent Advances in Constructing Three-Dimensional Graphitic Carbon Nitride Based Materials and Their Applications in Environmental Photocatalysis, Photo-Electrochemistry, and Electrochemistry","authors":"W. Xia, X. Li, M. Cheng, W. P. Xiong, B. Song, Y. Liu, Y. Yang, W. J. Wang, S. Chen, G. M. Zeng, C. Y. Zhou","doi":"10.3808/jei.202400505","DOIUrl":"https://doi.org/10.3808/jei.202400505","url":null,"abstract":"Recently, graphitic carbon nitride (g-C3N4), a promising visible-light-driven semiconductor material, has received enormous attention for photocatalytic water splitting, organic pollutant degradation, and CO2 reduction. However, the photocatalytic activity of bulk g-C3N4 is restricted due to the insufficient light adsorption, ineffective utilization of photogenerated charge carriers, and low specific surface area. Compared with bulk g-C3N4, the three-dimensional graphitic carbon nitride based materials (3D CNBMs) have outstanding physical and chemical characteristics, such as large specific area, plentiful active sites, and excellent electrical conductivity. This article reviews the latest achievements in 3D CNBMs, and presents the state-of-the-art advances in the synthetic methods of 3D CNBMs. Meanwhile, various applications of 3D CNBMs in photocatalysis, photo-electrochemistry, and electrochemistry are systematically reviewed and discussed. In addition, possible improvements and perspectives of 3D CNBMs are proposed. This review aims to summarize a panorama of the up-to-date processes of 3D CNBMs in environmental and energy applications and provide some innovative thoughts to accelerate the ground-breaking research and development of 3D CNBMs for a sustainable future.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"31 5 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977312","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}
With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.
{"title":"Optimal Configuration and Planning of Distributed Energy Systems Considering Renewable Energy Resources","authors":"H. Taraghi Nazloo, R. Babazadeh, M. Varmazyar","doi":"10.3808/jei.50-64","DOIUrl":"https://doi.org/10.3808/jei.50-64","url":null,"abstract":"With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.\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":"139656358","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}
W. Xia, X. Li, M. Cheng, W. P. Xiong, B. Song, Y. Liu, Y. Yang, W. J. Wang, S. Chen, G. M. Zeng, C. Y. Zhou
Recently, graphitic carbon nitride (g-C3N4), a promising visible-light-driven semiconductor material, has received enormous attention for photocatalytic water splitting, organic pollutant degradation, and CO2 reduction. However, the photocatalytic activity of bulk g-C3N4 is restricted due to the insufficient light adsorption, ineffective utilization of photogenerated charge carriers, and low specific surface area. Compared with bulk g-C3N4, the three-dimensional graphitic carbon nitride based materials (3D CNBMs) have outstanding physical and chemical characteristics, such as large specific area, plentiful active sites, and excellent electrical conductivity. This article reviews the latest achievements in 3D CNBMs, and presents the state-of-the-art advances in the synthetic methods of 3D CNBMs. Meanwhile, various applications of 3D CNBMs in photocatalysis, photo-electrochemistry, and electrochemistry are systematically reviewed and discussed. In addition, possible improvements and perspectives of 3D CNBMs are proposed. This review aims to summarize a panorama of the up-to-date processes of 3D CNBMs in environmental and energy applications and provide some innovative thoughts to accelerate the ground-breaking research and development of 3D CNBMs for a sustainable future.
{"title":"Recent Advances in Constructing Three-Dimensional Graphitic Carbon Nitride Based Materials and Their Applications in Environmental Photocatalysis, Photo-Electrochemistry, and Electrochemistry","authors":"W. Xia, X. Li, M. Cheng, W. P. Xiong, B. Song, Y. Liu, Y. Yang, W. J. Wang, S. Chen, G. M. Zeng, C. Y. Zhou","doi":"10.3808/jei.16-30","DOIUrl":"https://doi.org/10.3808/jei.16-30","url":null,"abstract":"Recently, graphitic carbon nitride (g-C3N4), a promising visible-light-driven semiconductor material, has received enormous attention for photocatalytic water splitting, organic pollutant degradation, and CO2 reduction. However, the photocatalytic activity of bulk g-C3N4 is restricted due to the insufficient light adsorption, ineffective utilization of photogenerated charge carriers, and low specific surface area. Compared with bulk g-C3N4, the three-dimensional graphitic carbon nitride based materials (3D CNBMs) have outstanding physical and chemical characteristics, such as large specific area, plentiful active sites, and excellent electrical conductivity. This article reviews the latest achievements in 3D CNBMs, and presents the state-of-the-art advances in the synthetic methods of 3D CNBMs. Meanwhile, various applications of 3D CNBMs in photocatalysis, photo-electrochemistry, and electrochemistry are systematically reviewed and discussed. In addition, possible improvements and perspectives of 3D CNBMs are proposed. This review aims to summarize a panorama of the up-to-date processes of 3D CNBMs in environmental and energy applications and provide some innovative thoughts to accelerate the ground-breaking research and development of 3D CNBMs for a sustainable future.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"38 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656384","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}
Z. B. Yu, S. Yin, J. H. Bai, C. Wang, G. Z. Chen, W. Wang, Y. Q. Wang, B. S. Cui, X. H. Liu, X. W. Li
How plant traits respond to environment changes has been given more concerns worldwide. However, it is hard to reveal the integrative responses of plants only based on independent plant traits without considering the close links among plant traits. Plant trait network (PTN) is emerging as a new way to study how plant traits adapt to changing environment and to find out the key plant trait. We collected soil and plant samples from five sampling zones in Suaeda salsa wetlands of the Yellow River Delta in China to construct hydrological connectivity index (HCI) by principal component analysis of eight indicators. PTNs were estimated by network analysis of nine plant traits. The results showed that five study areas had significant different HCIs. The PTNs showed the max tightness in areas with medium HCI and the complexity of PTNs decreased with the rise of HCI. Generally, PTNs exhibited the best performance in the areas with medium HCI in which were the most appropriate for plants to grow. Plant aboveground biomass was the central trait PTNs since it had a high degree as well as betweenness centrality. The findings indicate that Suaeda salsa takes different growth strategies under different hydrological connectivity conditions. Suaeda salsa enhanced the connections of different traits in areas which were the best for plants to grow while Suaeda salsa formed different groups of function modules in areas where hydrological connectivity was weak. This study may give new sights on how plant response to the change of hydrological connectivity.
{"title":"Suaeda salsa in Relation to Hydrological Connectivity: From the View of Plant Trait Networks","authors":"Z. B. Yu, S. Yin, J. H. Bai, C. Wang, G. Z. Chen, W. Wang, Y. Q. Wang, B. S. Cui, X. H. Liu, X. W. Li","doi":"10.3808/jei.41-49","DOIUrl":"https://doi.org/10.3808/jei.41-49","url":null,"abstract":"How plant traits respond to environment changes has been given more concerns worldwide. However, it is hard to reveal the integrative responses of plants only based on independent plant traits without considering the close links among plant traits. Plant trait network (PTN) is emerging as a new way to study how plant traits adapt to changing environment and to find out the key plant trait. We collected soil and plant samples from five sampling zones in Suaeda salsa wetlands of the Yellow River Delta in China to construct hydrological connectivity index (HCI) by principal component analysis of eight indicators. PTNs were estimated by network analysis of nine plant traits. The results showed that five study areas had significant different HCIs. The PTNs showed the max tightness in areas with medium HCI and the complexity of PTNs decreased with the rise of HCI. Generally, PTNs exhibited the best performance in the areas with medium HCI in which were the most appropriate for plants to grow. Plant aboveground biomass was the central trait PTNs since it had a high degree as well as betweenness centrality. The findings indicate that Suaeda salsa takes different growth strategies under different hydrological connectivity conditions. Suaeda salsa enhanced the connections of different traits in areas which were the best for plants to grow while Suaeda salsa formed different groups of function modules in areas where hydrological connectivity was weak. This study may give new sights on how plant response to the change of hydrological connectivity.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"299 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656161","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}
Z. B. Yu, S. Yin, J. H. Bai, C. Wang, G. Z. Chen, W. Wang, Y. Q. Wang, B. S. Cui, X. H. Liu, X. W. Li
How plant traits respond to environment changes has been given more concerns worldwide. However, it is hard to reveal the integrative responses of plants only based on independent plant traits without considering the close links among plant traits. Plant trait network (PTN) is emerging as a new way to study how plant traits adapt to changing environment and to find out the key plant trait. We collected soil and plant samples from five sampling zones in Suaeda salsa wetlands of the Yellow River Delta in China to construct hydrological connectivity index (HCI) by principal component analysis of eight indicators. PTNs were estimated by network analysis of nine plant traits. The results showed that five study areas had significant different HCIs. The PTNs showed the max tightness in areas with medium HCI and the complexity of PTNs decreased with the rise of HCI. Generally, PTNs exhibited the best performance in the areas with medium HCI in which were the most appropriate for plants to grow. Plant aboveground biomass was the central trait PTNs since it had a high degree as well as betweenness centrality. The findings indicate that Suaeda salsa takes different growth strategies under different hydrological connectivity conditions. Suaeda salsa enhanced the connections of different traits in areas which were the best for plants to grow while Suaeda salsa formed different groups of function modules in areas where hydrological connectivity was weak. This study may give new sights on how plant response to the change of hydrological connectivity.
{"title":"Suaeda salsa in Relation to Hydrological Connectivity: From the View of Plant Trait Networks","authors":"Z. B. Yu, S. Yin, J. H. Bai, C. Wang, G. Z. Chen, W. Wang, Y. Q. Wang, B. S. Cui, X. H. Liu, X. W. Li","doi":"10.3808/jei.202400507","DOIUrl":"https://doi.org/10.3808/jei.202400507","url":null,"abstract":"How plant traits respond to environment changes has been given more concerns worldwide. However, it is hard to reveal the integrative responses of plants only based on independent plant traits without considering the close links among plant traits. Plant trait network (PTN) is emerging as a new way to study how plant traits adapt to changing environment and to find out the key plant trait. We collected soil and plant samples from five sampling zones in Suaeda salsa wetlands of the Yellow River Delta in China to construct hydrological connectivity index (HCI) by principal component analysis of eight indicators. PTNs were estimated by network analysis of nine plant traits. The results showed that five study areas had significant different HCIs. The PTNs showed the max tightness in areas with medium HCI and the complexity of PTNs decreased with the rise of HCI. Generally, PTNs exhibited the best performance in the areas with medium HCI in which were the most appropriate for plants to grow. Plant aboveground biomass was the central trait PTNs since it had a high degree as well as betweenness centrality. The findings indicate that Suaeda salsa takes different growth strategies under different hydrological connectivity conditions. Suaeda salsa enhanced the connections of different traits in areas which were the best for plants to grow while Suaeda salsa formed different groups of function modules in areas where hydrological connectivity was weak. This study may give new sights on how plant response to the change of hydrological connectivity.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"42 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977244","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}
Driven by climate change, more frequent and extreme wildfires have brought a greater threat to humans globally. Fastspreading wildfires endanger the safety of residents in the wildland-urban interface. To mitigate the hazards of wildfires and facilitate early evacuation, a rapid and accurate forecast of wildfire spread is critical in emergency response. This study proposes a novel dualmodel deep learning approach to achieve a super real-time forecast of 2-dimensional wildfire spread in different scenarios. The first model utilizes the U-Net technique to predict the burnt area up to 5 hours in advance. The second model incorporates ConvLSTM layers to refine the forecasted results based on real-time updated input data. To evaluate the effectiveness of this methodology, we applied it to Sunshine Island, Hong Kong, and generated a numerical database consisting of 210 cases (12,600 samples) to train the deep learning models. The simulated wildfire spread database has a fine resolution of 5 m and a time step of 5 minutes. Results show that both models achieve an overall agreement of over 90% between numerical simulation and AI forecast. The real-time wildfire forecasts by AI only take a few seconds, which is 102 ~ 104 times faster than direct simulations. Our findings demonstrate the potential of AI in offering fast and high-resolution forecasts of wildfire spread, and the novel contribution is to leverage two models which can work in tandem and be utilized at various stages of wildfire management.
{"title":"Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method","authors":"Y. Z. Li, Z. L. Wang, X. Y. Huang","doi":"10.3808/jei.202400509","DOIUrl":"https://doi.org/10.3808/jei.202400509","url":null,"abstract":"Driven by climate change, more frequent and extreme wildfires have brought a greater threat to humans globally. Fastspreading wildfires endanger the safety of residents in the wildland-urban interface. To mitigate the hazards of wildfires and facilitate early evacuation, a rapid and accurate forecast of wildfire spread is critical in emergency response. This study proposes a novel dualmodel deep learning approach to achieve a super real-time forecast of 2-dimensional wildfire spread in different scenarios. The first model utilizes the U-Net technique to predict the burnt area up to 5 hours in advance. The second model incorporates ConvLSTM layers to refine the forecasted results based on real-time updated input data. To evaluate the effectiveness of this methodology, we applied it to Sunshine Island, Hong Kong, and generated a numerical database consisting of 210 cases (12,600 samples) to train the deep learning models. The simulated wildfire spread database has a fine resolution of 5 m and a time step of 5 minutes. Results show that both models achieve an overall agreement of over 90% between numerical simulation and AI forecast. The real-time wildfire forecasts by AI only take a few seconds, which is 102 ~ 104 times faster than direct simulations. Our findings demonstrate the potential of AI in offering fast and high-resolution forecasts of wildfire spread, and the novel contribution is to leverage two models which can work in tandem and be utilized at various stages of wildfire management.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"8 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977246","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}
Driven by climate change, more frequent and extreme wildfires have brought a greater threat to humans globally. Fastspreading wildfires endanger the safety of residents in the wildland-urban interface. To mitigate the hazards of wildfires and facilitate early evacuation, a rapid and accurate forecast of wildfire spread is critical in emergency response. This study proposes a novel dualmodel deep learning approach to achieve a super real-time forecast of 2-dimensional wildfire spread in different scenarios. The first model utilizes the U-Net technique to predict the burnt area up to 5 hours in advance. The second model incorporates ConvLSTM layers to refine the forecasted results based on real-time updated input data. To evaluate the effectiveness of this methodology, we applied it to Sunshine Island, Hong Kong, and generated a numerical database consisting of 210 cases (12,600 samples) to train the deep learning models. The simulated wildfire spread database has a fine resolution of 5 m and a time step of 5 minutes. Results show that both models achieve an overall agreement of over 90% between numerical simulation and AI forecast. The real-time wildfire forecasts by AI only take a few seconds, which is 102 ~ 104 times faster than direct simulations. Our findings demonstrate the potential of AI in offering fast and high-resolution forecasts of wildfire spread, and the novel contribution is to leverage two models which can work in tandem and be utilized at various stages of wildfire management.
{"title":"Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method","authors":"Y. Z. Li, Z. L. Wang, X. Y. Huang","doi":"10.3808/jei.65-79","DOIUrl":"https://doi.org/10.3808/jei.65-79","url":null,"abstract":"Driven by climate change, more frequent and extreme wildfires have brought a greater threat to humans globally. Fastspreading wildfires endanger the safety of residents in the wildland-urban interface. To mitigate the hazards of wildfires and facilitate early evacuation, a rapid and accurate forecast of wildfire spread is critical in emergency response. This study proposes a novel dualmodel deep learning approach to achieve a super real-time forecast of 2-dimensional wildfire spread in different scenarios. The first model utilizes the U-Net technique to predict the burnt area up to 5 hours in advance. The second model incorporates ConvLSTM layers to refine the forecasted results based on real-time updated input data. To evaluate the effectiveness of this methodology, we applied it to Sunshine Island, Hong Kong, and generated a numerical database consisting of 210 cases (12,600 samples) to train the deep learning models. The simulated wildfire spread database has a fine resolution of 5 m and a time step of 5 minutes. Results show that both models achieve an overall agreement of over 90% between numerical simulation and AI forecast. The real-time wildfire forecasts by AI only take a few seconds, which is 102 ~ 104 times faster than direct simulations. Our findings demonstrate the potential of AI in offering fast and high-resolution forecasts of wildfire spread, and the novel contribution is to leverage two models which can work in tandem and be utilized at various stages of wildfire management.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"231 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656163","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}
With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.
{"title":"Optimal Configuration and Planning of Distributed Energy Systems Considering Renewable Energy Resources","authors":"H. Taraghi Nazloo, R. Babazadeh, M. Varmazyar","doi":"10.3808/jei.202400508","DOIUrl":"https://doi.org/10.3808/jei.202400508","url":null,"abstract":"With increasing electricity demand, conventional centralized power generation systems encounter numerous challenges, including transmission and distribution losses, limited capacity, and high operational costs. In response, distributed energy systems have emerged as a promising solution by enabling electricity generation in close proximity to consumption points. These systems leverage renewable energy sources and minimize energy losses during transmission, presenting a more sustainable and efficient alternative. By utilizing diverse energy sources such as solar thermal panels, photovoltaic systems, geothermal energy, distributed energy systems enhance overall efficiency, and reduce power losses during transmission as well as greenhouse gas emissions. This research endeavor presents a novel approach employing mixed-integer linear programming to optimize distributed energy systems. The proposed model facilitates the determination of optimal dimensions of technologies, including combined heat and power systems, boilers, electric chillers, and absorption chillers, while simultaneously minimizing total costs and greenhouse gas emissions and adhering to real-world constraints. The findings of this study are validated through a real-world numerical example, confirming the model’s efficiency in configuring and planning distributed energy systems optimally, thereby enhancing their operational performance.\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":"139977245","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}
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