Y. Yoo, Green Materials, H. Park, Y. Choi, J. Jung, H. Song, J. Kim, H. Cho
This paper presents an image-processing-based model for calculating the interfacial-area concentration (IAC) of a low-pressure microbubble (LPMB) scrubber, which facilitates the determination of operational conditions of the scrubber via flow-pattern analysis. The LPMB scrubber maximizes the interfacial area of two-phase systems using the bubbly flow. Microbubbles have received attention due to their microscopic sizes, high residence time, and high mass-transfer efficiency. The LPMB scrubber maintains a negative outlet pressure to generate gas flow, which in turn generates microbubbles interrupting gas flow with three blocking plates in the atomizer. This gas flow generates a bubbly flux with different bubble sizes. To obtain bubble characteristics, we analyzed 20 atomizer images where this complex flux occurs. Bubble size, number of bubbles, gas void fraction, and IAC were calculated using an Open-CV Python algorithm. To validate the most appropriate bubble flow patterns, case studies were conducted at pressure difference of 240, 360, and 450 mmAq. The 360 mmAq condition had the lowest percentage of bubbles smaller than 50 µm, but the total number of bubbles, void fraction, and IAC were the highest. The results obtained in this study confirm that using an LPMB scrubber in an oxidizing solution facilitates reductions of 92.6, 93.9, and 99.9% in NOX, SOX, and dust, respectively. These results could be used to validate the bubble reactivity of other two-phase systems intended for commercial and practical applications.
{"title":"Method for Determining Optimum Operational Conditions of Microbubble Scrubber Using Image Processing","authors":"Y. Yoo, Green Materials, H. Park, Y. Choi, J. Jung, H. Song, J. Kim, H. Cho","doi":"10.3808/jei.202100457","DOIUrl":"https://doi.org/10.3808/jei.202100457","url":null,"abstract":"This paper presents an image-processing-based model for calculating the interfacial-area concentration (IAC) of a low-pressure microbubble (LPMB) scrubber, which facilitates the determination of operational conditions of the scrubber via flow-pattern analysis. The LPMB scrubber maximizes the interfacial area of two-phase systems using the bubbly flow. Microbubbles have received attention due to their microscopic sizes, high residence time, and high mass-transfer efficiency. The LPMB scrubber maintains a negative outlet pressure to generate gas flow, which in turn generates microbubbles interrupting gas flow with three blocking plates in the atomizer. This gas flow generates a bubbly flux with different bubble sizes. To obtain bubble characteristics, we analyzed 20 atomizer images where this complex flux occurs. Bubble size, number of bubbles, gas void fraction, and IAC were calculated using an Open-CV Python algorithm. To validate the most appropriate bubble flow patterns, case studies were conducted at pressure difference of 240, 360, and 450 mmAq. The 360 mmAq condition had the lowest percentage of bubbles smaller than 50 µm, but the total number of bubbles, void fraction, and IAC were the highest. The results obtained in this study confirm that using an LPMB scrubber in an oxidizing solution facilitates reductions of 92.6, 93.9, and 99.9% in NOX, SOX, and dust, respectively. These results could be used to validate the bubble reactivity of other two-phase systems intended for commercial and practical applications.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81118241","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 sediment delivery ratio (SDR) is an important index for understanding sediment erosion, transportation and deposition features in a river basin. Based on the commonly accepted definition of SDR and the characteristics of the sediment delivery process in hyper-concentrated flow areas of the Loess Plateau, China, a new model for computing the SDR is proposed. The model is a functional relation of fractional form, in which the denominator is the surface runoff of the basin, and the numerator is the water volume needed for saturated sediment discharge in the controlling hydrological station at the exit of the basin when the saturated sediment load is equal to the measured sediment load. Using the proposed SDR equation, the long-term series of SDRs in the Yanhe River basin, a typical hyper-concentrated flow basin in the Loess Plateau, were calculated from 1952 to 2010. The results show that the long-term annual average SDR in the Yanhe River basin was 0.92, which is consistent with the results of previous studies; this finding confirms the validity and effectiveness of the method. The proposed method only requires gauge data of sediment concentration, sediment delivery rate, sediment delivery volume, and runoff ratio from hydrological stations, which makes it easy to use, and it can be used to easily estimate soil erosion in the basin.
{"title":"A New Method for Computing the Sediment Delivery Ratio for the Hyper-Concentrated Flow Areas of the Loess Plateau, China","authors":"T. H. Li, W. Xie","doi":"10.3808/jei.202100456","DOIUrl":"https://doi.org/10.3808/jei.202100456","url":null,"abstract":"The sediment delivery ratio (SDR) is an important index for understanding sediment erosion, transportation and deposition features in a river basin. Based on the commonly accepted definition of SDR and the characteristics of the sediment delivery process in hyper-concentrated flow areas of the Loess Plateau, China, a new model for computing the SDR is proposed. The model is a functional relation of fractional form, in which the denominator is the surface runoff of the basin, and the numerator is the water volume needed for saturated sediment discharge in the controlling hydrological station at the exit of the basin when the saturated sediment load is equal to the measured sediment load. Using the proposed SDR equation, the long-term series of SDRs in the Yanhe River basin, a typical hyper-concentrated flow basin in the Loess Plateau, were calculated from 1952 to 2010. The results show that the long-term annual average SDR in the Yanhe River basin was 0.92, which is consistent with the results of previous studies; this finding confirms the validity and effectiveness of the method. The proposed method only requires gauge data of sediment concentration, sediment delivery rate, sediment delivery volume, and runoff ratio from hydrological stations, which makes it easy to use, and it can be used to easily estimate soil erosion in the basin.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89175609","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. Qin, J. E. Zhang, A. DiTommaso, J. Díez, Y. Zhao, F. Wang
Three ragweed species native to North America (Ambrosia artemisiifolia L., A. psilostachya DC, and A. trifida L.) that have been introduced into Asia are now spreading quickly in many regions. Predicting which specific areas may be vulnerable to the invasion of these allergenic Ambrosia species can provide valuable insights for early detection and for prioritizing preventive actions. Species distribution models, based on native and non-Asian occurrence records for these three Ambrosia species, were generated with the maximum entropy (Maxent) approach respectively. Spatial filtering and target-group background methods were used to address sampling bias. Models fitted with different levels of complexity under present conditions were compared and evaluated with independent Asian records. Models showing lower over-fitting and higher performance were then selected to assess their future distribution under two types of Representative Concentration Pathways (RCP4.5 and RCP8.5), using four General Circulation Models (GCMs). Predicted habitats for A. artemisiifolia in 2050 would contract in regions having been colonized, despite a limited increase in parts of China. This species may experience a southward range shift in China. Under all future climate scenarios, A. trifida was predicted to decrease its potential establishment while A. psilostachya would expand its range, especially in habitats being colonized currently. Special attention should be given to Hunan, Jiangxi Provinces and scattered along southeastern coastal regions of China as well as parts of Turkey and northwest Iran, Azerbaijan, considering that future potential distribution of A. artemisiifolia and A. psilostachya might increase in these areas respectively. The findings provide valuable information for assessing the risk that these three Ambrosia species pose to many Asian countries and for prioritizing early detection and prevention strategies.
原产于北美的三种豚草(Ambrosia artemisiifolia L.， A. psilostachya DC .和A. trifida L.)已被引入亚洲，目前正在许多地区迅速蔓延。预测哪些特定区域可能容易受到这些过敏性安氏菌物种的入侵，可以为早期发现和优先采取预防措施提供有价值的见解。利用最大熵(Maxent)法分别建立了三种Ambrosia的本地和非亚洲发生记录的物种分布模型。采用空间滤波和目标群体背景方法来解决抽样偏差。在当前条件下拟合不同复杂程度的模型与独立的亚洲记录进行了比较和评估。然后选择具有较低过拟合和较高性能的模型，使用四种一般循环模型(GCMs)评估它们在两种典型浓度路径(RCP4.5和RCP8.5)下的未来分布。预计到2050年，在已被殖民的地区，蒿属植物的栖息地将会收缩，尽管中国部分地区的蒿属植物数量会有有限的增长。本种在中国可能经历向南的范围转移。在未来的气候情景下，三叶草的潜在种群数量将会减少，而拟犀草的分布范围将会扩大，尤其是在目前已被殖民的生境中。应特别注意湖南、江西和分散在中国东南沿海地区以及土耳其部分地区和伊朗西北部、阿塞拜疆等地的蒿属植物，因为这些地区未来的潜在分布可能会增加。这些发现为评估这三种Ambrosia物种对许多亚洲国家构成的风险以及优先考虑早期发现和预防策略提供了有价值的信息。
{"title":"Predicting the Potential Distribution of Three Allergenic Invasive Ambrosia (ragweed) Species in Asia","authors":"Z. Qin, J. E. Zhang, A. DiTommaso, J. Díez, Y. Zhao, F. Wang","doi":"10.3808/JEI.202000444","DOIUrl":"https://doi.org/10.3808/JEI.202000444","url":null,"abstract":"Three ragweed species native to North America (Ambrosia artemisiifolia L., A. psilostachya DC, and A. trifida L.) that have been introduced into Asia are now spreading quickly in many regions. Predicting which specific areas may be vulnerable to the invasion of these allergenic Ambrosia species can provide valuable insights for early detection and for prioritizing preventive actions. Species distribution models, based on native and non-Asian occurrence records for these three Ambrosia species, were generated with the maximum entropy (Maxent) approach respectively. Spatial filtering and target-group background methods were used to address sampling bias. Models fitted with different levels of complexity under present conditions were compared and evaluated with independent Asian records. Models showing lower over-fitting and higher performance were then selected to assess their future distribution under two types of Representative Concentration Pathways (RCP4.5 and RCP8.5), using four General Circulation Models (GCMs). Predicted habitats for A. artemisiifolia in 2050 would contract in regions having been colonized, despite a limited increase in parts of China. This species may experience a southward range shift in China. Under all future climate scenarios, A. trifida was predicted to decrease its potential establishment while A. psilostachya would expand its range, especially in habitats being colonized currently. Special attention should be given to Hunan, Jiangxi Provinces and scattered along southeastern coastal regions of China as well as parts of Turkey and northwest Iran, Azerbaijan, considering that future potential distribution of A. artemisiifolia and A. psilostachya might increase in these areas respectively. The findings provide valuable information for assessing the risk that these three Ambrosia species pose to many Asian countries and for prioritizing early detection and prevention strategies.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82553714","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}
Global environmental quality is a rapidly developing and complicated subject in need of a new method for evolutionary analyses. This paper proposes an informetric dynamic co-word network to study the evolution of global environmental quality research (GEQR), making a significant contribution to the available literature. First, it was observed that GEQR has been vigorously developing and hotspots have emerged through a self-organized adjustment process, which other methods lacked. Second, small-world and scale- free effects, which are mechanisms of an evolving knowledge system (KS), were identified in GEQR’s KS. Third, the dynamic co- word network yielded topological patterns and robust clustering in GEQR. An assessment strategy map/table of GEQR has been ac- cordingly proposed as a mined innovative function of co-word networks, which provides new holistic understanding of global environ- mental quality. Thus, our studies on GEQR evolution using this informetric dynamic co-word network method may help researchers or managers learn GEQR development mechanisms, allowing for further topical selection and policy management of GEQR. Beyond GEQR, the informetric dynamic co-word network method may also provide a new universal method for evolutionary analysis of many other environmental problems.
{"title":"Comprehensive Study of Evolution of Global Environmental Quality Research Using Informetric Co-Word Network","authors":"Z. La, L. Chai","doi":"10.3808/JEI.202100449","DOIUrl":"https://doi.org/10.3808/JEI.202100449","url":null,"abstract":"Global environmental quality is a rapidly developing and complicated subject in need of a new method for evolutionary analyses. This paper proposes an informetric dynamic co-word network to study the evolution of global environmental quality research (GEQR), making a significant contribution to the available literature. First, it was observed that GEQR has been vigorously developing and hotspots have emerged through a self-organized adjustment process, which other methods lacked. Second, small-world and scale- free effects, which are mechanisms of an evolving knowledge system (KS), were identified in GEQR’s KS. Third, the dynamic co- word network yielded topological patterns and robust clustering in GEQR. An assessment strategy map/table of GEQR has been ac- cordingly proposed as a mined innovative function of co-word networks, which provides new holistic understanding of global environ- mental quality. Thus, our studies on GEQR evolution using this informetric dynamic co-word network method may help researchers or managers learn GEQR development mechanisms, allowing for further topical selection and policy management of GEQR. Beyond GEQR, the informetric dynamic co-word network method may also provide a new universal method for evolutionary analysis of many other environmental problems.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75478426","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}
In this paper, a non-linear mathematical model is proposed and analyzed to study the effects of mitigation options on the control of methane emissions in the atmosphere caused by rice paddies and livestock populations to reduce global warming. In the modeling process, it is assumed that the cumulative biomass density of rice paddies and the density of livestock populations follow logistic models with their respective growth rates and carrying capacities. The growth rate of concentration of methane in the atmos- phere is assumed to be directly proportional to the cumulative density of various processes involved in the production of rice paddies as well as the cumulative density of various processes used in the farming of livestock populations. This growth rate is also assumed to increase with natural factors such as wetlands but it decreases with the cumulative density of mitigation options, considered to be pro- portional to the increased level of methane concentration in the atmosphere. The non-linear model is analyzed by using the stability theory of differential equations and computer simulation. The analysis shows that mitigation options can control the methane emissions in the atmosphere caused by rice paddies and livestock populations considerably. The computer simulation of the model confirms this analytical result. The data from model prediction is compared with actual methane data in the atmosphere and found to be very satisfactory.
{"title":"Effects of Mitigation Options on the Control of Methane Emissions Caused by Rice Paddies and Livestock Populations to Reduce Global Warming: A Modeling Study and Comparison with Environmental Data","authors":"S. Sundar, Anshuman Mishra, J. Shukla","doi":"10.3808/JEI.202000447","DOIUrl":"https://doi.org/10.3808/JEI.202000447","url":null,"abstract":"In this paper, a non-linear mathematical model is proposed and analyzed to study the effects of mitigation options on the control of methane emissions in the atmosphere caused by rice paddies and livestock populations to reduce global warming. In the modeling process, it is assumed that the cumulative biomass density of rice paddies and the density of livestock populations follow logistic models with their respective growth rates and carrying capacities. The growth rate of concentration of methane in the atmos- phere is assumed to be directly proportional to the cumulative density of various processes involved in the production of rice paddies as well as the cumulative density of various processes used in the farming of livestock populations. This growth rate is also assumed to increase with natural factors such as wetlands but it decreases with the cumulative density of mitigation options, considered to be pro- portional to the increased level of methane concentration in the atmosphere. The non-linear model is analyzed by using the stability theory of differential equations and computer simulation. The analysis shows that mitigation options can control the methane emissions in the atmosphere caused by rice paddies and livestock populations considerably. The computer simulation of the model confirms this analytical result. The data from model prediction is compared with actual methane data in the atmosphere and found to be very satisfactory.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83179330","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}
R. Yao, J. S. Yang, X. P. Wang, Y. Zhao, H. Li, P. Gao, W. Xie, X. Zhang
Assimilation of proximally and remotely sensed information on soil salinization-related attributes into a hydrological model is essential to improve the forecast performance of the profiled soil salinity dynamics for developing appropriate soil amendment practices. Although the family of ensemble Kalman filters (EnKF) is widely used in data assimilation, their applicability and reliability for soil salinization estimation requires further experimental validation. Here, we evaluated the assimilation performance of apparent electrical conductivity (ECa) data obtained from an electromagnetic induction meter (EM38) into the HYDRUS hydrological model. Re-sults showed that the EnKF method improved the simulation accuracy of soil salinity at 0 ~ 100 cm soil depths, as indicated by the de-creased root-mean-square error of 32.6 ~ 76.7% and increased Nash–Sutcliffe efficiency of 9.6 ~ 71.2%. The HYDRUS-simulated values with EnKF were closer to the measured values than the values simulated by the HYDRUS model, and this benefitted from updating the running trajectory of the HYDRUS model. The EnKF values derived from measured ECa data were better than HYDRUS-simulated val-ues with EnKF. Soil salinity simulation was sensitive to ensemble size, error level, and ECa data depth. Considering the ensemble repre-sentativeness and computational efficiency, the optimal ensemble size was judged to be 50. The maximum acceptable observation error was 10%, and observation data to a depth of 100 cm was suggested in EnKF assimilation to minimize the root-mean-square error. It was concluded that proximally sensed EM38 data coupled with the EnKF algorithm is promising for improving the simulation performance and providing a prospective method for simulating large-scale ecological and hydrological processes by coupling multi-source data and hydrological models.
{"title":"Improving Soil Salinity Simulation by Assimilating Electromagnetic Induction Data into HYDRUS Model Using Ensemble Kalman Filter","authors":"R. Yao, J. S. Yang, X. P. Wang, Y. Zhao, H. Li, P. Gao, W. Xie, X. Zhang","doi":"10.3808/JEI.202100451","DOIUrl":"https://doi.org/10.3808/JEI.202100451","url":null,"abstract":"Assimilation of proximally and remotely sensed information on soil salinization-related attributes into a hydrological model is essential to improve the forecast performance of the profiled soil salinity dynamics for developing appropriate soil amendment practices. Although the family of ensemble Kalman filters (EnKF) is widely used in data assimilation, their applicability and reliability for soil salinization estimation requires further experimental validation. Here, we evaluated the assimilation performance of apparent electrical conductivity (ECa) data obtained from an electromagnetic induction meter (EM38) into the HYDRUS hydrological model. Re-sults showed that the EnKF method improved the simulation accuracy of soil salinity at 0 ~ 100 cm soil depths, as indicated by the de-creased root-mean-square error of 32.6 ~ 76.7% and increased Nash–Sutcliffe efficiency of 9.6 ~ 71.2%. The HYDRUS-simulated values with EnKF were closer to the measured values than the values simulated by the HYDRUS model, and this benefitted from updating the running trajectory of the HYDRUS model. The EnKF values derived from measured ECa data were better than HYDRUS-simulated val-ues with EnKF. Soil salinity simulation was sensitive to ensemble size, error level, and ECa data depth. Considering the ensemble repre-sentativeness and computational efficiency, the optimal ensemble size was judged to be 50. The maximum acceptable observation error was 10%, and observation data to a depth of 100 cm was suggested in EnKF assimilation to minimize the root-mean-square error. It was concluded that proximally sensed EM38 data coupled with the EnKF algorithm is promising for improving the simulation performance and providing a prospective method for simulating large-scale ecological and hydrological processes by coupling multi-source data and hydrological models.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87838713","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}
Nowadays, the demand for sustainable buildings is increasing. The main purpose of buildings is to provide a comfort- able living environment for their occupants, considering different aspects including thermal, visual, and acoustic comfort as well as In- door Air Quality. Decreasing carbon footprint and energy consumption rates while increasing comfort level can help to achieve better living and working environment for building users. This research proposes a framework that aims at improving building system energy performance using building information modeling (BIM) during buildings’ design stage by evaluating different alternatives for install- ed building systems. According to experts’ opinions, evaluating buildings’ energy performance by analyzing the energy consumption rates alone without including economic and environmental factors is insufficient. Therefore, in this paper, building systems are evalu- ated using four main criteria; operating cost savings, total energy consumption per year, Lifecycle cost savings, and carbon emissions. A Multiple Criteria Decision-making (MCDM) technique is applied using Superiority and Inferiority Ranking (SIR) to study the behav- ior of different alternatives. Sensitivity analysis is performed to detect the criticality and effectiveness of the different defined criteria that influence environmental concerns and building system energy performance. A case study is presented to demonstrate the use of the proposed framework on an academic building by considering four criteria which are Operating Costs, Life Cycle Cost, Energy Con- sumption, and Carbon Emissions. Sensitivity analysis is performed on the weights of the criteria to determine how critical each crite- rion is and how they affect the ranking of the alternatives. A total of 36 combinations are simulated, considering changing the weights and procedure (SAW vs. TOPSIS). The rank that has the top repetitive percentage is considered to identify the most dominating alternative.
如今，对可持续建筑的需求越来越大。建筑的主要目的是为居住者提供一个舒适的生活环境，考虑到不同的方面，包括热、视觉、声学舒适以及室内空气质量。在提高建筑舒适度的同时，减少碳足迹和能源消耗率，为建筑使用者创造更好的生活和工作环境。本研究提出了一个框架，旨在通过评估安装建筑系统的不同替代方案，在建筑设计阶段使用建筑信息模型(BIM)提高建筑系统的能源性能。专家们认为，仅通过分析能耗率而不考虑经济和环境因素来评价建筑的能源性能是不够的。因此，本文采用四个主要标准对建筑系统进行评价;运营成本节约、每年总能耗、生命周期成本节约和碳排放。应用多准则决策技术，利用优劣排序(SIR)来研究不同方案的行为。进行敏感性分析，以检测影响环境问题和建筑系统能源性能的不同定义标准的重要性和有效性。通过考虑运营成本、生命周期成本、能源消耗和碳排放四个标准，提出了一个案例研究，以展示在学术建筑上使用拟议框架。对标准的权重进行敏感性分析，以确定每个标准的关键程度以及它们如何影响备选方案的排名。总共模拟了36种组合，考虑改变权重和过程(SAW vs. TOPSIS)。具有最高重复百分比的排名被认为是识别最主要的替代方案。
{"title":"Evaluating Building Systems Energy Performance Superiority and Inferiority Ranking","authors":"M. Marzouk, I. Abdelbasset, K. Al-Gahtani","doi":"10.3808/JEI.202000448","DOIUrl":"https://doi.org/10.3808/JEI.202000448","url":null,"abstract":"Nowadays, the demand for sustainable buildings is increasing. The main purpose of buildings is to provide a comfort- able living environment for their occupants, considering different aspects including thermal, visual, and acoustic comfort as well as In- door Air Quality. Decreasing carbon footprint and energy consumption rates while increasing comfort level can help to achieve better living and working environment for building users. This research proposes a framework that aims at improving building system energy performance using building information modeling (BIM) during buildings’ design stage by evaluating different alternatives for install- ed building systems. According to experts’ opinions, evaluating buildings’ energy performance by analyzing the energy consumption rates alone without including economic and environmental factors is insufficient. Therefore, in this paper, building systems are evalu- ated using four main criteria; operating cost savings, total energy consumption per year, Lifecycle cost savings, and carbon emissions. A Multiple Criteria Decision-making (MCDM) technique is applied using Superiority and Inferiority Ranking (SIR) to study the behav- ior of different alternatives. Sensitivity analysis is performed to detect the criticality and effectiveness of the different defined criteria that influence environmental concerns and building system energy performance. A case study is presented to demonstrate the use of the proposed framework on an academic building by considering four criteria which are Operating Costs, Life Cycle Cost, Energy Con- sumption, and Carbon Emissions. Sensitivity analysis is performed on the weights of the criteria to determine how critical each crite- rion is and how they affect the ranking of the alternatives. A total of 36 combinations are simulated, considering changing the weights and procedure (SAW vs. TOPSIS). The rank that has the top repetitive percentage is considered to identify the most dominating alternative.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84925608","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}
M. Rodrigues, R. Martins, J. Rogeiro, A. Fortunato, Arnaldo S. R. Oliveira, A. Cravo, J. Jacob, A. Rosa, A. Azevedo, P. Freire
The concept of water observatories is extended to create a highly versatile tool for both the daily and the long-term man-agement of estuarine ecosystems. Coastal observatories are evolving from simple data repositories to include forecasts, scenarios’ ana-lyses and indicators, integrated in web platforms that provide multiple products and services. In a context of climate change (CC) and growing anthropogenic pressures, the assessment of the ecological health implies that the biogeochemical status is adequately quantified and incorporated in the coastal management decision-making procedure. This quantification requires accurate models for hydrodynamics and ecology that account for all relevant processes at the right scales. These models must be applied in forecast mode for emergency pur-poses and in hindcast mode to explore multiple scenarios as part of the CC adaptation strategy, creating a complex, vast amount of infor-mation to be shared with the coastal managers. A web-based portal supported by a comprehensive modeling and forecasting framework and materialized along the main water quality/biogeochemistry themes, from data to indicators, is developed and demonstrated in two distinct yet complex coastal systems: the Tagus estuary and the Ria Formosa lagoon. The paper starts with the requirements analysis from both ecological and computer science perspectives and then presents the overall multi-layered architecture of the framework and its key software components. The observatory portal implementation and demonstration explore its usefulness for coastal management.
{"title":"A Web-Based Observatory for Biogeochemical Assessment in Coastal Regions","authors":"M. Rodrigues, R. Martins, J. Rogeiro, A. Fortunato, Arnaldo S. R. Oliveira, A. Cravo, J. Jacob, A. Rosa, A. Azevedo, P. Freire","doi":"10.3808/JEI.202100450","DOIUrl":"https://doi.org/10.3808/JEI.202100450","url":null,"abstract":"The concept of water observatories is extended to create a highly versatile tool for both the daily and the long-term man-agement of estuarine ecosystems. Coastal observatories are evolving from simple data repositories to include forecasts, scenarios’ ana-lyses and indicators, integrated in web platforms that provide multiple products and services. In a context of climate change (CC) and growing anthropogenic pressures, the assessment of the ecological health implies that the biogeochemical status is adequately quantified and incorporated in the coastal management decision-making procedure. This quantification requires accurate models for hydrodynamics and ecology that account for all relevant processes at the right scales. These models must be applied in forecast mode for emergency pur-poses and in hindcast mode to explore multiple scenarios as part of the CC adaptation strategy, creating a complex, vast amount of infor-mation to be shared with the coastal managers. A web-based portal supported by a comprehensive modeling and forecasting framework and materialized along the main water quality/biogeochemistry themes, from data to indicators, is developed and demonstrated in two distinct yet complex coastal systems: the Tagus estuary and the Ria Formosa lagoon. The paper starts with the requirements analysis from both ecological and computer science perspectives and then presents the overall multi-layered architecture of the framework and its key software components. The observatory portal implementation and demonstration explore its usefulness for coastal management.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75435219","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}
A. Alsayed, M. Soliman, R. Shakir, E. Snieder, A. Eldyasti, U. Khan
. The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bioprocesses. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through developing and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of different input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bioprocesses are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study successfully utilized DDM to model methanotrophic-based bioprocesses. The findings of this research suggest that DDM are a powerful, alternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs.
{"title":"Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity","authors":"A. Alsayed, M. Soliman, R. Shakir, E. Snieder, A. Eldyasti, U. Khan","doi":"10.3808/jei.202000446","DOIUrl":"https://doi.org/10.3808/jei.202000446","url":null,"abstract":". The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bioprocesses. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through developing and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of different input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bioprocesses are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study successfully utilized DDM to model methanotrophic-based bioprocesses. The findings of this research suggest that DDM are a powerful, alternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77464643","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":"Assessment of Pollution and Ecological Risk Index of Heavy Metals in the Surface Sediment of Estuary and the Coastal Environment of Bay of Bengal","authors":"M. Khadanga, R. Mishra, B. K. Sahu","doi":"10.3808/jei.202100469","DOIUrl":"https://doi.org/10.3808/jei.202100469","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85635230","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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