瀬戸内海中部における堆積物コア情報に基づく水文プロセス及び人間活動によってコントロールされた沿岸堆積物中のリン蓄積量の評価と蓄積機構の解明

内容の要約広島大学(Hiroshima University)博士(学術)Philosophydoctora

Sources of nitrate in a heavily nitrogen pollution bay in Beibu Gulf, as identified using stable isotopes

Eutrophication, mainly caused by the oversupply of inorganic nitrogen and phosphate, has increased and become a serious environmental problem in the coastal bays of Beibu Gulf, a newly developing industry and port in South China. However, the sources of nitrate are poorly understood in the gulf. In this study, nitrate dual isotopes (δ15N-NO3- and δ18O-NO3-) and ammonium isotopes (δ15N-NH4+) were measured during the rainy season to identify the nitrate sources and elucidate their biogeochemical processes in Xi Bay, a semi-enclosed bay that is strongly affected by human activities in the Beibu Gulf. The results showed that a high dissolved inorganic nitrogen (DIN, 10.24-99.09 µmol L-1) was observed in Xi Bay, particularly in the bay mouth. The concentrations of DIN in the bay were 1.5 times higher than that in Qinzhou Bay and 1.7 times than that in Tieshangang Bay, which mainly influenced by the intensive human activities (i.e., industrial and port activities). In addition, lower values of δ15N-NO3- and δ18O-NO3- and higher values of δ15N-NH4+ were observed in the upper bay, suggesting that microbial nitrification occurs in the upper bay, which was the dominant nitrate source in the upper bay (39%). In addition to nitrification, external sources, including sewage and manure (33%), soil N (15%) and fertilizer (11%), contributed to the higher nutrients in the upper bay. In the lower bay, severe nitrogen pollution led to a weaker impact of biological processes on isotopic fractionation, although a high Chl a level (average of 7.47 µg L-1) was found in this region. The heavy nitrate pollution in the lower bay mainly originated from sewage and manure (54%), followed by soil N (26%) and fertilizer (17%). The contribution of the nitrate source from atmospheric deposition was relatively low in the bay (<3%). This study suggests that biogeochemical processes have little impact on nitrate dual isotopes under heavy nitrogen pollution, and isotopes are an ideal proxy for tracing nitrogen sources

Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Multi-Criteria Decision Making (MCDM) methods have rapidly developed and have been applied to many areas for decision making in engineering. Apart from that, the process to select fault-diagnosis sensor for Fuel Cell Stack system in various options is a multi-criteria decision-making (MCDM) issue. However, in light of the choosing of fault diagnosis sensors, there is no MCDM analysis, and Fuel Cell Stack companies also urgently need a solution. Therefore, in this paper, we will use MCDM methods to analysis the fault-diagnosis sensor selection problem for the first time. The main contribution of this paper is to proposed a fault-diagnosis sensor selection methodology, which combines the rank reversal resisted AHP and TOPSIS and supports Fuel Cell Stack companies to select the optimal fault-diagnosis sensors. Apart from that, through the analysis, among all sensor alternatives, the acquisition of the optimal solution can be regarded as solving the symmetric or asymmetric problem of the optimal solution, which just maps to the TOPSIS method. Therefore, after apply the proposed fault-diagnosis sensor selection methodology, the Fuel Cell Stack system fault-diagnosis process will be more efficient, economical, and safe

Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Multi-Criteria Decision Making (MCDM) methods have rapidly developed and have been applied to many areas for decision making in engineering. Apart from that, the process to select fault-diagnosis sensor for Fuel Cell Stack system in various options is a multi-criteria decision-making (MCDM) issue. However, in light of the choosing of fault diagnosis sensors, there is no MCDM analysis, and Fuel Cell Stack companies also urgently need a solution. Therefore, in this paper, we will use MCDM methods to analysis the fault-diagnosis sensor selection problem for the first time. The main contribution of this paper is to proposed a fault-diagnosis sensor selection methodology, which combines the rank reversal resisted AHP and TOPSIS and supports Fuel Cell Stack companies to select the optimal fault-diagnosis sensors. Apart from that, through the analysis, among all sensor alternatives, the acquisition of the optimal solution can be regarded as solving the symmetric or asymmetric problem of the optimal solution, which just maps to the TOPSIS method. Therefore, after apply the proposed fault-diagnosis sensor selection methodology, the Fuel Cell Stack system fault-diagnosis process will be more efficient, economical, and safe

Selection of Business Process Modeling Tool with the Application of Fuzzy DEMATEL and TOPSIS Method

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool

Selection of Business Process Modeling Tool with the Application of Fuzzy DEMATEL and TOPSIS Method

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool

Nitrogen dynamics in a highly urbanized coastal area of western Japan: impact of sewage-derived loads

Abstract In this study, we examined the nitrogen dynamics of a highly urbanized coastal area, focusing on the impacts of sewage-derived nitrogen. High levels of dissolved inorganic nitrogen were detected in seawater near treated sewage effluent (TSE) discharge points before decreasing in the offshore direction, suggesting that the impact zone of sewage effluent is about 1–2 km from the discharge point. The stable isotope ratios of nitrate and particulate organic nitrogen suggest nitrogen uptake by phytoplankton as well as dilution by offshore seawater, which contributed to a decrease in sewage-derived nitrogen levels. However, the extent of the impact zone was controlled by tidal variations and differences in temperature between the TSE and seawater. Our results also identify nitrogen transport processes, through exchange between seawater and sediment pore water, as an additional important source of nitrogen in the study area