This study addresses the discrepancy between conventional settling velocity equations, which assume spherical rigid particles, and the actual settling behavior of flocs in water treatment. It hypothesizes that variations in the ALT ratio (aluminum concentration in coagulant to suspended solids) affect floc morphology and settling velocity. Aluminum-kaolinite flocs were analyzed under five ALT conditions (0.025-0.20), measuring diameter, velocity, and circularity via image analysis. Results showed systematic changes in floc structure and behavior with increasing ALT ratio. A new settling velocity equation was developed by incorporating ALT-dependent drag coefficient and effective density, showing improved accuracy over conventional models, especially in R_e <10 where shape effects are significant. While its applicability to real source water flocs and practical design remains uncertain, the findings highlight the importance of floc morphology in sedimentation modeling and suggest potential for enhancing predictive accuracy in water treatment processes.

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Submerged plants mainly inhabit oligotrophic waters, and a method for purifying the aquatic environment that utilizes their functions is known as the buffer zone technique. In recent years, floating solar power generation has attracted attention, and there is a need to analyze and evaluate the growth characteristics of submerged plants under light-controlled shading when the water surface is covered with solar panels. In this study, to achieve advanced water treatment, we performed direct filtration from the aeration tank on the active sludge process of a sewage treatment plant using a ceramic flat membrane, and evaluated the growth characteristics of submerged plants under shading by the filtered water and the coexistence effects with aquatic organisms. As a result, we were able to confirm the growth of Submerged plant, Potamogeton maackianus A. Benn. Under low light intensity and the coexistence effects of Snail, Lymnaea Radix auricularia japonica.

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The introduction of food waste disposers without dedicated treatment (hereinafter referred to as “DP”), is expected to contibute to the waste treatment sector by reducing the amount of waste and so on. On the other hand, there are concerns about the burden on sewerage systems such as increased water quality loads at sewage treatment plants. As part of an evaluation of the optimization of food waste, the study conducted an impact assessment using greenhouse gas (GHG) emissions as an indicator in areas where DP has been introduced. It was found that although the introduction of DP is effective in reducing GHG emissions, there were differences in the effectiveness of this method depending on the conditions of the relevant facilities in the area when compared with other food waste recycling methods such as methane fermentation.

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The Sewage Blue Carbon Initiative aims to conserve marine resources and contribute to decarbonization through the operation and management of sewage treatment plants to deliver nutrients to seaweed beds. By promoting seaweed bed growth through nutrient delivery to nutrient-poor areas, the carbon sequestration amount from blue carbon ecosystems can be increased. Integration of simple and informative analytical methods can help monitor production of refractory dissolved organic carbon (RDOC) and labile dissolved organic carbon (LDOC) within dissolved organic carbon (DOC) in seawater, providing evidence for a major mechanism of carbon sequestration in coastal seaweed beds. However, measurement and characterization of DOC is challenging and often requires major equipment and complicated data-workup, creating barriers for application to field measurements. Excitation-emission matrix (EEM) has been used to measure DOC in environmental water samples and is capable of assessing changes in LDOC and RDOC concentrations. In this study, we attempted to monitor changes from marine microbe-assisted degradation of DOC, a process of DOC cycling that occurs naturally in seawater, by EEM. Since changes in fluorescent LDOC/RDOC observed by EEM showed poor correlation with changes in bulk DOC, additional monitoring methods should be used in conjunction with EEM.

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The sewage treatment process consumes large amounts of energy and produces significant carbon emissions. On the other hand, all Japanese sewerage industry has been to achieve carbon neutrality in 2050. Effective utilization of sewage sludge is essential for achieving the carbon neutrality in sewerage industry. In addition, the utilization of sewage sludge can contribute to the reduction of greenhouse gas emissions (GHGs) and life cycle costs (LCC), including beyond the boundaries of the sludge treatment facility, called external contribution, in case of replacing as conventionally used resources. However, few studies have focused on sludge treatment for GHGs and LCC for external contribution in sludge treatment facility. In this research, the reduction of GHGs emissions and LCC effects of using sewage sludge as a substitute for resources that rely on imports from overseas were evaluated based on the case of the effective utilization of sewage sludge.

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Satellite remote sensing enables the measurement of the horizontal distribution of surface chlorophyll-a (Chl-a) concentrations in lakes. Numerous algorithms have been developed in previous studies to estimate Chl-a concentrations from optical imagery. However, short-term environmental fluctuations remain difficult to monitor due to limitations such as low revisit frequency and high cloud cover.
　In this study, we developed machine learning models that incorporate both satellite optical data and in-situ observation data as input features to more accurately estimate the temporal dynamics of Chl-a concentrations in Lake Kasumigaura. The results demonstrate that decision tree-based approaches provided improved estimation accuracy compared to the method of previous studies. These findings highlight the effectiveness of machine learning techniques for time-series prediction of Chl-a concentrations.

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