Tohoku University
Environmental Water Quality Engineering

Outstanding Editorial Board Member for JAM in 2023

Award

Prof. Sano is awarded “Outstanding Editorial Board Member for JAM in 2023” by Applied Microbiology International.

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The WET Excellent Presentation Award
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Best Oral Presentation Award at ISFEV2024

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WET2024 Excellent Presentation Award

Marcus Sanchez and Katayoun Amirfard were honored  (WET Excellent Presentation Award) at the Water and Environment Technology Conference 2024 (July 20-21, 2024, Okayama University). Congratulations!!! Time-series forecasting of COVID-19 case number based on amplicon-based diversity indices from wastewater Marcus Joseph Sanchez, Yilei Wang, Yubing Li, Luyao Wang, Daisuke Sano Impact of ZnO powder on biofilm formation and conjugation frequency of…
Award

WET2025 Excellent Presentation Award

Marcus Sanchez was honored  (WET Excellent Presentation Award) at the Water and Environment Technology Conference 2025 (July 5-6, 2025, City Hall Plaza-Aôre Nagaoka). Congratulations!!! Incorporating Population-Based Emissions Within a Watershed Model to Estimate the Distribution of Contaminants of Emerging Concern (CECs) in Aquatic Environments Marcus Joseph Angeles SANCHEZ, Yohei MIURA, Wasitha DILSHAN, Yusuke HIRAGA, Janaka BAMUNAWALA, Mohan AMARASIRI, Daisuke SANO
Award

2021 Best Paper Award

The following paper received a best paper award for Year 2021 from Critical Reviews in Environmental Science and Technology (Taylor & Francis Group). Amarasiri, M., D. Sano, S. Suzuki. 2020. Understanding human health risks caused by antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG) in water environments: Current knowledge and question to be answered. 50:2016−2059. Best Paper Award 2020…
Award

Best Research Award at ISFEV2024

The following research was awarded (Best Research Award) at International Symposium for Food and Environmental Virology 2024 (Shimadzu Tokyo Innovation Plaza, June 9-14, 2024). Prediction of COVID-19 cases based on various explanatory variables using machine learning methods Takehito Iizuka, Wang Luyao, Zhu Yifan, Wakana Oishi, Daisuke Sano

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