| Project/Area Number |
22KJ1302
|
|---|
| Project/Area Number (Other) |
22J12590 (2022)
|
|---|
| Research Category |
Grant-in-Aid for JSPS Fellows
|
| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2022) |
|---|
| Section | 国内 |
|---|
| Review Section |
Basic Section 64010:Environmental load and risk assessment-related
|
|---|
| Research Institution | Tokyo Institute of Technology |
|---|
Principal Investigator |
GUO ZHONGYU 東京工業大学, 環境・社会理工学院, 特別研究員(DC2)
|
|---|
| Project Period (FY) |
2023-03-08 – 2024-03-31
|
| Project Status |
Completed (Fiscal Year 2023)
|
| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2023: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2022: ¥900,000 (Direct Cost: ¥900,000)
|
|---|
| Keywords | Dam reservoir / Bulk water property / Photochemistry / Dissolved organic matter / Radical production / Singlet oxygen / Excited triplet state / Photodegradation / photochemistry / photolysis / reactive intermediates / dissolved organic matter / reservoir / modeling / quantum yield / simulated sunlight |
|---|
| Outline of Research at the Start |
Organic micropollutants like herbicides have been detected in reservoirs including Japan. Since reservoir is closely related to human health, it is important to know their persistence. Our study focus on indirect photolysis of pollutants in reservoirs which help us know the persistence of pollutants
|
| Outline of Annual Research Achievements |
The proposed goals have been finished well. The results have been published on six peer-reviewed journals including four top journals and presented on three conferences (both domestic and international).
50 dam reservoirs have been sampled in Japan. Bulk water property has been characterized including pH, total organic carbon concentration, inorganic ions, conductivity, salinity, optical property.
Meanwhile, quantum yield for production of singlet oxygen and excited triplet of dissolved organic matter were determined using steady-state photochemical experiments. Validated prediction models (linear and machine-learning based) have been developed using water property to estimate the quantum yield . The models can be finally used to estimate photodegradation rate of pollutants in reservoirs.
|
