| Project/Area Number |
17K06571
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Hydraulic engineering
|
|---|
| Research Institution | Tokyo Institute of Technology |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2017-04-01 – 2023-03-31
|
| Project Status |
Completed (Fiscal Year 2022)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
|
|---|
| Keywords | 紫外可視分光光度計 / 河川水質 / 栄養塩 / 有機物 / 吸光度スペクトル / 統計回帰モデル / ANNモデル / 浮遊物質 / モニタリング / 水工水理学 / 水資源 |
|---|
| Outline of Final Research Achievements |
In this study, we examined models to accurately estimate the concentrations of nitrate nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS) using the absorbance spectra obtained from an indoor UV-Vis spectrometer on samples collected from actual rivers. Partial least squares regression (PLSR) using selected wavelengths and principal component regression (PCR) using all wavelengths were found to be suitable for estimating NO3-N and COD, respectively. An ANN model utilizing all wavelengths was suitable for predicting TN. For TP and SS, PLSR and PCR using all wavelengths were effective, respectively. Moreover, we demonstrated the application of these models to the spectra obtained from an in-situ UV-Vis spectrometer in actual rivers, showing that water quality could be reliably estimated with high confidence. The results suggest an efficient approach for water quality monitoring utilizing an in-situ UV-Vis spectrometer.
|
| Academic Significance and Societal Importance of the Research Achievements |
現場型の紫外可視吸光光度計は、環境中の水の吸光度スペクトルをリアルタイムで分析して複数の水質項目を監視できる大きな潜在能力を持っているが、流量や水質の変動の大きい河川を対象に吸光度スペクトルから複数水質項目を効率的かつ高い信頼性で推定する手法は確立されていない。本研究では室内の紫外可視吸光光度計で計測される河川水の吸光度スペクトルを用いて構築される最適な水質推定モデルが現場型の吸光光度計で計測される吸光度スペクトルにも適用できることを示しており、モデル構築時に現地でのスペクトル測定を行わずに複数水質の同時モニタリングを可能とするもので、今後の水質モニタリングに新たな道を開くものである。
|
