PMID- 37648802
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20231010
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Print)
IS  - 0167-6369 (Linking)
VI  - 195
IP  - 9
DP  - 2023 Aug 31
TI  - Development of statistical regression and artificial neural network models for 
      estimating nitrogen, phosphorus, COD, and suspended solid concentrations in 
      eutrophic rivers using UV-Vis spectroscopy.
PG  - 1114
LID - 10.1007/s10661-023-11738-0 [doi]
LID - 1114
AB  - River water quality monitoring is crucial for understanding water dynamics and 
      formulating policies to conserve the water environment. In situ 
      ultraviolet-visible (UV-Vis) spectrometry holds great potential for real-time 
      monitoring of multiple water quality parameters. However, establishing a reliable 
      methodology to link absorption spectra to specific water quality parameters 
      remains challenging, particularly for eutrophic rivers under various flow and 
      water quality conditions. To address this, a framework integrating desktop and in 
      situ UV-Vis spectrometers was developed to establish reliable conversion models. 
      The absorption spectra obtained from a desktop spectrometer were utilized to 
      create models for estimating nitrate-nitrogen (NO(3)-N), total nitrogen (TN), 
      chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). 
      We validated these models using the absorption spectra obtained from an in situ 
      spectrometer. Partial least squares regression (PLSR) employing selected 
      wavelengths and principal component regression (PCR) employing all wavelengths 
      demonstrated high accuracy in estimating NO(3)-N and COD, respectively. The 
      artificial neural network (ANN) was proved suitable for predicting TN in stream 
      water with low NH(4)-N concentration using all wavelengths. Due to the dominance 
      of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and 
      PCR methods utilizing all wavelengths effectively estimated TP and SS, 
      respectively. The determination coefficients (R(2)) of all the calibrated models 
      exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were 
      within 0.4. Our approach shows excellent efficiency and potential in establishing 
      reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This 
      approach eliminates the need for time-consuming and uncertain in situ absorption 
      spectrum measurements during model setup, which may be affected by fluctuating 
      natural and anthropogenic environmental conditions.
CI  - (c) 2023. The Author(s).
FAU - Lyu, Yanping
AU  - Lyu Y
AD  - Department of Transdisciplinary Science and Engineering, Tokyo Institute of 
      Technology, 4259 Nagatsuta-Cho, Midori-Ku, Yokohama, Kanagawa, 226-8503, Japan.
FAU - Zhao, Wenpeng
AU  - Zhao W
AD  - Department of Transdisciplinary Science and Engineering, Tokyo Institute of 
      Technology, 4259 Nagatsuta-Cho, Midori-Ku, Yokohama, Kanagawa, 226-8503, Japan. 
      wppzhao@gmail.com.
AD  - College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 
      225009, China. wppzhao@gmail.com.
FAU - Kinouchi, Tsuyoshi
AU  - Kinouchi T
AD  - Department of Transdisciplinary Science and Engineering, Tokyo Institute of 
      Technology, 4259 Nagatsuta-Cho, Midori-Ku, Yokohama, Kanagawa, 226-8503, Japan. 
      kinouchi.t.ab@m.titech.ac.jp.
FAU - Nagano, Tadahiro
AU  - Nagano T
AD  - Civil Engineering and Eco-Technology Consultants Co., Ltd, 2-23-2 
      Higashi-Ikebukuro, Toshima-Ku, Tokyo, 170-0013, Japan.
FAU - Tanaka, Shigeo
AU  - Tanaka S
AD  - Civil Engineering and Eco-Technology Consultants Co., Ltd, 2-23-2 
      Higashi-Ikebukuro, Toshima-Ku, Tokyo, 170-0013, Japan.
LA  - eng
GR  - 17K06571/Japan Society for the Promotion of Science/
PT  - Journal Article
DEP - 20230831
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
RN  - N762921K75 (Nitrogen)
RN  - 27YLU75U4W (Phosphorus)
SB  - IM
MH  - *Rivers
MH  - Biological Oxygen Demand Analysis
MH  - *Environmental Monitoring
MH  - Regression Analysis
MH  - Spectrophotometry, Ultraviolet
MH  - Neural Networks, Computer
MH  - Nitrogen
MH  - Phosphorus
PMC - PMC10468949
OTO - NOTNLM
OT  - Artificial neural network
OT  - In situ UV-vis spectroscopy
OT  - Statistical regression models
OT  - Water quality monitoring
OT  - Wavelength selection
COIS- The authors declare no competing interests.
EDAT- 2023/08/31 00:41
MHDA- 2023/09/01 06:43
PMCR- 2023/08/31
CRDT- 2023/08/30 23:27
PHST- 2023/06/08 00:00 [received]
PHST- 2023/08/17 00:00 [accepted]
PHST- 2023/09/01 06:43 [medline]
PHST- 2023/08/31 00:41 [pubmed]
PHST- 2023/08/30 23:27 [entrez]
PHST- 2023/08/31 00:00 [pmc-release]
AID - 10.1007/s10661-023-11738-0 [pii]
AID - 11738 [pii]
AID - 10.1007/s10661-023-11738-0 [doi]
PST - epublish
SO  - Environ Monit Assess. 2023 Aug 31;195(9):1114. doi: 10.1007/s10661-023-11738-0.