Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-Cho, Midori-Ku, Yokohama, Kanagawa, 226-8503, Japan.
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225009, China.
Environ Monit Assess. 2023 Aug 31;195(9):1114. doi: 10.1007/s10661-023-11738-0.
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-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-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH-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) 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.
河流水质监测对于了解水动力和制定水环境保护政策至关重要。原位紫外-可见（UV-Vis）光谱法在实时监测多种水质参数方面具有很大的潜力。然而，建立将吸收光谱与特定水质参数联系起来的可靠方法仍然具有挑战性，特别是对于在各种流量和水质条件下的富营养化河流。为了解决这个问题，开发了一个集成台式和原位 UV-Vis 光谱仪的框架，以建立可靠的转换模型。使用台式光谱仪获得的吸收光谱用于创建模型来估计硝酸盐氮（NO-N）、总氮（TN）、化学需氧量（COD）、总磷（TP）和悬浮固体（SS）。我们使用原位光谱仪获得的吸收光谱验证了这些模型。选择波长的偏最小二乘回归（PLSR）和使用所有波长的主成分回归（PCR）分别在估计 NO-N 和 COD 方面表现出很高的准确性。人工神经网络（ANN）被证明适合使用所有波长预测低 NH-N 浓度溪流水中的 TN。由于光响应磷物种主要被吸附在悬浮固体上，因此使用所有波长的 PLSR 和 PCR 方法可以有效地分别估计 TP 和 SS。所有校准模型的决定系数（R）都超过 0.6，大多数归一化均方根误差（NRMSE）都在 0.4 以内。我们的方法在建立同时监测氮、磷、COD 和 SS 的可靠模型方面表现出出色的效率和潜力。这种方法消除了在模型建立过程中需要进行耗时且不确定的原位吸收光谱测量的需要，而原位吸收光谱测量可能会受到波动的自然和人为环境条件的影响。
