Search
E-mail
RSS

Online submission Editor-in-chief Peer review Staff Login

Journal of Tropical Oceanography >

2022 , Vol. 41 >Issue 4: 20 - 30

DOI: https://doi.org/10.11978/2022006

Review

Research progress in the continuous measurement technology of suspended sediment concentration

  • LI Weihua ,
  • LI Jiufa ,
  • ZHANG Wenxiang
Expand
  • State Key Laboratory of Estuarine & Coastal Research Center, East China Normal University, Shanghai 200062, China
ZHANG Wenxiang. email:

Copy editor: YAO Yantao

Received date: 2022-01-12

  Revised date: 2022-02-16

  Online published: 2022-02-22

Supported by

Joint Key Funds of National Natural Science Foundation of China(U2040202)

Fundamental Research Funds for the Central Universities

Fold

Abstract

Continuous measurement technology of suspended sediment concentration is the most fundamental requirement in the research fields of hydraulics, environmental science, estuarine and coastal science, as well as marine science. The current popular technical routes including optical backscattering and transmission, specular reflection, remote sensing, acoustic backscattering and transmission attenuation, tuning fork resonance, pressure difference and gamma-ray attenuation and other principles of technical methods were summarized in this paper. The main advantages and problems of each technical method are then discussed, and the future research focus and development direction are prospected as: (1) the principle of optical backscattering is the optimal technical route for low-cost, miniaturized, and high-time-frequency measurement of suspended sediment concentration, and is necessary to focus on range expansion and particle size sensitivity weakening study; (2) low uncertainty suspended sediment concentration profile measurement relies on the development of the acoustic backscattering technical route; (3) the tuning fork resonance technical route is particularly suitable for ultra-high range application scenarios under turbid current and fluid mud conditions; (4) integrate multi-technology sensors and use the artificial intelligence algorithms to replace traditional inverse theory model, etc.

Key words： suspended sediment concentration; continuous measurement technology; optical turbidity measurement; acoustic suspended sediment concentration measurement; tuning folk densimeter

Cite this article

LI Weihua , LI Jiufa , ZHANG Wenxiang . Research progress in the continuous measurement technology of suspended sediment concentration[J]. Journal of Tropical Oceanography, 2022 , 41(4) : 20 -30 . DOI: 10.11978/2022006

悬沙运动与水下地貌演化、水体水质、生物地球化学过程、水力机械的淤塞和水力磨蚀以及水下结构物基础的局部冲刷等问题密切相关, 连续、高质量的悬沙浓度(suspended sediment concentration, SSC)测量已成为当前开展河流、湖泊、河口和近海区域泥沙输运与沉积过程、水质和水生系统修复等研究以及涉水工程设计、管理的重要基础需求之一(Rai et al, 2015; Felix et al, 2016)。近年来, 基于谐振、压差、放射性射线衰减、声学/光学透射与后向散射、遥感等原理的悬沙浓度连续测量技术(continuous measurement technology, CMT)取得了快速的发展。CMT利用悬浮泥沙性质和传感器电信号存在稳定定量关系的特点, 通过间接手段获得悬沙浓度信息, 是一种悬沙信息替代方法(Gray et al, 2009)。与现场取样后室内烘干称重的传统直接测量方法相比, CMT具有高时空分辨率、长期数据采集的成本和风险均较低的特点, 美国地质调查局自1982年开始引入悬沙浓度替代测量方法, 至2008年即已替代75%的传统悬沙浓度测量站位(Gray et al, 2009)。这使得现场连续的高分辨率悬沙浓度数据采集成为现实, 并且目前已广泛应用于地表水体场景的悬沙浓度现场观测作业中(Rai et al, 2015), 代表着今后长期监测水体悬沙浓度监测技术手段的重要发展趋势。
本文拟就当前基于光学、声学、音叉谐振、压差和放射性射线衰减等原理的悬沙浓度CMT的最新研究进展、优势与不足作详细介绍, 并归纳和展望未来发展的趋势, 以期为悬沙浓度CMT传感器的研发与应用提供参考。

1 光学技术路线

1.1 后向散射与透射原理测量

1.1.1 测量原理

(1) 后向散射(optical backscatter, OBS)
光后向散射(OBS)测量SSC的原理是通过利用光学传感器发出的特定频率红外光, 并接收其被悬浮泥沙颗粒后向散射(140~165°或90°)的光强来完成(Bin Omar et al, 2009)(图1), 传感器所接收的后向散射光强以模/数(analog and digital, AD)转换电压或浊度表征量(nephelometric turbidity unit, NTU)(ISO, 2016)表达(商用光学浊度计出厂时, 往往已由厂商把AD转换电压根据需要标定为NTU), 通过对AD转换电压或NTU与SSC建立定量标定关系, 可以获得水体的SSC信息(Haimann et al, 2014)。
图1 光后向散射、透射测量原理图(Campbell, 2018)

Fig. 1 Schematic of optical backscattering and transmission measurement (Campbell, 2018)

基于米氏散射理论(van de Hulst, 1981)的后向散射光强(F)是悬沙浓度(SSC)、后向散射体积(V)、光源源强(E)、泥沙密度(ρ)、泥沙粒径(D)及光散射效率(Qs)的函数:
$F=\frac{3}{2}\times \frac{V\cdot \operatorname{SSC}\cdot E\cdot {{Q}_{\text{s}}}}{\rho \cdot D}$
其中, 光散射效率与泥沙Munsell色度呈正相关关系(Sutherland et al, 2000), 而后向散射体积与泥沙颗粒形态关系密切。当局地水体环境内颗粒大小、形态和物质组成差异可近似忽略时, 理论上后向散射光强与SSC表现为良好的线性关系, 这是光后向散射原理测量SSC的重要基础。
(2) 透射衰减
光透射衰减原理测量SSC的理论基础为描述光能在材料中衰减规律的Lambert-Beer定律, 通过采集定距剩余透射光强(I)的方式实现(Ochiai et al, 2010)(图1):
$I={{I}_{0}}\cdot {{\text{e}}^{-k\cdot SSC}}^{\cdot l}$
式中: I0为入射光强度; l为发射与接收元器件的距离; k为衰减系数, 受水体物理特性、悬浮泥沙粒径、粒型、物质组成等影响。当水体泥沙特征恒定, 且光路距离不变时, k为常数, 此时透射光强与SSC表现为简单的指数型单调衰减关系。
必须指出, 水体中气泡对光散射性能的影响在0~80°区间最为严重, 0°接收角时的气泡响应强度是100~170°接收角情况下的104倍(Zhang et al, 2002)。这意味着光透射原理(0°散射角)在获得单解关系实现宽量程的同时, 应用场景存在局限性, 掺气较多的滨海湿地冲流带、多浪的水体表层和排水口等环境的适用性不佳。这使得当前主流传感器厂商基本不再研发、销售该类型的传感器。

1.1.2 优点与存在问题

光学传感器尤其是OBS类传感器因其低成本、高效率的优势, 已广泛应用于河流、水电站及近岸等不同水文条件下SSC值的连续测量(Rasmussen et al, 2009; Uhrich et al, 2014)。OBS类传感器标定后, 其测量SSC值可达到20g·L-1(Felix et al, 2012; Shao et al, 2017), 甚至更高(Gentile et al, 2010; Wang et al, 2020a)。但随着SSC的升高, 水体中的光吸收/衰减效应趋于增强并最终占据主导地位, 这导致后向散射光强与SSC的关系曲线依次表现为线性上升、指数上升和指数衰减三个阶段, 从而使得后向散射强度与SSC呈多解函数关系, 并在指数上升过渡至指数下降的量程段分辨率急剧下降(Downing, 2006; Shao et al, 2017; Wang et al, 2020a)。
现实中, 泥沙粒径通常是影响光学浊度传感器测量精度的最重要因素, 实验发现具有相同增益的OBS类传感器对1~500μm粒径泥沙的灵敏度变幅可达200倍(Downing, 2006)。而在极端情况下, 河流中因泥沙特性变化导致的光学浊度传感器的SSC观测值误差可能高达70%(Navratil et al, 2011)。受传感器特性和水沙环境影响, 泥沙粒径与OBS灵敏度的定量关系较为复杂。Landers等(2013)根据现场观测研究结果发现, 二者呈指数衰减效应, 且这种影响主要来自16μm以下的细颗粒泥沙组分; 而Merten等(2014)室内实验的结果则指出砂质含量与OBS灵敏度降幅呈线性相关关系。此外, 细颗粒泥沙絮凝导致的悬浮颗粒粒径变化对于光学SSC测量的影响是不可忽略的(Druine et al, 2018)。
后向散射角度的选择仍是一个共识不足的问题。Druine等(2018)认为90°散射角的测量精度相对低于140°散射角, Merten等(2014)则提出 90°接收角的灵敏度优于180°, 而Campbell公司(美国)则倾向于选择对微小气泡相对不敏感的140°散射角(Campbell, 2018)。
对于如何从标定方法上提高浊度与SSC转换的精度问题, 一方面, 较多学者强调现场同步取水标定的重要性(刘红 等, 2006; Landers et al, 2013; 林振镇 等, 2018); 另一方面, 亦有学者倾向于通过标定模型中加入泥沙粒径参数的方式补偿粒径对光接收敏感度的影响。如Su等(2016)即提出了一种使用两种同源已知级配泥沙样品、联合求解砂、粉砂两组分系数的定标模型和方法, 实验结果表明该方法的转换精度较传统方法在现场悬沙颗粒级配已知的条件下有明显提升。随着现场激光衍射粒度仪和多频超声后向散射反演悬沙粒径技术日趋进步, 悬沙粒径自容、连续观测已成为可能, 显然后一种标定方法在全天候、近底边界层的SSC连续观测方面有更好的发展前景。

1.2 遥感原理测量

自Ritchie等(1976)确认了优选700~800nm波段光反射率反演瞬态水表SSC场的可行性后, 因其同步、超广域、自动、非接触式测量和低成本的优势, 以及得益于MODIS、Landsat、SPOT、Sea WiFS、Sentinel和GF-1/4/5等卫星下发的光谱数据, 基于可见光和近红外波段光谱反射率的连续水表SSC反演在水色遥感和河口海岸动力沉积研究领域得到了广泛的关注( Fraser, 1998; Dekker et al, 2001; Doxaran et al, 2009; Wang et al, 2009; Teng et al, 2021), 且在已报道的遥感反演SSC模型中, 可见光波段与近红外波段单独或联合的反射率均有应用(Dekker et al, 2001; Doxaran et al, 2009; Wang et al, 2009; Teng et al, 2021)。遥感原理测量SSC的技术路线可分为经验模型和基于辐射转移理论的解析、半解析模型三大类。其中, 具有严格理论推导的解析模型具有普适性(Giardino et al, 2007; Sipelgas et al, 2009; Binding et al, 2012), 但模型的初始化参数较难确定, 反演精度相对弱于经验模型和半解析模型(Binding et al, 2012; Wang et al, 2020b), 当前主要应用于光学特性相对简单的湖泊水体, 河口海岸区域则较为少见(陈莉琼 等, 2012; Wu et al, 2013; Wang et al, 2020b); 早期的经验模型主要基于局地实测验证数据, 通过数理统计方法得出水体反射率与SSC的定量关系, 出于拟合精度的考量, 经验模型的方程形式多样(Fraser, 1998; Dekker et al, 2001), 显然其适用性仅局限于局地水域; 半解析模型则是解析模型的简化形式, 在兼顾经验模型精度的前提下仍具有一定的理论基础和物理意义(Loisel et al, 2014; Shen et al, 2014), 较经验模型的适用范围更广(Dogliotti et al, 2015), 在河口、近海和内陆水域方面均有成功的应用案例(Shen et al, 2014; Chen et al, 2015; Giardino et al, 2015), 是当前遥感反演SSC研究的主流技术路线和发展趋势(Wang et al, 2020b)。
因水体辐射传导特征与SSC的关系受局地水体的光学特性、泥沙的矿物成分和粒径变化、太阳天顶角、测量的空间分辨率和传感器观测角等影响(Bowers et al, 2006; Pavelsky et al, 2009; Long et al, 2013), 遥感反演水表SSC不可避免地依赖于对现场水体反射率的标定(Long et al, 2013)。换言之, 因受制于天然水体水沙特性的时间、空间变异性, 遥感方式反演SSC在时间和空间上的精度是非定常且不均匀的。此外, 卫星遥感光谱反射率相对于SSC的灵敏度不高, SSC每升高50mg•L-1, 反射率仅增加约1%量级(Wren et al, 2000), 这使得现场发生较大的SSC变化时, 可能只采集到很小的反射率波动信号, 进而有可能发生测量分辨率低于实际测量需求的情况。

2 声学技术路线

2.1 测量原理

(1) 后向散射
与光后向散射原理相似, 基于超声后向散射(acoustic backscatter, ABS)原理的悬沙浓度测量, 主要通过超声换能器主动向水体发射声脉冲并接收水体中悬浮颗粒反射和散射脉冲声波的方式实现(Thorne et al, 1993)。基于声散射理论, 水体中非粘性悬浮泥沙浓度(SSC)与后向散射回声强度(直观表征为压电转换电压Vm)的平方相关(Thorne et al, 2014), 基本函数关系如下:
$\text{SSC}=\left( \frac{r\psi }{KR} \right)V_{m}^{2}{{\text{e}}^{4r}}({{\alpha }_{\text{w}}}+{{\alpha }_{\text{s}}})$
式中: r为测量点与换能器的距离; ψ为无量纲近场修正因子, 与超声频率、换能器大小和r等相关; R为换能器硬件常数, 与换能器的灵敏度、波束角和超声频率等相关; αw为水体造成的超声衰减系数, 与超声频率和水体密度有关; αs为悬沙颗粒对声波反射、散射造成的超声衰减系数; K为悬浮泥沙后向散射特性参数。
悬沙超声衰减系数(αs)可由悬沙浓度沿程粘滞损耗与散射损耗的总和表达:
${{\alpha }_{\text{s}}}=\int_{0}^{r}{\left( {{\xi }_{\text{s}}}+{{\xi }_{\text{v}}} \right)\text{SSC}(r)}\text{d}r$
式中: ${{\xi }_{\text{v}}}$为粘滞损耗系数; ${{\xi }_{\text{s}}}$为散射损耗系数。粘滞损耗的量级是颗粒表面积、声波频率、水体粘度以及颗粒与流体密度之比的函数, 而散射损耗则取决于声波波长与颗粒周长之比(Landers, 2010)。粘滞损耗主要由细颗粒泥沙所致, 而散射损耗主要由较粗颗粒泥沙引起(Moore et al, 2012; Sahin et al, 2013)。
悬浮泥沙后向散射特性参数(K)与悬沙粒度和粒形等相关, 表征为悬沙颗粒形式函数密度(f)和平均粒径(a0)的经验关系:
$K=\frac{f}{\sqrt{\mathop{a}_{\text{0}}}}$
式(3)、式(4)和式(5)构成了悬沙浓度(SSC)和回声压电转换电压(Vm)的非闭合基本方程组。当水沙特性不变时, 悬浮泥沙后向散射特性参数(K)为与特定水沙环境相关的常数, 自近场向远场隐式迭代求解式(3)和式(4)即可实现SSC剖面的同步测量。而当悬沙粒度特征在垂向上不均匀时, 悬浮泥沙后向散射特性参数(K)不再一成不变, 泥沙粒度亦成为变量之一。基于不同频率超声后向散射性能差异的前提, 采用双频率超声能量比算法(Thorne et al, 2007)和3种以上频率超声最小浓度标准差算法(Hanes, 2016)或多频统计模型算法(Wilson et al, 2015)求解悬浮泥沙粒径是当前的主流做法。
(2) 透射衰减
对于均匀的水沙混合流体, 超声波声强(直观表征为压电转换电压Vm)在流体内随传播距离(r)同样遵循 Lambert-Beer定律而呈指数规律衰减(方彦军 等, 1990):
${{V}_{\text{m}}}={{V}_{\text{m}0}}{{\text{e}}^{-r(\alpha \text{w}+\alpha \text{s})}}$
式中: Vm0为换能器端的压电转换电压(初始发射声强)。
当水沙环境和超声波发射、接收换能器距离不变时, 式(4)等价于悬沙超声衰减系数(αs)与悬沙浓度(SSC)的一阶线性方程。从而式(6)可简化为:
${{V}_{\text{m}}}=a{{\text{e}}^{-b\cdot \text{SSC}}}$
式中: a、b为水沙环境和超声波发射、接收换能器距离不变条件下的常量系数, 与测试的水沙特性和传感器特性有关。
单频率超声透射衰减原理传感器是基于式(7)而实现的SSC测量, 其优势在于可实现超高量程(如500~800g·L-1)的测量(王爱霞 等, 2012)。
假定声衰减量为不同粒径组分的泥沙颗粒所致衰减量的代数累加, 则总泥沙衰减系数(αs)可表征为n个组分的百分含量f(i)加权相应颗粒组分衰减系数αs(i)的代数和:
${{\alpha }_{\text{s}}}=\sum\nolimits_{1}^{n}{{{f}_{(i)}}{{\alpha }_{\text{s}(i)}}}$
由式(4)、式(6)和式(8)组合可知, 当采用n+1种超声频率同时测量声衰减量时, 可联立求解获得n个泥沙颗粒组分的微分含量和SSC, Sympatec公司(德国)的OPUS超声粒度和含沙量测量仪即基于该原理而研制的(牛占 等, 2009; Sympatec, 2021)。

2.2 优点与存在问题

超声传感器对生物附着耐受和非接触式的远端测量方式, 以及后向散射原理可同步测量SSC和悬沙粒径剖面的特点, 使其在野外现场应用具有显著的独特优势。
但相对于光学传感器而言, 声学信号反演SSC和传感器标定作业过于复杂, 这使得声学传感器在CMT领域的应用存在一定局限性。因此, 除Aquatec公司(美国)的AQUAscat 1000以外, 其他商业产品鲜有报道。作为替代方式, 汪亚平等(1999)证实了利用宽带模式ADCP(acoustic doppler current profiler)回声强度反演SSC剖面是可行的, 且反演精度与光学OBS类传感器相当; Ha等(2011)的研究结果表明, 脉冲相干模式的ADCP能够胜任近底SSC剖面的高分辨率观测需求。Fugate等(2002)亦通过现场比测实验证实了6MHz频率的ADV后向散射声强反演SSC的可行性。概括而言, 当前声学反演SSC技术路线仍以超声多普勒流速(剖面)仪后向散射声强的二次开发利用为主。
与光学技术路线类似, 矿物成分和密度的差异对声学测量SSC影响显著, 简单假定为均一的石英矿物可导致明显的测量误差(Moate et al, 2012), 且单频超声散射性能同样与声波波长和颗粒尺寸相关 (陈星宇 等, 2018), 超声后向散射强度的灵敏度亦受悬浮泥沙粒径影响(Ha et al, 2011)。但单频后向散射声强的灵敏度存在相对平坦的粒径区间, 如6MHz超声后向散射声强的灵敏度在100~300μm区间近乎不变(Fugate et al, 2002), 而8MHz超声则为30~400μm (Manaster et al, 2020)。Siadatmousavi等(2013)则指出, 1.5MHz 超声对16μm以下的细颗粒泥沙不敏感, 故反演获得的SSC将缺失相应成分。
在算法方面, 多频声学反演算法的SSC反演精度较单频算法提升相对有限, 其价值更多地在于粒径的估算(Guerrero et al, 2011)。但必须指出, 声学反演悬沙粒径的精度显著低于激光衍射测量结果(LISST-SL)(Guerrero et al, 2014), 且声学反演悬沙粒径的量程与所使用的超声频率密切相关(Guerrero et al, 2011)。
此外, 黄河潼关水文站的比测结果(王爱霞 等, 2012)表明, 基于声衰减原理测量的悬沙颗粒级配与激光法测量的一致性较佳, 且反演SSC的量程上限可达800g·L-1。但受仪器构造和水沙条件限制, 以OPUS为代表的声衰减原理测量SSC和悬沙粒径仪器应用于野外现场环境时存在较大的局限性。

3 其他原理的技术路线

3.1 音叉谐振原理测量

音叉谐振原理测量SSC的基本思路在于, 认为水体的SSC或密度与音叉谐振频率的平方成反比, 通过测量音叉谐振频率的变化可以间接测得水体的SSC或密度(图2)(李先达 等, 2018); 同时音叉谐振频率的变化率亦与流体的屈服应力相关, 经过标定后亦可间接测得高含沙水体的屈服应力和粘性系数(Hsu et al, 2008; Zhang et al, 2020)。当前有关这一技术的产品主要有荷兰Stema公司的Rheotune音叉密度计(Werner, 2012)、美国Emerson公司的FDM音叉密度计(Emerson, 2021)和我国黄河水利委员会水文局研制的AEX-3型振动式悬移质测沙仪(河南黄河水文科技有限公司, 2020)。该类传感器对于SSC测量而言几乎不存在量程上限问题, 但现有成品仪器体积偏大且功耗偏高(Werner, 2012), 低SSC测量精度不高(FDM和Rheotune的标称密度精度均为1kg·m-3), 当前主要应用于疏浚溢流或浮泥类的非牛顿高含沙水体SSC或密度的连续测量(McAnally et al, 2007)。
图2 音叉密度传感器原理图(李先达 等, 2018)

Fig. 2 Schematic of tune sensor (Li et al, 2018)

3.2 压差原理测量

该技术是通过联测某一水层的上下压力差、水温和盐度来实现含沙水体的SSC或密度测量。其优势在于适用于SSC在10kg·m-3以上的高含沙水体, 无需标定, 不受悬沙粒度影响, 且没有生物污染和信号漂移问题(Rai et al, 2015)。Hsu等(2010)证明压差技术在SSC较高的JI-Ji大坝上能够取得较好的测量结果, 且更大的压力传感器测量间距明显有利于提高压差式SSC测量的精度(图3), 但这与高垂向测量分辨率的需求相左。Calhoun等(2001)指出脉动流速、水温和电导率是影响压差式SSC测量精度的首要因素。从实际情况来看, 因压差测沙传感器依赖的辅助测量要素较多而水体采样体积偏大, 因而商用产品极为少见(Gray et al, 2009), 美国地质调查局等机构甚至已暂时放弃对压差仪的进一步测试研究(Gray et al, 2010)。
图3 压差SSC传感器原理图(Hsu et al, 2010)

Fig. 3 Schematic of differential pressure densimeter (Hsu et al, 2010)

3.3 γ射线衰减原理测量

该技术利用γ射线穿透物质的能力与物质密度成反比的原理来实现水体的SSC或密度测量。其优点在于不受泥沙特性、水温、压力、粘度等物理性质的影响, 是我国现行河流SSC测量国标(中华人民共和国住房和城乡建设部, 2016)中在SSC大于20kg·m-3时推荐使用的CMT传感器。但近年来, γ射线衰减原理传感器因其存在安全风险和环境污染问题而已趋于衰落(Rai et al, 2015)。

4 现状的不足与研发方向展望

(1) 低成本、小型化和高时频的光后向散射原理测量SSC技术路线
光后向散射原理的SSC测量是当前满足低成本、小型化和高时频的最优技术路线, 其技术难点在于扩增量程和削弱粒径敏感度。
光后向散射原理测量仪器的核心元器件为发光二极管/激光管和光敏接收元件。较其他技术而言, 其成本最低, 并且随着微机电系统(micro electromechanical system, MEMS)技术的发展, Vishay(美国)、Ti(美国)、Maxim(美国)、滨松(日本)和亿光(中国台湾)等厂商推出的高AD转换精度、高频收发一体数字IC和光纤的使用, 使得传感器探针式封装和高时频测量成为可能, 甚至已有厂商推出多传感器阵列式封装产品用于床面底边界层SSC剖面和床面冲淤观测, 如Argus公司(德国)推出的ASM-Ⅳ型悬浮物测量仪(邢超锋 等, 2015)。但如1.1.2节所述, 该技术方法的粒径敏感度偏高且量程偏低, 这是当前光后向散射原理技术亟须解决的关键问题。
实际应用中, 一方面后向散射强度与SSC呈多解函数关系, 并在过渡量程段分辨率急剧下降, 不同后向散射角度传感器标定曲线的过渡段极值亦存在差异, 越接近于透射方向极值出现时的SSC越低。因此, 目前主流的宽量程OBS传感器解决消解和消除量程间断问题, 主要是通过同时测量多个后向散射角度的光强来实现, 如美国Campbell公司的OBS5+(Campbell, 2021)、日本JFE株式会社的ATU75W2(JFE-Advantech, 2017)以及美国哈希公司的TSS(HACH, 2018)等高量程的浊度传感器。
另一方面, 对于SSC测量需求而言, 散射光强的AD转换电压换算为浊度中间量的做法并非必要, 不同传感器在同一水沙条件下的浊度-SSC标定关系无法保证一致(Downing, 2006; Fettweis et al, 2019)。这意味着使用光后向散射传感器阵列观测SSC剖面时, 须严格标定每一个传感器。若简单将所有传感器预先标定至浊度(NTU)后, 仅使用单一传感器的NTU-SSC标定关系来代表全部传感器的做法(邢超锋 等, 2015; 周晓妍 等, 2020)存在一定瑕疵, 或将导致人为的垂向SSC差异。
此外, 目前在削弱光后向散射测量SSC的粒径敏感度方面的研究倾向于3种截然不同的方向。其一, 利用声、光后向散射强度的粒径敏感度在30μm以下截然相反的响应模式和高频超声在30~500μm粒径区间的粒径敏感度趋于恒定的特点, 联用高频超声传感器和光后向散射传感器, 从而将联合传感器的粒径敏感度大幅减弱, Sequoia公司(美国)推出的LISST-AOBS浊度传感器即基于该思路而研发(Sequoia Scientific, 2021); 其二, 借助其他现场粒度测量仪器直接测量悬浮泥沙的平均粒径, 从而将粒径敏感度问题转换为标定关系曲线库检索问题, 如使用Sequoia公司(美国)生产的LISST现场激光粒度仪与浊度传感器的联合测量(张文祥 等, 2019); 其三, 与多频超声技术类似, 基于Mie光谱后向散射理论, 悬浮泥沙后向散射系数是悬浮泥沙平均粒径和光波波长的函数, 同步测量多光谱后向散射系数可以实现悬沙粒径和SSC的求解(Kostadinov et al, 2009), 这一思路目前主要在多光谱水色遥感研究领域有所尝试(Kostadinov et al, 2009; 杨曦光 等, 2015)。但近年来, 随着TAOS(美国)、Maxim(美国)、AMS(奥地利)、滨松(日本)等公司陆续推出医疗健康领域用途的微型多光谱集成IC(如TCS3200、MAX30102、AS7341和C12666MA等), 将其替代或集成至光后向散射传感器, 从而实现粒径敏感度的降低, 这将是一个值得尝试的研发方向。
(2) 有助于实现低不确定度SSC垂向剖面观测的超声后向散射原理技术路线
低不确定度SSC垂向剖面观测是浑水水槽实验、河流泥沙输移通量和河口、海岸近底边界层动力沉积过程研究等领域极为普遍的基础需求和难点。泥沙粒径在垂向上因沉降导致的天然不均匀分布特性, 决定了以光学传感器阵列方式测量SSC剖面时难以保持垂向一致的不确定度(精度)。而相较于其他类型的传感器, 多频声学后向散射传感器具有的耐受生物附着、可非侵入式独立同步测量SSC和粒径垂向剖面的独特优点(Thorne et al, 2014), 使其成为解决上述问题的关键甚至唯一技术路线。
但必须指出, 当前基于声学后向散射原理的SSC测量技术仍远未达到成熟可靠的程度。其一, 声后向散射强度对粘性细颗粒泥沙的响应规律仍不明晰, 絮凝粒径的表达甚至存在争议, 这严重降低了其在河口海岸区域的适用性。如Fugate等(2002)基于现场观测结果, 提出粒径对声后向散射强度的影响来自絮凝体内散粒而非整体(Fugate et al, 2002); 而Ha等(2011)则认为絮凝体密度是多变的, 并提出了相反的论点(Ha et al, 2011)。其二, 当前基于多频超声的最小浓度标准差算法反演SSC和悬沙粒径的精度相对最高(钱仁亮, 2019), 但声散射性能与声波波长和颗粒尺寸相关, 每一超声频率均有其适用的悬沙粒径敏感响应区间(陈星宇 等, 2018), 因此具有普适性的优选超声频率谱段仍有待进一步深入研究。其三, 后向散射声强反演SSC的算法依赖于悬沙粒度形函数的准确表达, 当前研究中往往采用单一概率分布函数概化处理(Thorne et al, 2014), 但天然泥沙形态特征的多变性使得这一概化处理存在不可忽视的SSC反演误差(Vergne et al, 2021)。如何在悬沙粒度形函数中正确表达河口海岸区域常见的悬浮泥沙粒度多峰分布, 并兼顾泥沙粒形特征, 是一个关系到能否进一步提升测量精度的问题。
(3) 适合高含沙水流和浮泥工况下的超高量程CMT应用场景的音叉谐振原理技术路线
γ射线衰减原理、音叉谐振原理和超声衰减原理是3个可实现超高量程SSC连续测量的技术方向。但γ射线衰减原理的传感器存在放射性安全风险, 超声衰减原理传感器则因换能器存在余振盲区而使得传感器侵入测量的间距相对偏大(方彦军 等, 1990), 且多频超声标定操作和相应的SSC与粒径反演计算极为复杂、繁琐, 因而此类传感器产品的应用研究甚少。相反, 谐振原理的密度/SSC测量传感器在化工、食品和钻探行业的塑性流体密度实时测量领域已得到了广泛应用(Hsu et al, 2008; 李先达 等, 2018; Zhang et al, 2020), 是目前唯一能够同时测量流体的流变特性和密度/含沙量两类物理量的技术手段。显然这对于同属非牛顿流体的高含沙水流和浮泥工况下的超高量程SSC测量需求是契合的, Stema(荷兰)生产的Rheotune密度仪甚至已成为国际上港口航道浮泥测量的应用标准(McAnally et al, 2007)。相应地, 当前谐振原理的密度/SSC测量传感器主要的短板在于成品仪器体积偏大且功耗偏高(McAnally et al, 2007), 尚不能胜任无人值守的自容测量需求。另一方面, 随着汽车智能控制领域的发展, 国际上的大型半导体公司开始着力研发发动机油路用途的小型的音叉密度和粘度传感器。如TE(英国)公司推出的FPS2800B12C4型传感器, 在标称精度与主流音叉密度计相当的前提下, 其叉臂长度缩短至11mm, 功耗则降至70mA@12VDC(TE Connectivity, 2015), 较Stema Rheotune音叉密度计的叉臂长度缩短了20%, 功耗降低了98%(Stema Systems, 2016)。这说明当前谐振原理的密度/SSC测量传感器在降低功耗、缩小体积方面不存在显著的技术障碍, 仍有较大的改进空间。
(4) 多技术路线传感器融合和使用人工智能算法模型替代正向反演模型
SSC连续观测的技术和应用研究已明显表现出向多技术原理传感器集成化、智能化方向发展的趋势。当前在实践中联合使用声学、光学技术原理传感器的做法已极为普遍, 而随着人工智能算法的发展和计算硬件性能的提升, 当训练数据足够丰富时, 人工智能算法模型在SSC输出精度上的表现往往优于传统的正向反演模型结果(Ying et al, 2020), 这为解决单一技术原理的局限性和规避正向反演算法中因过于抽象概化或机理尚不明确而引入误差的问题提供了很好的借鉴, 无疑具有较好的发展前景。

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
陈莉琼, 陈晓玲, 田礼乔, 等, 2012. 鄱阳湖水体悬浮颗粒物散射光谱分解方法研究[J]. 光谱学与光谱分析, 32(3): 729-733.

CHEN LIQIONG, CHEN XIAOLING, TIAN LIQIAO, et al, 2012. Partitioning of the suspended particulate spectral scattering coefficient in Poyang Lake[J]. Spectroscopy and Spectral Analysis, 32(3): 729-733. (in Chinese with English abstract)

[2]
陈星宇, 黄善和, 何昊哲, 2018. 探测频率对多频声学测沙技术测量误差的影响[J]. 浙江大学学报(工学版), 52(2): 307-316.

CHEN XINGYU, HUANG SHANHE, HE HAOZHE, 2018. Measurement error due to frequency selection in multi-frequency suspended sediment measurement system[J]. Journal of Zhejiang University (Engineering Science), 52(2): 307-316. (in Chinese with English abstract)

[3]
方彦军, 唐懋官, 1990. 超声衰减法含沙量测试研究[J]. 泥沙研究, (2): 1-12.

FANG YANJUN, TANG MAOGUAN, 1990. Ultrasonic attenuation method for measuring sediment concentration[J]. Journal of Sediment Research, (2): 1-12. (in Chinese with English abstract)

[4]
河南黄河水文科技有限公司, 2020[2020-01-13]. Aex型振动式悬移质测沙仪[EB/OL]. http://www.hhsw.cn/ArticleShow.asp?ArticleID=113.

[5]
李先达, 魏赞庆, 2018. 谐振式音叉密度仪的测量原理及电路设计[J]. 化学工程与装备, (12): 222-223. (in Chinese)

[6]
林振镇, 陈蕴真, 杨日魁, 等, 2018. 悬沙浓度标定相关性问题的探索[J]. 水文, 38(1): 53-57.

LIN ZHENZHEN, CHEN YUNZHEN, YANG RIKUI, et al, 2018. Study on correlation of suspended sediment concentration[J]. Journal of China Hydrology, 38(1): 53-57. (in Chinese with English abstract)

[7]
刘红, 何青, 王元叶, 等, 2006. 长江口浑浊带海域OBS标定的实验研究[J]. 泥沙研究, (5): 52-58.

LIU HONG, HE QING, WANG YUANYE, et al, 2006. OBS situ calibration research in the turbidity maximum of the Changjiang Estuary, China[J]. Journal of Sediment Research, (5): 52-58. (in Chinese with English abstract)

[8]
牛占, 吉俊峰, 和瑞莉, 等, 2009. Opus原理结构与检测运行[J]. 水利水文自动化, (2): 1-7.

NIU ZHAN, JI JUNFENG, HE RUILI, et al, 2009. Discussion on OPUS principle structure and measurement operation[J]. Automation in Water Resources and Hydrology, (2): 1-7. (in Chinese with English abstract)

[9]
钱仁亮, 2019. 悬浮泥沙多频声学测量方法研究[D]. 杭州: 浙江大学.

QIAN RENLIANG, 2019. Research on multi-frequency acoustic measurement of suspended sediment[D]. Hangzhou: Zhejiang University. (in Chinese with English abstract)

[10]
王爱霞, 李海霞, 窦敏, 等, 2012. Opus在线粒度分析仪比测试验[C]// 中国水文科技新发展——2012中国水文学术讨论会. 南京:471-477 (in Chinese)

[11]
汪亚平, 高抒, 李坤业, 1999. 用ADCP进行走航式悬沙浓度测量的初步研究[J]. 海洋与湖沼, 30(6): 758-763.

WANG YAPING, GAO SHU, LI KUNYE, 1999. A preliminary study on suspended sediment concentration measurements using an ADCP mounted on a moving vessel[J]. Oceanologia et Limnologia Sinica, 30(6): 758-763. (in Chinese with English abstract)

[12]
邢超锋, 何青, 郭磊城, 等, 2015. ASM在近底泥沙浓度剖面观测中的应用研究[J]. 泥沙研究, (6): 46-51.

XING CHAOFENG, HE QING, GUO LEICHENG, et al, 2015. Application of ASM at the bottom observation of suspended sediment concentration[J]. Journal of Sediment Research, (6): 46-51. (in Chinese with English abstract)

[13]
杨曦光, 黄海军, 严立文, 等, 2015. 近岸水体表层悬浮泥沙平均粒径遥感反演[J]. 武汉大学学报(信息科学版), 40(2): 164-169.

YANG XIGUANG, HUANG HAIJUN, YAN LIWEN, et al, 2015. Average grain size inversion of suspended sediment in offshore waters[J]. Geomatics and Information Science of Wuhan University, 40(2): 164-169. (in Chinese with English abstract)

[14]
张文祥, 黄远光, 程武风, 等, 2019. 高浓度悬沙观测的室内标定实验与对比[J]. 泥沙研究, 44(5): 21-26.

ZHANG WENXIANG, HUANG YUANGUANG, CHENG WUFENG, et al, 2019. Experimental study on high concentration suspend sediment calibration[J]. Journal of Sediment Research, 44(5): 21-26. (in Chinese with English abstract)

[15]
中华人民共和国住房和城乡建设部, 2016. GB/T 50159-2015 河流悬移质泥沙测验规范[S]. 北京: 中国计划出版社.

Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2016. GB/T 50159-2015 Code for measurements of suspended sediment in open channels[S]. Beijing: China Planning Press. (in Chinese with English abstract)

[16]
周晓妍, 戴志军, 庞文鸿, 等, 2020. ASM-Ⅳ仪器在河口近底层悬沙浓度观测分析中的应用研究[J]. 应用海洋学学报, 39(2): 221-228.

ZHOU XIAOYAN, DAI ZHIJUN, PANG WENHONG, et al, 2020. Application study of ASM-Ⅳ instrument at the near-bed suspended sediment concentration measurement in estuary[J]. Journal of Applied Oceanography, 39(2): 221-228. (in Chinese with English abstract)

[17]
BINDING C E, GREENBERG T A, BUKATA R P, 2012. An analysis of MODIS-derived algal and mineral turbidity in Lake Erie[J]. Journal of Great Lakes Research, 38(1): 107-116.

DOI

[18]
BIN OMAR A F, BIN MATJAFRI M Z, 2009. Turbidimeter design and analysis: a review on optical fiber sensors for the measurement of water turbidity[J]. Sensors, 9(10): 8311-8335.

DOI

[19]
BOWERS D G, BINDING C E, 2006. The optical properties of mineral suspended particles: A review and synthesis[J]. Estuarine, Coastal and Shelf Science, 67(1-2): 219-230.

DOI

[20]
CALHOUN D L, RASMUSSEN T C, 2001. Densimetric monitoring of suspended-sediment concentrations, northeastern Georgia[C]// Proceedings of the seventh federal interagency sedimentation conference. Reno, Nevada, USA:III86-III93.

[21]
CAMPBELL, 2018[2018-05-09]. OBS-3A system operator's manual[EB/OL]. https://s.campbellsci.com/documents/ca/manuals/obs-3a_man.pdf.

[22]
CAMPBELL, 2021[2021-09-25]. OBS-5+ system operator's manual[EB/OL]. https://s.campbellsci.com/documents/us/manuals/obs-5+.pdf.

[23]
CHEN JUN, QUAN WENTING, CUI TINGWEI, et al, 2015. Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone[J]. Estuarine, Coastal and Shelf Science, 155: 104-113.

DOI

[24]
DEKKER A G, VOS R J, PETERS S W M, 2001. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes[J]. Science of the Total Environment, 268(1-3): 197-214.

DOI

[25]
DOGLIOTTI, A I, RUDDICK, K G, NECHAD, B, et al, 2015. A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters[J]. Remote sensing of environment, 156, 157-168.

DOI

[26]
DOWNING J, 2006. Twenty-five years with OBS sensors: The good, the bad, and the ugly[J]. Continental Shelf Research, 26(17-18): 2299-2318.

DOI

[27]
DOXARAN D, FROIDEFOND J M, CASTAING P, et al, 2009. Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data[J]. Estuarine, Coastal and Shelf Science, 81(3): 321-332.

DOI

[28]
DRUINE F, VERNEY R, DELOFFRE J, et al, 2018. In situ high frequency long term measurements of suspended sediment concentration in turbid estuarine system (Seine Estuary, France): Optical turbidity sensors response to suspended sediment characteristics[J]. Marine Geology, 400: 24-37.

DOI

[29]
EMERSON, 2021[2021-12-01]. Micro Motion FDM Fork Density and Concentration Meters[EB/OL]. https://www.emerson.com/documents/automation/brochure-density-concentration-meters-micro-motion-en-64220.pdf.

[30]
FELIX D, ALBAYRAK I, ABGOTTSPON A, et al, 2012. Suspended sediment and Pelton turbine wear monitoring[C]// Experimental investigation of various optical and acoustic devices and begin of the case study Fieschertal. Wien, Austria: 483-494.

[31]
FELIX D, ALBAYRAK I, BOES R M, 2016. Continuous measurement of suspended sediment concentration: Discussion of four techniques[J]. Measurement, 89: 44-47.

DOI

[32]
FETTWEIS M, RIETHMÜLLER R, VERNEY R, et al, 2019. Uncertainties associated with in situ high-frequency long-term observations of suspended particulate matter concentration using optical and acoustic sensors[J]. Progress in Oceanography, 178: 102162.

DOI

[33]
FRASER R N, 1998. Multispectral remote sensing of turbidity among Nebraska Sand Hills lakes[J]. International Journal of Remote Sensing, 19(15): 3011-3016.

DOI

[34]
FUGATE D C, FRIEDRICHS C T, 2002. Determining concentration and fall velocity of estuarine particle populations using ADV, OBS and LISST[J]. Continental Shelf Research, 22(11-13): 1867-1886.

DOI

[35]
GENTILE F, BISANTINO T, CORBINO R, et al, 2010. Monitoring and analysis of suspended sediment transport dynamics in the Carapelle torrent (Southern Italy)[J]. CATENA, 80(1): 1-8.

DOI

[36]
GIARDINO C, BRANDO V E, DEKKER A G, et al, 2007. Assessment of water quality in Lake Garda (Italy) using Hyperion[J]. Remote Sensing of Environment, 109(2): 183-195.

DOI

[37]
GIARDINO C, BRESCIANI M, VALENTINI E, et al, 2015. Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake[J]. Remote Sensing of Environment, 157: 48-57.

DOI

[38]
GRAY J R, GARTNER J W, 2009. Technological advances in suspended-sediment surrogate monitoring[J]. Water Resources Research, 45(4): W00D29.

[39]
GRAY J R, GARTNER J W, 2010. Overview of selected surrogate technologies for high-temporal resolution suspended-sediment monitoring[C]// Proceedings of the joint federal interagency conference 2010: Hydrology and sedimentation for a changing future:Existing and emerging issues. Las Vegas, Nevada, USA.

[40]
GUERRERO M, RÜTHER N, ARCHETTI R, 2014. Comparison under controlled conditions between multi-frequency ADCPs and LISST-SL for investigating suspended sand in rivers[J]. Flow Measurement and Instrumentation, 37: 73-82.

DOI

[41]
GUERRERO M, SZUPIANY R N, AMSLER M, 2011. Comparison of acoustic backscattering techniques for suspended sediments investigation[J]. Flow Measurement and Instrumentation, 22(5): 392-401.

DOI

[42]
HA H K, MAA J P Y, PARK K, et al, 2011. Estimation of high-resolution sediment concentration profiles in bottom boundary layer using pulse-coherent acoustic Doppler current profilers[J]. Marine Geology, 279(1-4): 199-209.

DOI

[43]
HACH, 2018[2021-09-25]. TSS SC user manual[EB/OL]. https://www.hach.com/asset-get.download.jsa?id=7639982983.

[44]
HAIMANN M, LIEDERMANN M, LALK P, et al, 2014. An integrated suspended sediment transport monitoring and analysis concept[J]. International Journal of Sediment Research, 29(2): 135-148.

DOI

[45]
HANES D M, 2016. Acoustic attenuation due to bi-modal size distributions of suspended sediment[J]. Journal of Coastal Research, 75: 23-27.

DOI

[46]
HSU Y S, CAI JUNFENG, 2010. Densimetric monitoring technique for suspended-sediment concentrations[J]. Journal of Hydraulic Engineering, 136(1): 67-73.

DOI

[47]
HSU Y S, HWANG Y F, HUANG J H, 2008. An exploratory study of using external fluid loading on a vibrating tube for measuring suspended sediment concentration in water[J]. Journal of Physics D: Applied Physics, 41(16): 165504.

DOI

[48]
ISO, 2016. ISO 7027-1:2016 Water quality — Determination of turbidity — Part 1: Quantitative methods[S]. Geneva, Switzerland.

[49]
JFE-ADVANTECH, 2017[2021-09-25]. INFINITY-Turbi ATU75 W2-USB/CAR/CAD data sheet[EB/OL]. https://www.jfe-advantech.co.jp/eng/assets/img/products/ocean-infinity/INFINITY-Turbi(E)_201704.pdf.

[50]
KOSTADINOV T S, SIEGEL D A, MARITORENA S, 2009. Retrieval of the particle size distribution from satellite ocean color observations[J]. Journal of Geophysical Research: Oceans, 114(C9): C09015.

[51]
LANDERS M N, 2010. Review of methods to estimate fluvial suspended sediment characteristics from acoustic surrogate metrics[C]// Proceedings of the 2nd joint federal interagency conference. Las Vegas, Nevada, USA: 1-2.

[52]
LANDERS M N, STURM T W, 2013. Hysteresis in suspended sediment to turbidity relations due to changing particle size distributions[J]. Water Resources Research, 49(9): 5487-5500.

DOI

[53]
LOISEL H, MANGIN A, VANTREPOTTE V, et al, 2014. Variability of suspended particulate matter concentration in coastal waters under the Mekong's influence from ocean color (MERIS) remote sensing over the last decade[J]. Remote Sensing of Environment, 150, 218-230.

DOI

[54]
LONG C M, PAVELSKY T M, 2013. Remote sensing of suspended sediment concentration and hydrologic connectivity in a complex wetland environment[J]. Remote Sensing of Environment, 129: 197-209.

DOI

[55]
MANASTER A E, STRAUB T D, WOOD M S, et al, 2020. Field evaluation of the Sequoia Scientific LISST-ABS acoustic backscatter sediment sensor[R]. Open-File Report 2020-1096.

[56]
MCANALLY W H, TEETER A, SCHOELLHAMER D, et al, 2007. Management of fluid mud in estuaries, bays, and lakes. Ⅱ: measurement, modeling, and management[J]. Journal of Hydraulic Engineering, 133(1): 23-38.

DOI

[57]
MERTEN G H, CAPEL P D, MINELLA J P G, 2014. Effects of suspended sediment concentration and grain size on three optical turbidity sensors[J]. Journal of Soils and Sediments, 14(7): 1235-1241.

DOI

[58]
MOATE B D, THORNE P D, 2012. Interpreting acoustic backscatter from suspended sediments of different and mixed mineralogical composition[J]. Continental Shelf Research, 46: 67-82.

DOI

[59]
MOORE S A, LE COZ J, HURTHER D, et al, 2012. On the application of horizontal ADCPs to suspended sediment transport surveys in rivers[J]. Continental Shelf Research, 46: 50-63.

DOI

[60]
NAVRATIL O, ESTEVES M, LEGOUT C, et al, 2011. Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment[J]. Journal of Hydrology, 398(3-4): 246-259.

DOI

[61]
OCHIAI S, KASHIWAYA K, 2010. Measurement of suspended sediment for model experiments using general-purpose optical sensors[J]. CATENA, 83(1): 1-6.

DOI

[62]
PAVELSKY T M, SMITH L C, 2009. Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada[J]. Water Resources Research, 45(11): W11417.

[63]
RAI A K, KUMAR A, 2015. Continuous measurement of suspended sediment concentration: Technological advancement and future outlook[J]. Measurement, 76: 209-227.

DOI

[64]
RASMUSSEN P P, JOHN G R, GLYSSON G D, et al, 2009. Guidelines and procedures for computing time-series suspended-sediment concentrations and loads from in-stream turbidity-sensor and streamflow data[R]. Rolla, USA: U.S. Geological Survey.

[65]
RITCHIE J C, SCHIEBE F R, MCHENRY J R, 1976. Remote sensing of suspended sediments in surface waters[J]. Photogrammetric Engineering and Remote Sensing, 42(12): 1539-1545.

[66]
SAHIN C, SAFAK I, HSU T J, et al, 2013. Observations of suspended sediment stratification from acoustic backscatter in muddy environments[J]. Marine Geology, 336: 24-32.

DOI

[67]
SEQUOIA SCIENTIFIC, 2021[2021-12-01]. LISST-AOBS Super-Turbidity Sensor[EB/OL]. https://www.sequoiasci.com/product/lisst-aobs/.

[68]
SHAO YUYANG, MAA J P Y, 2017. Comparisons of different instruments for measuring suspended cohesive sediment concentrations[J]. Water, 9(12): 968.

DOI

[69]
SHEN, FANG, ZHOU, YUNXUAN, PENG, XIANGYI, et al, 2014. Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean, China. International Journal of Remote Sensing, 35(11-12), 4173-4192.

[70]
SIADATMOUSAVI S M, JOSE F, CHEN QIN, et al, 2013. Comparison between optical and acoustical estimation of suspended sediment concentration: Field study from a muddy coast[J]. Ocean Engineering, 72: 11-24.

DOI

[71]
SIPELGAS L, OSSIPOVA V, RAUDSEPP U, et al, 2009. A bio-optical model for the calculation of suspended matter concentration from MODIS data in the Pakri Bay, the Gulf of Finland[J]. Boreal Environment Research, 14: 415-426.

[72]
STEMA SYSTEMS, 2016[2021-12-01]. RheoTune Specifications: Density and yield stress measurement[EB/OL]. https://stema-systems.nl/wp-content/uploads/2016/03/Rheotune-2017.pdf.

[73]
SU MIN, YAO PENG, WANG ZHENGBING, et al, 2016. Conversion of electro-optical signals to sediment concentration in a silt-sand suspension environment[J]. Coastal Engineering, 114: 284-294.

DOI

[74]
SUTHERLAND T F, LANE P M, AMOS C L, et al, 2000. The calibration of optical backscatter sensors for suspended sediment of varying darkness levels[J]. Marine Geology, 162(2-4): 587-597.

DOI

[75]
SYMPATEC, 2021[2021-12-01]. OPUS: Real-time particle size and concentration analysis in process environments from below 0.1µm to 3,000µm[EB/OL]. https://www.sympatec.com/en/particle-measurement/sensors/ultrasonic-extinction/opus/.

[76]
TE CONNECTIVITY, 2015[2021-10-16]. FPS2800B12C4 fluid property sensor data sheet[EB/OL]. https://www.te.com/content/dam/te-com/documents/sensors/global/FPC012_M_FPS2800B12C4%20Data%20Sheet.pdf.

[77]
TENG LIZHI, CHENG HEQIN, DE SWART H E, et al, 2021. On the mechanism behind the shift of the turbidity maximum zone in response to reclamations in the Yangtze (Changjiang) Estuary, China[J]. Marine Geology, 440: 106569.

DOI

[78]
THORNE P D, AGRAWAL Y C, CACCHIONE D A, 2007. A comparison of near-bed acoustic backscatter and laser diffraction measurements of suspended sediments[J]. IEEE Journal of Oceanic Engineering, 32(1): 225-235.

DOI

[79]
THORNE P D, HARDCASTLE P J, SOULSBY R L, 1993. Analysis of acoustic measurements of suspended sediments[J]. Journal of Geophysical Research: Oceans, 98(C1): 899-910.

[80]
THORNE P D, HURTHER D, 2014. An overview on the use of backscattered sound for measuring suspended particle size and concentration profiles in non-cohesive inorganic sediment transport studies[J]. Continental Shelf Research, 73: 97-118.

DOI

[81]
UHRICH M A, KOLASINAC J, BOOTH P L, et al, 2014. Correlations of turbidity to suspended-sediment concentration in the Toutle River Basin, near Mount St. Helens, Washington, 2010-11[R]. Reston, VA: U.S. Geological Survey.

[82]
VAN DE HULST H C, 1981. Light scattering by small particles[M]. New York: Dover Publications: 496

[83]
VERGNE A, BERNI C, LE COZ J, et al, 2021. Acoustic backscatter and attenuation due to river fine sediments: Experimental evaluation of models and inversion methods[J]. Water Resources Research, 57(9): e2021WR029589.

[84]
WANG CHONGYANG, WANG DANNI, YANG JI, et al, 2020b. Suspended sediment within estuaries and along coasts: A review of spatial and temporal variations based on remote sensing[J]. Journal of Coastal Research, 36(6): 1323-1331.

[85]
WANG FAN, ZHOU BIN, XU JIANMING, et al, 2009. Application of neural network and MODIS 250m imagery for estimating suspended sediments concentration in Hangzhou Bay, China[J]. Environmental Geology, 56(6): 1093-1101.

DOI

[86]
WANG YUNWEI, PENG YUN, DU ZHIYUN, et al, 2020a. Calibrations of suspended sediment concentrations in high-turbidity waters using different in situ optical instruments[J]. Water, 12(11): 3296.

DOI

[87]
WERNER C, 2012. Application of high resolution acoustics for determination of the physical properties of fluid sediments[C]// Hydro12 - Taking care of the sea. Rotterdam, Netherland: 269.

[88]
WILSON G W, HAY A E, 2015. Acoustic backscatter inversion for suspended sediment concentration and size: A new approach using statistical inverse theory[J]. Continental Shelf Research, 106: 130-139.

DOI

[89]
WREN D G, BARKDOLL B D, KUHNLE R A, et al, 2000. Field techniques for suspended-sediment measurement[J]. Journal of Hydraulic Engineering, 126(2): 97-104.

DOI

[90]
WU GUOFENG, CUI LIJUAN, DUAN HONGTAO, et al, 2013. An approach for developing Landsat-5 TM-based retrieval models of suspended particulate matter concentration with the assistance of MODIS[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 85: 84-92.

DOI

[91]
YING JIANYUN, LIANG KEWEI, WU QINGSONG, et al, 2020. Calculation of suspended sediment concentration based on deep learning and OBS turbidity[J]. Journal of Coastal Research, 115(S1): 627-630.

DOI

[92]
ZHANG MI, CHEN DEHUA, HE XIAO, et al, 2020. A hydrodynamic model for measuring fluid density and viscosity by using quartz tuning forks[J]. Sensors, 20(1): 198.

DOI

[93]
ZHANG XIAODONG, LEWIS M, LEE M, et al, 2002. The volume scattering function of natural bubble populations[J]. Limnology and Oceanography, 47(5): 1273-1282.

DOI

Options
Outlines

/

〈 〉