Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Turbidity is a key environmental parameter that is used in the determination of water quality. The turbidity of a water body gives an indication of how much suspended sediment is present, which directly impacts the clarity of the water (i.e., whether it is cloudy or clear). Various commercial nephelometric and optical approaches and products exist for electronically measuring turbidity. However, most of these approaches are unsuitable or not viable for collecting data remotely. This paper investigates ways for incorporating a turbidity sensor into an existing remote aquatic environmental monitoring platform that delivers data in near real-time (i.e., 15-min intervals). First, we examine whether an off-the-shelf turbidity sensor can be modified to provide remote and accurate turbidity measurements. Next, we present an inexpensive design for a practical light attenuation turbidity sensor. We outline the sensor’s design rationale and how various technical and physical constraints were overcome. The turbidity sensor is calibrated against a commercial turbidimeter using a Formazin standard. Results indicate that the sensor readings are indicative of actual changes in turbidity, and a calibration curve for the sensor could be attained. The turbidity sensor was trialled in different types of water bodies over nine months to determine the system’s robustness and responsiveness to the environment.

[1]  Jarrod Trevathan,et al.  The integration, analysis and visualization of sensor data from dispersed wireless sensor network systems using the SWE framework , 2015 .

[2]  シュナイダー，デイビッド・アンソニー,et al.  Dishwasher with a turbidity sensing mechanism , 1996 .

[3]  Yang Liu,et al.  Design of an MCU-controlled laser liquid turbidimeter based on OPT101 , 2009, International Conference on Optical Instruments and Technology.

[4]  Trina S. Myers,et al.  SEMAT — The Next Generation of Inexpensive Marine Environmental Monitoring and Measurement Systems , 2012, Sensors.

[5]  Bruce A. McCarl,et al.  Costs of water treatment due to diminished water quality: A case study in Texas , 1998 .

[6]  O. Postolache,et al.  SDI-12 based turbidity measurement system with field calibration capability , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[7]  Mohd Zubir MatJafri,et al.  The Swift Turbidity Marker , 2011 .

[8]  Kwok-Wing Chau,et al.  Uncertainty Analysis on Hybrid Double Feedforward Neural Network Model for Sediment Load Estimation with LUBE Method , 2019, Water Resources Management.

[9]  John F. Orwin,et al.  An inexpensive turbidimeter for monitoring suspended sediment , 2005 .

[10]  Amir Mosavi,et al.  Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters , 2018, Engineering Applications of Computational Fluid Mechanics.

[11]  Christoforos Panayiotou,et al.  A Nephelometric Turbidity System for Monitoring Residential Drinking Water Quality , 2009, SENSAPPEAL.

[12]  Jarrod Trevathan,et al.  Smart Environmental Monitoring and Assessment Technologies (SEMAT)—A New Paradigm for Low-Cost, Remote Aquatic Environmental Monitoring , 2018, Sensors.

[13]  Daoliang Li,et al.  Design and characterization of a smart turbidity transducer for distributed measurement system , 2012 .

[14]  Kwok-Wing Chau,et al.  A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States , 2015, Environmental Monitoring and Assessment.

[15]  Kwok-wing Chau,et al.  Effect of river flow on the quality of estuarine and coastal waters using machine learning models , 2018 .

[16]  Antonio García,et al.  A New Design of Low-Cost Four-Beam Turbidimeter by Using Optical Fibers , 2007, IEEE Transactions on Instrumentation and Measurement.

[17]  Nélia Alberto,et al.  Optical Sensors Based on Plastic Fibers , 2012, Sensors.

[18]  S. Mylvaganaru,et al.  TURBIDITY SENSOR FOR UNDERWATER APPLICATIONS Sensor Design and System Performance with Calibration Results , 1998 .

[19]  James W. O'Dell,et al.  METHOD 180.1 – DETERMINATION OF TURBIDITY BY NEPHELOMETRY , 1996 .

[20]  Jarrod Trevathan,et al.  Allocating Sensor Network Resources Using an Auction-Based Protocol , 2016, J. Theor. Appl. Electron. Commer. Res..

[21]  S. Geoffrey Schladow,et al.  Water clarity modeling in Lake Tahoe: Linking suspended matter characteristics to Secchi depth , 2006, Aquatic Sciences.

[22]  Graham K. Hargrave,et al.  A low-cost bench-top research device for turbidity measurement by radially distributed illumination intensity sensing at multiple wavelengths , 2019, HardwareX.

[23]  Jarrod Trevathan,et al.  Developing low-cost intelligent wireless sensor networks for aquatic environments , 2010, 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[24]  João L. Pinto,et al.  Turbidity sensor for determination of concentration, ash presence and particle diameter of sediment suspensions , 2011, International Conference on Optical Fibre Sensors.

[25]  Mohamad Javad Alizadeh,et al.  Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models , 2017, Environmental Science and Pollution Research.

[26]  Theofanis P. Lambrou,et al.  A low-cost system for real time monitoring and assessment of potable water quality at consumer sites , 2012, 2012 IEEE Sensors.

[27]  Daniel Kahn,et al.  An Affordable Open-Source Turbidimeter , 2014, Sensors.

[28]  Cátia Leitão,et al.  Plastic optical fibre sensor for quality control in food industry , 2013, Other Conferences.

[29]  Amir Mosavi,et al.  Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms , 2020, Engineering Applications of Computational Fluid Mechanics.