Water turbidity sensing using a smartphone

This paper demonstrates a rapid, cost-effective and field-portable smartphone based turbidimeter that measures turbidity of water samples collected from different natural water resources and in drinking water. The working of the designed sensor is based on a Mie-scattering principle where suspended micro (μ-) particles in water medium scatter a strong light signal along the normal direction of the incoming light signal, which can be detected by an infra-red (IR) proximity sensor embedded in the smartphone. Two freely available android applications were used to measure the irradiance of the scattered flux and analyse the turbidity of the medium. With the designed sensor, water turbidity variation as low as 0.1 NTU can be measured accurately in the turbidity value ranging from 0 to 400 NTU. The sensor responses for these ranges of turbid media are found to be linear. A high repeatability in the sensor characteristics is also been observed. The optics design involved for the development of the proposed smartphone turbidimeter is simple and is robust in operation. The designed sensing technique could emerge as a truly portable, user-friendly and inexpensive turbidity sensing tool that would be useful for different in-field applications.

[1]  Lin Zhang,et al.  Smartphone-based point-of-care testing of salivary α-amylase for personal psychological measurement. , 2015, The Analyst.

[2]  F. W. Gilcreas,et al.  Standard methods for the examination of water and waste water. , 1966, American journal of public health and the nation's health.

[3]  Jerry E. Ongerth Evaluation of Treatment for Removing Giardia Cysts , 1990 .

[4]  Paul S. Francis,et al.  Mobile phone-based electrochemiluminescence sensing exploiting the ‘USB On-The-Go’ protocol , 2015 .

[5]  Aydogan Ozcan,et al.  Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools. , 2014, Lab on a chip.

[6]  Subash C B Gopinath,et al.  Bacterial detection: from microscope to smartphone. , 2014, Biosensors & bioelectronics.

[7]  Steve Feng,et al.  Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning. , 2015, Lab on a chip.

[8]  M. Hartmann,et al.  Light scattering by small particles. Von H. C. VANDE HULST. New York: Dover Publications, Inc. 1981. Paperback, 470 S., 103 Abb. und 46 Tab., US $ 7.50 , 1984 .

[9]  Ahmad Fairuz Omar,et al.  Turbidimeter Design and Analysis: A Review on Optical Fiber Sensors for the Measurement of Water Turbidity , 2009, Sensors.

[10]  Huibin Cao,et al.  The design of rapid turbidity measurement system based on single photon detection techniques , 2015 .

[11]  Rudolph W. Preisendorfer,et al.  Secchi disk science: Visual optics of natural waters1 , 1986 .

[12]  Tu San Park,et al.  Smartphone-based, sensitive µPAD detection of urinary tract infection and gonorrhea. , 2015, Biosensors & bioelectronics.

[13]  Pabitra Nath,et al.  Ground and river water quality monitoring using a smartphone-based pH sensor , 2015 .

[14]  Pabitra Nath,et al.  Smartphone-based platform optical setup measuring π/256 optical phase difference in an interference process. , 2015, Applied optics.

[15]  Jihea Moon,et al.  Development of a Smartphone-based reading system for lateral flow immunoassay. , 2014, Journal of nanoscience and nanotechnology.

[16]  B. Wilén,et al.  Short term effects of dissolved oxygen concentration on the turbidity of the supernatant of activated sludge , 1998 .

[17]  P. M. Lane,et al.  The calibration of optical backscatter sensors for suspended sediment of varying darkness levels , 2000 .

[18]  P. Nath,et al.  Label-free biodetection using a smartphone. , 2013, Lab on a chip.