International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Developing a Low-Cost Salinity Sensor Using Locally Sourced Materials

Abstract

We report the design and construction of a locally constructed hydro-meteorological sensor for surface and underwater vehicles. This salinity sensor consists of four electronics units: the power, input (sensor), gain (amplifies the output signal), and output units. Each unit/block utilizes various low-power integrated circuits. The calibration equation of the salinity sensor was 1055.6exp-1.062 ml/mg/volt (correlation coefficient r = 0.9897). The performance and the coefficient of efficiency of the constructed sensor were compared with a standard sensor, showing the Mean Bias Error (MBE), Root Mean Square Error, and Standard Deviation of -0.5535, 1.3825, and 3.4839 ml/mg, the error margins were relatively small, indicating an excellent performance by the sensor. However, the negative MBE suggests a slight underestimation of the standard. Conclusively, the sensor is efficient in hydro-meteorological studies, capable of monitoring solution conductivity and measuring salinity (and total dissolved salt) in the ocean or brackish water. 

Country : Nigeria

1 Okunlola B.A.2 Ewetumo T.3 Okogbue E. C.4 Olabanji O. M.

  1. Department of Meteorology and Climate Science, the Federal University of Technology, Akure, Nigeria
  2. Department of Physics, the Federal University of Technology, Akure, Nigeria
  3. Department of Meteorology and Climate Science, the Federal University of Technology, Akure, Nigeria
  4. Department of Physics, the Federal University of Technology, Akure, Nigeria

IRJIET, Volume 5, Issue 8, August 2021 pp. 102-106

doi.org/10.47001/IRJIET/2021.508017

Full Paper
Download

References

  1. Booij, K., Robinson, C. D., Burgess, R. M., Mayer, P., Roberts, C. A., Ahrens, L., et al. (2016). Passive sampling in regulatory chemical monitoring of nonpolar organic compounds in the aquatic environment. Environ. Sci. Technol. 50, 3–17. doi: 10.1021/acs.est.5b04050.
  2. Griffith, J. F., and Weisberg, S. B. (2011). Challenges in implementing new technology for beach water quality monitoring: lessons from a California demonstration project. Mar. Technol. Soc. J. 45, 65–73. doi: 10.4031/MTSJ.45.2.13.
  3. Kröger, S., Parker, E. R., Metcalfe, J. D., Greenwood, N., Forster, R. M., Sivyer, D. B., et al. (2009). Sensors for observing ecosystem status. Ocean Sci. 5, 523–535. doi: 10.5194/os-5-523-2009.
  4. Lim, J., and Choi, M. (2015). Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environ. Monit. Assess. 187:384. doi: 10.1007/s10661-015-4616-1.
  5. Mills, G., Fones, G. R., and Kröger, S. (2014). “In-situ sensors for monitoring the marine environment,” in Measurement, Instrumentation and Sensors Handbook, eds J. G. Webster and H. Eren (Boca Raton, FL: CRC Press Taylor & Francis Group), 71–72.
  6. Murphy, K., Sullivan, T., Heery, B., and Regan, F. (2015). Data analysis from a low-cost optical sensor for continuous marine monitoring. Sens. Actuat. B Chem. 214, 211–217. doi: 10.1016/j.snb.2015.02.023.
  7. Oinonen, S., Hyytiäinen, K., Ahlvik, L., Laamanen, M., Lehtoranta, V., Salojärvi, J., et al. (2016). Cost-effective marine protection - a pragmatic approach. PLoS ONE 11:e0147085. doi: 10.1371/journal.pone.0147085.
  8. Peck, M. A., Arvanitidis, C., Butenschön, M., Canu, D. M., Chatzinikolaou, E., Cucco, A., et al. (2016). Projecting changes in the distribution and productivity of living marine resources: a critical review of the suite of modelling approaches used in the large European project VECTORS. Estuar. Coast. Shelf Sci.doi: 10.1016/j.ecss.2016.05.019. [Epub ahead of print].
  9. Radu, A., Anastasova, S., Fay, C., Diamond, D., Bobacka, J., and Lewenstam, A. (2010). Low cost, calibration-free sensors for in situ determination of natural water pollution. IEEE Sensorsdoi: 10.1109/ICSENS.2010.5690357.
  10. Roy, H. E., Pocock, M. J. O., Preston, C. D., Roy, D. B., Savage, J., Tweddle, J. C., et al. (2012). Understanding Citizen Science and Environmental Monitoring: Final Report on Behalf of UK Environmental Observation Framework..
  11. Sendra, S., Parra, L., Lloret, J., and Jiménez, J. M. (2015). Oceanographic multisensor buoy based on low cost sensors for posidonia meadows monitoring in Mediterranean Sea. J. Sensors 2015:920168. doi: 10.1155/2015/920168.
  12. Shutler, J. D., Warren, M. A., Miller, P. I., Barciela, R., Mahdon, R., Land, P. E., et al. (2015). Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: the AlgaRisk demonstration service. Comput. Geosci. 77, 87–96. doi: 10.1016/j.cageo.2015.01.010.
  13. Su, T.-C., and Chou, H.-T. (2015). Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: a case study of tain-pu reservoir in kinmen, Taiwan. Remote Sens. 7, 10078–10097. doi: 10.3390/rs70810078.
  14. Thomsen, P. F., Møller, P. R., Sigsgaard, E. E., Knudsen, S. W., Jørgensen, O. A., and Willerslev, E. (2016). Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE 11:e0165252. doi: 10.1371/journal.pone.0165252.
  15. UNEP (2016). Marine Plastic Debris and Microplastics – Global Lessons and Research to Inspire Action and Guide Policy Change. Nairobi.
  16. Van der Molen, J., Ruardij, P., and Greenwood, N. (2016). Potential environmental impact of tidal energy extraction in the Pentland Firth at large spatial scales: results of a biogeochemical model. Biogeosciences 13, 2593–2609. doi: 10.5194/bg-13-2593-2016.
  17. Woody, C., E. Shih, J. Miller, T. Royer, L.P. Atkinson, R.S. Moody, 2000. Measurements of Salinity in the Coastal Ocean: A Review of Requirements and Technologies. Marine Technology Society Journal, 34(2), 26-33.
  18. William J. W. (1974). The development of the chlorinity/salinity concept in oceanography. Elsevier Oceanography SeriesVolume 7, 1974, Pages 105-133. https://doi.org/10.1016/S04229894(08)70980-2.
  19. Wright, S., Hull, T., Sivyer, D. B., Pearce, D., Pinnegar, J. K., Sayer, M. D. J., et al. (2016). SCUBA divers as oceanographic samplers: The potential of dive computers to augment aquatic temperature monitoring. Sci. Rep. 6:30164. doi: 10.1038/srep30164.
  20. Yokota, F., and Thompson, K. M. (2004). Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Anal. 24, 635–650. doi: 10.1111/j.0272-4332.2004. 00464.x.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies