The Utilization OpenCV to Measure the Water Pollutants Concentration

Riri Asyahira Sariati Syah, Rijal Hakiki

Abstract


Abstract. Intensive water quality determination needs to be adjusted with technological developments to meet today's society's needs and increased water pollution due to urbanization. Therefore, early detection is essential for in site water quality determination and as a critical consideration in making health and environmental decisions. OpenCV is a library programming feature for Computer Vision which focuses on extracting information from images in real-time, this can be considered to be potential to measure the pollutant concentration. Objectives: This study identify the potential of colorimetry analysis method by using OpenCV as an alternative method for pollutant concentration measurement. Method and results: First stage, this study collecting the data of NH3 phenate and Pt-Co CU from the spectrophotometer. The first stage also was including the development of an OpenCV code. Then, the data was collected were processed to get the concentration of NH3 and Pt-Co both using OpenCV and spectrophotometer; factors that influence the Pt-Co sample image measurement process by using OpenCV-Python was analyzed too. Then in the analysis stage, the result of the two measurement method was tested by statistic determine its significant difference. The conclusion found whether OpenCV could be potential to measure the pollutant concentration or not. Conclusion: the OpenCV has potential to be use as alternative colorimetry measurement method to determine water pollutant as there is no significant difference in the spectrophotometric method results and the results from OpenCV for Pt-Co sample.  Meanwhile, in this study found that the result of NH3 from spectrophotometer is nonlinear different with from OpenCV that is linear. Thus, further research is needed to test the validity of OpenCV method.  The factor influence of measurement using OpenCV code is when determining the Region of Interest (ROI) and determining the pixel values for the normalized box filter

Full Text:

PDF

References


G. Luka et al., “Microfluidics integrated biosensors: A leading technology towards lab-on-A-chip and sensing applications,” Sensors (Switzerland), vol. 15, no. 12, pp. 30011–30031, 2015, doi: 10.3390/s151229783.

E. Samiei, G. S. Luka, H. Najjaran, and M. Hoorfar, “Integration of biosensors into digital microfluidics: Impact of hydrophilic surface of biosensors on droplet manipulation,” Biosens. Bioelectron., vol. 81, pp. 480–486, 2016, doi: 10.1016/j.bios.2016.03.035.

D. N. Myers, “Why monitor water quality?,” United States Geol. Surv., pp. 240–241, 2014, doi: 10.1016/j.jcrs.2013.12.013.

P. M. K. R. Indonesa, “STANDAR BAKU MUTU KESEHATAN LINGKUNGAN DAN PERSYARATAN KESEHATAN AIR UNTUK KEPERLUAN HIGIENE, SANITASI, KOLAM RENANG, SOLUS PER AQUA, DAN PEMANDIAN UMUM,” Peratur. Menteri Kesehat. Republik Indones., 2017.

C. Oleari, Standard Colorimetry Definition ,Algorithms, and Software, 1st ed. United Kingdom: John Wiley & Sons, Ltd, 2016.

D. Harvey, Modern Analytical Chemistry, 1st ed. United States: McGraw-Hill Higher Education, 2000.

K. L. Diehl and E. V. Anslyn, “Array sensing using optical methods for detection of chemical and biological hazards,” Chem. Soc. Rev., vol. 42, no. 22, pp. 8596–8611, 2013, doi: 10.1039/c3cs60136f.

S. Capel-Cuevas et al., “A compact optical instrument with artificial neural network for pH determination,” Sensors (Switzerland), vol. 12, no. 5, pp. 6746–6763, 2012, doi: 10.3390/s120506746.

C. L. Lonsdale et al., “The Use of Colorimetric Sensor Arrays to Discriminate between Pathogenic Bacteria,” PLoS One, vol. 8, no. 5, pp. 2–9, 2013, doi: 10.1371/journal.pone.0062726.

A. Abbaspour, M. A. Mehrgardi, A. Noori, M. A. Kamyabi, A. Khalafi-Nezhad, and M. N. S. Rad, “Speciation of iron(II), iron(III) and full-range pH monitoring using paptode: A simple colorimetric method as an appropriate alternative for optodes,” Sensors Actuators, B Chem., vol. 113, no. 2, pp. 857–865, 2006, doi: 10.1016/j.snb.2005.03.119.

L. Sun, The structural behavior of overlaid asphalt pavements. 2016.

Acharya, Image Processing: Principles and Applications [book review], 1st ed., vol. 18, no. 2. Canada: John Wiley & Sons, Ltd, 2005.

R. Hakiki and T. Wikaningrum, “The Prospect of Digitally Enhanced Colorimetry As an Analytical Method for Water Quality Determination,” Indones. J. Urban Environ. Technol., vol. 2, no. 2, pp. 146–163, 2019, doi: 10.25105/urbanenvirotech.v0i0.4362.

G. S. Luka, E. Nowak, J. Kawchuk, M. Hoorfar, and H. Najjaran, “Portable device for the detection of colorimetric assays,” R. Soc. Open Sci., vol. 4, no. 11, 2017, doi: 10.1098/rsos.171025.

T. Ringenson, M. Höjer, A. Kramers, and A. Viggedal, “Digitalization and environmental aims in municipalities,” Sustain., vol. 10, no. 4, pp. 1–16, 2018, doi: 10.3390/su10041278.

A. Pajankar, Raspberry Pi Computer Vision Programming. MUMBAI: Packt Publishing Ltd, 2015.

G. B. Garcia, Learning Image Processing with OpenCV. Birmingham-Mumbai: PACKT, 2015.

O. J. Frp et al., “for Face Recognition,” vol. 6, pp. 485–490, 2020.

N. Gupta, P. Sharma, V. Deep, and V. K. Shukla, “Automated Attendance System Using OpenCV,” pp. 1226–1230, 2020, doi: 10.1109/icrito48877.2020.9197936.

Ashwini, V. Balaji, S. Srinivasan, and K. Monisha, “Real Time Facial Recognition System,” New Trends Comput. Vis. Bio-inspired Comput., vol. 5, no. 8, pp. 1721–1726, 2020, doi: 10.1007/978-3-030-41862-5_175.

“OpenCV-Python Tutorials Documentation Release beta,” 2017.

D. Apriyanti, V. Indria Santi, and Y. Dianinayati Siregar, “Pengkajian Metode Analisis Amonia Dalam Air Dengan Metode Salicylate Test Kit,” J. Ecolab, vol. 7, no. 2, pp. 60–70, 2013, doi: 10.20886/jklh.2013.7.2.60-70.

M. Douglas C, P. Elizabeth A, and V. G. Geoffrey, Introduction To Linear Regression Analysis, Fifth. Canada: John Wiley & Sons, Inc., 2012.

M. Pagano, “Simple linear regression and correlation, continued.,” Nutrition, vol. 11, no. 3, p. 319, 1995.

NURYADI, TUTUT DEWI ASTUTI, ENDANG SRI UTAMI, and MARTINUS BUDIANTARA, Dasar-Dasar Statistika Penelitian. SIBUKU MEDIA, 2017.




DOI: http://dx.doi.org/10.33021/jenv.v6i2.1475

Copyright (c) 2021 Riri Asyahira Sariati Syah

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Journal of Environmental Engineering and Waste Management Published by PresUniv Press, in collaboration with IESA and APIK Indonesia Network