Performance of sensors for quality analysis of irrigation water

Mádilo Passos; Arthur Breno Rocha Mariano; Daniela Andreska da Silva; Alan Bernard Oliveira de Sousa

doi:10.18011/bioeng.2022.v16.1094

Authors

Mádilo Passos Universidade Federal do Ceará
Arthur Breno Rocha Mariano Graduating in Agronomy, Federal University of Ceará (UFC), Fortaleza, CE, Brazil
Daniela Andreska da Silva Master's student in the Postgraduate Program in Agricultural Engineering, Federal University of Ceará (UFC), Fortaleza, CE, Brazil.
Alan Bernard Oliveira de Sousa Department of Agricultural Engineering, Federal University of Ceará (UFC), Fortaleza, CE, Brazil.

DOI:

https://doi.org/10.18011/bioeng.2022.v16.1094

Keywords:

pH, turbidity, total dissolved solids

Abstract

Monitoring the quality of irrigation water can help in the maintenance of filters and irrigation systems, avoiding clogs and uniformity problems. The objective of this work was, thus, to evaluate the performance of sensor modules for monitoring irrigation water quality variables. For that, three sensors were evaluated, and their performance was rated from the adjustment of calibration equations, obtained through linear regression analysis (y_i = b₀ + b₁x_i + ε_i), using the ordinary least squares method (OLS) to estimate its parameters (β₀ and β₁). The first sensor evaluated was the Ph4502c for pH measurement. Direct methodology was used, using standard pH solutions (1.79; 4.5; 6.88; 12.13; and 13.99) and an electrode type BNC probe. The second evaluated sensor was turbidity model TSW30. To evaluate the total dissolved solids (TDS) sensor, the direct method was applied, using solutions with electrical conductivity of 0.50, 1.0, and 2.0 dS m^-1. To investigate the assumptions of independence, homoscedasticity, and normality of the residuals of the linear regression models, the Durbin-Watson, Breusch-Pagan, and Kolmogorov-Smirnov tests were respectively used. In the evaluation of the statistical performance, the indicators of the root-mean-square error, coefficient of determination, correlation coefficient, confidence index, and index of agreement were adopted. The ordinary least squares method did not produce the best unbiased linear estimators for the calibration equations of the pH, turbidity, and TDS sensors, due to the violation of the regression assumptions. The adjustments showed good accuracy for water quality assessment, according to high performance statistics and models classified as ‘Excellent’.

Downloads

Download data is not yet available.

References

Almeida, O. A. de. (2010). Qualidade da água de irrigação. Embrapa Mandioca e Fruticultura, Cruz das Almas. 234p.

Assis, F. N., Arruda, H. V. de, Pereira, A. R. (1996). Aplicações de estatística à climatologia: teoria e prática. Pelotas: UFPel, 1996. 161p.

Brasil. (2014). Ministério da Saúde. Fundação Nacional de Saúde. Manual de controle da qualidade da água para técnicos que trabalham em ETAs.

Camargo, A. P., Sentelhas P. C. (1997). Avaliação do desempenho de diferentes métodos de estimativas da evapotranspiração potencial no Estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, Santa Maria, 5(1), 89-97.

Carvalho, F. P., Maia, V. M., Louzada, L. C., Gonçalves, M. A. (2017). Desempenho setorial de empresas brasileiras: um estudo sob a ótica do ROE, Q de Tobin e Market to Book. Revista de Gestão, Finanças e Contabilidade, 7(1), 149-163. http://dx.doi.org/10.18028/2238-5320/rgfc.v7n1p149-163 DOI: https://doi.org/10.18028/2238-5320/rgfc.v7n1p149-163

Durbin, J., Watson, G. S. (1951). Testing for serial correlation in least squares regression ii. Biometrika, 38(1-2), 159-178. https://doi.org/10.1093/biomet/58.1.1 DOI: https://doi.org/10.1093/biomet/38.1-2.159

Francisco, P. R. M., Mello, V. D. S., Bandeira, M. M., De Macedo, F. L., Santos, D. (2016). Geoespacialização de cenários pluviométricos através de krigagem utilizando distribuição gama incompleta e teste Kolmogorov-Smirnov. Congresso Técnico Científico da Engenharia e da Agronomia CONTECC 2016. Paraná, 5 p. DOI: https://doi.org/10.26848/rbgf.v9.1.p047-061

Figueiredo Filho, D., Nunes, F., Rocha, E. C., Santos, M. L., Batista, M., Silva Júnior, J. A. (2011). O que fazer e o que não fazer com a regressão: pressupostos e aplicações do modelo linear de Mínimos Quadrados Ordinários (MQO). Revista Política Hoje, 20(1), 44 – 99.

Kutner, M. H.; Nachtsheim, C. J.; Neter, J.; Li, W. (2004). Applied linear statistical models. 5. ed. New York: McGraw-Hill, 1396p.

Leal Junior, W. B., Araujo, H. X., Tavares, F. M. (2020). Monitoramento da qualidade da água utilizando plataforma de internet das coisas. Humanidades & Inovação, 7(9), 46-53.

Luo, R., Misra, M., Himmelblau, D. M. (1999). Sensor fault detection via multiscale analysis and dynamic PCA. Industrial & Engineering Chemistry Research, 38(4), 1489-1495. https://doi.org/10.1021/ie980557b DOI: https://doi.org/10.1021/ie980557b

Mueller, T. G., Pierce, F. J., Schabenberger, O., Warncke, D. D. (2001). Map quality for site-specific fertility management. Soil Science Society of America Journal, Madison, 65(5), 1547-1558. https://doi.org/10.2136/sssaj2001.6551547x DOI: https://doi.org/10.2136/sssaj2001.6551547x

Pratami, L. W. D., Ariswati, H. G., Titisari, D. (2020). Effect of Temperature on pH Meter Based on Arduino Uno With Internal Calibration. Journal of Electronics, Electromedical, and Medical Informatics, 2(1), 23-27. https://doi.org/10.35882/jeeemi.v2i1.5 DOI: https://doi.org/10.35882/jeeemi.v2i1.5

Pule, M., Yahya, A., Chuma, J. (2017). Wireless sensor networks: A survey on monitoring water quality. Journal of Applied Research and Technology, 15(6), 562–570. https://doi.org/10.1016/j.jart.2017.07.004 DOI: https://doi.org/10.1016/j.jart.2017.07.004

Rietmanm, A., Kaplan, M. L., Cava, R. J. (1985). Lithium ion-poly (ethylene oxide) complexes. I. Effect of anion on conductivity. Solid State Ionics, 17(1), 67-73. https://doi.org/10.1016/0167-2738(85)90124-9 DOI: https://doi.org/10.1016/0167-2738(85)90124-9

Sousa, J. T. D., Leite, V. D., & Luna, J. G. D. (2001). Desempenho da cultura do arroz irrigado com esgotos sanitários previamente tratados. Revista Brasileira de Engenharia Ambiental, 5(1), 107-110. DOI: https://doi.org/10.1590/S1415-43662001000100019

Trentin, C. V., Souza, J. L. M. (2006). Possibilidade de utilização da irrigação com água residuária em torno das principais estações de tratamento de efluentes da Região Metropolitana de Curitiba, Estado do Paraná, Brasil. Acta Sci. Agron, 28(2), 291-298. https://doi.org/10.4025/actasciagron.v28i2.1132 DOI: https://doi.org/10.4025/actasciagron.v28i2.1132

Walker J. P., Willgoose G. R., Kalma J. D. (2004) In situ measurement of soil moisture: a comparison of techniques. Journal of Hydrology, 293(1-4), 85-99. https://doi.org/10.1016/j.jhydrol.2004.01.008 DOI: https://doi.org/10.1016/j.jhydrol.2004.01.008

Willmott, C. J., Ackleson, S. G., Davis, J. J. Feddema, K.; Klink, D. R. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, Ottawa, 90(5), 8995-9005. https://doi.org/10.1029/JC090iC05p08995 DOI: https://doi.org/10.1029/JC090iC05p08995