Study of the Impact of sanitary decisions over water quality using Bayesian Belief Networks in Upper Pantanal Wetland Basin – Brazil

Authors

DOI:

https://doi.org/10.33448/rsd-v11i3.26309

Keywords:

Sewage; Urbanization; River ecology.

Abstract

Bayesian Belief Networks (BBN) modeling the water quality has become popular due to advances in computational techniques. For this instance, BBN is a useful tool to modeling the relationship between water quality data and population or urbanization parameters on a watershed scale. This method can combine primary water quality data and decision parameters and help scientists and decision-makers analyze several scenarios on a watershed, including the effect of scale. This paper aims to analyze and discuss the application of Bayesian Belief Network (BBN) on the relationship between watershed water quality and sanitary management indicators, studying a case on the Pantanal Wetland tributary watershed. Two scales BBN were constructed using ten years of water quality and sewage management datasets. Both BBNs were responsive and sensitive to water quality parameters. The Total Nitrogen and E. coli were de most essential parameters to simulate changes in water quality scenarios. The simulated scenarios showed structural limitations about the Pantanal Wetland Cities' sanitary system in the present study. We strongly recommend a review of the goals of sanitary structure and services and alert to the risk of a sanitary crisis in Pantanal Wetland.

References

Ancione, G., Bragatto, P., & Milazzo, M. F. (2020). A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments. Journal of Loss Prevention in the Process Industries, 104080. 10.1016/j.jlp.2020.104080

Avila, R., Horn, B., Moriarty, E., Hodson, R., & Moltchanova, E. (2018). Evaluating statistical model performance in water quality prediction. Journal of Environmental Management, 206, 910–919. 10.1016/j.jenvman.2017.11.049

Borrero-Ramírez, Y., & Mosquera-Becerra, J. (2020). Emancipation versus normality in the Global South. International Journal of Public Health. 10.1007/s00038-020-01466-4

Farooqi, Z. U. R., Sabir, M., Latif, J., Aslam, Z., Ahmad, H. R., Ahmad, I., Imran, M., & Ilić, P. (2019). Assessment of noise pollution and its effects on human health in industrial hub of Pakistan. Environmental Science and Pollution Research, 27(3), 2819–2828. 10.1007/s11356-019-07105-7

Fasaee, M. A. K., Berglund, E., Pieper, K. J., Ling, E., Benham, B., & Edwards, M. (2021). Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach. Water Research, 189, 116641. 10.1016/j.watres.2020.116641

Forio, M. A. E., Landuyt, D., Bennetsen, E., Lock, K., Nguyen, T. H. T., Ambarita, M. N. D., & Goethals, P. L. M. (2015). Bayesian belief network models to analyse and predict ecological water quality in rivers. Ecological Modelling, 312, 222–238. 10.1016/j.ecolmodel.2015.05.0

Garcia, B. H. Y., Olinda, R. A., Barbosa, D. S., & Mioto, C. L. (2020). Substantive audit testing of sewer systems using Brazilian open database: stat methods for compliance screening. Revista Ibero Americana de Ciências Ambientais, 11(6),716-724. 10.6008/CBPC2179-6858.2020.006.0057

Kang, G., Qiu, Y., Wang, Q., Qi, Z., Sun, Y., & Wang, Y. (2020). Exploration of the critical factors influencing the water quality in two contrasting climatic regions. Environmental Science and Pollution Research. 10.1007/s11356-020-07786-5

Liu, J., Liu, R., Zhang, Z., Cai, Y., & Zhang, L. (2019). A Bayesian Network-based risk dynamic simulation model for accidental water pollution discharge of mine tailings ponds at watershed-scale. Journal of Environmental Management, 246, 821–831. 10.1016/j.jenvman.2019.06.060

Mayfield, H. J., Bertone, E., Smith, C., & Sahin, O. (2019). Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions. Mathematics and Computers in Simulation. 10.1016/j.matcom.2019.07.005

Panidhapu, A., Li, Z., Aliashrafi, A., & Peleato, N. M. (2019). Integration of weather conditions for predicting microbial water quality using Bayesian Belief Networks. Water Research, 115349. 10.1016/j.watres.2019.115349

Pivello, V. R., Vieira, I., Christianini, A. V., Ribeiro, D. B., da Silva Menezes, L., Berlinck, C. N., Melo, F. P. L., Marengo, J. A., Tornquist, C. G., Tomas W. M., & Overbeck, G. E. (2021). Understanding Brazil’s catastrophic fires: Causes, consequences and policy needed to prevent future tragedies. Perspectives in Ecology and Conservation, 19(3), 233–255. 10.1016/j.pecon.2021.06.005

Ramin, M., Labencki, T., Boyd, D., Trolle, D., & Arhonditsis, G. B. (2012). A Bayesian synthesis of predictions from different models for setting water quality criteria. Ecological Modelling, 242, 127–145. 10.1016/j.ecolmodel.2012.05.0

Salman, R., Nikoo, M. R., Shojaeezadeh, S. A., Beiglou, P. H. B., Sadegh, M., Adamowski, J. F., & Alamdari, N. (2021). A novel Bayesian maximum entropy-based approach for optimal design of water quality monitoring networks in rivers. Journal of Hydrology, 603, 126822. 10.1016/j.jhydrol.2021.126822

Silva, M. O., Olinda, R. A., Mioto, C. L., & Barbosa, D. S. (2020). Análise plurianual da qualidade das águas de bacia tributária do Pantanal brasileiro. Revista Ibero Americana de Ciências Ambientais, 11(2), 172-181. 10.6008/CBPC2179-6858.2020.002.0019

Sha, J., Li, Z., Swaney, D. P., Hong, B., Wang, W., & Wang, Y. (2014). Application of a Bayesian Watershed Model Linking Multivariate Statistical Analysis to Support Watershed-Scale Nitrogen Management in China. Water Resources Management, 28(11), 3681–3695. 10.1007/s11269-014-0696-x

Souza, A. V. V., & Loverde-Oliveira, S. M. (2014). Analysis of the water quality of the Rio Vermelho in Mato Grosso: during the flood season in 2014. Biodiversity, 13(2), 115-126.

Wan, R., Cai, S., Li, H., Yang, G., Li, Z., & Nie, X. (2014). Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China. Journal of Environmental Management, 133, 1–11. 10.1016/j.jenvman.2013.11.035

Wijesiri, B., Deilami, K., McGree, J., & Goonetilleke, A. (2018). Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach. Environmental Pollution, 233, 655–661. 10.1016/j.envpol.2017.10.076

Zhang, M., Zhi, Y., Shi, J., & Wu, L. (2018). Apportionment and uncertainty analysis of nitrate sources based on the dual isotope approach and a Bayesian isotope mixing model at the watershed scale. Science of The Total Environment, 639, 1175–1187. 10.1016/j.scitotenv.2018.05.2

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Published

19/02/2022

How to Cite

SILVA, M. O. da; BARBOSA, D. S.; OLINDA, R. A. de; MIOTO, C. L. Study of the Impact of sanitary decisions over water quality using Bayesian Belief Networks in Upper Pantanal Wetland Basin – Brazil . Research, Society and Development, [S. l.], v. 11, n. 3, p. e21011326309, 2022. DOI: 10.33448/rsd-v11i3.26309. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/26309. Acesso em: 22 nov. 2024.

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Section

Engineerings