Study of the Impact of sanitary decisions over water quality using Bayesian Belief Networks in Upper Pantanal Wetland Basin – Brazil
DOI:
https://doi.org/10.33448/rsd-v11i3.26309Keywords:
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Mayara Oliveira da Silva; Domingos Sávio Barbosa; Ricardo Alves de Olinda; Camila Leonardo Mioto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.