Probabilistic backward location for the identification of multi-source nitrate contamination

• Published in: Stochastic environmental research and risk assessment 2021-04, Vol.35 (4), p.941-954
• Main Authors: Teramoto, Elias Hideo, Engelbrecht, Bruno Zanon, Gonçalves, Roger Dias, Chang, Hung Kiang
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Aquifers; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Contaminants; Contamination; Earth and Environmental Science; Earth Sciences; Environment; Flow simulation; Geology; Groundwater; Groundwater pollution; Markov chains; Math. Appl. in Environmental Science; Mathematical models; Nitrates; Original Paper; Particle tracking; Physics; Probability theory; Probability Theory and Stochastic Processes; Septic tanks; Sewage; Spills; Statistics for Engineering; Stochasticity; Urban areas; Urban environments; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-020-01966-y

Nitrate represents the most widespread contaminant in shallow aquifers, especially in urban areas, and poses risks to human health, when the contaminated groundwater is ingested. In urban environments, the release of nitrate in groundwater can occur from multiple sources and is frequently associated with sewage leakage and septic tank infiltration. The Rio Claro Aquifer, located on the campus of the São Paulo State University at Rio Claro, offers an attractive example of a shallow aquifer impacted by nitrate contamination. Old sewage spills are considered to be the main sources of contamination; however, their locations remain largely unknown. Because of the scarce data and heterogeneous aquifer geology, the direct backward location approach is unsuitable in this case. Aiming to predict the probable locations of contamination sources, we developed a probabilistic backward location approach to identify the backward location in multiple geological scenarios using stochastic simulations. The numerical flow simulation and backward particle tracking were conducted based on 100 stochastic scenarios generated with Markov chains using lithological data from core descriptions. The multiple backward locations generated by stochastic simulations allowed us to build a density map to identify the region most likely to contain the contamination sources, thus simplifying the investigation and mitigation of the sewage spills.