DEFINITION OF TOLERANCE THRESHOLDS FOR EMERGENCY CONDITIONS BASED ON TURBIDIMETER DATA IN ONLINE MONITORING OF MINING DAMS
Abstract
The safety of mining dams is a critical issue in environmental and operational contexts, particularly in sensitive regions such as the Amazon. Continuous monitoring of environmental variables is essential for the early detection of changes related to structural risks and impacts on water resources. This study aimed to analyze water turbidity behavior in a mining dam using a high-frequency time series dataset collected from June 2024 to March 2026. The methodology was based on statistical data processing, including descriptive analysis, outlier detection, moving averages, and the definition of operational thresholds using percentiles (P90, P95, and P99). The results indicated a predominantly stable system, with a mean turbidity of 14.19 NTU, but with the occurrence of extreme events, evidenced by a positively skewed distribution and a maximum value of 199.94 NTU. Temporal analysis revealed critical periods associated with the Amazonian rainy season, characterized by increased precipitation and sediment transport. The classification into operational ranges proved effective for data interpretation; however, the maximum observed value remained below the emergency threshold (> 221.54 NTU), indicating that no emergency events were recorded during the study period. It is concluded that robust statistical approaches combined with temporal and seasonal analysis enhance environmental monitoring and support decision-making in mining dam management.
Author Biographies
Master’s student in Dam Engineering and Environmental Management at the Federal University of Pará (UFPA).
Master’s student in Infrastructure Engineering and Energy Development at the Federal University of Pará (UFPA), Belém, PA, Brazil.
Postdoctoral researcher in Geology and Geotechnics at the Federal University of Pará (UFPA).
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