Physico-Chemical Influence of Surface Water Contaminated by Acid Mine Drainage on the Populations of Diatoms in Dams (Iberian Pyrite Belt, SW Spain)

Self Cite

The Odiel River (SW Spain) is one of the most cited rivers in the scientific literature due to its high pollution degree, generated by more than 80 sulphide mines’ (mostly unrestored) contamination in the Iberian Pyritic Belt (IPB), that have been exploited for more than 5000 years. Along the river and its tributaries, the physico-chemical parameters and diatoms, from 15 sampling points, were analyzed in the laboratory. Physico-chemical parameters, water chemical analysis, together with richness and Shannon–Wiener indexes were integrated in a matrix. An initial graphical treatment allowed the definition and proposal of a functioning system model, as well as the establishment of cause–effect relationships between pollution and its effects on biota. Then, the proposed model was statistically validated by factor analysis. For acidic pH waters, high values of Eh, TDS, sulphate, ∑REE and ∑Ficklin were found, while diatomologic indicators took low values. Thus, factor analysis was a very effective tool for graphical treatment validation as well as for pollution–biota interaction models’ formulation, governed by two factors: AMD processes and water balance suffered by the studied river. As a novelty, the cause–effect relationships between high barium concentration and low diversity and richness were demonstrated in the IPB, for the first time.

Section: Graphical Treatmentsupporting

confidence: 70%

Section: Hydrological and Mining Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Odiel River (SW Spain), a Singular Scenario Affected by Acid Mine Drainage (AMD): Graphical and Statistical Models to Assess Diatoms and Water Hydrogeochemistry Interactions

Grande

Luís

Córdoba

et al. 2021

Self Cite

“…Rainwater erosion and surface runoff in mining areas lead to pollutant diffusion and, consequently, to groundwater pollution. The risks and paths of pollutant diffusion caused by surface runoff can be monitored and simulated through image data [108][109][110], thus providing a basis for the prevention and governing of water pollution. Conventional monitoring of water pollution in mining areas usually includes band combination transformation calculation by convenient empirical models to extract suspended substances in water or inversion of the water pollution status by combining the measured data of water sample points (Table 2).…”

Section: Water Pollution and Air Quality Monitoringmentioning

confidence: 99%

Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas

Song

et al. 2020

Based on the results of an extensive literature research, we summarize the research progress of remote sensing monitoring in terms of identifying mining area boundaries and monitoring land use or land cover changes of mining areas. We also analyze the application of remote sensing in monitoring the biodiversity, landscape structure, vegetation change, soil environment, surface runoff conditions, and the atmospheric environment in mining areas and predict the prospects of remote sensing in monitoring the ecological environment in mining areas. Based on the results, the accurate classification of land use or land cover and the accurate extraction of environmental factors are the basis for remote sensing monitoring of the ecological environment in mining areas. In terms of the extraction of ecological factors, vegetation extraction is relatively advanced in contrast to the extraction of animal and microbial data. For the monitoring of environmental conditions of mining areas, sophisticated methods are available to identify pollution levels of vegetation and to accurately monitor soil quality. However, the methods for water and air pollution monitoring in mining areas still need to be improved. These limitations considerably impede the application of remote sensing monitoring in mining areas. The solving of these problems depends on the progress of multi-source remote sensing data and stereoscopic monitoring techniques.

“…Acid mine drainage (AMD) is one of the main environmental problems caused by the oxidation of sulfide-rich deposits after their extraction, when they are exposed to oxygen, water and biological weathering. In many cases, mining waste can continue to affect the environment negatively for centuries after mine closure, contaminating the surrounding soil, surface water and groundwater [ 1 , 2 , 3 ]. Acid mine drainage is related to the oxidation of pyrite and other poly-metallic sulfides, resulting in an acid lixiviate with high concentrations of metals and metalloids.…”

Section: Introductionmentioning

confidence: 99%

Application of a Fuzzy Logic Based Methodology to Validate the Hydrochemical Characterization and Determining Seasonal Influence of a Watershed Affected by Acid Mine Drainage

Dávila

Sarmiento

Aroba

et al. 2021

Self Cite

The Odiel River Basin, located in the Iberian Pyrite Belt (IPB), is heavily affected by acid mine drainage (AMD), which occurs when pyritic minerals from sulfide mining areas are exposed to atmospheric, hydrological or biological weathering. This paper presents a hydrochemical characterization of parameters in the Odiel River Basin by means of Fuzzy Logic and data mining methodologies to determine the seasonal influence of AMD in polluted waters that have not been used before for a basin in this environmental area. This technique was proven to be effective, providing results that could not be achieved by using classic statistics, because it allows us to characterize the different parameters separately and also their relationships in waters affected by AMD in a qualitative manner based on the antecedents and according to the conditions (rules) imposed by the consequents (in this case, the Fe(II) and accumulated rainfall over 30 days). Thus, it was possible to confirm that hydrochemistry is greatly affected by seasonal changes, with a higher pH in the wet season (up to 8.59) compared to 2.12, the minimum pH value reached in the dry season. Accordingly, higher concentrations of most of the metals were observed in the dry season (e.g., up to 4000 mg/L of Fe (II)), with the exception of the values found after the first rains that occur in the early fall. With the use of the Fuzzy Logic technique, it was observed that, during the wet season, lixiviates with a higher Fe content have higher metal concentrations, and in the dry season, the behavior is the opposite.