Abstract
Few studies have assessed mining-associated water pollution using spectral characteristics. We used high-resolution multispectral data acquired by unmanned aerial drones combined with in situ chemical data to assess water quality parameters in 12 relatively small water bodies located in the Tharsis complex, an abandoned mining area in the Iberian pyrite belt (SW Spain). The spectral bands of Micasense RedEdge-MX Dual and spectral band combinations were used jointly with physicochemical data to estimate water quality parameters and develop reliable empirical models using regression analysis. Physicochemical parameters including pH, ORP, EC, Al, Cu, Fe, Mn, S, Si, and Zn were estimated with high accuracy levels (0.81 < R2 < 0.99, 4 < RMSE% < 75, 0.01 < MAPE < 0.97). In contrast, the observed and modelled values for Ba, Ca, and Mg did not agree well (0.42 < R2 < 0.70). The best-fitted models were used to generate spatial distribution maps, providing information on water quality patterns. This study demonstrated that using empirical models to generate spatial distribution maps can be an effective and easy way to monitor acid mine drainage.
Highlights
Metalliferous mining has left a severe environmental legacy of numerous abandoned mining districts containing metalrich wastes that remain as long-term sources of acidic and metal-polluted water known as (Nieto et al 2007; Runkel et al 2012; Yang et al 2020)
Acid mine drainage (AMD) is the main environmental pollution problem associated with coal and metal-bearing mineral mining and it is of international concern (Acharya and Kharel 2020; Qian and Li 2019)
Most of them have been based on satellite datasets (Barrett and Frazier 2016; Bonansea et al 2015; Hansen et al 2015; Philipson et al 2016), and most recently, unmanned aerial systems (UAS) platforms have been tested for this purpose (Arango and Nairn 2020; Castro et al 2020; Olivetti et al 2020; Su and Chou 2015)
Summary
Metalliferous mining has left a severe environmental legacy of numerous abandoned mining districts containing metalrich wastes that remain as long-term sources of acidic and metal-polluted water known as (Nieto et al 2007; Runkel et al 2012; Yang et al 2020). UAS is becoming increasingly popular in environmental monitoring due to the acquisition flexibility, the high spatial and temporal resolution achieved, and the possibility to acquire data unaffected by cloud cover. In this context, we decided to calibrate empirical models through regression analysis to predict water quality parameters using in situ physicochemical parameters and spectral reflectance values obtained by the commercial Micasense RedEdge-MX Dual sensor. The different compositions of the numerous acid drainages affects the color of the water, making the IPB an ideal scenario for testing water quality monitoring techniques using remote sensing (Riaza et al 2014; Sanchez España 2008). It is important to highlight that this is an auxiliary tool for monitoring the acid drainage generation process, and does not replace conventional environmental monitoring
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