Remediation Experiment of Ecuadorian Acid Mine Drainage: Geochemical Models of Dissolved Species and Secondary Minerals Saturation

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ “This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11356-019-06539-3” 1 Remediation experiment of Ecuadorian acid mine drainage: geochemical models of dissolved 1 species and secondary minerals saturation. 2 Joaquín Delgado1, 2, Cinta Barba-Brioso3*, Diana Ayala 4, Tomasz Boski2, Sandra Torres5, Edmundo 3 Calderón5, Flavio López5, 6 4 1 Department of Physical, Chemical and Natural Environments. Faculty of Experimental Sciences. Pablo de Olavide 5 University 41013, Seville, Spain. 6 2 Centre for Marine and Environmental Research. Faculty of Science and Technology. Algarve University, 8005-139, 7 Faro, Portugal. . 8 3 Department of Crystallography, Mineralogy and Agricultural Chemistry. Faculty of Chemistry. Seville University. 9 41012, Seville, Spain. 10 4 Department of Civil and Environmental Engineering. Biogeochemistry Dual Major Program. The Pennsylvania 11 State University, 16802, PA, USA. 12 5 National Research Institute of Geology, Mining and Metallurgy, Quito, Ecuador. 13 6 Central University of the Ecuador, 170129, Quito, Ecuador. 14 *Corresponding author: Cinta Barba-Brioso, [email protected].Telephone-fax numbers: (+34) 954556321; 954557141 15 ABSTRACT: Acid mine drainage is one of the main environmental hazards to ecosystems worldwide and 16 it is directly related to mining activities. In Ecuador, such acidic-metallic waters are drained to rivers 17 without treatment. In this research, we tested a laboratory combined (Ca-Mg) Dispersed Alkaline Substrate 18 (DAS) system as an alternative to remediate acid drainage from the Zaruma-Portovelo gold mining site, at 19 El Oro, Ecuador. The system worked at low and high flow hydraulic rates during a period of 8 months, 20 without signs of saturation. . Analysis of physico-chemical parameters and water composition (ICP-OES, 21 ICP-MS)demonstrated that treatment effectively increased water pH and promoted the retention of about 22 80 % of Fe, Al, Mn and Cu. Under acid conditions As, Cr and Pb concentrations decreased with Fe and 23 possible precipitation of jarosite and schwertmannite. However, the homogeneous depletion of Cr at pH 24 above 6 could be related to ferrihydrite or directly with Cr(OH)3 precipitation. After DAS-Ca, sulphate, 25 phosphate and rare earth elements (REE) concentrations decreased to 1912, 0.85 and 0.07 mg/L 26 respectively, while DAS-Mg contributed to form a complex model of minor carbonate and phosphate 27 phases as main sink of REE. DAS-Mg also promoted the retention of most divalent metals at pH values 28 over seven. Thus, this low cost treatment could avoid environmental pollution and international conflicts. 29 Anyway, further investigations are needed to obtain higher Zn retention values. 30 31 Keywords: Zaruma-Portovelo mining district; Hydro-geochemistry; AMD; Water remediation; Metal 32 removal; Secondary mineral cycles; mine waste revalorization. 33 2 1. Introduction 34 Acid mine drainage (AMD) generated from abandoned mining activities and uncontrolled tailing dumps is 35 a global socio-environmental concern. Acidic waters are produced from the oxidation of pyrite and other 36 metal sulphides disposed as million tons of sulphate-rich wastes worldwide. Such wastes once exposed to 37 oxidation under atmospheric conditions generate highly acid leachates with extreme concentrations of 38 metal-metalloids and sulphates known as AMD (Ayala et al., 2015; Younger et al., 2002). Extended areas 39 of streams directly affected by AMD present poor water quality incapable of sustaining a healthy macro- 40 life (e.g., fish) (Bigham and Cravotta, 2016). 41 AMD mainly affects the quality of waters draining mining sites. In Ecuador, for example, mining sites 42 located in the southern part have affected the Puyango River basin, one of the most important basins that 43 flows to the Pacific Ocean crossing southern Ecuador and northern Peru (Betancourt et al., 2005). In its 44 upper course, the Puyango River flows through the Zaruma-Portovelo gold mining site (Fig. 1). The climate 45 of the area is characterised by a high variability of temperature and precipitation with a mega-thermal dry 46 weather dominating Portovelo (mean annual temperatures of 24 ºC and precipitation oscillating between 47 500 and 2000 mm). Zaruma-Portovelo is located over the palaeozoic metamorphic complex of El Oro, 48 covered by cretaceous volcanic rocks and subsequently intruded by cenozoic igneous complexes. The ore 49 at this site consist mainly of gold and silver, included in quartz veins associated to sulfides aspyrite, 50 chalcopyrite, galena, sphalerite (PRODEMINCA, 1999). Metallurgical processing plants implies a variety 51 of methods for precious metal recovery from ore extracted within and outside the Zaruma-Portovelo district. 52 Such variability of ore sources and ore processing methods have led to the release of Fe, Cu, Pb, Zn, As, 53 Cd, and Bi from geochemically heterogeneous tailings, harder to dispose and difficult to monitor (Delgado 54 et al., 2018). Around 87 to 110 small-scale active processing plants (Fig. 1) in the Zaruma-Portovelo mining 55 site (Pazmiño, 2013) store tailings in dams located at the river edges or release them directly to the river. A 56 potential product from the oxidation of such tailings is AMD. Recent studies demonstrated how mining 57 activities impacted negatively on Amarillo River and the Calera River, effluents of the Puyango watershed 58 (Tarras-Wahlberg et al. 2001; Tarras-Wahlerberg and Lane, 2003; Guimarães et al., 2011; Ayala and López, 59 2014). 60 In 2008, considering the Binational Convention Puyango-Tumbes (1971), the Republic of Peru promoted 61 an international claim against Ecuador, due to the extreme pollution detected in the lower part of Puyango 62 watershed. Since then, Ecuadorian government has promoted continuous efforts to address this situation. 63 3 However, wider knowledge on the toxic elements cycles and development of novel techniques for 64 environmental remediation are necessary to implement a correct basin management. 65 In this sense, several remediation strategies have been designed to reduce the environmental impact of mine 66 tailings around the wold. One option is the application of active treatments consisting of controlled addition 67 of chemicals into the acidic discharge and/or wastes to increase pH and decrease metals mobility. However, 68 active treatments are expensive (Skousen et al., 1990), impracticable at abandoned sites, and generate 69 products even harder to dispose. On the other hand, passive treatment systems (PTS) do not need continuous 70 addition of reagents and require occasional maintenance, becoming an economic option for AMD 71 remediation. Typical AMD treatment systems tends to lose reactivity or permeability due to the formation 72 of precipitates coating the limestone grains or filling the pore space of the system (Simon et al., 2005; Pérez- 73 López et al. 2007). To address these problems, Rötting, Thomas, Ayora and Carrera (2008) developed the 74 Dispersed Alkaline Substrate (DAS), which consists of a reactive mixture of wood shavings and limestone 75 sand. The high metal removal performance of this reactive mixture has been tested both in laboratory 76 columns (Rötting, Thomas et al., 2008) and field-scale experiments (Rötting, Caraballo, Serrano, Ayora 77 and Carrera, 2008; Caraballo, Rötting, Nieto and Ayora, 2009). Nevertheless, DAS could be insufficient to 78 remediate high concentrations of divalent metals such as Zn, Mn, Ni or Cd (Rötting, Ayora and Carrera, 79 2008) since: (i) under field conditions, calcite dissolution only raises pH values around 7, insufficient to 80 precipitate divalent metals (Cortina et al., 2003). (ii) At extremely high concentrations, metals could be 81 toxic to sulphate reducing bacteria, responsible for inducing precipitation of metal-sulphides (Cabrera et 82 al., 2006). (iii) Sorption and exchange sites may be rapidly saturated under high metal loads (Gibert et al., 83 2005). 84 Rötting et al. (2006) conducted laboratory column experiments that demonstrated that caustic magnesia 85 (MgO) could be used to remove high concentrations of divalent metals from solution. Reactivity of MgO 86 was improved when mixed with quartz sand or wood fragments, which favoured the creation of a more 87 reactive and permeable substrate. In fact, a combined DAS (Ca-Mg) created a more reactive substrate 88 system with a higher-porosity matrix and a large surface area that accelerated reaction kinetics and 89 enhanced its permeability, two great advantages for a passive AMD treatment system such as DAS. 90 This technology has been successfully applied at sites with moderate-high acidity and metal concentrations 91 under humid climate (i.e. Rötting et al., 2006; Rötting, Ayora and Carrera, 2008; Rötting, Caraballo, et al. 92 2008; Rötting, Thomas et al., 2008; Caraballo, Rötting, Macías, Nieto and Ayora 2009; Caraballo, Rötting, 93 4 Nieto et al. 2009, Caraballo et al. 2011; Macías et al., 2012). However, the effectiveness of the passive 94 treatment (DAS-type) has not been tested in dry-megathermic climate conditions or AMD generated from 95 tailings generated in the Zaruma-Portovelo mining district. 96 On the other hand, Rare Earth Elements (REE) have been frequently used as environmental tracers because 97 they help us to estimate the affection degree of polluted sites by mining activities (i.e., Olías et al., 2005; 98 López-González et al., 2012; Delgado et al., 2012). In addition, REE (especially Eu, Tm, Lu and Y) are 99 now widely used in several technologies, from clean energy tomilitary systems (Zhou et al., 2016). This 100 trend will lead to a continuous increase in demand for REE in the coming decades (Alonso et al., 2012), 101 which will be traduced in a higher request for global production of REE. Normally, REE are associated 102 with Ca, Th and U, so their appearance is not consistent with the demand for individual elements. This 103 poses restrictions on the survey of REE projects (Zhou et al., 2017). For this reason, recent studies are 104 lending special interest in their behaviour under acidic environments condition (e.g. Ayora et al., 2016; 105 Macias et al., 2017) and focusing their effort to determine the degree of REE recuperation from AMD 106 treatment systems, providing added value to the process by reusing the waste. REE concentration in waters 107 decreases as pH increase, which is usually the result of simultaneous processes as sorption to suspended 108 particles (Gammons et al., 2005), coprecipitation with Al-and Feoxyhydroxides and the complexation 109 with humic substances flocculants (Åström et al., 2012). This pH dependence has been observed by 110 analyzing AMD neutralization processes at both laboratory (Verplanck et al., 2004) and basin scales 111 (Delgado et al., 2012), concluding that there exists a preferential HREE (heavy rare earth) partition over 112 LREE (light) in the solid phase as pH increases above 6. Nevertheless, few studies aboard REE fractionation 113 at alkaline conditions, suggesting that carbonates (Ayora et al., 2016) and phosphates (Delgado et al., 2012) 114 exert a certain control in the processes of fractionation of REE at the end of the series. 115 Therefore, the aims of the present study were: 1) to test the effectiveness of a combined DAS (Ca-Mg) 116 reactive substrate system to decontaminate AMD, by monitoring hydrogeochemical and thermo-hydraulic 117 conditions at laboratory scale. 2) To set design guidelines for a future pilot-field scale implementation, 118 based on the laboratory efficiency results under the existing climate conditions (dry-megathermic climate). 119 3) To gain a better understanding of REE cycles in environmental systems affected by AMD production 120 through the study of the fractionation between aqueous and solid phases, that could be used in future studies 121 of waste reusing. 122 123 5 2. Materials and methods 124 2.1. Experimental design – Laboratory Columns 125 A combined DAS (Ca-Mg) reactive substrate system to treat AMD (Rötting, Ayora and Carrera, 2008; 126 Rötting, Thomas et al., 2008) was implemented at the National Research Institute of Geology, Mining and 127 Metallurgy (INIGEMM) laboratories in Quito-Ecuador, as shown in Fig. 2a, 2a’. The matrix of the DAS- 128 Ca column was composed of 25% (v/v) of 94% purity CaCO3 sand (0.1–2 mm grain size) and 75% (v/v) 129 wood chips (thickness up to 1 mm, 0.5 mm mean grain size). The matrix of the Das-Mg column was a 130 mixture of 12.5% (v/v) commercial reactive magnesium oxide (MgO) dust (0.15 mm median particle size) 131 and 87.5% (v/v) wood chips. The column bottoms were filled with local quartz gravels (3–5 mm diameter). 132 AMD collected from two sources (section 2.2) was stored under laboratory conditions in plastic containers 133 to create a Natural Fe-Oxidizing Lagoon (NFOL) where part of the Fe(II) is biotically and abiotically 134 oxidized and hence removed from solution (Macías et al., 2012). A peristaltic pump (LongerPump® 135 BT100-1 L) pumped the partially treated effluent to the DAS-Ca column, from which it flowed to the DAS- 136 Mg column. The experience was conducted in duplicate (Fig. 2a). 137 To start the experiment, both columns were slowly saturated with AMD and a permanent level of solution 138 was maintained as a supernatant (see Fig. 2b). The system performed a low flow rate period (7 months) and 139 a high flow rate period (4 weeks) to test the efficiency of the columns with different hydraulic residence 140 times. Assuming a mean porosity of 50%, (Rotting, Ayora and Carrera, 2008) and Darcy constant velocities 141 of 0.8 ml/min and 1.4 ml/min, the mean flow rates were 0.096 m3/m2d (0.66 L/day, with 8 days residence 142 time) and 0.21 m3/m2d (1.44 L/day, assuming 3 days residence time) for low and high flow rate, 143 respectively. 144 145 2.2. Field water analysis and monitoring 146 Two AMD-sources located in the Zaruma-Portovelo mining site (ZPMS) were selected for water samples 147 collection (Fig. 1). The first point, AMD-Buza consisted on a leachate derived from environmental 148 liabilities (located near the confluence of Amarillo and Calera rivers). The strong seasonality (hot-dry 149 climate conditions) and lack of rain over a long period produced the drying of this creek. Therefore, the 150 AMD sampling site changed to the nearby AMD-Torata mine shaft, characterized by a permanent flow. 151 6 This change allowed us simulating the response of the combined DAS (Ca-Mg) reactive to variable 152 hydrochemical characteristics of input acid leachates derived from geochemically heterogeneous tailings 153 (Delgado et al., 2018). 154 During the experiment, the system was working at open laboratory conditions (environmental variation of 155 temperature). Climatic data were obtained from INANHI (National Institute of Meteorology and 156 Hydrology) for inter-annual treatment period. Water samples along the columns were collected from the 157 sampling ports showed in Fig. 2b. The inflow container (AMD-Input; #1), supernatant water (Supra-; #2 158 and #7), drain pipe (Out-#6 and #11), and Output container (Out-Final; #12) were sampling every 15 days. 159 Samples from the intermediate sampling ports located in the reactive zones (#3, 4, 5, 8, 9 and 10) were 160 taken monthly. 161 Physico-chemical parameters pH, electrical conductivity (E.C.), redox potential (Eh), dissolved oxygen 162 (D.O.) and temperature (T) were measured using a multi-parameter sensor (HANNA HI9828). Major 163 cations and trace elements were measured in filtered water samples (0.2 μm) by inductively coupled 164 plasma–atomic emission spectroscopy (ICP-OES, model Optima 8300, Perkin Elmer, detection limits in 165 Table 1). REE were determined by inductively coupled plasma–mass spectroscopy (ICP-MS, model 166 Agilent 7500ce). Dissolved sulphate (SO42-) and phosphate (PO43-) were determined by UV-Vis 167 spectrometry using a HACH DR6000 spectrophotometer with 10 mL quartz cells. Sulphate analysis was 168 based on a turbidimetric method 4500-SO42-E methodology with barium chloride (APHA, 2012a). 169 Phosphate was quantified following the APHA method 4500-PE and adapted with a standard addition 170 technique to eliminate the arsenate interference (APHA, 2012b). 171 Certified ICP Multi-Element standard solutions from AccuStandard, SQS-01-1CRM and SQS-02-R1- 172 1CRM according to the EPA 200.7 and EPA 6010 Series, were used for calibration and accuracy control. 173 Additionally, certified reference material for Effluents/Inorganic/Metal (nº1244) and inter-laboratory test 174 for Trace Metals (Water Pollution/Trace Metals nº586) (CRM-European environmental production) are 175 also measured as internal periodic laboratory control. Beside, nine replicates (one for each sampled matrix) 176 were analysed to check the quality of the analysis whose relative percentage difference (% RPD) revealed 177 low mean values for Al, Fe, Mn, SO42- (<5%) and for Mg, Co, Ni, Pb, Zn, Cu (<10%) and acceptable values 178 for As, Cd, Cr (<18%). Total alkalinity was measured using CHEMetrics “Titrets” test kits (range 10-100 179 or 100-1000 mg/L as CaCO3, with 5% accuracy). Hydraulic conductivity K (cm/s) was calculated using 180 Darcy’s Law to track the reactivity of the system (Equation 1), where Q (ml/s) is flow rate, A (cm2) is the 181 7 tank cross-section area perpendicular to flow, and L and Δh (cm) are distances and head-loss between 182 control surfaces, respectively. 183 K=QL/A Δh [1] 184 Net acidity (mg/L as CaCO3 equivalents) was calculated according to Kirby and Cravotta (2005) (Equation 185 2), where CX are molar concentrations (mol/L) and alk is measured as total alkalinity (mg/L as CaCO3 186 equivalents). 187 Net acidity = 50,045 × (3cAl + 2cFe + 2cMn + 2CZn + 10−pH) − alk [2] 188 Absolute net acidity elimination and relative metal removal (r), where Csup is AMD-Input concentration 189 (mg/L) and Cout is Out-Final concentration (mg/L), were also calculated (Equation 3 and 4, respectively). 190 Net acidity elimination = Supernatant net acidity − Drain pipe net acidity [3] 191 r = (Csup – Cout/Csup) x 100 [4] 192 193 2.3. Geochemical Modelling of major ions, trace elements and rare earth elements. 194 Activity and chemical speciation of dissolved species (AS, aqueous species) and the saturation indexes (SI, 195 Equation 5) of minerals in solution present in the two columns (DAS-Ca and DAS-Mg) of the system at 196 the end of the treatment (Torata last sampling, 20/02/2015), were calculated using PHREEQC modelling 197 (version 3.1.7; Parkhurst and Appelo, 1999). The thermodynamic database of PHREEQC was enlarged 198 with data from geochemical code WATEQ4F (Ball and Nordstrom, 1991). Solubility constants (KS) for 199 schwertmannite were extracted from literature (Bigham et al., 1996) and SI were obtained following the 200 detailed methodology described in Delgado et al., (2009). 201 SI = log (IAP/ KS) [5] 202 Where SI the saturation index, IAP the ion activity product, and KS the solubility constant. 203 REE concentrations were normalized to the upper crust using the North-American Shale Composite 204 (NASC) values. Finally, partition processes of the main lanthanides aqueous species from the AMD Out- 205 Final solution were established using CHEAQS-Next modelling (Verweij, 2007) with stability constants of 206 the REE sulphates complexes from NIST compilation (Martell et al., 2004). 207 8 2.4. Middle REE enrichment. 208 The enrichment/depletion in middle REE (MREE) gives upward concave (V-type) and convex (-type) 209 quantifiable patterns according to the traditional NASC-normalized (La/Gd)NASC ratio. However, using a 210 single rare earth as representative of a set could hamper the interpretation of REE patterns in the case of 211 exclusive fractionation affecting only that rare earth. Therefore, it would be crucial to assess the significance 212 of the curvature effect in the MREE whole segment (Pérez-López et al., 2010). For this reason, we have 213 calculated the index EMREE (Enrichment of middle REE) to quantify this effect as the normalized maximum 214 vertical difference between the polynomial curve fitting of the MREE region and its theoretical Y-axis 215 position in the absence of enrichment or depletion (see details of the calculation method in Pérez-López et 216 al. (2010) and Delgado et al. (2012). EMREE < 0 and EMREE > 0 represent depletion (concave pattern) and 217 enrichment (convex pattern) in MREE, respectively, and thus around zero the flat patterns. The quality of 218 the fit, and hence the significance of the curve formed by data points, was quantified by the squared 219 correlation coefficient, R2. 220 221 3. Results and Discussion 222 3.1. Hydrochemical evaluation of the combined DAS system to treat Ecuadorian mine waters. 223 The combined DAS (Ca-Mg) reactive substrate system was constantly monitored (Table 1). The control 224 points appear numbered in Fig.2b. The pH of AMD-Input (#1) and Supra-Ca (#2) solutions had mean values 225 from 2.7 to 2.4 and 4.1 to 2.3, respectively and values of E.C. characteristic of high-mineralized waters, 226 subjected to moderate or high Ecuadorian restrictions for use in crops (MAE, 2000). After the DAS-Ca, an 227 increase in water pH (up to 6.2), alkalinity (up to 263 mg/L) and Ca concentrations (up to 1128 mg/L), as 228 well as a decrease in acidity, took place due to limestone dissolution (Fig. 3a, b). This process induced the 229 removal of Fe and Al (Fig. 3c, d) and the adsorption-coprecipitation of As, Cr and Pb even at low 230 concentrations (Fig. 3e, f). 231 The decrease in concentration of Al and sulphates (Fig. 3a, b) during the early stages of the treatment, led 232 to a subsequent protons removal, pH increase and could be related with Cu removal (Fig.3c) by 233 coprecipitation processes (Hammarstrom et al., 2005). On the other hand, the circum-neutral conditions 234 observed in the DAS-Ca was not enough for divalent metal hydroxides to form (Cortina et al., 2003). At 235 the supernatant of DAS-Mg (Supra-Mg #7) pH increased up to 6.7, related with the dissolution of reactive 236 15 3.4 Efficiency of treatment and legislation issues 414 Results of Table 1 have been compared with reference values for irrigation waters (FAO, 1985) and 415 consumption (WHO, 2011), and with the maximum values of discharges of elements in natural water bodies 416 adopted by the Government Ecuadorian (MAE, 2003). 417 pH and acidity values after treatment were in the range proposed by the FAO and WHO (6.5 to 8.5). 418 Although sulphate retention is high compared to recent studies (Rötting, Thomas et al., 2008; Macias et al., 419 2012), final SO42- concentration (1850 mg/L) was still above the established limit of 1000 mg/L (MAE, 420 2003). According to Cabrera et al. (2006), these systems are ineffective in eliminating large amounts of 421 sulphate due to the high metallic load, because these metals are toxic for the establishment of bacterial 422 communities that reduce sulphate. 423 A metal removal around 100% was achieved for Fe, Al and Cu, and higher than 70% for the rest of elements 424 except Cr (50%) and SO42- (25%) (Fig. 8). Therefore, the combined DAS system is suitable to treat AMD 425 of Ecuadorian mining areas, and probably any other in the world. Comparing the element concentrations 426 of the Out-Final water with the guideline values for toxic elements in irrigation and drinking waters (Table 427 1), chemical quality of the treated waters accomplished these recommendations for most elements. Some 428 values were slightly superior to the guidelines, but only Mn and Zn concentrations clearly surpassed those 429 limits. Taking into account that drinking waters should be subjected to a drinking treatment, an additional 430 analysis after those treatments should define their adjustment to human consumption. However, the results 431 have also been compared with the maximum values of element discharges in natural waterbeds, adopted by 432 the Ecuadorian Government (MAE, 2003). 433 As previously discussed, the DAS-Mg columns reflected a passivation stage by the end of the experiment, 434 which specially affected to Zn retention. By 16/09/2014, Zn retention was around 95%, and on 20/02/2015, 435 it decreased to 43% (Table 1). The rest of divalent metals did not undergo major changes, overcoming 436 overall effective retentions of 80%. In this case, the final concentrations of Mn and Zn surpass these 437 referenced limits. The discharge of these waters to rivers, as well as their use for agriculture, should be 438 preceded by additional treatments for Mn and Zn reduction. 439 The high metal removal can be related to the sulphate reducing conditions achieved in the DAS-Mg, which 440 is confirmed by the negative values of net-acidity and p.e. values of the Out-Final flow (Table 2: acidity - 441 285 mg/L as CaCO3eq; p.e. 7.23). Passive treatments are usually not effective removing large quantities of 442 sulphate from AMD due to the high-metallic load because metals could be toxic for sulphate reducing 443 16 microbial (SRM) communities (Utgikar et al., 2003; Cabrera et al., 2006). In addition, there might not have 444 been strict anaerobic conditions for SRM to thrive. The development of fermenters is also crucial to provide 445 organic acids as carbon sources to be utilized by SRM. Therefore, the addition of a final step to promote 446 the enrichment of SRM should be tested, without dismiss other system as adsorption or ion exchange 447 processes. 448 Additionally, the control of hydraulic conductivity reflected the capability of the system of working under 449 low and high flows (Fig. 9), proving the existence of an adequate permeability during long treatment 450 periods. Moreover, the effectiveness of the treatment seemed not to be controlled by the climatic conditions, 451 even if an increase of precipitation was registered (October 2014 15th and February 20th 2015). 452 Taken into account the good response of STP, the recovery of some base metals and REE have been 453 calculated and reported in the Table 4. The high concentration obtained for some metals make it possible 454 to consider the potential recovery of metals in acid system treatment, such as Al, Cu, Fe or Zn. The potential 455 possibility of recover metals in treatment of mine waters has previously been reported by other studies 456 (Smith et al., 2013), which proposed metal recovery by solubilization of the sludge from AMD treatment 457 under soft acid conditions. This possibility can be brought to this experience, since metals are finally 458 retained in poorly crystalline oxy-hydroxides and/or oxy-hydroxysulfates. Moreover, the solid-waste could 459 contain remarkable concentrations (0.11 gr during 8 month of treatment) of high-tech metals such as REE, 460 which could be also considered as valuable by-products (Macias et al., 2017). In fact, the potential metal 461 recovery of Torata flow would be 24 kg/yr. Moreover, other valuable industrial metals such as Mn (tons/yr) 462 could increase the potential value of Zaruma-Portovelo mine waste. 463 464 5. Conclusions. 465 The passive treatment system responded positively during the 8 months of experimentation in the 466 laboratory, removing quantities of metals even higher than previously achieved by other systems. This fact 467 justifies the implementation of the DAS at field-pilot plant for real flows. 468 The visual observation of precipitated layers shown the important role of Fe (III) on metal retention along 469 the first centimetres of the DAS-Ca column, in form of low-crystalline oxyhydroxydes mainly jarosite, 470 schwertmannite and lepidocrocite (geochemical model support this statement). Aluminium is trapped 471 deeper as hydroxysulfate, as well as gypsum. In fact, white precipitates observed also confirm this idea. 472 17 Data of the second state of treatment (DAS-Mg) suggested that carbonates (calcite-dolomite, and in lesser 473 extent azurite-malachite) were involved on coprecipitation-sorption processes, affecting solubility of Zn, 474 Mn and other divalent metals. Moreover, the abundance of Zn would condition the existence of Zn- 475 sulphates. High percentages of Zn2+ and ZnSO4 registered in solution supported these statements. In 476 general, the experiment confirmed that DAS-Mg column contributes to a total retention of divalent metals. 477 In addition, the behaviour of toxic elements could be also controlled by minority phosphate and carbonate 478 phases. 479 Finally, since the concentration of sulphates after treatment contravenes existing local legislation, an 480 improvement of the system that promotes sulphate remotion is necessary. 481 Concerning REE fractionation processes, the study confirms the traditional trend of these elements 482 associated to sorption onto Fe-Al rich precipitates. However, REE partition continued even when Fe and 483 Al were consumed, so the role of carbonates and phosphates was also depicted in this REE partition. Finally, 484 a reutilization of the solid wastes obtained from the columns can be a new-fangled source of metals and 485 REE to the current market and could significate an excellent revalorisation of the solid-wastes. For this 486 reason, a new approach, by mean of mineralogical characterization and selective extraction, it is actually 487 being done to test the possibility of these wastes as a secondary source of metals and REE with economic 488 interest. 489 490 Acknowledgements 491 The authors acknowledge the material and human resources displayed from INIGEMM (Ecuadorian 492 National Research Institute of Geology, Mining and Metallurgy) and CIMA (Centre for Marine and 493 Environmental Research, Algarve University) to favour this research. 494 495 Funding 496 This work was supported by PROMETEO Ecuadorian program (Secretary of Superior Education, Science, 497 Technology and Innovation) in the framework of the project "Experiencia Piloto en la Remediación y 498 Mitigación de la Oxidación de Sulfuros y la Generación de AMD en Relaveras del Distrito Minero de 499 Zaruma-Portovelo". 500 18 References 501 Acero P, Ayora C, Torrento C, Nieto JM (2006) The behaviour of trace elements during schwertmannite 502 precipitation and subsequent transformation into goethite and jarosite. 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US Geological Survey Water- 682 Resources Investigations Report 99- 4018A, 83–92. 683 Figure 3 Click here to access/download;Figure;Figure 3.tif Figure 4 Click here to access/download;Figure;Figure 4.tif Figure 5-revised Click here to access/download;Figure;Figure 5.tif Figure 5CONT-revised Click here to access/download;Figure;Figure 5_CONT.tif Figure 6-revised Click here to access/download;Figure;Figure 6.tif Figure 7-revised Click here to access/download;Figure;Figure 7.tif Figure 8-revised Click here to access/download;Figure;Figure 8.tif Figure 9-revised Click here to access/download;Figure;Figure 9.tif Code Ag Al As Ca Cd Co Cr Cu Fe Mg Mn Ni Pb Zn SO4 2- d.l. 0,013 0,02 0,05 0,005 0,008 0,005 0,018 0,043 0,005 0,005 0,019 0,017 0,044 0,17 2 AMD-Input 2,7 4,7 <20 4361 0,02 531 0,07 416 7,03 2,12 0,13 335 121 202 66,8 0,24 0,19 698 4707 Supra-Ca 4,1 4,1 <20 2200 0,03 194 0,05 999 6,41 2,21 0,06 203 4,98 218 63,2 0,22 0,12 654 3757 Out-Ca 5,9 3,9 183 973 0,05 1,26 0,05 1128 6,36 2,39 0,05 61,2 0,42 212 68,8 0,28 0,08 668 3212 Supra-Mg 6,7 4,1 190 283 0,02 0,16 <d.l. 788 2,66 0,99 0,03 16,9 0,13 699 35,8 0,02 0,03 296 3176 Out-Mg 8,8 4,4 76 -70 <d.l. 0,08 <d.l. 389 0,01 0,01 0,01 0,20 0,17 1114 1,71 0,01 0,02 1,05 3254 Out-Final 8,1 4,6 0,05 0,09 <d.l. 246 0,01 0,01 0,01 0,19 0,14 1089 1,73 0,01 0,02 0,91 3633 AMD-Input 2,5 3,0 20 426 0,01 83,5 2,06 95,1 0,62 1,00 0,01 29,8 474 109 21,3 0,92 0,19 71,7 2934 Supra-Ca 2,5 2,9 20 429 0,1185 95,6 2,33 122 0,84 1,02 0,01 37,1 456 123 22,7 0,92 0,20 91,7 2108 Out-Ca 5,8 2,2 190 34,5 <d.l. 0,97 0,10 729 1,26 1,06 0,01 56,5 8,59 120 24,0 0,97 0,08 109 2001 Supra-Mg 6,0 1,9 <d.l. 0,26 <d.l. 695 1,55 1,09 0,01 33,3 0,60 138 25,4 0,95 0,04 132 1904 Out-Mg 9,3 3,1 <d.l. 0,01 <d.l. 206 0,04 0,03 0,01 0,73 0,16 431 0,83 0,02 0,02 2,85 1730 Out-Final 8,5 3,3 190 -116 <d.l. 0,01 0,04 220 0,01 0,08 0,01 0,12 0,14 413 1,86 0,03 0,01 3,88 1733 AMD-Input 2,4 2,6 20 1307 0,21 57,6 12,4 129 0,38 1,23 0,04 13,6 488 134 25,7 1,39 0,17 43,9 3075 Supra-Ca 2,3 1,9 20 1089 0,12 53,6 4,64 101 0,28 1,10 0,04 10,7 391 107 21,3 1,21 0,34 34,2 2500 Out-Ca 6,2 1,5 263 -173 0,11 0,01 <d.l. 824 0,30 1,10 0,02 8,56 0,01 117 21,7 1,16 0,01 31,9 2150 Supra-Mg 6,7 2,1 0,04 0,01 <d.l. 835 0,23 0,91 0,02 6,69 0,01 133 21,4 0,99 0,01 26,5 1950 Out-Mg 7,1 1,7 255 -188 0,07 0,01 <d.l. 602 0,04 0,76 0,02 1,07 0,01 288 15,9 0,48 0,01 24,5 1825 Out-Final 8,0 1,6 343 -285 0,09 0,01 <d.l. 494 0,06 0,39 0,02 0,57 0,01 334 9,83 0,28 0,01 25,1 1850 FAO 6.5-8.4 n.d. 5 0,1 n.d. 0,01 0,05 0,1 0,2 5 n.d. 0,2 0,2 5 2 n.d. WHO 6.5-8.5 n.d. 0,9 <d.l. n.d. 0,003 n.d. 0,05 2 2,0 n.d. 0,4 0,07 0,01 3 n.d. TULSMA 0,1 5 0,1 n.d. 0,02 0,5 0,5 1 10 n.d. 2,0 2 0,2 5 1000 pH E.C. Alk Acidity Buza/First sampling (15/07/2014) E.C.: Electrical conductivity (mS/cm); Alk: Total alkalinity as mg/L of CaCO3; n.d. no data reported; d.l. detection limit; Data expressed in mg∙L-1 Torata/First sampling (16/09/2014) Torata/Last sampling (20/02/2015) n.d. Imput source Legal limits Table 1 AMD‐Input Supra‐Ca Mid1‐Ca Mid2‐Ca Mid3‐Ca Out‐Ca Supra‐Mg Mid1‐Mg Out‐Mg Out‐Final 1 234567 81112 pH 2,4 2,3 2,4 5,8 6,4 6,3 6,6 7,3 7,0 8,0 Temp(ºC) 21,5 21,5 21,7 21,7 21,7 21,2 21,5 21,7 21,6 21,9 EhH(mV) 821 794 772 344 460 507 445 406 465 409 pe 13,9 13,4 13,1 5,82 7,78 8,57 7,52 6,86 7,85 6,91 Alkalinity* 20 n.d. n.d. n.d. n.d. 366 n.d. n.d. 341 409 Ca 118 125 153 804 1027 873 857 177 602 714 Mg 119 125 140 142 177 162 242 547 394 366 K 3,12 3,17 3,75 3,5 3,65 4,00 3,99 4,57 4,25 4,56 Fe 230 257 259 6,82 <d.l. 0,02 <d.l. 0,02 0,02 0,01 Mn 26,3 27,9 31,1 30,0 31,7 27,5 28,0 6,47 19,0 9,64 Cr 0,06 0,05 0,07 0,03 0,02 0,03 0,03 0,01 0,02 0,02 Co 1,54 1,54 1,61 1,67 1,18 1,40 1,24 0,11 0,66 0,61 S(6)** 4535 4579 3177 2454 1959 1842 1567 1900 2088 1512 P*** 7,3 7,9 n.d. n.d. n.d. 0,90 0,90 n.d. 0,90 1,00 Ag 0,2 0,25 0,22 0,07 0,12 0,07 0,18 0,05 0,10 0,20 Al 61,2 64,3 72,4 58,6 0,08 0,08 0,09 0,06 0,08 0,09 As 7,05 7,50 4,18 0,03 0,06 0,03 0,04 0,01 0,02 0,03 Cd 0,40 0,41 0,39 0,33 0,15 0,19 0,21 0,11 0,07 <d.l. Cu 16,0 16,1 17,8 33,1 0,54 1,32 0,56 0,28 0,22 0,24 Ni 1,72 1,71 1,81 1,87 1,25 1,45 1,33 0,09 0,56 0,68 Pb 0,74 0,22 0,19 0,13 <d.l. 0,01 <d.l. <d.l. <d.l. <d.l. Zn 46,8 48,8 55,0 52,0 13,4 12,5 13,7 7,02 3,90 3,05 Concetrationsareexpressedinmg∙L‐1(*AlcalnityasHCO3 ‐**S(6)asSO4 ‐2***PasPO4 ‐3)/n.d.notdetermined;<d.l.belowdetectionlimitCalculationsbasedonthe Oxidation‐reductionpotential:EhH(mV)=Ehpl(mV)+223.8‐1.02*t(ºC);pe=16.9*(EhH/1000) Sampling Port Table 2-Revised