Identifiable causes of osteoporosis
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). 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The prevalence of OP is approximately 10.3% in the general population and affects over 200 million women worldwide [1]. Approximately 30% of postmenopausal women suffer from OP in Western nations. About 50% of affected women and at least 15% of affected men will undergo fragility fractures in their lifetime. OP is chronic and does not present with apparent symptoms before osteoporostic fractures occur. This can cause disability, a decreased quality of life, and even death. As a result, OP imposes a heavy financial burden on society that includes not only the direct cost of osteoporostic fractures but also the indirect costs of disablement. In the United States, the direct cost of OP was over USD 13.7 billion in 2005 and may reach USD 25.3 billion by 2025. Therefore, OP is a pressing public health concern and greater understanding of its mechanisms is imperative.
Population ageing is accelerating worldwide, and OP is becoming increasingly prevalent. However, the risk factors that contribute to OP are not clear. Many factors have been suggested to affect the likelihood of OP, including genetics, gender, age, poor diet, smoking and medications (Figure 1). It has been reported that the genetic heritability of bone loss in humans is up to 56% [2,3]. This has been found through comparisons of bone mineral density (BMD), the hallmark trait of OP, in monozygotic (MZ) and dizygotic (DZ) twins after menopause. Additionally, a genome-wide association study (GWAS) of 37,534 individuals in Europe and North America has confirmed that LRP5 (lipoprotein-receptor-related protein) significantly increases the risk of OP and osteoporotic fractures [4]. Therefore, genetic factors play a significant role in the aetiology of OP and its complications.
Bone mass is affected by genetic (60%~80% contribution) and other factors (20%~40% contribution) including exercise, sunlight, nutrition, smoking, alcohol, medication and ageing. Factors along the upline potentially contribute to higher bone mass and along the downline cause lower bone mass.
OP is characterized by reduced BMD, impaired microarchitecture of bone tissue and increased risk of fractures. OP has a wide range of phenotypes including abnormal BMD, bone turnover, osteoporotic fracture, skeletal growth, and fracture risk. Although OP genetics aims to identify pathological genes that increase the chance of bone fragility, osteoporotic fractures are not appropriate candidate phenotypes for heritability studies. As fractures occur due to a wide variety of reasons, not necessarily as a result of bone fragility, fractures alone are insufficient qualifiers for studies. In contrast, previous studies have reported a strong genetic correlation between BMD and bone mass/fracture phenotypes [5]. To this date, most OP studies have focused on BMD because of the high heritability and relative ease of measurement. This chapter will discuss the hereditary factors in pursuit of a new understanding of pathological genes in OP, including an overview of current technology at the cutting-edge of OP testing.
OP may be caused by primary and secondary factors (Table 1). Mostly, OP results from primary factors; in approximately 20%~40% of patients the condition has secondary causes [5,6]. Primary OP has two categories: type I (postmenopausal) and type II (senile) OP. Type I OP is believed to be associated with oestrogen deficiency and usually occurs in women between the ages of 51 and 75. It results in excessive bone resorption and fractures, including in the trabecular bone in the vertebrae and distal radius. Type II OP usually results from age-related vitamin D deficiency and affects women and men over the age of 70. It mainly causes hypocalcaemia, increased parathyroid hormone (PTH) release, bone resorption and fractures, including in both the trabecular and cortical bone on the long bones, causing fractures of the femoral neck, proximal, humerus and tibia, and pelvis.
\n\t\t\t\tIdentifiable Causes\n\t\t\t | \n\t\t|
\n\t\t\t\tPrimary cause\n\t\t\t | \n\t\t\t\n\t\t\t\tSecondary cause\n\t\t\t | \n\t\t
Personal condition | \n\t\t\tChronic glucocorticoid therapy | \n\t\t
Oestrogen deficiency | \n\t\t\tThyrotoxicosis | \n\t\t
Low weight and body mass index | \n\t\t\tVitamin D deficiency | \n\t\t
Increased age | \n\t\t\tAlcoholism and smoking | \n\t\t
Gender | \n\t\t\tHypogonadism (men) | \n\t\t
Family history | \n\t\t\tMalnutrition | \n\t\t
Identifiable causes of osteoporosis
As discussed in detail in other chapters of this book, OP is a chronic and complex disease that is influenced by both non-genetic and genetic factors. Non-genetic factors are common and worthy of consideration (Table 2). One of the most important is oestrogen, which changes with age [6, 7]. Oestrogen acts directly on oestrogen receptors in osteoblasts and its deficiency affects BMD, which leads to the risk of OP in postmenopausal women and contributes to the development of OP in elderly men. The chemical element lead is another potential risk factor for OP. Lead impairs cell proliferation and viability by affecting the response of cells to hormonal stimuli, which interferes with hormone and cytokine signal-transduction processes. This causes a toxic effect in skeleton systems and influences bone mineral homeostasis and growth. Calcium is one of our most common body minerals and is required for normal skeletal growth and maintenance [8]. A low-calcium diet causes loss of trabecular bone by affecting the function and phenotype of bone-marrow cells [8-10]. This has been shown to further accelerate bone loss in ovariectomized (OVX) rats, though BMD levels were partially rescued by calcium supplements [8-10]. Cadmium is toxic and causes bone loss after the start of dietary cadmium exposure [11-12]. It has been demonstrated that cadmium exposure leads to an increase in serum calcium, phosphorous and PTH levels in concomitant with significant reduction in serum vitamin D (3), osteocalcin (OC) levels and bone-specific alkaline phosphatase (BALP) activity [11-12]. Reduced BMD is usually associated with kidney impairment in response to cadmium exposure [12]. Although the toxic effects of aluminium overload on bone metabolism were first reported with a severe form of osteomalacic osteodystrophy [13], recently it has been reported that accumulated aluminium content in bone during life does not substantially influence the level of BMD [14-15]. Additionally, no clinical symptoms of bone disease were found in individuals with aluminium contamination, but its accumulation in tissue was significant [14-15]. Interestingly, dual X-ray absorptiometry (DXA) was found to induce BMD recovery in osteopenic rats affected by aluminium [16]. Alcohol slows cell proliferation and viability to arrest longitudinal bone growth, and affects dry weight, mineral content and mechanical integrity [17-18]. This may increase the risk of developing OP – further confirmation is given by epidemiological findings. Additionally, studies on animals have reported that alcohol suppresses young bone growth and inhibits adult bone formation – alcohol has greater deleterious effects on bone formation than on bone resorption [18]. Smoking, particularly heavy smoking, may affect nutritional absorption, decrease absorption of calcium, and interfere with oestrogen. This eventually results in lower oestrogen levels, lower bone density, a dramatic decrease in the bone mass/mineralization, and a higher incidence of bone fractures [19-21]. Notably, smoking duration was not associated with BMD in 1,054 subjects, including 26.2% of current smokers (n=276), 17.7% of former smokers (n=187), and 56.1% of never smokers (n=591) [21].
Non-genetic factors in the risk of osteoporosis.
Although non-genetic factors play a significant role in determination of the risk of OP, twin and family studies have reported that up to 60~80% of the variance in BMD is attributable to genetic factors. Non-genetic factors vary markedly among individuals who show different bone response to stimuli, and genetic factors appear to be more dominant than the combination of non-genetic factors in the pathogenesis of OP. The search for pathogenic genes that cause OP remains one of the greatest challenges and the most active scientific area in musculoskeletal research. Despite the impact of environmental factors, it has been confirmed that genes related to OP exist and have pathogenicity. These are multiple genes with small individual effects; not more than 10% are associated with BMD. Therefore, the impact of genetic factors in the pathogenesis of OP is still unclear. To date, different methods have been used to identify the susceptible genes related to OP (Fig. 2.). These are elaborated below.
Strategies in the identification of genetic variants of osteoporosis and other complex diseases. Linkage studies in families and sibling pairs refer to phenotyping members of extended families for the feature of interest (e.g., BMD). The control study looks for the association of a marker allele with disease by comparison among unrelated subjects. Experimental crosses in animals are performed to prove the findings from linkage and candidate gene studies.
Ever since Mendel first elucidated the concept of inheritance in the pea plant, genetic analyses have used families, which often share similar genes, to study gene characteristics. With the development of genomic technology, the discovery of genetic markers spanning the entire human genome for Mendelian diseases and traits has been achieved after widespread mapping efforts. Major monogenic skeletal disorders with OP that have been identified to date include osteogenesis imperfecta, Bruck syndrome, osteopetrosis, high-bone-mass syndrome, osteoporosis-pseudoglioma syndrome, von Buchem disease, sclerosteosteosis, familial expansile osteolysis, juvenile Paget’s disease, hypophosphatasia, neonatal hyperparathyroidism and pyknodysostosis. These diseases affect BMD and can cause either high- or low-mass syndrome.
Although many pathogenic genes have been identified (Table 3), many do not qualify as osteoporosis genes due to a lack of robustness. Low-density lipoprotein receptor-related 5 (LRP5) protein is a transmembrane low-density lipoprotein receptor which functions in receptor-mediated endocytosis [22-24]. The LRP5 gene was identified as the cause of the monogenic disease osteoporosis-pseudoglioma by mapping and candidate gene screening [22-24]. Inactivation of LRP5 leads to the suppression of mechano-responsiveness and reduces bone mass and Young’s modulus in osteoporosis-pseudoglioma [25, 26]. Activation of LRP5 increases bone mass in osteosarcoma. Furthermore, population-based and cohort studies have confirmed the significant association of LRP5 with BMD in Asian and European populations [27-29]. Bone fragility and risk of fracture have been reported with the mutation of LRP5 in families of affected patients. Patients with multiple thoracic vertebral fractures were confirmed to have about two compound heterozygous missense mutations in LRP5 [30, 31]. Meta-analyses and genome-wide studies carried out for separate large populations have revealed consistency between LRP5 variants in BMD and fracture risk – this will be further addressed in the next part. In addition to LRP5, low-density lipoprotein-related 4 (LRP4) protein was identified as a candidate sclerostin interaction partner by an unbiased proteomics approach [32]. The mutation of LRP4 has been reported to be associated with bone overgrowth by impairing the sclerostin facilitator function.
AR | \n\t\t\tAHSG | \n\t\t\tApoE | \n\t\t\tBMP2 | \n\t\t\tBGP | \n\t\t\tCASR | \n\t\t
CLCN7 | \n\t\t\tCOL1A1 | \n\t\t\tCOL1A2 | \n\t\t\tCT | \n\t\t\tCYR1B1 | \n\t\t\tCOMT | \n\t\t
CYP1A1 | \n\t\t\tCYP17 | \n\t\t\tCTR | \n\t\t\tCYP19 | \n\t\t\tCCR2 | \n\t\t\tDBP | \n\t\t
DRD4 | \n\t\t\tERbeta | \n\t\t\tERalpha | \n\t\t\tFRA-1 | \n\t\t\tGH1 | \n\t\t\tGnRH | \n\t\t
HLA-A | \n\t\t\tIL-6 | \n\t\t\tLRP5 | \n\t\t\tLRP4 | \n\t\t\tLCT | \n\t\t\tIL-1RA | \n\t\t
LEPR | \n\t\t\tIL-10 | \n\t\t\tI-TRAF | \n\t\t\tIRAK1 | \n\t\t\tIGF-1 | \n\t\t\tIGF-II | \n\t\t
Klotho | \n\t\t\tMMP-1 | \n\t\t\tMGP | \n\t\t\tMTHFR | \n\t\t\tMMP-9 | \n\t\t\tNPY | \n\t\t
NCOA3 | \n\t\t\tOSCAR | \n\t\t\tOPG | \n\t\t\tP57(KIP2) | \n\t\t\tPON1 | \n\t\t\tPDE4 | \n\t\t
PPARG | \n\t\t\tPLDO1 | \n\t\t\tPTHR1 | \n\t\t\tPTH | \n\t\t\tPDE4D | \n\t\t\tQPCT | \n\t\t
RIL | \n\t\t\tRUNX2 | \n\t\t\tSox4 | \n\t\t\tSERT | \n\t\t\tSOST | \n\t\t\tTNFalpha | \n\t\t
TNFR2 | \n\t\t\tTCIRG1 | \n\t\t\tTGFbeta | \n\t\t\tWRN | \n\t\t\t\n\t\t\t | \n\t\t |
Genes associated with BMD/osteoporosis. These genes are identified through association studies and correlate with BMD and osteoporosis-related phenotype.
Vitamin D receptor (VDR) is one of the trans-acting transcriptional regulatory factors that regulate proteins involved in bone mineral homeostasis. It has been reported that the polymorphism of the third region of VDR is significantly associated with BMD, and the allelic variation of this gene may contribute 75% of the genetic effect on BMD change [33]. Additionally, the polymorphism of the VDR gene, including Bsml, Apal, Fokl and Taq genotypes, regulates BMD and is associated with OP [34-37]. VDR polymorphisms emerge in the rheumatoid-related OP and the ApaI, BsmI and TaqI polymorphisms may be susceptible risk factors for rheumatoid arthritis (RA). Interestingly, the Ff genotype may be responsible for development of OP in RA [38, 39].
Type I collagen is a constituent major-bone protein, and the genes (COLIA1 and COLIA2) are believed to be candidates for genetic control of BMD. The COLIA1 gene polymorphism is suggested to be implicated in reduced BMD and increased fracture incidence [40]. Mutations in the two genes for type I collagen have been confirmed to cause osteogenesis imperfecta, and are associated with OP in postmenopausal women [41, 42]. Population-based studies have indicated that COL1A1 Sp1 polymorphism may contribute to the development of OP such that Sp1 polymorphic variants of COL1A1 gene are associated with BMD values [43-46]. The “ss” and “TT” genotypes possess lower lumbar-spine BMD [46].
The oestrogen-receptor gene encodes an oestrogen receptor (ESR), a member of the superfamily of nuclear receptors that are involved in DNA and hormone binding and activation of transcription. The ESR can interact with oestrogen and transcription factors including SP-1, AP-1 and NF-κB. As oestrogens are important endocrine regulators in skeletal growth and maintenance, oestrogen-deficient animals exhibit reduced BMD and oestrogen substitution shows restored bone compartments [47-49]. One case of OP in a male patient was reported to show an inactivating mutation of the ESR gene, which was parallel with the male knockout mice with null alleles at the ESR locus [50]. However, inactivation of ESR alpha, specifically in nervous tissue, the main ER for oestrogenic bone effects, causes increased BMD in mouse trabecular and cortical bone [51]. Interestingly, ER alpha in osteocytes has been shown to play an osteoprotective role in the trabecular bone formation, confirmed by tests on ER alpha-deletion mice [52].
Although many genes have been identified in monogenic skeletal disorders connected to OP, the linkage data have not been sufficiently robust due to sample size and significance. Studies in OP genetics need to facilitate more powerful and more sophisticated approaches, such as GWAS studies, to achieve the identification of OP heritable factors.
Genome-wide association study (GWAS) aims to identify disease-associated loci called single nucleotide polymorphisms (SNPs), which contribute to small variations of BMD in the genome. If an allele of an SNP occurs significantly more or less frequently in people with a particular disease than in people without the disease, the allele is associated with the disease traits. Although previous linkage and candidate-gene studies have provided few replicated loci for OP, genome-wide association approaches have produced clear and reproducible findings.
Two SNPs, rs3736228 on chromosome 11 in the LRP5 gene and rs4355801 on chromosome 8 near the TNFRSF11B gene, were reported to cause risk of OP and osteoporotic fracture in 2,094 British women [53]. The rs3736228 in the LRP5 gene was identified to correlate with decreased BMD in lumbar spine and femoral neck. The rs4355801 near the TNFRSF11B gene correlated with decreased BMD for lumbar spine and femoral neck [53]. In a study of 583 postmenopausal women, the polymorphism of three variants TNFRSF11B (rs4355801, rs2073618, and rs6993813) and one of LRP5 (rs3736228) was further confirmed to be associated with BMD variations [54]. In a prospective study of 37,534 subjects in Europe and North America, variants of LRP5 (rs3736228, rs4988321) and one variant of LRP6 (rs2302685) were examined in terms of their effect on BMD and contribution to risk of fracture. The rs3736228 and rs4988321 variants of LRP5 were associated with reduced lumbar-spine BMD, femoral-neck BMD, and fractures. The rs2302685 of LRP6 polymorphism was not associated with any OP phenotype, including reduced BMD and fracture [55, 56]. This was further confirmed in a study of 944 postmenopausal Spanish women, where the rs2302685 variant of the LRP6 polymorphism was not significantly associated with lumbar-spine or femoral-neck BMD [57]. LRP4 was also analysed for SNP in cohort studies of European populations. One associated SNP of LRP4 (rs6485702) was significant in hip and whole-body BMD, modulated through the Wnt and BMP signalling pathways [58, 59].
Other gene polymorphisms associated with changes in BMD included the Cerberus 1 (CER1) gene, ADAM metallopeptidase with thrombospondin type 1 motif 18 (ADAMTS18), transforming growth factor beta receptor III (TGFBR3), high-mobility group (HMG), signal transducer and activator of transcription 1 (STAT1), aldehyde dehydrogenase (ALDH), PTH, and sperm-ion channel gene CatSper channel auxiliary subunit beta (CATSPERB). In a cohort of 1,083 human subjects, one SNP (rs3747532) in human CER1 gene was reported to play a role in the increased risk of low BMD in women prior to menopause and a vertebral fractures cohort [60]. In an association test of 379,319 SNPs in 1,000 unrelated American white individuals, ADAMTS18 and TGFBR3 were reported as BMD candidate genes in meta-analyses [61]. Additionally, individuals with normal skeletal fractures were different to individuals with non-union skeletal fracture in the expression of both genes [61]. In two cohorts of 1,548 Caucasian American men and 1,680 Afro-Caribbean men, the rs1042725 of the HMGA2 polymorphism was reported to be associated with decreased tibia trabecular volumetric BMD [62]. In a GWAS of 1,000 unrelated Caucasians, the STAT1 gene was significantly associated with BMD variation and was upregulated in the low BMD group rather than the high BMD group variation [63]. In a study of 700 elderly Chinese Han individuals (350 with hip osteoporotic fracture and 350 healthy controls), one SNP, rs13182402, within the ALDH7A1 gene on chromosome 5q31, was significantly associated with osteoporotic fractures [64]. This was further examined in relation to the relevance of hip BMD in Caucasian and Chinese populations (n=9,962), finding a consistent association with hip BMD [64]. The Interleukin 21 receptor (IL21R) encodes a cytokine receptor for interleukin 21 that activates multiple downstream signalling molecules, including STAT1, STAT3, JAK1 and JAK3. In a GWAS of 983 unrelated white subjects, the polymorphisms of the PTH gene (rs9630182, rs2036417, and rs7125774) and the polymorphisms of the IL21R gene (rs7199138, rs8061992 and rs8057551) were associated with changed femoral-neck BMD [65]. The polymorphisms of CATSPERB (rs1298989, rs1285635) were reported to be associated with femoral-neck BMD in 1,524 European-American premenopausal women and 669 African-American premenopausal women [66]. In particular, the rs1285635 in the European-American women was consistent with that in the African-American women [66].
Furthermore, meta-analysis of GWAS has shown more candidate genes and BMD loci reaching genome-wide significance. A meta-analysis of 19,195 Northern European subjects from five GWAS revealed nine genes from 150 identified genes associated with changed BMD: TNFSF11, TNFRSF11A, TNFRSF11B, LRP4, LRP5, ESR1, SPP1, ITGA1, and SOST [67]. Additionally, 13 new BMD loci were identified and included: 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4, ARHGAP1, F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1) [68]. In a meta-analysis of 18,098 subjects from six European-descent populations and an Asian population, rs2273061 of the Jagged1 (JAG1) gene was identified to be associated with BMD for lumbar spine and for femoral neck. The JAG1 gene therefore becomes a new candidate gene in the regulation of BMD and a new risk factor for bone-fracture pathogenesis [69]. In a meta-analysis of GWAS based on 3,657 Caucasian men and 7,633 Caucasian women, two genes and three new loci were identified as associated with OP-related traits and BMD in women, including SOX6, GPR177 gene, 2q11.2 (TBC1D8), chromosome 1p13.2 and 18q11.2 (OSBPL1A) [70]. A meta-analysis of GWAS data from European and Chinese individuals identified further genes associated with changed BMD located at 1q21.3, 9q22, 9q33.2, 20p13, and 20q12. This significantly correlates with the development and functionality of muscle, skeleton and connective tissue [71]. In a meta-analysis of 17 GWAS based on 32,961 subjects of East-Asian and European ancestry, 32 new loci were associated with BMD at genome-wide significance and were localized in 1q24.3, 2p21, 2q13, 2q14.2, 3q13.2, 3q25.31, 4p16.3, 6p21.1, 6p22.3, 7q31.31, 7q31.31, 7q36.1, 8q13.3, 9q34.11, 10p11.23, 10q21.1, 10q22.3_1, 10q24.2, 11p14.1_1, 12p11.22, 12p13.33, 12q13.12, 12q23.3, 14q32.12, 16p13.11, 16p13.3_1, 16p13.3_2, 16q12.1, 17p13.3, 17q24.3, 18p11.21, 19q13.11 and Xp22.31 [72]. Moreover, in a meta-analysis of 27,061 individuals, two new loci were of genome-wide significance: 14q24.2 (rs227425, SMOC1) in the whole sample and 21q22.13 (rs170183, CLDN14) in the female-specific sample. These were also shown to be significant in the results of the GEnetic Factors for Osteoporosis Consortium (GEFOS, n = 32,960) [73].
Thus, GWAS has been successfully used in uncovering key genes or markers associated with OP in humans and animals. Although it has confirmed the existence of relevant SNPs associated with changed BMD and the risk of OP, GWAS still cannot be absolutely accurate in the prediction of OP.
The first large-scale genome-wide studies of copy-number variants (CNVs) in humans were performed about a decade ago, and CNVs now make a larger contribution to genome variations than SNPs [74, 75]. Recent studies have focused on CNVs that may modulate gene function and affect disease risk. CNVs are structural genetic variants in the genome that alter the number of copies of one or more sections of DNA. This corresponds to large regions of the genome with deletion or duplication on certain chromosomes, and results in phenotypic variation.
Although CNV has been reported to correlate with many complex human diseases, the contribution of CNV to OP has not been revealed yet. A Chinese study of 700 elderly patients, comprising 350 individuals with homogeneous hip osteoporostic fracture and 350 control individuals, reported that CNV 4q13.2 significantly correlated with osteoporostic fracture (P<0.0002). Additionally, a variant of UGT2B17 in CNV 4q13.2 was further proven to correlate with hip osteoporostic fracture in both white (1000 individuals) and Chinese (689 individuals), with consistently significant results (P=0.0005-0.021) [76].
Another genome-wide study of 1,000 Caucasian individuals found that a CNV in VPS13B gene, encoding a potential transmembrane protein involved in vesicle-mediated transport, was significantly associated with hip, spine and femoral-neck BMD. Interestingly, individuals with two copies of the CNV in the genome exhibited a higher level of BMD in the hip, spine and femoral neck, compared with one-copy subjects [77].
Another genome-wide CNV study tested 5,178 subjects in a prospective cohort in the Netherlands. It identified 210 kb deletion located on chromosome 6p25.1 that correlated with the risk of fracture (P=0.0000869). This deletion has geographic specificity, not affecting the populations of Australia, Canada, Poland, Iceland, Denmark, or Sweden. It has been found and is prevalent in Ireland (0.06%), England (0.15%), USA (0.23%), Scotland (0.10%), and Spain (0.33%), with insufficient significance of fracture risk. However, the role of the 6p25.1 locus in the prediction of risk of bone fracture needs to be tested in a larger and more diverse population to confirm the findings [78].
A study on 2,286 Caucasian individuals, and replicated in 1,627 Chinese individuals, identified two CNVs (CNV2580 and CNV1191) that correlated with appendicular lean mass (ALM), the main component of skeletal muscle. CNV1191 resides in the gene encoding GTPase of the immunity-associated protein family (GIMAP1), and is significantly associated with ALM (P=0.0226). CNV2580 is localized in the serine hydrolase-like protein (SERHL) gene and is also significantly associated with ALM (P=0.00334). Both genes are important for skeletal-muscle growth and may be relevant to OP. Although the two new CNVs are responsible for variation in ALM, more evidence is required to confirm their significance in relation to OP [79].
Therefore, the use of CNV-based GWAS in relation to OP is a cutting-edge technology and strongly supports the importance of CNV in the pathogenesis of OP. Although the results in this area are well established, it is necessary to focus on the association between classical genotypes, and actual experimentation is needed for validation. Additionally, high-throughput, sophisticated genotyping approaches for CNVs need to be optimized for further genetic screening.
OP is one of the most common diseases and is becoming more prevalent with the ageing of the world’s population. BMD is the hallmark of OP and exhibits high heritability; efforts to understand OP genetic determinants have therefore been increased. OP represents a paradigm where the effects of multiple genetic factors dominate the phenotype. Although monogenic approaches that identify genes by rare mutations have contributed to the understanding of OP, the genetic background of OP is characterized by polymorphisms and variations in multiple genes; more-powerful and high-throughput technologies are needed to analyse these aspects. Recent genetic studies in OP have facilitated the understanding of the aetiology of OP. The discovery of novel genetic factors through the use of genome sequencing will contribute to understanding the modulation of BMD and bone fragility with potential therapeutic targets. Combined with behavioural and environmental factors, findings in genetic studies can be validated and used in the development of clinical treatments of OP worldwide. There is no doubt that genetic tests have achieved significant progress in bone biology and are likely to become even more important within the next decade. To optimize accuracy, we should highlight elements including careful phenotyping, sophisticated study design, adequately powered cohorts, and multi-collaboration in future research.
This study was supported by the Seeding Fund of the Shandong Academy of Sciences, China, Natural Science Foundation of China (81201405, 31470904, 81472078) and the Basic Research Project of Shenzhen (JCYJ20120615125154107).
Sulfide ore minerals are generally concentrated by milling and flotation, which produces tailings containing gangue minerals and residual sulfides. Milling involves crushing and grinding to reduce particle size and liberate ore minerals from the rock matrix. After ore minerals have been extracted and concentrated, the resulting tailings are commonly dewatered and deposited in sub-aerial tailings, impoundments, or stockpiles [1]. They are piled up as less than 5 cm thick layers and are slightly differentiated by compositional and/or granulometric features. Although the metal content is removed in the metallurgic process, some ore sulfides (e.g. pyrite, galena, sphalerite, chalcopyrite, arsenopyrite, etc.) can be deposited, either because they were not sufficiently high-grade for use, or due to a deficient extraction technology. They entail both an accumulation and a potential emission source of trace elements (e.g., Cu, Fe, Pb, and Zn). Oxidation of the sulfide minerals accumulated in the abandoned mine tailings may cause: (a) highly contaminating acid mine drainage (AMD) from leakages and (b) mobilization of significant quantities of trace elements such as As, Cd, Cu, Hg, and Pb. It becomes necessary to identify and characterize these hazardous areas where large quantities of potentially toxic elements can be released into the environment. Mine ponds are, therefore, an important environmental problem, especially if they are abandoned.
\nSpain has a long mining tradition dating from pre-historic times up to the present day. A large amount of mine installations, galleries, and waste deposits were abandoned until the 1980s by the cessation of mining activity. Pollution from these sources can originate via mining spills, leakages, or wind-blown dust, and toxic elements with a high mobility can cause huge environmental problems: accumulation in flora and fauna, reducing the quality of streams and groundwater.
\nAn inventory of the abandoned mine waste deposits has been prepared by the Spanish Ministry of the Economy, through the Directorate General of Energy Policy and Mining [2]. The most relevant contribution of the inventory consists in the classification of the existing mine waste deposits based on their hazard potential for infrastructure and the human population. Another significant aim is focused on providing a qualitative geotechnical and environmental assessment of the elements at risk and an associated description. Further knowledge of the current status of the highest potential risk deposits is required because of the preliminary nature of the inventory, carried out by means of visual surveys and without sampling or testing.
\nIn summary, a complete geoenvironmental characterization of the affected areas is crucial for any proposal of effective measures that could help to minimize environmental impact and concern.
\nSeven mine areas from the most important metallic mining district in Spain have been selected (Figure 1): La Naya, Monte Romero and Mina Concepción from Iberian Pyrite Belt [3, 4], Brunita from Cartagena-La Unión [5], San Quintín from Alcudia Valley [6], and San Cristóbal and Las Moreras from Mazarrón [7].
\nLocation and field photographs of the different studied mine areas: (a) location of the sulfide mine ponds in Iberian Pyrite Belt (LN—La Naya, MC—Mina Concepción, and MR—Monte Romero), Cartagena-La Unión (BR—Brunita), Alcudia Valley (SQ—San Quintín), and Mazarrón (SC—San Cristóbal and LM—Las Moreras); (b) Monte Romero; (c) acid leakage from Mina Concepción tailings; (d) detail of tailings in La Naya; (e) La Naya; (f) tailing dune in San Quintín; (g) Las Moreras; (h) borehole core samples from Brunita tailings; (i) San Cristóbal; and (j) Brunita.
One of the largest concentrations of massive sulfide mineralizations in the world is hosting at the volcano-sedimentary rocks of the Iberian Pyrite Belt, in the southwest (SW) of the Iberian Peninsula. The main ore mineral is pyrite, although lower quantities of sphalerite, galena, chalcopyrite, and arsenopyrite are also found. An intense mining activity in this province is related to the exploitation of S, Cu, Pb, Zn, Ag, and Au from the sulfide ore minerals. Different studies have pointed out the significant concentrations of certain trace elements in sediments and soils surrounding mining or waste sites in the Iberian Pyrite Belt district [8]. The waters of the Tinto and Odiel fluvial systems are also affected [9]. Remediation has only been conducted in a few of the mine areas, although they still display significant environmental issues [4]. In 1998, an environmental disaster occurred in the SW of Spain, when the tailing dam of one of the bigger mines from the district was ruptured. Around 2 × 106 m3 of heavy metal-bearing sludge and ~4 × 106 m3 of acidic waters were released [10].
\nMonte Romero (Figure 1) comprises two mine ponds located at the Cueva de la Mora mine site, where Pb- and Zn-bearing minerals were benefited [3]. La Naya (Figure 1) is a mine pond located to the southeast of Minas de Riotinto town, and is one of the largest deposits mined during the extensive works in this mining group. The main ores extracted were Cu-bearing minerals and pyrite. Another mine site studied was Mina Concepción, a restored mine pond located on the SE of the Almonaster la Real village (Figure 1). Tailings from the metallurgical treatment and benefit of pyrite were piled up over metavolcanic lithologies [4]. The contention dyke retaining them is 8 m high, and three collector pipes going through the dyke controlled the drainage. The physical restoration and the landscape integration of the mine pond were achieved by sealing and reforestation with pine trees, substituting the original vegetation.
\nOne of the most significant places of geochemical pollution and geotechnical instability in Spain’s abandoned mining heritage is the Cartagena-La Unión district, southeast of Spain [2]. The Brunita mine pond is one of the numerous tailing deposits in the district, affecting the surrounding watercourses that reach La Manga coastline, a major tourism location in SE Spain. Human beings, fauna, flora, groundwater, and agricultural soils are negatively stilted [5].
\nThe mine tailings were produced from grinding and metallurgical treatment of mineral from Eloy and Brunite mines between 1952 and 1981 [2]. The main ore minerals are pyrite, sphalerite, galena, marcasite, and pyrrhotite. Other minor sulfides include arsenopyrite, minerals of the tetrahedrite-tennantite group, chalcopyrite, and stannite [11]. In October 1972, an extreme rainfall event caused damage in the Brunita mine pond (Figure 1). A tailing flash flood killed one person and caused serious material damage. As a result, a retaining wall of the coarse-grained tailings was built. Since 1981, when the mine was closed, no further works on restoration or reclamation have been carried out.
\nThe San Quintín abandoned mine area, located at the Alcudia Valley (Ciudad Real province, Spain), is crossed by the Don Quixote Route [12], a tourist set of itineraries created in 1995 to celebrate the IV Centenary of the publishing of “El ingenioso hidalgo Don Quijote de La Mancha”. This route, the longest ecotourist route in Europe, was recently declared as Cultural Itinerary by the Council of Europe. It could soon reach the rank of Humanity Heritage because of its high cultural and environmental quality. These features make the San Quintín area a busy tourist route, and the environmental characterization of potential hazards is so necessary.
\nThe ore mainly comprised Ag-bearing galena and sphalerite as major phases of a hydrothermal mineralization also including pyrite, chalcopyrite, marcasite, pyrrotine, bournonite, siderite, boulangerite, and ankerite [13]. The exploitation was performed from 1887 to 1934, date of the mining closure. In 1973, a new treatment plant was installed for re-working of approximately three million tons of tailings. Several tons of cinnabar from the Almadén mine (Ciudad Real, Spain) were experimentally treated in this new plant with successful results. At present, several mine tailings resulting from re-working together with the ruins of the mine structures are clearly visible. AMD from the tailings is recognized. Furthermore, a tailing dune, formed over one of the ponds, is migrating and toward the agricultural soils surrounding the mine area (Figure 1). The course of the Arroyo de la Mina stream, crossing the mining area, was altered and presently runs along the limits of the mine ponds.
\nMazarrón is located 4 km from the Mediterranean coast in SE Spain, and was one of the most important mining districts in the area [14]. It was exploited from Roman times to the early 1960s for Pb, Al, Ag, and Zn. Together with mining activities, the Mazarrón area is characterized by intensive farming and tourist pressure. Mining deposits caused significant water and soil pollution, and led to negative effects on both agricultural and tourism land uses. A correct geo-environmental characterization of the affected area is important for any proposal of restoration and remediation focused on minimizing environmental impacts.
\nThe ponds are located on the hill slopes of the San Cristóbal and Los Perules hills, situated near a watercourse that drains to the Las Moreras watercourse, in turn flowing into the Mediterranean Sea (Figure 1). The main ore minerals were sphalerite, pyrite, and Ag-bearing galena. Other minor sulfides were arsenopyrite, chalcopyrite, the tetrahedrite-tennantite group, stibnite, cinnabar, and berthierite. The mine tailing ponds, near the Las Moreras dry watercourse, are situated on Quaternary alluvial and colluvial deposits (Figure 1). Although the total level amount of rainfall is not high, the area is subjected to strong stormy events each year, which can induce flash flooding phenomena.
\nMineralogical and geochemical techniques normally used to determine the composition of mine tailings, soils, waters, and watercourse sediments and the possible occurrence of AMD are described. In the case of high Hg contents, gaseous mercury emissions were analyzed too. Sampling features such as methods, sampling depth, analytical techniques, etc. are summarized in Table 1 and described below.
\nMine district | \nStudy mine | \nOre mineralogy | \nSample type | \nSampling | \nAnalytical techniques | \nComplementary techniques | \n
---|---|---|---|---|---|---|
Iberian Pyrite Belt | \nLa Naya | \nPy, Sp, and Ga | \nTailings | \nManual sampler (2.7 m depth) | \nXRD, ESEM+EDX, INAA, pH | \nERT (1 profile) | \n
Monterromero | \nSp, Ga, and Py | \nTailings | \nManual sampler (2 m depth) | \nXRD, ESEM+EDX, INAA, pH | \nERT (2 profiles) | \n|
Mina Concepción | \nPy, Sp, Ga, Apy, Cpy, and Mg | \nTailings | \nManual sampler (1 m depth) | \nXRD, INAA | \nERT (6 profiles) | \n|
Water | \nLeakage | \nICP-MS, pH, EC | \n||||
Cartagena-La Unión | \nBrunita | \nPy, Sp, Ga, Ma, Pyr, Te, Cpy, Apy, and St | \nTailings | \nBorehole (24 m depth) | \nXRD, ICP-MS, pH, EC | \nERT (6 profiles), aerial photographs | \n
Water | \nWater table | \nICP-MS, pH, EC | \n||||
Alcudia Valley | \nSan Quintín | \n(Ag)-Ga, Sp, Py, Ma, Cpy, and Pyr | \nTailings/colluvial | \nBoreholes (10–12 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \nERT (7 profiles), aerial photographs | \n
Water | \nBorehole (8.5 m depth); watercourse; AMD | \nICP-MS, pH | \n||||
Soil | \nSurround; Blank | \n\n | ||||
Air | \nSummer-winter | \nZAAS-HFM | \n||||
Mazarrón | \nSan Cristóbal | \nPy, Sp, (Ag)-Ga, Cpy, Apy, Te, Ci, and St | \nTailings | \nBorehole (5.5 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \nERT (1 profile) | \n
Watercourse sediments | \n||||||
Water | \nWater table | \nICP-MS, pH, EC | \n||||
Las Moreras | \nTailings | \nBorehole (6 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \n|||
Watercourse sediments | \nERT (1 profile) | \n|||||
Water | \nWatercourse | \nICP-MS, pH, EC | \n
Main sampling features of the studied mine district in Spain: Iberian Pyrite Belt, Alcudia Valley, Cartagena-La Unión, and Mazarrón [3, 4, 5, 6, 7].
XRD, X-ray diffraction; ESEM, environmental scanning electron microscopy; EDS, energy dispersive X-ray; INAA, instrumental neutron activation analysis; ICP-MS, inductively coupled plasma-mass spectrometry; EC, electrical conductivity; and ZAAS-HFM, atomic absorption spectrometer with Zeeman effect.
At Brunita, San Quintín, and San Cristóbal-Las Moreras areas, nondisturbed rock drill core tailing samples were collected from boreholes using a rotary drilling machine with a core bit diameter between 86 and 100 mm. Sampling was carried out by digging down below the surface of each pond, eliminating the surficial sealing to prevent falling material inside the borehole during drilling. Sampling depth of the unaltered samples varies between 0.5 and 1 m, depending on the borehole depth. All samples were air-dried for 7 days, passed through a 2-mm sieve, homogenized, and stored in plastic bags at room temperature prior to analyses. Below mine tailings, colluvial sediments (2–4 m) in San Quintín and watercourse sediments in San Cristóbal (2.5 m)—Las Moreras (4.5 m) were drilled, collected, and analyzed to obtain a complete geoenvironmental characterization of the area. Where a rotary drilling machine is not possible to place, samples were collected with an Eijkelkamp soil core manual sampler. Sampling was sequential with a centimeter vertical constant spacing and lower in depth than boreholes. This is the case of tailings studied at the Iberian Pyrite Belt district (Table 1). In many occasions, soils surrounding mine facilities show evident signals of contamination from different sources (tailings, ponds, open shafts, etc.) and pathways (wind erosion, water flows, etc.) affecting different receptors (agricultural soils, colluvial sediments, humans, etc.). In these scenarios, it is necessary to collect representative sample soils from the studied zone, and from a natural soil far enough of the mining area as a background sample (blank), in the case of San Quintín mine area. This is necessary to compare the potentially toxic element contents with the natural amount in the surrounding soils.
\nWater sampling is also necessary to check the metal amount in watercourse affected by the mining operations, as well as the possible AMD generation from the tailings. Several water samples have been collected depending on the features of the studied mine site: water sample collected at 8.5 m depth from a borehole (San Quintín), water samples from the tailing pond (Brunita and San Cristóbal), water sample from the watercourse crossing the mining area (San Quintín and Las Moreras), or water sample from the leakage of an abandoned (San Quintín) and a restored mine pond (Mina Concepción). All water samples were collected in 250 ml plastic bottles, were kept in a refrigerator at 4°C and, prior to analysis, and were filtered with 45 μm pore spacing. In San Quintín mine site (Alcudia Valley district), total gaseous mercury (TGM) was also measured using a 200 m sample spacing grid during both summer and winter by means of an atomic absorption spectrometer with Zeeman effect (ZAAS-HFM). The geostatistical treatment of data was performed applying block kriging to obtain interpolation maps of the study area.
\nMineralogical characterization of borehole and soil samples was performed by X-ray diffraction (XRD) using a Philips X’Pert powder device with a Cu anticathode and standard conditions: speed 2° 2θ/min between 2° and 70° at 40 mA and 45 kV. The whole sample was examined by crystalline nonoriented powder diffraction on a side-loading sample holder. Semi-quantitative results were obtained by the normalized reference intensity ratio (RIR) method. The mineralogy of the samples was also studied by environmental scanning electron microscopy (ESEM), coupled with energy dispersive X-ray analysis (EDX), using a Philips XL30 microscope. The ESEM was operated at a low-vacuum mode, at a pressure between 0.5 and 0.6 Torr under a water vapor atmosphere and an operating voltage of 20 kV. The XRD and ESEM-EDX analyses were performed at the Centro de Apoyo Tecnológico (CAT Universidad Rey Juan Carlos, Móstoles, Spain). From the total list of major, minor, and trace elements analyzed, Ag, As, Cd, Co, Cu, Fe, Hg, Ni, Pb, S, Sb, Sn, and Zn were specially chosen because of their abundance in these types of sludges and because most of them are included in the priority contaminant list of environmental protection agencies. They were analyzed by total digestion (TD) or lithium metaborate/tetraborate fusion (FUS), inductively coupled plasma-mass spectrometry (ICP-MS), and instrumental neutron activation analysis (INAA) at the Activation Laboratories Ltd. (1428 Sandhill Drive, Ancaster, Ontario, Canada). Quality control at the Actlabs laboratories is performed by analyzing duplicate samples and blanks to check the precision, whereas accuracy is determined using Certified Reference Materials (GXR series; see [15]). Detection limits for the analyzed elements are as follows (data in μg g−1): Ag (0.3), As (5), Cd (0.5), Co (1), Cu (1), Fe (100), Hg (0.005), Ni (1), Pb (5), S (10), Sb (0.5), Sn (1), and Zn (1). Pb content higher than 5000 μg g−1 (above the ICP-MS maximum detection limits) was measured by ICP-OES or atomic absorption.
\nWater samples were analyzed by ICP-MS at Activation Laboratories Ltd. The pH was measured using an electronic pH meter (CRISON) that was calibrated using standard buffer solutions at two points: pH: 7 and pH: 4. This parameter was determined in a slurry system with an air-dried sample (10 g) mixed with distilled water (25 mL). Before reading the pH values, these solutions were vigorously stirred in a mechanical shaker for 10 min and left to stand for 30 min.
\nElectrical resistivity tomography (ERT) imaging is a near surface nondestructive technique designed to be widely used in many different geological applications, including the determination of the materials constituting the bedrock, unraveling the stratigraphical record of the basins and locating hidden faults, among others [16]. A resistivity profile is obtained from many different measurements using different available electrode arrays, with the data acquisition being controlled by means of a computer. These measurements provide data about the variations of apparent resistivity values at different depths, in such a manner that when the spacing between the electrodes increases, the resistivity data correspond to a greater depth of investigation. After data acquisition, the apparent resistivity values are converted to an image of true resistivity variations against depth. The resistivity meter used to obtain the data for this study was a Syscal Junior Switch 48. As mentioned before, different electrode arrays are available, with differences in relation to the depth of investigation and signal-to-noise ratio (e.g. [17]). From the different electrode arrays available, a Wenner-Schlumberger array has been selected because it provides a good penetration depth, the signal to noise ratio is good, and both vertical and horizontal resolutions are also reasonable. Moreover, different authors have previously used this array successfully in several similar studies [3, 4, 5, 6, 7, 18] because it shows a high contrast between the resistivity values of the vase of the mine ponds and the resistivity values of the infilling. From the field data, the information obtained about the resistance measurements between the different electrodes and distances between them is used to calculate the apparent resistivity values. Then, a plot of the apparent resistivity values vs. depth, named pseudosection, is constructed. Previously to be interpreted, the pseudosections need to be converted into profiles where true resistivity values are plotted against depth. The conversion from apparent to true resistivity values is performed by means of the RES2DINV code. As a first step of this inversion procedure, the data are filtered to remove bad data points, and then the topography information along the profile is also included. The code uses the L1 norm for the data misfit and the inversion was performed using the L1 norm (robust) for the model roughness filter [19]. The choice of the robust inversion is justified because this kind of inversion is more accurate when sharp boundaries in the model exist, and this is just the case involved in this study because of the large contrasts expected in the electrical properties of the materials. The method uses a finite element scheme for solving the 2-D forward problem and blocky inversion method for inverting the ERT data. The code RES2DINV finally provides an inverted resistivity image for each profile. The inverted profile is the one used to obtain the final interpretation about the variations of the subsurface lithology.
\nAerial photographs, georeferenced and integrated into a GIS, are used to map morphologic elements and to detect the main changes in the environment through time. The evolution of mine deposits in the Brunita site was carried out to estimate anthropogenic changes in the landscape [5]. We worked with aerial photographs taken in 1929 (photogrammetric flight by Ruiz de Alda), 1946, and 1956 (flights by the Geographic Service of the Spanish Army), and 1973, 1981, 2004, and 2013 (flights by the Spanish National Geographic Institute), and anaglyphs of orthoimages from years 1946, 1956, 1981, and 2004 [20]. On the other hand, the study of a mine pond and a tailing sand dune at the San Quintín mine permitted to evidence the eolian dispersion of contaminants to the surroundings [21]. Several aerial photographs corresponding to different years were also analyzed (1957, Geographic Service of the Spanish army, 1977, 1984, Spanish National Geographic Institute, 2006, digital orthophoto IGN).
\nThe results of the mineralogical and geochemical characterization of the samples collected from tailings, soils, air, water, and watercourse sediments are presented and discussed here. Morphological evolution over time from Brunita and San Quintín mine ponds is presented too, as well as the geophysical study concerning the structure and infilling of ponds and the possible presence of acidic water flows.
\nThe mineralogical composition of tailings, colluvial, watercourse sediments, and soil samples has been inferred from the X-ray diffraction studies (Table 2). The following nomenclature has been used for tailing-mineral identification: primary minerals, those minerals that constitute ore and gangue assemblages originally deposited in the waste dumps, and secondary minerals, those deposited within the dumps by precipitation from metal-rich waters derived from acid mine drainage.
\nMine district | \nIberian Pyrite Belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n||||||
---|---|---|---|---|---|---|---|---|---|---|
Study area | \nLN | \nMR | \nMC | \nBR | \nSQ | \nSQ | \nSC | \nSC | \nLM | \nLM | \n
Sample | \nTa | \nTa | \nTa | \nTa | \nTa | \nCo | \nTa | \nWT | \nTa | \nWT | \n
Quartz | \n85 | \n35 | \n35 | \n30 | \n70 | \n80 | \n40 | \n45 | \n50 | \n40 | \n
Illite | \n— | \n10 | \n5 | \n5 | \n15 | \n10 | \n— | \n— | \n— | \n20 | \n
Feldspar | \n— | \n10 | \n5 | \n— | \n— | \n— | \n5 | \n30 | \n5 | \n5 | \n
Chlorite | \n5 | \n5 | \n5 | \n10 | \n10 | \n10 | \n— | \n— | \n5 | \n10 | \n
Calcite | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n20 | \n
Siderite | \n— | \n— | \n— | \n15 | \n— | \n— | \n— | \n— | \n— | \n— | \n
Pyrite | \n— | \n25 | \n35 | \n30 | \n— | \n— | \n10 | \n5 | \n15 | \n— | \n
Sphalerite | \n— | \n10 | \n5 | \n5 | \n— | \n— | \n5 | \n5 | \n10 | \n— | \n
Galena | \n— | \n— | \n— | \n— | \n— | \n— | \n10 | \n5 | \n5 | \n— | \n
Arsenopyrite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Magnetite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Jarosite | \n5 | \n5 | \n— | \n— | \n— | \n— | \n15 | \n5 | \n5 | \n— | \n
Rozenite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Goethite | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Hematite | \n— | \n— | \n— | \n— | \n— | \n— | \n5 | \n— | \n5 | \n— | \n
Alunite | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n5 | \n— | \n— | \n
Gypsum | \n— | \n— | \n5 | \n5 | \n5 | \n— | \n10 | \n— | \n— | \n5 | \n
Semi-quantitative mineralogical composition (wt%) of the studied samples.
La Naya (LN), Monterromero (MR), Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM). Ta: tailings; Co: colluvial; and WS: watercourse sediments.
The nearly homogeneous mineralogical composition of mine tailings is mainly composed of primary gangue minerals from the volcanic or metamorphic host rocks: quartz (30–85 wt%), illite (5–15 wt%), feldspar (5–10 wt%), and chlorite (5–10 wt%). Minor gangue minerals appear in important amounts in some of the areas: siderite (15 wt%) in Brunita. The most important feature of the mineralogical composition of these deposits is the metallic ore mineral contents (25–40 wt%). Significant amounts of pyrite (10–35 wt%), sphalerite (5–10 wt%), and/or galena (5–10 wt%) have been identified in mine tailings (Table 2). These high values are probably related to inefficient metallurgical processing of the benefited ore during the operational years. Because of the re-working of tailing mine areas, San Quintín area shows the lower ore mineral content. In other cases, like Brunita deposit, different ore minerals amounts are associated with the two different mines exploited and dumped: Brunita and Eloy mines. Cinnabar was identified by X-ray diffraction in one borehole sample from San Quintín. Its presence is due to the experimental metallurgical works carried out during the last period of operations in the Almadén mine (Ciudad Real, Spain). Secondary mineralogy is mainly represented by Fe-sulfates (jarosite and rozenite), Ca-sulfates (gypsum), and Al-sulfates (alunite). Fe-bearing sulfide oxidation increases the metal mobility from these materials compared to the levels mainly composed by sphalerite and galena. Significant amounts of secondary gypsum are typically found in this type of sulfide tailings. Fe-oxides and Fe-hydroxides have also been identified.
\nIn some occasions, the ore minerals are not identified by X-ray diffraction, or are identified in low amounts. In these cases, a detailed study by environmental scanning electron microscopy (ESEM) coupled with energy dispersive X-ray analysis (EDX) is necessary. Four examples of the application of ESEM-EDX are presented in Figure 2. Primary sulfide minerals (e.g. galena) identified in low amounts by XRD were also recognized by ESEM-EDX in Monte Romero tailings. Galena occurred as cubic crystals commonly showing octahedron faces. Other sulfide phases such as arsenopyrite, chalcopyrite, and galena were not detected by X-ray diffraction in La Naya tailings. Secondary mineral phases recognized by ESEM-EDX were Fe-oxyhydroxides. Cryptocrystalline Fe-oxyhydroxides frequently occurred around other minerals such as quartz, completely or partially replacing primary sulfides (pyrite and sphalerite). In San Quintín mine, primary ore minerals were not identified by XRD due to the optimized mining works. Pyrite, galena, chalcopyrite, and gangue minerals (barite) were identified by ESEM-EDX (Figure 2). In San Cristóbal tailings, ore and gangue minerals were identified, as well as oxide minerals such as goethite. In Las Moreras samples, low quantities of Fe-oxides (hematite and magnetite) and carbonates (siderite) were identified by ESEM.
\nBackscattered electron (BSE) images: (a) galena crystal from Monte Romero; (b) pyrite crystal from San Quintín; (c) altered faces of a pyrite crystal from San Cristóbal; (d) subidiomorphic magnetite from Las Moreras.
With respect to the colluvial sediments drilled at the San Quintín boreholes, the mineralogical composition was totally composed by primary silicates, as well as watercourse sediments from Las Moreras area (Table 2). The ore mineral content is low enough to be identified by XRD analysis. In contrast, the semi-quantitative mineralogical composition of watercourse sediment from San Cristóbal mine included ore minerals (pyrite, sphalerite, and galena) and secondary sulfates (jarosite and alunite).
\nTotal ferric iron (Fe2O3total), S, and trace element (Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn) concentrations, and pH values from tailing samples of the four mine district are summarized in Table 3. All samples showed a pH range of 2.2–5.6. This value range reflects the typical acid character of stored mine tailings. The composition of all tailing samples is characterized by the high contents of ore-bearing elements in each district: As, Cu, and Pb in the Iberian Pyrite Belt, Pb and Zn in Cartagena-La Unión and Alcudia Valley, and As, Pb, and Zn in Mazarrón. The total ferric iron content is significantly high in analyzed samples from all mine districts due to the omnipresence of Fe-bearing minerals like pyrite. The significantly high contents of potentially hazardous elements like Fe, Cu, Pb, and/or Zn are due to the nature of the mined ore, which is mainly composed of pyrite, chalcopyrite, sphalerite, and galena (Table 2). The highest metal contents are related to the mining history of each district, and the efficiency of the metallurgical processing in the benefited ore during the operational period of time. In the case of San Quintín mine, approximately 3 million tons of minerals from the tailings were re-worked. Then, the lowest Pb and Zn contents are located at the upper levels of the ponds. High Hg content measured in the San Quintín mine tailings is related to the experimental metallurgical works previously cited (Section 2). Significant Hg values were measured in Monte Romero mine related to the formation of a replacive mineralization. Pb (up to 21,130 μg g−1) and Zn (41,841 μg g−1) contents in the tailings from Mazarrón district (Table 3) as well as the significant Ag content from San Cristóbal mine related to the exploitation of Ag-bearing galena deserve special mention.
\nMine district | \nIberian Pyrite Belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|---|---|
Study area | \nLN | \nMR | \nMC | \nBR | \nSQ | \nSC | \nLM | \n
Ag | \nb.d. | \n31–81 (48) | \nb.d. | \n2–8 (4) | \n5–29 (12) | \n20- > 100 (55) | \n14–26 (20) | \n
As | \n191–909 (472) | \n1110–2740 (1650) | \n223–1080 (613) | \n39–385 (195) | \n16–54 (28) | \n400–633 (490) | \n181–630 (366) | \n
Au | \n49–100 (70) | \n311–744 (558) | \n22–86 (53) | \nn.a. | \nn.a. | \nn.a. | \nn.a. | \n
Cd | \nn.a. | \nn.a. | \nn.a. | \n3–67 (22) | \n5–22 (13) | \n4–833 (110) | \n97–373 (186) | \n
Cu | \n306–4511 (998) | \n914–16,582 (4874) | \nn.a. | \n76–323 (179) | \n42–381 (171) | \n121–882 (406) | \n168–356 (230) | \n
Fe2O3 total | \n8.51–14.1 (11.5) | \n2.12–27.2 (14.57) | \n6.11–24.1 (14) | \n22.3–52.6 (38.7) | \n4.5–6.3 (5.6) | \n9.8–22.9 (19.2) | \n17.7–28.5 (23.7) | \n
Ni | \nb.d. | \nb.d. | \nb.d. | \n11–40 (22) | \n29–61 (44) | \n11–41 (21) | \n41–59 (49) | \n
Pb | \n51–222 (133) | \n295–12,610 (4615) | \n71–475 (264) | \n1610–5950 (3211) | \n1510–10,500 (3992) | \n5987–39,877 (21,130) | \n3940–5239 (4665) | \n
S | \nn.a. | \nn.a. | \nn.a. | \n3.8–19.1 (11.8) | \n0.3–1.0 (0.4) | \n3.5–53.0 (6.6) | \n7.1–12.8 (9.3) | \n
Sb | \n31.3–50.4 (35.2) | \n168–861 (338) | \n16.2–67.5 (40) | \n3–54 (30) | \n36–162 (79) | \n108- > 200 (149) | \n54–136 (93) | \n
Sn | \nb.d. | \nb.d. | \nb.d. | \n14–244 (76) | \n3–7 (5) | \n8–121 (83) | \n24–37 (30) | \n
Zn | \n50–260 (143) | \n0.5–6.9 (4) | \n200–970 (511) | \n2020–2112,150 (6315) | \n1250–4470 (2418) | \n2500–11,405 (4101) | \n12,810–41,841 (27,738) | \n
pH | \n2.8–3.5 (3.2) | \n2.5–3.2 (3.0) | \nn.a. | \n2.4–3.7 (3.0) | \nn.a. | \n2.2–3.6 (2.5) | \n2.8–5.6 (4.0) | \n
Fe2O3 total, trace element content, and pH values in the studied tailings.
Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn in μg/g. Fe2O3total and S in wt%. Mean in brackets. b.d.: below detection; n.a.: not analyzed. La Naya (LN), Monterromero (MR), Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM).
The Mina Concepción samples were collected with a manual sampler from the first meter in depth. That is the reason for the lower metal contents to be associated with the more recent and efficient metallurgical works. Related to the Iberian Pyrite Belt district, relevant variations as a function of depth were identified in all of the analyzed element contents from Monte Romero samples. Possible explanations for these variations could be argued: (a) periods with higher mineral benefit, due to improvements in metallurgic processes or to a higher grade mineralogy and (b) a change in the exploitation targets, originally focused on galena (Pb) mining but later re-directed to pyrite (Fe) and sphalerite (Zn) mining due to environmental policies that do not recommend the use of lead in many industrial fields.
\nTotal ferric iron, S and trace element concentrations, and pH values from colluvial and watercourse sediment, and soil samples of the Alcudia Valley and Mazarrón districts are summarized in Table 4.
\nMine district | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|
Study mine | \nSan Quintín | \nSan Cristóbal | \nLas Moreras | \n||
Sample | \nColluvial | \nSoil | \nBlank | \nSediment | \nSediment | \n
Ag | \n0–3 (2) | \n0–3 (1) | \n0.2 | \n21–60 (38) | \n0–7 (3) | \n
As | \n8–24 (17) | \nb.d.-26 (15) | \n11 | \n216–312 (259) | \n15–131 (33) | \n
Cd | \n1–7 (3) | \nb.d.-7 (b.d.) | \nb.d. | \n2–4 (3) | \n1–6 (3) | \n
Cu | \n38–196 (76) | \n5–39 (17) | \n18 | \n70–122 (96) | \n45–482 (177) | \n
Fe2O3 total | \n5.3–9.5 (6.9) | \n1.7–4.7 (3.5) | \n4.2 | \n7.6–33.5 (18.3) | \n4.0–12.0 (5.9) | \n
Ni | \n46–93 (59) | \n13–46 (34) | \n30 | \n10–14 (12) | \n30–61 (47) | \n
Pb | \n79–577 (315) | \n41–1110 (318) | \n34 | \n9395–16,193 (12,800) | \n157–1880 (555) | \n
S | \n0–0.2 (0.1) | \n0–0.3 (0.1) | \n0.01 | \n2.7–4.5 (3.9) | \n0.1–3.0 (1.0) | \n
Sb | \n5–40 (24) | \n2–30 (9) | \n2 | \n98–124 (111) | \n2–48 (14) | \n
Sn | \n3–5 (4) | \n2–3 (3) | \n2 | \n5–10 (7) | \n2–6 (4) | \n
Zn | \n186–844 (503) | \n34–1180 (335) | \n49 | \n815–1660 (1334) | \n452–10,693 (2907) | \n
pH | \nn.a. | \n5.7–6.2 (5.9) | \n5.1 | \n3–3.4 (3.2) | \n7.0–7.7 (7.4) | \n
Fe2O3 total, trace element content, and pH values in the studied colluvial, soil, and watercourse sediment samples.
Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn in μg/g. Fe2O3 and S in wt%. Mean in brackets. b.d.: below detection. n.a.: not analyzed.
The highest contents were found in the pond samples, and the intermediate contents in the colluvial samples from San Quintín area. The ponds were not waterproofed, and hazardous metals from the upper ponds have percolated through the underlying colluvial sediments. Cu, Pb, and Zn contents show significant amounts. Five representative soil samples were analyzed in order to determine the importance of contamination (Table 4). Two mine soil samples show similar metal and As content to the upper tailing samples. The other two were agricultural soil samples, showing significantly lower metal and As content, but higher Hg and Pb contents than the local background sample (blank in Table 4), collected from an agricultural soil 4.5 km to the south-east. However, remarkably high As, Pb, and Zn contents were still found in this background sample, suggesting that the surrounding agricultural soils are also contaminated. In fact, Ag, Cd, Pb, and Zn contents from agricultural soil samples are higher than geochemical baselines reported by [22] for this Spanish region.
\nIn Mazarrón district, both tailings and watercourse sediments showed high amounts of potentially toxic elements, slightly lower at the sedimentary level (3.0–5.5 m depth). The total iron content ranged between 4.0 and 33.5 wt%, Pb was 157–16,193 μg g−1, and the Zn content ranged between 815 and 10,693 μg g−1 (Table 4). Other trace elements that displayed high values were: As, Cu, and Sb. The sediments mark a defined geochemical limit with the tailing unit. The upper mine tailings are significantly concentrated in Fe2O3total and heavy metals, whereas the sediments display marked lower values. This decrease is not absolutely regular, with major peaks in Cu and Zn contents, and minor increases in As, Cu, Fe2O3total, Pb, and Zn. The amount of calcite in the Las Moreras sedimentary unit (Table 2) controls the pH, buffering to within a small range of 7.2–7.7 (Table 4). In turn, the upper tailing unit of Las Moreras shows much lower pH values (2.8–5.6), due to the sulfide content and the complete absence of calcite.
\nThe composition of leakage sample waters of a restored mine pond (Mina Concepción) indicates that these waters represent acid mine drainage, as reflected by their very low pH (<2.6) (Table 5). Trace element contents are very high for Cu and Zn (>2 mg/l), both higher than the EPA’s maximum recommended limits for irrigation waters (0.2 mg/l for Cu and 2 mg/l for Zn [23]). This is also the case for As in samples leaking from the dyke wall and the puddle. Lead also goes beyond the legislation limits in samples from the dyke wall and the drainage pipes. Acid mine drainage was observed in the northern part of the Brunita mine pond (pH < 2.4) (Table 5). The concentrations were very high for Cu, Zn, Cd, Ni, and Fe in the water sample. Results from complementary techniques (ERT), shown in Section 4.3 of this chapter, have confirmed the formation of AMD waters in Mina Concepción and Brunita mines.
\nMine district | \nIberian pyrite belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|---|---|
Study mine | \nMC | \nBR | \nSQ | \nSC | \nLM | \n||
Sampling | \nLK (5) | \nWT | \nBO | \nWT (6) | \nAMD (3) | \nWT | \nWC | \n
As | \n>2000 | \n79 | \n2.6 | \n>1.5 | \n2.3 | \n>2000 | \nb.d. | \n
Cd | \n14–54 | \n483 | \n3.8 | \n0.1–26.6 | \n>3200 | \n6470 | \n1.5 | \n
Cu | \n>2000 | \n>2000 | \n12.3 | \n1–12.6 | \n>8300 | \n>2000 | \n52 | \n
Fe2O3 total | \n>100,000 | \n>100,000 | \n90 | \n30–100 | \n>189,000 | \n>100,000 | \n100 | \n
Ni | \n30–143 | \n559 | \n80.1 | \n1.8–22.3 | \n>3500 | \n5620 | \n120 | \n
Pb | \n0–67 | \n199 | \n8.5 | \n0.4–21.6 | \n>2800 | \n0.5 | \n1 | \n
Sb | \n0–1 | \nb.d. | \n6.1 | \n0.1–3.0 | \n1.3 | \n11.8 | \n1.4 | \n
Sn | \nb.d. | \nb.d. | \nb.d. | \nb.d. | \nb.d. | \n3 | \n2 | \n
Zn | \n>2500 | \n>2500 | \n93.6 | \n57–2520 | \n>550,000 | \n>2500 | \n365 | \n
pH | \n2–2.6 | \n2.4 | \n5.7 | \n6.2–7.1 | \n2.5–4.3 | \n1.8 | \n8.3 | \n
Fe2O3 total, trace element content, and pH values in the water samples.
Values in μg/L. b.d.: below detection. Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM). LK: leakage; WT: water table; BO: borehole; AMD: acid mine drainage; and WC: watercourse.
One water sample was collected at 8 m depth in the borehole from San Quintín mine (Table 5). The high EC and acidic pH values are consistent with water from ore deposits retained in tailing ponds. Three samples showing low pH and significantly high trace element contents indicate AMD flowing from the remaining tailings. AMD was not observed in samples from the watercourse crossing the mining zone (Table 5). pH values in these waters are circumneutral, and EC values and metal contents are significantly lower than in samples from the tailings. Samples collected up- and downstream display the lowest trace element contents. Higher metal contents have been measured in the rest of watercourse samples, denoting that trace element contamination occurs through the mining area.
\nWith regard to the San Cristóbal mine pond, AMD was clearly detected in the water sample: pH < 2, high redox potential, high EC, and Total dissolved salt values. Concentrations of trace elements were very high for As and Cu (>2000 μg/L), Zn (>2500 μg/L), Cd and Ni (>5600 μg/L), and Fe (>100,000 μg/L). Water from the seasonal watercourse of Las Moreras was also analyzed. Significant contents of metallic elements (Cu, Fe, Ni, and Zn) were measured, all beyond the established limits for irrigation waters.
\nA singular case occurs in San Quintín mine where significantly high Hg content has been identified in the mine area. Gaseous Hg emissions were measured from the tailings and surrounding soils (Figure 3). The total gaseous mercury distribution in the studied area significantly changes between summer and winter. The area affected by TGM values up to 100 ng m−3 is restricted to the surroundings of the cinnabar stockpile in winter, but the affected area is 0.16 km2, and extends into the Don Quixote Route in summer. TGM values are lower than the limit recommended for the general population by the World Health Organization (WHO) (1000 ng m−3) for the worst scenario [24]: higher temperature and solar radiation during summer.
\nTotal gaseous mercury (TGM) seasonal distribution in the San Quintín area: (a) summer and (b) winter values. Modified from Martín-Crespo et al. [6].
Multivariate analysis has been carried out on the significant metal contents from samples of tailings (Brunita), tailings + colluvial sediments (San Quintín), and tailings + watercourse sediments + bedrock (San Cristóbal and Las Moreras) (Figure 4). Statistical data processing was carried out using Minitab® 16 software. The multivariate analysis was based on clustering (group average linkage dendrograms, Euclidean distance) of the set of samples and significant trace elements (Ag, As, Cd, Cu, Pb, Sb, Sn, and Zn). The dendrogram of the metals and As in the Brunita tailing samples shows the metallic signature of the district ores: Ag-Pb-Cd-Zn, Cu, and As-Sb-Sn, with As being mainly related to Sb (tetrahedrite-tenanntite mineral group). Ag-Pb-Cd-Zn signature is clearly defined due to the mineral source. In the case of San Quintín, the dendrogram from tailings and colluvial sediments reflects again the metallic signature of the district (Pb-Ag-Sb, Cu, and Zn-Cd to a certain extent [13]), with As mainly related to Sb (bournonite and boulangerite) and Pb-Ag (galena). Some samples display a strong affinity to the Ag-Pb-Sb-As association, whereas other samples display Cd-Zn affinity. The same metallic signature has been obtained from tailings and colluvial sediments, reflecting the same origin for both kinds of samples. The external origin of Hg is reflected by the highest obtained distances. The same occurs for the samples from Mazarrón district, reflecting the metallic signature of the district (Pb-Ag-Sb, Zn-Cd, and Cu to a certain extent [14]) with As being mainly related to the Sb (stibnite and sulfosalts) and Pb and Ag (galena). Some slight differences are displayed in the samples from sediments and bedrocks, particularly the larger range of Cu content.
\nDendrograms (distance Euclidean) of metals: (a) Brunita tailings; (b) San Quintín tailings and colluvial; (c) Mazarrón tailings; and (d) Mazarrón watercourse sediments and bedrocks.
All dendrograms presented in Figure 4 are in good agreement with field data, and mineralogical and geochemical features of tailings and watercourse deposits (Tables 2, 3, 4 respectively). In summary, the metallic signature of the three districts is clearly defined in the samples from tailings and affected sediments.
\nAdditional information about the characteristics of the mine tailing deposits can be obtained from electrical resistivity tomography (ERT) data. The major pieces of information that this method provides are related to both the thickness of the deposits and the occurrence of AMD (both inside the mine pond as flowing out through the dyke or the base). Figure 5 shows several examples of the type of information derived from the application of ERT to different mine ponds, resulting in a valuable tool that completes the information derived from mineralogical and geochemical techniques.
\nERT profiles obtained at four different mine sites. Each profile provides information about the thickness of the mine deposits and the presence or not of acidic water.
As a general rule, the materials that constitute the mine pond infilling are characterized by a medium to fine texture and high-water content. Moreover, due to oxidization of sulfide minerals, the pH of the water stored in the mine pond infilling is frequently acidic (pH < 5) in character. Opposite to this, the host rock where the mine pond is placed should be the host rock of the mineralization, typically metamorphic and/or igneous rocks of coarse texture and extremely low water content. Thus, a high resistivity contrast between the mine pond infilling (low to very low resistivity values) and the host rock (medium to high resistivity values) exists, allowing an accurate characterization of the boundary between both rock types and providing good estimations of the thickness of the mine pond deposits. Different mine pond thickness values and bottom geometries (dashed white lines) are imaged in Figure 5. Monte Romero and San Quintin mine ponds show simple bowl-shaped geometries with a thickness of ~3 and 10 m, respectively (confirmed with data from a borehole in the case of San Quintin mine pond 1), whereas Mina Concepcion and Brunite mine ponds exhibit a stepped bottom geometry with variable thickness (~6–10, and ~5–12 m, respectively). Where different rock units are present below the mine pond, instead of a homogeneous lithology, an estimation of the thickness of the different units can also be obtained. This is the case for San Quintin mine ponds, where a ~10 m thick sedimentary unit of colluvial deposits overlies the metasediments that constitute the regional basement. As mentioned before, a low pH value for the water contained in the mine pond deposits is also frequent, resulting in lower resistivity values in comparison with water with circumneutral pH. Therefore, the occurrence of acidic water inside a mine pond is revealed by extremely low resistivity values, normally lower than 1 ohm m. This is the case for Monte Romero, Mina Concepcion, and Brunita mine ponds where areas of <1 ohm m inside the mine pond correspond to the presence of water with pH ranging from 2 to 3 (see Table 5). On the other hand, the higher (>5 ohm m, and mainly >10 ohm m) resistivity values of the infilling of San Quintin mine ponds are associated with circumneutral pH (Table 5).
\nFinally, the strong resistivity contrast between the acidic water and the host rock results to be very useful to detect if AMD is flowing through the bottom of the mine pond. Where the sealing of the mine pond is correct, the host rock shows homogenous high resistivity values along the whole boundary with the pond infilling, such as the case of Monte Romero and San Quintin mine ponds. However, where AMD flows through the host rock, discrete areas of resistivity values much lower than the ones associated with the host rock are imaged, revealing the occurrence and sense of flow of the AMD. The latter is nicely imaged in both the cases of Mina Concepcion mine pond, where AMD flows from the inner central part of the pond toward the northern edge (confirmed during the field inspection of the dyke that exhibits AMD trough it), and Brunita mine pond, where AMD flows toward the east through the host rock.
\nDumping of huge piles of tailings and debris since the beginning of mining operations at the Brunita area leads to a deep transformation of the landscape with the whole disappearance of the original reliefs [5] (Figure 6). A few studies are recognized in the 1929 and 1946 aerial photographs, but mine ponds were not yet operational. The orography consisted of smooth hills (~40 height difference) separated by NNW–SSE and WNW-ESE ravines (<500 m long). However, by 1956, tailings from the mineral treatment plant were dumped in four stepped ponds along a NNW–SSE valley. Due to the continued mine activity, the tailings leveled the land up to the highest pond between 1973 and 1981, even burying the adjacent hills, and debris from the mine quarry began to cover the easternmost NNW–SSE ravines and hills. At this stage, there were two ponds, a large one with three stepped dikes and a small one in the highest part of the valley. The landslide scar in the dikes and the back sunken area, due to the flowage of tailings that caused the disastrous 1972 flash flood, are also visible. The sunken area was crisscrossed by gullies due to subsequent water erosion. Although the mine was closed in 1981, the reinforcement of the pond perimeter, with the sealing of the broken area, and the strong accumulation of debris to the east completely buried the ravines and hills and leveled the topography by 2004. Changes in the gully drainage pattern and the retreat of scarps indicate that tailing erosion persists.
\nLandscape evolution in the surroundings of the Brunita mine pond from aerial photographs taken in: 1929 (Ruiz de Alda photogrammetric flight); 1956 (Geographic Service of the Spanish Army); and 1981 and 2004 (Spanish National Geographic Institute). Blue lines, mine ponds in 1956; orange lines, mine ponds in 1981; brown dashed lines, badlands and landslide scar in 1981; and white line, spoil tips in 2004.
Eolian dispersion of contaminants from a tailing sand dune (Figure 1) is the most important environmental concern at the San Quintín area [21]. By 1977, the mine pond was divided into three sectors through dikes. One of these dikes will be the obstacle over which the dune will be developed when the mine operations ceased. The dune has been growing and migrating by the dominant winds since 1984. As the tailings are not replaced, the dune is losing its pollutant particles by dispersion toward the nearby river and agricultural soils, so its decrease in size or disappearance is expected.
\nThe results obtained from the mineralogical and geochemical characterization of the samples collected from tailings, soils, air, water, and watercourse sediments allow identifying the potential environmental concerns that would affect the different mine districts. These potential environmental concerns can be classified according to three main types: (a) ecosystem risks, (b) human health risks, and (c) physical hazards. Ecosystem risks are mainly related to the negative effect of both the acidic water and metals. To properly evaluate the potential volume of metals susceptible to produce negative effects on the ecosystems, the mineralogical and geochemical characterization of the tailings is crucial. From the cartographic (area) and ERT (general geometry and thickness) studies, an infilling volume of 912,000 m3 has been calculated for the Brunita mine pond. The maximum amounts of potential contaminants were obtained taking into account the mean content of potentially toxic elements (Table 3), the previously calculated volume, and the mass of the waste. A mean bulk density of 2.65 g/cm3 was calculated from the mineral particle density and assuming a porosity of 40%, which is the value for mine ponds originating from the processing of this type of deposit. From the mean trace element content shown in Table 3, the Brunita impoundment contains more than 24,250 t of potentially toxic elements such as (470 t), Cd (52 t), Cu (430 t), Ni (53 t), Pb (7753 t), Sb (71 t), Sn (184 t), and Zn (15,245 t). Release of these amounts of toxic elements would be catastrophic for the environment and the community (death, serious material damage, coastal areas, and farm land). Similar studies in the Iberian Pyrite Belt district show amounts of potentially toxic elements of 5900 t in La Naya, and 2100 t in Monte Romero ponds.
\nIn order to evaluate the contaminating degree of tailings, the geo-accumulation index (Igeo) was calculated. Müller [25] defined Igeo and enabled the assessment of sediment contamination by comparing current and pre-industrial concentrations of heavy metals. This index is mathematically expressed as Igeo = log2 Cn/1.5Bn, where Cn is the concentration of an element in the sample and Bn is the background concentration of the corresponding element in the Earth’s crust, according to [26]. Müller [25] suggested six descriptive classes for this index: uncontaminated (Igeo ≤ 0), uncontaminated to moderately contaminated (0 < Igeo < 1), moderately contaminated (1 < Igeo < 2), moderately to strongly contaminated (2 < Igeo < 3), strongly contaminated (3 < Igeo < 4), strongly to extremely contaminated (4 < Igeo < 5), and extremely contaminated (Igeo > 5). The Igeo index was calculated for tailings from Brunita, and tailings and colluvial from San Quintín (Figure 7). As, Cd, Pb, Sb, and Zn from Brunita tailings show extreme contamination (Igeo > 5), whereas Cu and Sn show moderate to strong contamination (1 < Igeo < 4). Ag is classified as a nonpollutant. The contamination classes are two levels higher than those obtained for similar tailings in Spain [6]. Cd, Hg, Pb, and Sb show extreme contamination (Igeo > 5), and As and Zn show moderate to heavy contamination (1 < Igeo < 5) in the tailings and colluvial sediment from San Quintín. Cu shows moderate to heavy contamination, and Ag is classified as unpolluted (Figure 7). Sutherland [27] proposed the enrichment factor (EF) to assess the level of contamination and the possible anthropogenic impact. To identify anomalous metal concentration, geochemical normalization of the heavy metal data to a conservative element, such as Fe, was employed (geochemical normalization). EF was calculated using the formula EF = (M/Fe)sample/(M/Fe)background, where (M/Fe)sample is the ratio of metal to Fe concentrations in the sample and (M/Fe)background is the ratio of metal to Fe concentrations of the background (blank; Table 4). Sutherland [27] proposed five contamination categories: minimal enrichment (EF < 2), moderate enrichment (2 < EF < 5), significant enrichment (5 < EF < 20), very high enrichment (20 < EF < 40), and extremely high enrichment (EF > 40). The San Quintín samples show very high to extremely high enrichment in Ag, Cd, Hg, Pb, Sb, and Zn. EF values for As are significantly lower than for the rest of elements, reflecting the lack of As-bearing minerals. Figure 8 shows Igeo and EF for San Quintín representative soil samples. Agricultural soil samples (S-06 and S-53) and the background sample (S-00) show the same features: they are moderately contaminated by As, Cd, Pb, and Sb and not contaminated by Ag, Cu, and Zn. The Igeo for Hg was strong for agricultural soils and extreme for mine soils. Agricultural soil samples (S-06; S-53) and mine soil sample (S-37) show minimal or moderate EF for Ag, As, Cd, Cu, Pb, Sb, and Zn. The EF values for Hg were significant or very high for agricultural soils and extremely high for mine soils. These data highlight the significant metal contents of the mine site, which can become especially hazardous due to eolian dispersion.
\n(a) Geoaccumulation index for Brunita tailings, (b) geoaccumulation index for San Quintín tailings and colluvial; (c) enrichment factor for San Quintín tailings and colluvial. Modified from Martín-Crespo et al. [5, 6].
Representative soils from San Quintín mine area: (a) geoaccumulation index and (b) enrichment factor. Modified from Martín-Crespo et al. [6].
The occurrence of AMD inside the tailings and its flow through the mine deposits toward the surrounding environment represents a major risk for the ecosystems. In this sense, several zones have been affected by metal mobilization though acidic water and its percolation from tailings to riverbed deposits, resulting in the affection of watercourses (Mina Concepcion) and groundwater (Brunita). Consequently, Mazarrón and Iberian Pyrite Belt districts show water metal contents beyond the EPA’s maximum recommended limits in irrigation waters. Where AMD is confined inside the mine tailings (Monte Romero and San Quintín), metal mobilization also occurs but the affection to the environment is limited. However, the large volume of acidic water with high metal contents stored at these deposits represents a major potential ecosystem risk. If a failure of the dam occurs, or the sealing of the mine pond fails, the ecosystem, watercourses, and riverbed sediments would be largely affected by the release of acidic water and its dissolved hazard metals.
\nRegarding human health risks, they are mainly associated with the eolian dispersion of contaminants. San Quintin mine ponds represent the area with the higher risk due to the combined effect of both the eolian dispersion of metals from the dune developed on the mine tailings, affecting the surrounding agricultural soils, and the gaseous mercury emissions. As previously mentioned in Section 4.2.2, agricultural soils surrounding San Quintín mine display As, Cd, Pb, and Zn contents higher than geochemical baseline. Therefore, they are contaminated and can be considered as a potential human health risk by the metal input to the olive tree crops. Nevertheless, metal contents in water from the watercourse crossing the mining area are below recommended limits for irrigation waters, denoting not significant affection by AMD. Although this zone is not remediated and not in a condition for public transit, the San Quintín mine has been reported as one of the points to be visited on the longest Eco-tourist Itinerary in Europe, named “Don Quixote Route, a place for adventure”. Section four of the route crosses the San Quintín mining area, exhibiting ruinous mine structures. This mine has become a representative example of the socio-economic and cultural benefits that its restoration could confer to this zone. Although these types of tourist initiatives are remarkable in terms of geological heritage, a previous characterization and reclamation study has not been carried out.
\nPhysical hazards are mainly related to the presence of open shafts and unstable ponds and have also been identified in the different mine districts. Alcudia valley and Mazarrón districts contain many abandoned open shafts and tunnels. Unstable ponds have also been identified as in the case of Brunite mine pond, where a previous dam failure occurred [5]. Similar to this, the outflow of acidic water through the dam of Mina Concepcion mine pond would represent a source of instability resulting in a potential physical hazard.
\nThis work revealed that the joint use of mineralogical, geochemical, and geophysical techniques can provide an environmental characterization of abandoned mine sites, allowing for estimations of potential pollution and the extent of affected zones.
\nSignificant potentially hazardous element contents have been identified in all studied mine districts, not only in the mine tailings but also in the underlying colluvial and alluvial sediments and surrounding soils. Mineralogical and geochemical signatures of the ore mineralization are clearly recognized in all analyzed samples. Pyrite, sphalerite, and galena are the main ore minerals identified in the mine tailings. Gangue minerals (quartz, illite, feldspar, and chlorite) and secondary minerals (Fe-sulfates, gypsum, and Fe-sulfates) have also been identified by XRD and/or ESEM-EDX. Significantly high contents of As, Cu, Pb, and Zn have been identified in the majority of the mine tailings, reflecting the related environmental hazards associated with all of these abandoned deposits. Moreover, significant potentially toxic element content has been analyzed in tailings from restored mine pond like Mina Concepción. Agricultural soil samples show lower metal and As content but higher Hg and Pb content than in the background sample in the San Quintín area. AMD has been clearly identified not only flowing from the remaining tailings, but also from a restored mine pond, denoting that environmental hazard persists.
\nERT provides valuable additional information about the mine deposits. The strong resistivity contrast between the infilling and the underlying rock allows obtaining both the thickness of the infilling as the geometry of the bottom mine pond. Moreover, if the infilling deposits contain water, the resistivity values provide information about both the acidic character of the water and the occurrence or not of AMD flow outside the mine pond. The mapping of mine deposits from time series of aerial images reveals the strong impact of mining on the landscape due to the dumping of large amounts of polluting wastes and their mobilization thereof to the surrounding areas by several geological processes (mass movement, gully erosion, and eolian dispersion).
\nMajor environmental hazards are associated with different main pathways (wind erosion and water flows) and several receptors (bathing waters, agricultural soils, humans, and sediments) depending on the specific mine area. In summary, this type of abandoned deposits need to be characterized, monitored, and restored in order to avoid mobilization of tens thousands of tons of potentially hazardous elements.
\nThis work has been accomplished on the frame of projects URJC-RNT-063-1, URJC-CM-2006-CET-0636, and URJC-CM-2008-CET-3644 funded by Comunidad de Madrid and Universidad Rey Juan Carlos. The Open Access Publishing Fee has been funded by the Universidad Rey Juan Carlos.
\nThe authors declare no conflict of interest in this chapter.
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