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Journal of Environmental Sciences 93 (2020) 120–12 8
Contents lists available at ScienceDirect
Journal of Environmental Sciences
journal homepage: www.elsevier.com/locate/jes
Current situation and forecast of environmental risks of a typical
lead-zinc sulfide tailings impoundment based on its geochemical
characteristics
Tao Chen
1
, Zi-Ang Yan
2
, Damao Xu
3
, Minghui Wang
3
, Jian Huang
1
, Bo Yan
1 , 3 , ∗,
Xianming Xiao
4
, Xunan Ning
2
1
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of
Environmental Theoretical Chemistry, South China No rmal University, Guangzhou 510 0 06, China
2
School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou 510640, China
3
State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
4
China University of Geosciences, Beijing 10 0 083, China
a r t i c l e i n f o
Article history:
Received 1 September 2019
Revised 18 March 2020
Accepted 18 March 2020
Available online 10 April 2020
Keywo rds:
Pb-Zn tailings impoundment
Geochemical characteristics
Distribution of the metals
Weathering process
Environmental risk assessment
a b s t r a c t
The potential environmental implications of a Pb (Lead)-Zn (Zinc) sulfide tailing impoundment were
found to be dependent on its geochemical characteristics. One typical lead-zinc sulfide tailing impound-
ment was studied. Ten boreholes were set with the grid method and 36 tailings were sampled and tested.
According to the results of metal content analysis, the tailing samples contained considerably high con-
tents of heavy metals, ranging from 6.99 to 89.0 mg/kg for Cd, 75.3 to 602 mg/kg for Cu, 0.53% to 2.63%
for Pb and 0.30% to 2.54% for Zn. Most of the heavy metals in the sample matrix showed a uniform
concentration distribution, except Cd. Cd, Pb, Zn, and Mn were associated with each other, and were con-
sidered to be the dominant contributors based on hierarchical cluster analysis. XRD, SEM and XPS were
employed for evaluation of the tailing weathering characteristics, confirming that the tailings had un-
dergone intensive weathering. The maximum potential acidity of the tailings reached 244 kg H
2
SO
4
/ton;
furthermore, the bioavailability of heavy metals like Pb, Cd, Cr, Cu, and Zn was 37.8%, 12.9%, 12.2 %, 5.95%,
and 5.46% respectively. These metals would be potentially released into drainage by the weathering pro-
cess. Analysis of a gastrointestinal model showed that Pb, Cr, Ni and Cu contained in the tailings were
high-risk metals. Thus, control of the heavy metals’ migration and their environmental risks should be
planned from the perspective of geochemistry.
©2020 Published by Elsevier B.V. on behalf of The Research Centre for Eco-Environmental Sciences,
Chinese Academy of Sciences.
Introduction
Numerous heavy metals have been detected in Pb-Zn sulfide
mines; these metals occur in minerals such as sphalerite, galena,
chalcopyrite, nickel pyrite etc. When the minerals are processed,
these metals are delivered into the tailing impoundment as associ-
ated elements in the tailings. Multiple heavy metals present in tail-
ings could severely threaten ecological security and human health
due to their acute toxicity and carcinogenicity ( Yang et al., 2014 ;
Brooks et al., 2019 ). The extended migration of metals from tailing
∗Corresponding author at: Building 3, South China Normal University, Guangzhou
University City, Panyu District, Guangzhou, China.
E-mail addresses: tao.chen@m.scnu.edu.cn (T. Chen), bo.yan@m.scnu.edu.cn
(B. Ya n) .
impoundments has been studied extensively. In these works, the
studied samples included the soil adjacent to the impoundment,
as well as vegetation and organisms (including microorganisms)
downstream of the impoundment ( Wei et al., 2009 ; Yan g et al.,
2019 ; Wang et al., 2018 ; Zhang et al., 2018 ). However, the endoge-
nous migration of heavy metals contained in the impoundment is
not sufficiently understood ( Zhang et al., 2016 ; Azhari et al., 2017 ;
El et al., 2017 ; Zheng et al., 2019 ). Metals’ endogenous migration
is often dependent on their geochemical characteristics, including
the spatial distribution ( Chen et al., 2018 ), mineral character and
buffering components ( Romero et al., 2014 ; Roseby et al., 2017 ).
Once a typical impoundment is chosen, metals’ geochemical char-
acteristics should be studied intensively. Thus, the detailed distri-
bution patterns, geochemical relationships and environmental im-
plications of heavy metals in mine tailings should be investigated.
https://doi.org/10.1016/j.jes.2020.03.010
1001-0742/© 2020 Published by Elsevier B.V. on behalf of The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences.
T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128 121
Fig. 1. Sampling points (A1, B1-B3, C1-C4, D1-D2) of the impoundment (Drill No: drilling number; FK TI: Fan -Kou tailings impoundment).
It has been reported that the negative environmental consequences
associated with metalliferous tailings deposits depend heavily on
their geochemistry and composition ( Lindsay et al., 2015 ). So far,
sample collections have concentrated on surface samples ( El et al.,
2017 ), while the tailings have piled up for decades and some are
perennial; thus, inadequate information exists on the vertical dis-
tribution, geochemical forms and mineralogical characteristics of
the heavy metals bound in the mine tailings. Thus, it is meaningful
to focus on the spatial patterns of heavy metals in the vertical pro-
files of mine tailing piles. Also, it has been well demonstrated that
the bio-accessibility of heavy metals is an indicator of the potential
environmental risk ( Pascaud et al., 2014 ; Unda-Calvo et al., 2017 ;
Xu et al., 2019 ). Metals’ bio-accessibility would be promoted by the
weathering of the minerals, while the migration would be buffered
by the presence of alkaline materials like Ca, Mg etc. As a conse-
quence, qualitative prediction of the potential environmental im-
plications of HMs contained in the stored tailings is of great prac-
tical significance ( Meunier et al., 2010 ; Zhang et al., 2014 ; Pascaud
et al., 2014 ).
This study was initiated with the following detailed objectives:
(1) to measure metal contents in legacy tailing samples and deter-
mine the distribution patterns of multiple elements in tailing pro-
files; (2) to identify the ecological risks of heavy metals contained
in the tailings; (3) evaluate the potential risks of heavy metal mi-
gration from a mineralogical perspective.
1. Materials and methods
1.1. Sampling site description and sampling procedure
The present study was focused on the FanKou Pb (Lead)-Zn
(Zinc) mining district, one of the largest and most representative
polymetallic sulfide mines in Asia, north of Guangdong Province,
South China. The mining district is globally known for its prof-
itable mineral deposits and tremendous production, and the abun-
dant sulfide ores have been continuously mined and smelted
to extract economically valuable polymetallic sulfides. An aban-
doned tailing impoundment (between 113 °39
18
E-113 °39
54
E
and 25 °02
24
N-25 °03
18
N), with reserves of approximately 4.92
million tonnes of Pb-Zn mine tailings, was selected as the targeted
impoundment. A simplified geological map is depicted in Fig. 1 .
Ten boreholes ((labeled as R
C where R (Rows) = A, B, …, F); C
(Columns) = 1, 2, …, 6) were drilled using a mechanical excavator
within the dried beach of the impoundment. The boreholes were
set by a grid distribution point method. The 36 core tailing samples
were collected at regular depth increments of 5 m from the top
down, to the maximum bottom depth (labeled as R
C
-t from top to
bottom, R
C
(borehole number) = A1, B1, …, D2; t (depth) = a, b, …,
f). The bottom depth was determined by the underlying surface of
the impoundment. When the final depth of the tailings profile was
less than 5 m, the core samples were combined to form an indi-
vidual sample. All samples were air-dried at ambient temperature
in the laboratory and lightly crushed, followed by screening with
a 2-mm nylon sieve to remove coarse debris. Thereafter, all the
sieved samples were further mechanically pulverized and homoge-
nized adequately using an agate mortar and pestle so that all par-
ticles could pass through a 0.15-mm nylon sieve for further chemi-
cal analysis. Shortly after processing, one representative composite
tailing sample (RTS) was prepared by the coning and quartering
method to conduct the geochemical form, morphological and min-
eralogical investigations.
1.2 . Analytical methods and sample characterization
Tailings were wet-digested in by microwave digestion equip-
ment (WX-80 0 0, CEM, America) by Method 3051A. The metal con-
tents in the digestion and also the extraction solutions were deter-
mined using a flame atomic absorption spectrophotometer (AAS,
ZA30 0 0, Hitachi, Japan), while trace elements at significantly low
contents were detected through inductively coupled plasma mass
spectrometry (ICP-MS, 7500, Agilent, America), and major ele-
ments like Ca, Mg, Al were determined with an inductively cou-
pled atomic emission spectrometer (ICP-AES, ICAP-70 0 0, Thermo,
America).
Water content was determined by drying the sample at 105 °C
for 24 hr. pH was determined in a 1:2.5 ( W / V ) ratio of dry tail-
ing sample to deionized water suspension using a pre-calibrated
digital pH meter (pH-3C, Shanghai Precision and Scientific Instru-
ment Ltd., China). Total sulfur ( C
tot-sulfur
, %) and organic carbon
122 T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128
Fig. 2. Statistical distributions of metal contents (The outer box indicates mean ±standard error, the whiskers indicate the 95% confidence interval and data points show
the measured concentration of heavy metals in 36 samples).
(TOC) contents were determined by combustion and infrared de-
tection using an elemental analyzer (MicroCube Elementar, Ger-
many). According to the requirements of standard EN1744-1, sul-
fate contents ( C
sulfate
,%) were determined by gravimetric measure-
ments of BaSO
4 precipitate by adding excess BaCl
2 after extraction
with 10% dilute HCl. Sulfide sulfur ( C
sulfide
,%) contents were cal-
culated as the difference between C
tot-sulfur and C
sulfate ( Lawrence,
2010 ). The maximum potential acidity (MPA, kg/ton) of the tailing
was calculated with the C
tot-sulfur
, and the inherent buffering ca-
pacity was determined using the acid-neutralization capacity (ANC,
kg/ton) method of Lawrence et al. (2010) , where the tailing sample
was reacted with a known excess of HCl, then back titrated with
NaOH to quantify the acid-neutralizing capacity of the sample. The
net acid-producing potential (NAPP, kg H
2
SO
4
/ton) was calculated
using Eqs. (1) and ( 2 ) ( Çelebi and Öncel, 2016 ; Wang et al., 2017 ;
Roseby et al., 2017 ):
MPA = C
tot-sulfur
×30.625 (1)
NAPP = MPA- ANC (2)
The particle-size distribution was measured using a laser
diffraction Particle Analyzer (JL-1177, Chengdu Jingxin Powder Test-
ing Equipment Co., Ltd., China). The primary mineralogical phases
were identified by an X-ray diffractometer (XRD, Brucker D8, Ger-
many) in the continuous scan mode using Cu K αradiation, within
a range of 3 °< 2 θ< 80 °, with a 0.01 °2 θstep size and an analy-
sis time of 1 sec per step. The powdered samples were further in-
vestigated by optical microscopy on polished sections (DMRX, LE-
ICA, Germany). Scanning electron microscopy (SEM) was employed
for mineralogical identification (S-4800, Hitachi, Japan). X-ray pho-
toelectron spectroscopy (XPS) measurements were performed on
an ESCALAB 250X (Thermo-Fisher Scientific, America) spectrome-
ter with monochromatic Al K αradiation.
The chemical fractions of heavy metals were determined
through the five-step sequential leaching procedure (Tessier
method) ( Tessier et al., 1979 ; Lee et al., 2014 ; Yang et al., 2014 ).
Furthermore, the bio-accessibility of heavy metals was determined
through the human gastrointestinal tract model ( Oomen et al.,
2002 ; Ruby et al., 1999 ); the gastric phase (GP) and the gastroin-
testinal phase (GIP) of the metals were tested.
2. Results and discussion
2.1. Geochemical data and statistics
The depth of the ten boreholes ranged from 7. 20 m (D1) to
26.50 m (C2). The tailings profiles consisted mainly of grey and
fine silty clay, with a color gradient from reddish to dark grey.
The basic descriptive statistics for heavy metal contents in tail-
ings samples are shown in Fig. 2 . The mass abundance of heavy
metals followed the following sequence: Fe (1.21 ×10
5 mg/kg)
> Al (2.25 ×10
4 mg/kg) > Pb (0.958 ×10
4 mg/kg) > Zn
(0.617 ×10
4 mg/kg) > Mg (0.545 ×10
4 mg/kg) > Mn (564 mg/kg)
> Cu (210 mg/kg) > Cr (40.6 mg/kg) > Cd (18.5 mg/kg) > Ni
(12.1 mg/kg). There was no pronounced difference between the to-
tal metal contents, except for Cd; its contents varied over a broad
range among sample matrices (from 6.99 to 89.0 mg/kg), with the
maximum concentration being 12.7 times higher than the mini-
mum concentration. This result might be related to the contin-
uous oxidation and weathering of the sulfide minerals, such as
pyrite, chalcopyrite, sphalerite, and galena, which might accelerate
the migration of heavy metals. Low coefficients of variation (CV)
were found for Cr, Ni, Cu, Pb, Mn, Fe, Mg, and Al, suggesting in-
significant variations in contents over all profiles and depths. Sim-
ilar geochemical variations were observed for Mn (CV = 0.19), Fe
(CV = 0.05), Mg (CV = 0.18) and Al (CV = 0.16); that is, they can
T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128 123
Tabl e 1
Distribution characteristic of metals within the different tailing boreholes.
Borehole Depth
∗
Major metal (%) Trace metal (g/ton)
Mg Al Fe Pb Zn Mn Cu Cr Cd Ni
A1 a 0.422 1.99 12.0 0.697 0.400 426 144 37.3 12.1 8.40
B1 a 0.549 2.61 11.6 0.800 0.485 637 172 45.1 14.2 10.4
B2 a 0.467 1.83 13.1 0.681 0.417 574 118 35.9 12.0 9.50
B3 a 0.782 1.85 11.8 0.583 0.302 439 184 29.2 8.8 7.96
C1 a 0.491 1.77 13.2 1.66 1.34 619 155 39.2 43.1 10.7
C2 a 0.550 2.37 12.8 0.949 0.631 745 234 49.6 20.0 18.9
C3 a 0.455 1.87 11.8 0.877 0.574 654 172 35.5 18.8 11.5
C4 a 0.441 2.66 13.2 0.792 0.424 491 196 89.5 11.5 15.9
D1 a 0.496 1.75 11.6 1.57 1.35 579 192 37.6 43.0 16.3
D2 a 0.485 2.41 13.4 1.31 0.948 671 233 39.5 31.2 9.2
A1 b 0.437 1.47 12.9 0.875 0.865 477 110 30.5 26.0 10.3
B1 b 0.538 2.60 13.5 0.810 0.431 642 181 45.0 10.7 11.4
B2 b 0.525 2.55 11.7 0.885 0.525 739 198 56.0 15.9 13.5
B3 b 0.649 2.06 11.9 0.799 0.483 477 266 35.4 14.2 9.7
C1 b 0.522 2.39 11.6 0.887 0.478 607 188 35.9 12.6 15.7
C2 b 0.471 2.46 12.2 0.961 0.653 632 199 45.8 19.0 20.2
C3 b 0.573 2.33 13.2 0.810 0.454 590 171 42.3 12.9 10.8
C4 b 0.492 2.64 13.2 0.801 0.461 586 208 87.4 13.0 21.9
D2 b 0.541 1.60 12.3 2.63 2.54 848 297 36.5 89.0 12.1
A1 c 0.593 1.77 13.1 0.527 0.457 646 75 28.5 13.7 10.8
B1 c 0.507 2.22 12.0 0.731 0.425 589 156 49.6 12.3 8.70
B2 c 0.910 1.86 13.5 0.557 0.317 471 208 31.5 9.3 10.4
B3 c 0.588 2.27 11.8 0.782 0.305 493 147 28.2 7.6 8.80
C2 c 0.483 2.71 12.3 1.31 0.864 547 249 34.7 22.7 16.4
C3 c 0.497 2.90 12.3 1.03 0.624 726 225 44.8 17.7 14.1
C4 c 0.672 2.11 11.7 1.10 0.631 489 352 36.1 18.2 12.7
D2 c 0.491 2.68 12.7 1.62 1.08 664 221 60.8 32.8 10.0
A1 d 0.524 2.28 13.3 0.681 0.335 367 161 28.9 8.3 8.17
B1 d 0.521 2.53 13.1 0.843 0.323 496 190 35.1 7.0 9.52
B2 d 0.513 2.45 11.9 0.805 0.361 464 169 35.6 10.0 10.5
C2 d 0.542 2.72 13.4 0.816 0.452 544 188 36.0 10.9 9.94
C3 d 0.613 2.49 12.8 0.817 0.333 544 163 30.9 8.2 11.4
B2 e 0.597 2.01 12.7 0.920 0.550 414 278 30.3 14.6 13.5
C2 e 0.632 2.25 12.0 1.03 0.713 489 602 34.3 20.7 15.3
C3 e 0.541 2.29 12.6 0.800 0.385 451 207 31.4 9.9 10.3
C2 f 0.583 2.14 12.0 0.858 0.487 469 365 30.4 13.1 12.1
∗a, b, c, d, e, and f mean 0–5 m, 5–10 m, 10–15 m,15–20 m, 20–25 m, 25–30 m depth interval, respectively.
serve as the most conservative elements because the most abun-
dant contents tend to be uniform both regionally and locally. The
ratio of alkaline oxides to acidic oxides ((CaO + MgO)/(SiO
2
+ Al
2
O
3
))
was 0.733; thus, the tailings were still alkaline. Furthermore, sol-
uble alkaline elements, such as Na and K, could still be detected.
The acid-neutralizing capacity and the NAPP should be considered
in assessing its weathering process ( Chen et al., 2018 ).
2.2. Distribution characteristics of major and trace elements in mine
tailings
Quantitative comparison of the geochemical characteristics ex-
hibited uniform elemental distributions for all the studied profile
tailings, mostly associated with spatial heterogeneities in composi-
tional distribution. The spatial distribution of metals contained in
the tailings is depicted in Table 1 . The contents of Cd, Pb and Zn
were generally in the ranges of 6.99–43.0 6 mg/kg, 0.53%-1.6 6% and
0.3%-1.35%, respectively, with the highest contents of 2.63%, 2.54%
and 89.01 mg/kg occurring in the 5–10 m interval (Sample D2-
b). Major metals were evenly distributed: 367–848 mg/kg for Mn,
11.6%-13.5% for Fe, 0.420%-0.913% for Mg and 1.47%-2.90% for Al.
The high contents of Fe mainly came from the host-rock-forming
siltstone and limestone, whereas Mg and Al were consistent with
the trace existence of dolomite and muscovite. Relatively lower
metal contents were obtained in the upper layer (a layer) of the
tailings profile. Due to the improvement of production technology,
the amount of metal resources in the tailings should be gradually
reduced, so that there is a vertical distribution phenomenon with
high metal content at the bottom. However, the total metal con-
tents did not show an expected or obvious dependency on increas-
ing depth. Two exceptions occurred with Cd and Mn, whose con-
tents decreased along the depth gradient. This phenomenon might
be caused by endogenous migration. The interpretation for the dis-
tribution was that the chemical fractions and geochemical charac-
teristics of metals, as well as unfavorable physicochemical proper-
ties, fine physical textures and mineralogical composition of mine
tailings, had a profound influence on the remobilization and mi-
gration of heavy metals across these tailing profiles ( Moncur et al.,
2015 ; Saryg-Ool et al., 2017 ).
2.3. Process mineralogical analysis of the representative tailings
sample
The main physicochemical properties of the representative
composite tailing sample (RTS) are presented in Table 2 . The
C
tot-sulfur
, TOC, and the moisture content of the RTS were 4.98%,
3.13%, and 2.14%, respectively.
The grain size distribution of the representative composite tail-
ing sample (RTS) is shown in Fig. 3 a; the tailings were composed
of fine particles generally between 0.050 and 92.6 μm. As shown
in Table 2 , the main mineralogical composition provided reason-
able evidence for considerable amounts of SiO
2 (33.1%) and CaO
(13.1%) as well as Fe
2
O
3 (21.4%). Al
2
O
3 (5.17%), K
2
O (1.96%) and
MgO (1.08%) also occurred in moderate amounts. Some detectable
minerals in mine tailings might be conventionally inherited from
the dominant ore compositions. As indicated by the XRD patterns
in Fig. 3 b, the principal crystalline phases of the tailings were
quartz, calcite, pyrite, gypsum, dolomite, muscovite and ettringite,
124 T. Chen, Z.-A. Ya n and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128
Tabl e 2
Physicochemical, acid-base accounting and chemical composition analysis for representative composite tailing sample.
Normal Index Value (%, except pH) Composition Content (%) Composition Content (%)
pH 7.39 SiO
2 33.1 MnO 0.122
Moisture 2.14 Fe
2
O
3 21.4 CuO 0.0393
TOC 3.13 MgO 1.08 ZnO 1.31
LOI 14.1 Al
2
O
3 5.17 As
2
O
3 0.156
C
tot-sulfur 7.98 K
2
O 1.96 P
2
O
5 0.138
C
sulfate 4.57 Na
2
O 0.185 Cr
2
O
3 0.0124
C
sulfide 3.41 CaO 13.1 SrO 0.0127
Ga
2
O
3 0.0116 PbO 1.22
Rb
2
O 0.00731 Cl 0.0296
TiO
2 0.352 SO
3 19.9
TOC: total organic carbon content; LOI: weight loss during the 110 0 °C ignition.
Fig. 3. Geochemical characteristics of the representative composite tailing sample. (a) Particle size distribution; (b) XRD analysis; (c) XPS spectra of Fe2p; (d) XPS spectra of
S2p.
which were further confirmed by reflected light microphotographs
and SEM images ( Fig. 4 ). No reflections of galena and sphalerite
were observed in the XRD patterns, while they were found by the
reflected light microphotographs. In contrast, gypsum was not de-
tectable in the SEM images; however, its presence was observable
in XRD analyses. A detailed description of sulfide oxidation and the
weathering process of AMD formation are outlined in a previous
publication ( Lindsay et al., 2015 ). The self-progressive dissolution
of pyrite is the primary mechanism for the generation of strong
acids (H
2
SO
4
); oxy-hydroxide iron precipitates are depicted in Re-
actions ( 3 ) and ( 4 ):
FeS
2(s)
+ 3.75O
2(g)
+ 3.5H
2
O
(l)
= Fe(OH)
3(s)
+ 2SO
4
2 −+ 4H
+
(aq)
(3)
CuFeS
2
+ 2.75 O
2(g)
+ 1.5 H
2
O
(l)
= Cu
2 + (aq)
+ Fe(OH)
3(s)
+ 2SO
4
2 −(aq) (4)
Some elements structurally incorporated by adsorption or pre-
cipitation onto the residual ferric minerals casually cover and ad-
here to the primary mineral surfaces, alleviating their oxidative
dissolution rates. The mineralogical transformation of Ca-bearing
carbonates to gypsum by consuming H
+ occurs by Reactions ( 5 )
and ( 6 ) ( Lindsay et al., 2015 ; Liu et al., 2017 ):
CaCO
3(s)
+ H
2
SO
4(aq)
+ H
2
O
(l)
= CaSO
4
·2H
2
O
(s)
+ CO
2(g) (5)
CaMg(CO
3
)
2(s)
+ 2H
2
SO
4(aq)
+ 9H
2
O
(l)
= MgSO
4
·7H
2
O
(s)
+ CaSO
4
·2H
2
O
(s)
+ 2H
2
CO
3(aq) (6)
T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128 125
Fig. 4. Reflected light microphotographs and SEM images of the representative composite tailing sample (a–d: Optical microscopy of a polished thin-section; e-f: Represen-
tative SEM images).
XPS spectra of the RTS were studied to identify its surface
weathering. The valence states of typical elements (Fe and S) are
shown in Fig. 3 c and d. The deconvolution of the Fe2p peaks, in-
cluding two shakeup satellites, yielded a peak at 706.76 eV for
FeS
2
, a peak at 711.06 eV for Fe
2
O
3
, and a peak at 714.35 eV for
Fe
2
(SO
4
)
3
after fitting analysis ( Fig. 3 c). The S2p peaks showed two
distinct fitting peaks at 162.08 and 168.89 eV, corresponding to the
metal sulfides and metal sulfates, respectively ( Fig. 3 d). These re-
sults further confirmed that the mine tailings had undergone in-
tensive weathering and corrosion, resulting in the gradual migra-
tion of heavy metals ( Chen et al., 2018 ).
As clearly represented in Fig. 4 , some sulfide assemblages, such
as detectable subordinate pyrite and chalcopyrite, were microscop-
ically observed in the reflected color of whitish-yellow with ir-
regular rims and corrosion-like striations. There were also numer-
ous submicron-scale grains of metal sulfides with less oxidation
or even unaltered, especially for pyrite and chalcopyrite. The al-
teration zones and voids were occluded in newly-formed phases,
preserving inclusions of a matrix of Fe (oxy) hydroxides that had
strongly cemented the sulfide particles into massive aggregates of
tabular crystals ( Fig. 4 a and b). Moreover, the grain boundaries
were randomly coated by the generated nodular and flaky crys-
tals of mineral weathering products, which were relicts of sul-
fide oxidation. It was observed that the mineral surface showed
irregular margins and large cracks. Meanwhile, some isolated poly-
metallic minerals were found to be inlaid on the surface of large
126 T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128
Fig. 5. Multivariate statistical analysis of the tailing samples.
granular minerals under SEM inspection (
Fig. 4 e and f). This
might be because they were incompletely shed during the flotation
process.
2.4. Multivariate statistical analysis of the tailing samples
An explorative hierarchical cluster analysis (HCA, Fig. 5 a)
showed a significantly low distance for all of the 36 tailing sam-
ples; the distance was less than 0.10, which could be related to
their uniform mineralogical composition. The accuracy of metal
content analysis was illustrated by this interesting phenomenon.
Principal component analysis (PCA) was applied to clarify the envi-
ronmental impact of geochemical variables and describe their con-
centration distribution in space. As shown in Fig. 5 b, the tested
heavy metals were subdivided into three significant clusters. Clus-
ter 1 contained Cu and Mg, and Cluster 2 included Cr, Ni, and Al.
Cluster 3 comprised Cd, Pb, Zn, and Mn, whereas Fe was solely
grouped into Cluster 4. As we know, Cu, Ni, and Cr were the trace
elements in the mine, and these metals were not accompanied
metals of the Pb-Zn mine, while Mg and Al were the main ele-
ments of the surrounding rocks ( Çelebi et al., 2016 ); thus, Cu, Ni,
and Cr have little relationship with Clusters 3 and 4. The group
with the sole member of Fe was mainly caused by its susceptibil-
ity to oxidation ( Chen et al., 2018 ) and the poorly crystalline na-
ture of Fe minerals ( Panda et al., 2006 ). The association of cluster
3 was likely because of similarities in the geochemical behaviors
and distribution trends. As seen in Fig. 5 c, two principal compo-
nents (PCs) cumulatively explained 57.7% of the total variance in
the data. Cd, Pb, Zn, and Mn were deemed to be the predominant
elements. These facts showed that Cr and Ni were preferentially
associated with Al (oxy) hydroxides in the core samples, which co-
incided well with the cluster analysis. However, Fe had no positive
geochemical association with the other studied metals. Correlation
analysis between the elements in tailings will be the focus of our
coming study.
2.5. Environmental risk assessment of the tailings
As shown in our previous study ( Chen et al., 2018 ), the concen-
tration of Pb exceeded the maximum permissible level (MPL) of Pb,
while Cu and Hg were lower than their corresponding MPLs. The
drainage water sample analysis indicated that surface runoff from
the tailing impoundment area was the main route for the migra-
tion of heavy metals. The leaching of the metals was connected
with the MAP and ANC values of the tailings ( Wang et al., 2017 ).
MAP and ANC obtained for RTS were 244 kg H
2
SO
4
/ton and 139 kg
H
2
SO
4
/ton, respectively. The metals dissolved in the weathering
process would migrate with the acid drainage. The NAPP value of
the RTS was 105 kg H
2
SO
4
/ton, and the NAG-pH was 6.85. Ac-
cording to the systematic acid rock drainage method of classifica-
tion, RTS indicated an uncertain (UC) generation of AMD. However,
the NAG solution pH was higher than 4.5, which conflicts with
the highly aggressive NAPP values. The conclusive results should
be carefully considered ( Lindsay et al., 2015 ; Park et al., 2018 ). As
an exception, the oxidative dissolution of sphalerite and galena did
not contribute to the acidity in the presence of O
2
, only metal-free
ions and SO
4
2 −, as shown in Reactions ( 7 ) and ( 8 ):
ZnS
(s)
+ 2O
2(g)
= Zn
2 + (aq)
+ SO
4
2 −(aq) (7)
PbS
(s)
+ 2O
2(g)
= Pb
2 + (aq)
+ SO
4
2 −(aq) (8)
T. Chen, Z.-A. Yan and D. Xu et al. / Journal of Environmental Sciences 93 (2020) 120–128 127
Fig. 6. Chemical partitioning and bio-accessible characteristics of the representative composite tailing sample.
The Tessier leaching tests and human gastrointestinal tract
model were utilized together to predict the environmental risks
of the tailings. The potential mobilization, bioavailability, and tox-
icity of heavy metals in mine tailings are strongly tied to their
geochemical form or their binding strength rather than their total
contents. The Tessier sequential leaching procedure is often used
to analyze metals’ chemical fractions ( Tessier et al., 1979 ), while
the risk assessment code (RAC) is widely used to assess the poten-
tial mobility and availability of exchangeable (F1) and carbonate-
associated fractions (F2) ( Narwal et al., 1999 , Liu et al., 2017 ). The
results for RTS are shown in Fig. 6 a . As presented in Fig. 6 a, the
heavy metal bioavailability decreased in the order Pb (37.8%) >
Mn (24.4%) > Ni (16.1%) > Cd (12.86%) > Cr (12.16%) > Cu (5.95%)
> Zn (5.46%) > Fe (0%), revealing that more mobile Cd, Pb and
Mn species were potentially released into AMD by the weathering
process.
Bioavailability is a direct indicator of the ecological risk of
heavy metals. As shown in Fig. 6 b, the bioavailable metals as a
proportion of the total ranged from 2.95% for Zn to 43.6% for Cr
in the gastric phase (GP) and 0.481% for Fe to 62.5% for Pb in
the gastrointestinal phase (GIP). Considering GP and GIP, the bio-
accessible contents of the tested metals in GIP, except for Ni, Cu,
Pb and Mn, were lower than those in GP, due to the much more
acidic nature of the gastric extractant and the addition of biologi-
cal components in GIP ( Cai et al., 2016 ). On the basis of the bio-
accessible metal fractions in GIP, the sequence of bio-accessible
metals (from high to low) was Pb (62.5%) > Cu (44.2%) > Cr
(33.9%) > Ni (31.9%) > Mn (14.7%) > Cd (14.5%) > Zn (2.63%) >
Fe (0.481%). The general trend of the bio-accessibility evaluation
results matched well with the results obtained from BCR leach-
ing results. Bio-accessibility ranges could be interpreted as follows
( Silva et al., 2015 ): low risk (1%-15%, LR), moderate risk (16%-30%,
MR), high risk (31%-50%, HR); very high risk ( > 50%, VHR). Based
on the bio-accessibility classification for heavy metals, the results
showed MR for Cd, Zn, and Mn, HR for Cr, Ni, and Cu, and VHR for
Pb.
3. Conclusions
Geochemical characteristics and ecological risks of mine tail-
ings were tested. As the distribution characteristics of the metals
showed, the tailing samples contained considerably high contents
of toxic contaminants, ranging from 6.99 to 89.01 mg/kg for Cd,
75.29 to 602.39 mg/kg for Cu, 0.53% to 2.63% for Pb and 0.30%
to 2.54% for Zn. There were no noticeable differences found over
all profiles and depths for the total metal contents, except for Cd.
Weathering of the minerals was identified by SEM and XRD anal-
ysis. With the Valence state analysis of Fe and S, it could be fur-
ther confirmed that the tailings had undergone intensive weath-
ering and corrosion, resulting in the gradual migration of heavy
metals. MAP of the tailings reached 244 kg H
2
SO
4
/ton, and the
metals dissolved in the weathering process would migrate with
the acid drainage. According to the results of the Tessier leach-
ing procedure, the exchangeable and carbonate-associated frac-
tions of the tested metals followed the order Pb (37.8%) > Mn
(24.4%) > Ni (16.1%) > Cd (12.86%) > Cr (12.16%) > Cu (5.95%) >
Zn (5.46%). Furthermore, the gastrointestinal model showed that
the bio-accessible fractions varied according to Pb (62.5%) > Cu
(44.2%) > Cr (33.9%) > Ni (31.9%) > Mn (14.7%) > Cd (14.5%) >
Zn (2.63%) > Fe (0.481%). Pb, Cr, Ni, and Cu contained in the tail-
ing were high-risk metals. First, the mineral weathering was iden-
tified by its geochemical characteristics. Secondly, the heavy met-
als could be leached according to their chemical fraction. Last but
most important, the tailings have potential acidity. Therefore, the
metals contained in the tailings should be removed, and proactive
management strategies should be stringently conducted to avoid
ongoing damage.
Declaration of Competing Interest
The authors declare that they have no competing, personal and
financial interests in this manuscript.
Acknowledgments
This work was supported by the National Key Research and De-
velopment Plan (No. 2018YFC1802803), Guangdong Provincial Sci-
ence and Technology Program (No. 2015B020237003), the 2017
Central Special Fund for Soil, Preliminary Study on Harmless Treat-
ment and Comprehensive Utilization of Tailings in Dabao Mountain
(18HK0108). The authors declare that they have no competing, per-
sonal and financial interests in this manuscript.
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