ANALYTICAL SCIENCES JANUARY 2004, VOL. 20 2004 © The Japan Society for Analytical Chemistry
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Heavy-Metal Distribution in River Waters and Sediments around a “Firefly Village”, Shikoku, Japan: Application of Multivariate Analysis Katsuro ANAZAWA,*† Yasuhiko KAIDA,** Yoshinori SHINOMURA,*** Takashi TOMIYASU,* and Hayao SAKAMOTO* *Department of Earth and Environmental Sciences, Faculty of Science, Kagoshima University, 1-21-35 Korimoto, Kagoshima 890–0065, Japan **National Institute of Advanced Industrial Science and Technology, Kyushu, 807-1 Shuku, Tosu, Saga 841–0052, Japan ***Graduate School of Frontier Sciences, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113–0033, Japan
River water and sediment samples were collected at the same site in a vicinity of an abandoned mine, and the concentrations of major elements and heavy metals were determined. The chemical correlations were observed by principal component analysis (PCA), and the samples were classified by cluster analysis (CA) based on the PCA scores. The PCA results presented a macroscopic viewpoint of covariance structure, i.e., the chemical elements could be classified into three groups: 1) major elements and heavy metals in the river water, 2) Cd, Fe and Mn in the sediments, and 3) Cu and Zn in the sediments. The CA results implied a similarity of chemical compositions in most parts of the study area, except the ranges close to the abandoned copper mine. At the mixing location of mining water with natural river water, major elements and cadmium showed simple physical mixing (conservative mixing). Other heavy metals (Cu, Fe, Mn and Zn) showed the massive precipitation at the mixing event. The PCA structure was mainly interpreted in terms of the mixing process between mining water and diluted natural river water. (Received September 17, 2003; Accepted November 10, 2003)
The water and soil pollution of heavy metals has become a question of considerable public and scientific concern in the light of evidence of their extreme toxicity to human health and to biological systems. The principal need for the heavy metal in situ observation is due to the fact that the problems depend strongly on the specific local industrial processes. For the field observation, sediments have been widely used as environmental indicators, and their ability to trace contamination sources is widely recognized.1–6 In order to understand the hydrochemical environment in terms of heavy metals, the combined research work between water and sediment is essential. Misato village is known as a “firefly village”, and Genji BOTARU (Luciola cruciata), which is designated as a national protected species, is observed in countless numbers in a basin of the Kawata River and branches of the Higashiyamatani River from the end of May to early in June. However, no flight of fireflies is observed in the vicinity of the main stream of the Higashiyamatani River. Various reasons for the lopsided geographical distribution of fireflies have been considered, such as vegetation or toxicity of agricultural chemicals. However, no geochemical investigation on the aqueous environment has been conducted in this region. Among those potential factors, we noticed that waste water containing large amounts of heavy metals flowing from an abandoned copper mine into the upstream of the Higashiyamatani River, as a leading factor. In the present study, we have collected water and sediment † To whom correspondence should be addressed. E-mail:
[email protected]
samples from the two major rivers in Misato village and conducted chemical analysis of major and trace elements. The chemical data was analyzed by multivariate analysis, and the samples selected by the statistical analysis were put into thorough investigation quantitatively. The objectives of this study are firstly to quantify the geographical distribution of heavy metals in water, and secondly, to clarify the chemical behaviors of heavy metals in physical and chemical events according to river flow. The present study also tried to evaluate the applicability of multivariate analytical techniques to environmental chemical issues.
Experimental Sampling site The location of Misato village is centered at latitude 34˚01′N and longitude 134˚15′E, 13 km wide in east-west direction and 8 km in north-south direction with the total area of 50.47 km2 with a population of 1440 as of March 31, 2003 (Fig. 1). Surrounded by the mountain ridge in Shikoku Mountains, the major rivers of the village, The Higashiyamatani River and the Kawata River converge at the central part of the village and flow into the Yoshino River. Although geographical features are steep at the northern part in general, the southern part serves as a loose inclination ground. The study area is geologically located in Sambagawa metamorphic zone, and the major basement rock consists of crystalline schist, i.e. green schist, piedmontite quartz schist, glaucophane schist and black schist.
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Fig. 1
Location map of the sampling sites.
There is an abandoned copper mine in the northeast part of the village, and waste water with high-concentrated heavy metal is still flowing into the Higashiyamatani River, which flows across the central part of the village. The stream formed by the discharge of the mine is called Akamizutani (red water valley) River. The sample collections were performed on river water and sediment at the mining discharge point and at several stations along the Higashiyamatani River in April of 2002. Some samples were also collected along the Kawata River, which is the other major stream in Misato village, for comparison and assessment of background level in this area. Reagents The working standard solutions were prepared from analytical reagent-grade chemicals using deionized water obtained from a Millipore Milli-Q SP water-purification system. Analytical procedure River water. The temperature, pH and EC (electrical conductivity) were determined at the sampling points by a digital pH-EC meter (Horiba pH·EC meter D-24), and alkalinity (as HCO3–) was measured by titration with H2SO4. The water samples were filtered and placed in polyethylene bottles and immediately transported to the laboratory. The filtered (0.20 µm) samples were analyzed for major anions by Ion Chromatography (Hitachi L7000 pump, L-7470) at the laboratory. The silica determination was performed by molybdenum ammonium colorimetric method at a wavelength of 410 nm on a spectrophotometer (Hitachi U-1000). The samples for major cations and heavy metals were filtered and acidified (1 vol% HNO3) in acid-washed polyethylene bottles. The major cations were measured by atomic absorption spectrophotometer (Shimadzu KK, AA-646) and the heavy metals were determined by inductively-coupled plasma spectrometry (ICP-AES, Seiko-Instruments KK SPS-1200AR). The selected elements (Cd, Cu, Fe, and Zn) were analyzed both in ICP-AES and AA spectrophotometer (Shimadzu KK, AA646) to check the analytical results. River sediment. The < 500 µm sediments (by sieving) were dried at 60˚C, then ground and homogenized. Around 1 mL of concentrated H2SO4 and 2 mL of concentrated HF were added to sub-samples of 0.2 g, with two replicates, in a Teflon (PTFE)
vessel. The vessel was placed inside the microwave and was heated for 20 min, then cooled for 30 – 40 min. The cool solution was transferred to the Teflon beaker and was evaporated to dryness over a hotplate for around 1 – 2 h. After the evaporation, cooling followed and finally, the solution was filtered (0.45 µm) and diluted to 50 mL using 0.1 M HNO3. Concentrations of the heavy metals were analyzed by ICP-AES. Multivariate analysis Until recently, multivariate analysis has not been widely used, largely due to the massive computation capacity required. The availability of faster and cheaper computers is now enabling us to apply this set of statistical methods to environmental chemistry.7–10 In this research work, principal component analysis (PCA) was applied to summarize the statistical correlation among components in the water and sediment samples. The concentration orders among elements differ greatly and the statistical result would be highly biased by the elements with high concentration. Therefore, standardization was made on each chemical prior to statistical analyses. The calculation was performed based on the correlation matrix of chemical components and the PCA scores were obtained from the standardized analytical data. For classification of samples, cluster analysis (CA) was performed based on the PCA score to eliminate the variate biases. The Ward method, which is a part of hierarchy cluster analysis, was applied for grouping, and the dissimilarity is defined by Euclidean distance. The details of those statistical methods are found in the literatures.11,12 Calculation of the conservative mixing The heavy metals dissolved in mining water are subjected to the mixing effect of river water. At the confluence of the rivers, the concentration of heavy metals would be changed not only by simple physical mixing (conservative mixing) of waters, but by removal from aqueous phase, i.e., precipitation and/or adsorption. Generally speaking, the pattern of element solubility can be explained in terms of charge and ionic radius (z/r). Ions with low z/r values, e.g. Na+, Ca2+, and Mg2+, are highly soluble and ions with high z/r values form complex oxyanions, e.g. SO42–, and again are soluble. Those ions tend to behave conservatively at the mixing processes. Heavy metal
ANALYTICAL SCIENCES JANUARY 2004, VOL. 20 Table 1(a)
81
Concentrations of major elements in river water (n = 2) Major elements in river water/mg dm–3
Sampling site
EC (mS/m)
K-7 K-8 K-9 K-10 K-11 K-12 H-1 H-2 H-3 H-4 H-5 H-6 H-7 H-8 H-9 H-10
13.70 14.87 17.83 15.09 14.96 15.98 12.02 13.89 51.4 18.8 12.25 15.39 10.77 16.82 17.07 16.30
Table 1(b)
Concentrations of heavy metals in river water and sediments (n = 3)
pH 8.22 8.46 8.32 8.58 8.89 8.95 7.83 8.12 4.90 6.83 7.37 7.83 7.87 7.80 8.25 8.57
Na+
K+
Mg2+
Ca2+
Cl–
NO3–
SO42–
HCO3–
Si
3.84 4.22 4.99 4.48 4.55 4.62 3.99 4.65 4.88 4.58 4.45 4.66 4.28 4.71 4.70 4.78
0.82 0.93 1.69 1.16 1.04 0.98 0.50 0.98 1.08 0.89 0.71 0.82 0.53 0.88 0.87 0.93
2.64 2.84 3.14 2.92 2.94 3.58 1.71 2.58 20.0 5.54 3.65 3.50 2.23 4.31 4.20 4.26
17.4 18.7 21.1 19.2 19.1 19.3 15.4 17.1 47.0 21.1 13.1 19.4 12.2 18.6 18.7 18.7
3.89 4.16 6.59 4.81 4.83 4.95 3.70 4.93 2.88 4.57 5.52 3.56 3.44 4.78 4.74 5.07
3.42 3.62 11.8 5.45 4.89 4.36 2.50 4.37 0.20 2.43 2.39 4.53 1.93 3.15 3.02 3.46
11.3 13.6 16.3 14.2 14.3 16.6 8.85 11.3 224 41.9 6.98 6.84 8.67 22.6 21.2 20.3
44.7 48.1 44.9 46.4 47.6 46.2 29.5 32.9 < 1.0 31.7 33.2 42.2 27.6 43.4 44.7 47.6
4.06 4.06 4.06 3.73 3.69 3.81 5.02 3.85 17.0 5.40 4.98 4.19 4.52 4.69 4.56 4.60
Heavy metals in river water/mg dm–3
Sampling site
Cd
Cu
Fe
K-7 K-8 K-9 K-10 K-11 K-12 H-1 H-2 H-3 H-4 H-5 H-6 H-7 H-8 H-9 H-10
0.0038 < 0.002 < 0.002 < 0.002 0.0046 < 0.002 < 0.002 0.0021 0.0113 0.0039 0.0041 0.0038 0.0038 < 0.002 0.0024 0.0045
0.0053 0.0052 0.0075 0.0012 0.0058 0.0092 0.0066 0.0063 2.64 0.1039 0.0115 0.0082 0.0059 0.0208 0.0227 0.0142
0.017 0.015 0.252 0.094 0.026 0.041 0.078 0.035 0.187 0.015 0.089 0.144 0.024 0.020 0.023 0.026
Heavy metals in sediments/mg kg–1
Mn < 0.0006 < 0.0006 0.0124 0.0037 < 0.0006 0.0063 < 0.0006 < 0.0006 1.16 0.0667 0.0022 0.0022 < 0.0006 0.0173 0.0127 0.011
ions with intermediate charge/(ionic radius), e.g. Fe3+, on the other hand, are relatively insoluble and easily precipitate in neutralization processes (non-conservative behavior).13 In order to evaluate the chemical behavior of heavy metals at water mixing locations, the volumetric flow fraction (Fus) of the mining stream (Akamizutani River) and the natural river (Higashiyamatani River) were estimated. Using a “conservatively behaved” element (e.g. major cations, SO42– or EC) as an index component, the mixing of the two streams of water is described by the following mass-balance equation.14
Zn
Cd
Cu
Fe (g kg–1)
Mn
Zn
0.0029 0.0018 0.0034 0.0011 0.0022 0.0095 0.0055 0.0025 3.77 0.118 0.006 0.0034 0.0011 0.0419 0.0312 0.018
3.93 2.76 2.65 3.65 4.85 4.46 0.45 3.08 4.16 3.98 2.49 3.69 2.20 3.02 3.27 4.91
39.5 34.1 30.9 24.6 33.5 81.1 30.4 32.7 196 595 21.9 42.8 26.2 105 82.8 99.9
38.3 22.7 20.5 20.5 32.4 29.0 12.1 24.5 40.0 22.3 17.5 32.7 16.4 25.1 30.3 48.0
504 566 418 470 893 649 423 350 503 465 304 432 170 650 578 1074
81.3 71.4 62.8 57.9 76.3 186 59.6 70.0 206 808 60.0 78.1 60.3 232 203 251
Here, the underlying assumption is that the downstream water is well mixed and the concentration of index component (Cds) is obtained by simply physical mixing, i.e., removal (e.g. precipitation) or addition of the component does not proceed at the mixing location. In order to determine the clear Fus solution, a component having a large difference between Cus and Ctr should be chosen as an index component. Using the above Fus, the ideal concentration of any solutes in the downstream can be calculated: Cdsi = FusCusi + (1 – Fus)Ctri
CusFus + Ctr(1 – Fus) = Cds
C, concentration of an index element; Fus, volumetric flow fraction of main upstream; us, upstream of the confluence (mainstream); tr, upstream of the confluence (tributary); ds, downstream of the confluence. The above equation induces the volume fraction of the upstream water: Fus = (Cds – Ctr)/(Cus – Ctr)
i: solute component
(3)
(1)
(2)
If the solute component i behaves “conservatively”, the ratio of the actual concentration (Cdsi*) versus ideal concentration (Cdsi) of downstream can be interpreted as follows: Cdsi*/Cdsi = 1 conservative mixing Cdsi*/Cdsi < 1 removal from river water Cdsi*/Cdsi > 1 addition to river water
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Table 2 Eigenvectors and eigenvalues on the correlation matrix of the chemical concentration in water and sediments Element Water
Sediment
Na+ K+ Mg2+ Ca2+ Cl– NO3– SO42– HCO3– Si Cd Cu Fe Mn Zn Cd Cu Fe Mn Zn
Eigenvalue Proportion (%) Cum. Proportion (%)
P1
P2
P3
0.12 0.04 0.32 0.31 –0.16 –0.14 0.32 –0.26 0.32 0.29 0.32 0.12 0.32 0.32
–0.38 –0.43 –0.03 –0.10 –0.36 –0.33 0.00 –0.24 0.07 0.03 0.04 –0.12 0.03 0.04
–0.10 –0.26 –0.03 –0.08 –0.11 –0.30 –0.04 0.02 –0.06 0.05 –0.09 –0.40 –0.08 –0.09
0.10 0.12 0.14 0.01 0.09
–0.36 –0.12 –0.24 –0.32 –0.16
0.18 0.41 0.10 0.15 0.45
9.4 47.0 47.0
3.5 17.6 64.6
3.3 16.3 80.9
Calculation of saturation index The equilibrium calculations were performed by the PHREEQC program15 with a thermodynamic database, WATEQ4F.DAT, to evaluate the degree of saturation for heavy metals. The PHREEQC program is widely used to simulate chemical reactions and transport processes in natural or polluted water. The results of the PHREEQC calculations are presented in terms of the saturation index (SI) for each element. SI is defined by SI = log(IAP/Ksp), where IAP is the ion activity product of the dissolved mineral constituents in a solubility reaction, and Ksp is the corresponding solubility product of the dissolved mineral constituents. Thus SI > 0 implies supersaturation with respect to the mineral, whereas SI < 0 means undersaturation. In this work, SI calculation was conducted to assess the heavy metal behavior as “conservative” or “non-conservative” thermodynamically.
Results and Discussion Chemistry of water and sediment The temperature, EC (electrical conductivity), pH, and chemical composition of the water samples are presented in Table 1. The EC and concentration of most elements of st.H-3 (Akamizutani River) sample, which is the river water flowing out from the closed mining area, are remarkably higher than those of other water samples. The river sediment sample of st.H-4, which is the first downstream point of the confluence between the Higashiyamatani (st.H-2) and the Akamizutani River (st.H-3), shows high concentrations of heavy metals. Principal component analysis (PCA) and cluster analysis (CA) The result of the PCA based on the correlation matrix of chemical components is expressed in Table 2. The first three components account for over 80% of the variance in the data set. Therefore, three vectors were extracted for statistical
Fig. 2 Dendrogram for cluster analysis based on the PCA scores. The dissimilarity is defined by Euclidean distance and the combination of cluster is based on Ward method.
consideration. The eigenvectors classified the chemical elements into three groups: 1) major elements and heavy metals in the river water, 2) Cd, Fe and Mn in the sediments, and 3) Cu and Zn in the sediments. In other words, the heavy metals in solution demonstrate similar behavior with major components, and the heavy metals in the river sediments are divided into two other independent components. The result of CA (cluster analysis) based on the PCA scores is shown in Fig. 2 as a dendrogram graph. The dendrogram clarifies the abnormality of the samples of st.H-3 and st.H-4, both of which are highly influenced by the water flow from the abandoned mine. The samples from the downstream locations of those two stations make one group. The mutual dissimilarities among other sample groups are very little comparing with H-3 and H-4. In order to interpret the PCA structure in detail, the following geochemical considerations were developed. Overview of the chemical process in downstream of mining discharge The analytical data set was standardized in order to compare the aspect of the variation of each chemical species. Among the standardized data, the downstream samples of the Akamizutani River, whose water source is the overflow of closed mine, are shown in Figs. 3(a) – (d). Here, the H-3 sample, which is the Akamizutani River sample, shows an extraordinary high value of EC, as well as high concentrations of Ca2+, Mg2+ and SO42–, whereas the samples are low in pH and HCO3– (> 3s). The standardized concentrations for those elements as well as other species of H-4 and downstream samples are almost equivalent to the mean value of each element within 1s. The heavy metals in river water show similar behavior to that of the major element, and all heavy metals except Fe of H-3 sample show extraordinary higher concentration with over 3s. The minimum value is taken by Fe, but is also significantly high (1.7s; Fig. 3(c)). On the other hand, the several heavy metals (Cu and Zn) in sediments, however, show rather low value at H-3 (< 1s) and highest peak at H-4, which is the first sampling station from the junction of the Akamizutani River and the Higashiyamatani main stream. Other metals (Cd, Fe and Mn) show rather moderate variation, where even at H-3 and H-4 the value is within 1s. The mixing process between low (st.H-2) and high (st.H-3) concentrated solution The calculation of mixing process was performed at the confluence between the Higashiyamatani River (st.H-2) and the
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(a)
(b)
(c)
(d)
Fig. 3 Standard unit of chemical concentrations in waters and sediments downstream of the Akamizutani River. The standard unit (z) is defined as z = (x – u)/S: where x is the raw concentration data, u is the mean value and S is the standard deviation. (a) EC (electrical conductivity), pH and major anions in waters, (b) major cations and silicon in waters, (c) heavy metals in waters, (d) heavy metals in sediments.
Akamizutani River (st.H-3) by using the index component of EC and SO42–. Both results were almost identical (relative difference of mixing ratio results is < 1%) and the EC result is shown in Fig. 4. The volume fraction (F) is 0.83 for the Higashiyamatani River and 0.17 for the Akamizutani River. Most of the major elements in the water show conservative behaviors as Cdsi*/Cdsi = 1. On the other hand, Cdsi*/Cdsi of heavy metals except Cd show values less than 1.0; such values represent removal of heavy metals from solution at the mixing event. Indeed, Cu and Zn in sediments show extraordinary high concentrations at st.H-4; these are higher than those at st.H-3, whereas the concentrations in waters decrease drastically from H-3 to H-4 samples (Table 1(b), Fig. 3(c) – (d)). This chemical process is interpreted as the precipitation of those heavy metals at the mixing event of the river waters. The Fe and Mn also show small Cdsi*/Cdsi. However, unlike Cu and Zn, the concentrations in sediments show only small changes between H-3 and H-4. Here, the mean concentration ratio of sediment to water is much higher in Fe and Mn than in other metals (Fig. 5). Since the concentration of those metals in sediments is high in nature, the addition by the precipitation from river water changes the concentration in sediment only slightly at the event. Among those heavy metals, only Cd shows conservative mixing, with Cdsi*/Cdsi = 1.14. To understand Cd behavior, saturation index (SI) was calculated in a mixing solution of H-2 and H-3. The calculation was performed by PHREEQC with WATEQ4F database at a volume fraction of 0.83:0.17 to foresee the possible precipitate from the solution (Table 3). The result shows that most of the heavy metals are oversaturated in the mixing solution on several solid phases, but only Cd is undersaturated for all solid phases found in WATEQ4F database. Even the highest SI value on Cd is –2.33 for Otavite
Fig. 4 Concentration patterns for each element in river water, using EC to derive an ideal conservative mixing line. The value of 1 shows ideal conservative mixing line. The plot above the line shows the addition to solution and below the line shows removal from solution.
(CdCO3). Therefore, the conservative behavior of Cd, which is far different from other heavy metals, is justified by its low concentration in terms of the saturation index. Taking the above discussion into consideration, we propose the following interpretation of PCA structure. Interpretation of PCA structure in terms of the behavior of heavy metals The first component of PCA shows the similarity between major elements and heavy metals in solution (Table 2). In a macroscopic point of view, the heavy metals and major elements in solution behave similarly, i.e., high concentrations of both major elements and heavy metals in mining water decreased at the confluent point with the main stream of the Higashiyamatani River. The concentrations of heavy metals and major elements in the downstream part of the Higashiyamatani River and in the inflow streams are almost the
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ANALYTICAL SCIENCES JANUARY 2004, VOL. 20 Table 3 Possible precipitates that form from the mixing of H-2 (natural river water) and H-3 (overflow of mining water), under the assumption of volume fraction as 0.83:0.17
Fig. 5 Average concentration ratios of heavy metals in water to sediment.
same as in the other major stream of the Kawata River. The first component is interpreted as a macroscopic overview of the similar behavior among the solutes. The second component shows the unique behavior of Cd, Fe and Mn in sediments. The difference between this metal group and the other group is that Cd, Fe and Mn show relatively small variation among the samples compared with Cu and Zn (Fig. 3(d)). As discussed in the foregoing section, the small variation of Cd is interpreted by its conservative behavior. Among the other heavy metals, only Cd shows lower concentration in the mining water than the saturated concentration, and Cd does not precipitate during neutralization processes at the confluences. For Fe and Mn, on the other hand, their high concentrations in sediments make the fluctuation of those elements in solution negligible. The third component is loaded by Cu and Zn in sediments. Those heavy metal concentrations in mining stream sediment are rather low and only slightly higher than background level (Fig. 3(d)). The highest concentration in sediment is found at the downstream of the confluence point of the mining stream and the natural river. The relative concentration of water to sediment is high enough to detect the precipitation effect in the sediment at the mixing process. Therefore, the second and third components show the difference of the heavy metal behavior in the mixing process between mining water and natural river water.
Conclusions The geographical distribution of heavy metals in this region is highly controlled by the discharge flow from an abandoned copper mine. The PCA results classified the chemical elements into three groups; 1) major elements and heavy metals in the river water, 2) Cd, Fe and Mn in the river sediments, and 3) Cu and Zn in the sediments. The PCA structure was mainly interpreted by the different types of behavior of the elements during the mixing process between high-concentration water and low-concentration water. Multivariate analysis, such as PCA or CA, provides us with good perspective on multivariate data set: nevertheless, detailed observation by chemical consideration is essential to interpret what the statistical results mean.
Acknowledgements The authors are indebted to Mr. Masakatsu SATO, Hotaru Museum in Misato village, Mr. Haruo TAKEMAE, office of Misato village, and other village people for their assistance in
Phase
SI
IAP
KT
Chemical formula
Hematite Cupric Ferrite Pyrolusite Nsutite Magnetite Goethite Bixbyite Maghemite Birnessite Fe(OH)2.7Cl0.3 Cuprous Ferrite Hausmannite Manganite Fe(OH)3 ZnSiO3 Malachite Tenorite Otavite
16.66 14.73 8.1 7.56 7.38 7.34 7.3 6.57 6.52 6.26 5.16 4.58 3.84 1.59 1.51 0.39 0.26 –2.33
39.19 47.22 50.12 50.12 37.84 19.59 58.36 39.19 50.12 16.33 6.69 66.6 29.18 19.59 4.62 –4.63 8.03 –14.43
22.53 32.49 42.02 42.56 30.46 12.26 51.07 32.62 43.6 10.08 1.53 62.02 25.34 18.01 3.11 –5.02 7.77 –12.1
Fe2O3 CuFe2O4 MnO2 MnO2 Fe3O4 FeOOH Mn2O3 Fe2O3 MnO2 Fe(OH)2.7Cl0.3 CuFeO2 Mn3O4 MnOOH Fe(OH)3 ZnSiO3 Cu2(OH)2CO3 CuO CdCO3
Saturation indices of minerals including heavy metals were calculated by PHREEQC ver.2 program on the basis of WATEQ4F database.15
the field survey. We would like to express our appreciation to Ms. Jeneper LO for critical discussion and helpful comments. We also thank Dr. Kazunari ARIMA, Mr. Takuji KIYOHARA, Mr. Hisanori ARAYA, and Ms. Yoko ISHIDA, Kagoshima University, for their cooperation during the sample collections.
References 1. M. Wardas, L. Budek, and E. H. Rybicka, Appl. Geochem., 1996, 11, 197. 2. Z. Borovec, Sci. Total Environ., 1996, 177, 237. 3. K. C. Cheung, B. H. T. Poon, C. Y. Lan, and M. H. Wong, Chemosphere, 2003, 52, 1431. 4. M. Singh, G. Muller, and I. B. Singh, J. Geochem. Explor., 2003, 80, 1. 5. H. M. V. M. Soares, R. A. R. Boaventura, A. A. S. C. Machado, and J. C. G. E. Silva, Environ. Pollut., 1999, 105, 311. 6. C. K. Jain, J. Hydrology, 1996, 182, 105. 7. K. Loska and D. Wiechua, Chemosphere, 2003, 51, 723. 8. P. L. Nimis, F. Fumagalli, A. Bizzotto, M. Codogno, and N. Skert, Sci. Total Environ., 2002, 286, 233. 9. W. X. Liu, X. D. Li, Z. G. Shen, D. C. Wang, O. W. H. Wai, and Y. S. Li, Environ. Pollut., 2003, 121, 377. 10. Y. Lin, T. Teng, and T. Chang, Landsc. Urb. Plann., 2002, 62, 19. 11. K. Anazawa and M. Yoshida, Geochem. J., 1994, 28, 307. 12. K. Anazawa and M. Yoshida, Geochem. J., 1997, 30, 355. 13. J. E. Andrews, P. Brimblecombe, T. D. Jickells, and P. S. Liss, “An Introduction to Environmental Chemistry”, 1995, Blackwell Science, Cambridge, Massachusetts, USA, 90. 14. D. Langmuir, “Aqueous Environmental Geochemistry”, 1997, Prentice Hall, Upper Saddle River, New Jersey, 298. 15. D. L. Parkhurst and C. A. J. Appelo, “User’s guide to PHREEQC (Version 2)—A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations, Water-Resources Investigations Report 99-4259”, 1999, U.S. Geological Survey, Denver, Colorado.
