Response of microbial characteristics to heavy metal pollution of mining soils in central Tibet, China

Applied Soil Ecology 45 (2010) 144–151

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Response of microbial characteristics to heavy metal pollution of mining soils in central Tibet, China Fu-Ping Zhang a,b,c,1 , Cheng-Fang Li a,b,1 , Le-Ga Tong a,b , Li-Xin Yue a,b , Ping Li d , Yang-Jin Ciren d , Cou-Gui Cao a,b,∗ a

Key Laboratory of Huazhong Crop Physiology, Ecology and Production, Ministry of Agriculture, Wuhan 430070, PR China College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China College of Resources and Environment, Tibet Agriculture and Animal Husbandry College, Nyingchi 860000, PR China d College of Plant Science and Technology, Tibet Agriculture and Animal Husbandry College, Nyingchi 860000, PR China b c

a r t i c l e

i n f o

Article history: Received 29 October 2009 Received in revised form 7 March 2010 Accepted 13 March 2010 Keywords: Heavy metal pollution Microbial activity Multivariate analysis Central Tibet

a b s t r a c t Soil microbial activity plays a crucial role in soil microbiological processes, which can be used as a useful indicator to determine the ecological effects of heavy metal pollution on soils. The objective was to determine the effects of heavy metal pollution on mining soils at the Lawu mine of central Tibet, China on soil enzyme activities (sucrase, urease and acid phosphatase), microbial biomass C, N and P (MBC, MBN, and MBP), basal respiration, metabolic quotients, and N mineralization. Sixteen soil samples around the mine were sampled, and one soil sample, 2 km from the mine center, was taken as the control. Compared to the control, mining soils were polluted by heavy metals, Cu, Zn, Pb and Cd, resulting in decreases of sucrase activities, urease activities, acid phosphatase activities, MBC, MBN, MBP, and N mineralization, and increases of basal respiration and qCO2 . Multivariate analysis (cluster analysis [CA], principle component analysis [PCA] and canonical correlation analysis [CCA]) indicated nine microbial variables were only reduced to one principal component explaining 72% of the original variances, and MBC (R2 = 0.93) had the highest positive loadings on the principal component. Mining soils polluted by heavy metals were perfectly clustered into four groups, which were highly distinguished by MBC. There were significant canonical correlations between soil heavy metals and microbial indexes on two canonical variates (R1 = 0.99, p < 0.001; R2 = 0.97, p < 0.01), which further demonstrated significant correlations between soil heavy metal contents and microbial characteristics. Hence, our results suggested that MBC may be used a sensitive indicator for assessing changes in soil environmental quality in metal mine of central Tibet. Crown © 2010 Published by Elsevier B.V. All rights reserved.

1. Introduction Mining activities produce major solid wastes in China, and thus are of environmental concern due to potential hazards of surface or groundwater pollution by heavy metals (Shu et al., 2000). These heavy metals resulting from mining activities have a clear negative influence on biologically mediated soil processes (Lee et al., 2002) and thus have toxic effects on both natural and man-made environment ecosystems (Giller et al., 1998; Majer et al., 2002). Soil microorganisms are important components of terrestrial ecosystems because they play central roles in organic matter decomposition and nutrient cycling. An increasing body

∗ Corresponding author at: College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, P.R. China. Tel.: +86 27 87283775; fax: +86 27 87282131. E-mail address: [email protected] (C.-G. Cao). 1 These authors contributed equally to this work.

of evidence suggests that microorganisms are far more sensitive to heavy metal stress than soil animals or plants growing on the same soils (Giller et al., 1998). Therefore, knowledge of heavy metals affecting soil microorganisms is fundamental for a sustainable environmental management (Vásquez-Murrieta et al., 2006). It has been documented that heavy metals exhibit toxic effects on key microbial processes (Obbard, 2001) and the structure and diversity of soil microbial community (Hinojosa et al., 2005). A number of soil microbiological parameters, notably enzyme activity, microbial biomass, C and N mineralization, basal respiration, and microbial community structure (Kızılkaya et al., 2004; GilSotres et al., 2005; Liao and Xie, 2007; Wang et al., 2007; Zhang et al., 2008), have been suggested as possible indicators of soil environmental quality, and have been employed in national and international monitoring programs (Yao et al., 2000). Soil enzyme activity is involved in nutrient cycling and availability to plants, and is sensitive to heavy metals pollution (Renella et al., 2003; Mikanova, 2006; Chaperon and Sauvé, 2007; Li et al., 2009), which

0929-1393/$ – see front matter. Crown © 2010 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.apsoil.2010.03.006

F.-P. Zhang et al. / Applied Soil Ecology 45 (2010) 144–151

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Table 1 Dominant species in the sampling sites. Sampling sites

Dominant species of frutices

Dominant species of herbages

3

Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.)

4

–

5

–

6

–

7 8 9

Procumbent Juniper (Sabina vulgaris Ant.) Procumbent Juniper (Sabina vulgaris Ant.) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.) and Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid) Procumbent Juniper (Sabina vulgaris Ant.), Barberry Root (Berberis sargentiana Schneid)

Kobresia Humilis (Kobrbresiapygmaea), Kentucky (Poa pratensis), Chinese Cinquefoil Herb (Potentilla L.) Kobresia Humilis (Kobrbresiapygmaea), Kentucky (Poa pratensis), Chinese Cinquefoil Herb (Potentilla L.) Kobresia Humilis (Kobrbresiapygmaea), Kentucky (Poa pratensis), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Largeleaf Knotweed (Polygonum sphaerostachyum Meisn.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Largeleaf Knotweed (Polygonum sphaerostachyum Meisn.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Largeleaf Knotweed (Polygonum sphaerostachyum Meisn.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Largeleaf Knotweed (Polygonum sphaerostachyum Meisn.) Kobresia Humilis (Kobrbresiapygmaea), Largeleaf Knotweed (Polygonum sphaerostachyum Meisn.) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi) Kobresia Humilis (Kobrbresiapygmaea), Red Fescue (Festuca rubra L.), Guing-Tibetan Sedge (Carex kobomugi Ohwi), Herba Oxytropls Falcatae (Oxytropis)

1 2

10 11 12 13 14 15 16 17

can be used as an index of soil functioning (Nannipieri et al., 2003). Soil microbial biomass, which serves as a pool of nutrients and plays an important role in ecosystem sustainability, has been found to be a sensitive indicator of microbial changes in soil polluted by increased heavy metal concentrations (Vig et al., 2003) and has been widely used as an approach to evaluate soil quality. Also, decomposition processes (C and N mineralization, respiration) are also designated as good indicators of soil pollution by heavy metal (Filip, 2002; Gil-Sotres et al., 2005; Liao et al., 2007). Thus, by using such approaches it might be possible to determine whether the natural ecosystem is being altered by heavy metal pollutants. However, more data of microbial characteristics are needed before this will be possible. The central Tibet, distributing in the Brahmaputra watershed, including Lhasa, Higatse, and Zedang, etc., is the primary agricultural and industrial region of Tibet. This region is ecologically frail due to unique landform and climates (Cai, 2002). Recently, in central Tibet, mining activities have generated large quantities of mine waste materials, which were left as is, without any proper treatment. Oxidation of minerals exposed to weathering not only led to severe contamination of soil and plants in the vicinity of the mines, which induces a decline in biodiversity and an increase in frangibility of ecosystems of the mines, but has also led to health problems in people living downstream of the mines (Cheng and Tian, 1993). Thus, it is necessary to restore mine ecosystems of central Tibet. However, to our knowledge, very little information is available on the effects of heavy metals on the environment or the microbial characteristics in Tibet, especially in central Tibet. Therefore, the objective of our study was (1) to assess effects of heavy metals induced by mining activities on soil enzyme activities, microbial biomass, basal respiration, metabolic quotient (qCO2 ) and N mineralization, and through using multivariate analysis, (2) to evaluate whether these microbial characteristics can be used as possible indicators of soil pollutions by heavy metals in Lawu mine of central Tibet, China.

2. Materials and methods 2.1. Site description and soil sample collection Lawu Cu–Zn–Pb mine was exploited since 2003, which is situated at Dangxiong County, Lhasa City, Tibet Province, China, and locates at 30◦ 27.35 -N latitude and 91◦ 41.63 -E longitude. Its altitude varies from 4360 to 5464 m above sea level with 0.75 km2 in area. The Lawu River, a branch of Brahmaputra River, flows through this region. This region (plateau subfrigid zone) has a semiarid and subhumid monsoon climate with an average annual temperature of 1.3 ◦ C, total hours of sunshine of 2650 h, and a mean annual precipitation of 420 mm with most of the rainfalls occurring between May and August. Since the vegetation growth restarts from early May, soil samples were collected on May 10, 2009 according to the distributions of dominant species of vegetations in the mining region (Table 1). The sample sites included Sites 1–6 and 15 (around the mine center), Sites 12 and 13 (about 100 m from the mine center), Sites 7–11, 14 and 16 (about 200 m from the mine center), and Site 17 (about 2 km from the mine center). Each sample included 5 cores of 5 cm diameter and 0–10 cm depth. Field moist soils (moisture contents ranging 11.9–14.1%) were sieved through a mesh <2 mm and large pieces of plant material, stones and soil animals were removed. Part of samples was kept moist in the dark at 4 ◦ C to determine soil enzyme activity and microbial biomass. The remaining soil was air-dried, crushed and sieved through 0.2 mm mesh to analyze pH, organic C, and total and available heavy metals. The main soil properties of the sampling sites are shown in Table 2. 2.2. Soil analysis Soil pH was determined in H2 O (soil:water = 1:2) with a grass electrode using a Mettler–Toledo pH meter. Soil organic C was analyzed by dichromate oxidation and titration with ferrous

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Table 2 Soil properties of Lawu mine in central Tibet. Sampling sites

pH

Organic C (g kg−1 )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

5.3 5.7 5.7 6.1 6.2 6.4 6.4 6.4 6.3 6.4 6.3 6.3 6.4 6.1 6.0 6.3 6.6

12.9 11.5 9.1 11.3 10.6 5.5 8.2 8.4 7.0 7.0 6.5 4.9 4.2 10.7 4.9 6.2 5.4

Total (mg kg−1 )

Available (mg kg−1 )

Cu

Zn

Pb

Cd

Cu

Zn

Pb

Cd

198.2 136.3 200.3 138.2 118.4 176.0 45.0 106.5 132.2 105.7 58.9 272.5 155.7 149.6 495.9 122.7 13.1

398.0 396.1 401.2 445.4 394.7 357.8 154.5 185.4 147.1 165.5 236.8 377.5 159.1 151.6 481.9 248.6 19.7

54.4 44.6 36.6 49.6 44.0 42.5 17.9 27.3 22.6 24.0 29.0 42.6 33.1 28.3 135.7 94.9 7.0

33.0 31.8 59.4 25.3 32.8 37.7 15.9 9.5 27.1 9.1 22.8 45.6 11.6 18.7 45.4 26.9 3.7

19.0 14.2 35.2 31.9 11.1 13.0 7.3 9.5 8.5 9.6 31.4 7.4 20.8 41.1 47.0 36.9 1.3

76.8 79.6 98.3 95.4 84.1 77.8 70.1 75.0 60.4 75.7 67.6 94.4 75.1 87.9 89.8 85.7 3.3

23.5 21.8 26.1 25.5 22.3 22.7 11.4 10.6 7.8 8.9 11.2 15.7 19.0 16.8 13.7 14.8 0.6

13.2 12.8 11.6 10.3 10.2 8.5 2.4 2.6 2.2 3.2 2.2 9.3 5.5 11.4 11.7 11.2 1.1

ammonium sulfate (Bao, 2000). Total Cu, Zn, Pb and Cd contents were determined by the atomic adsorption spectrophotometer (AAS) after digestion with a mixture of HNO3 –HCl–HClO4 (Bao, 2000). Since the measurement method that uses HCl solution for extracting available heavy metal is fitted for acid soils (Bao, 2000), available Cu, Zn, Pb and Cd were extracted with 0.1 mol L−1 HCl solution and analyzed by AAS. Microbial biomass C, N and P (MBC, MBN and MBP) were determined according to the CHCl3 fumigation-extraction method (Vance et al., 1987; Yao, 2006). Carbon concentrations of fumigated and non-fumigated samples were determined through oxidation with potassium dichromate, and the difference of carbon concentration of fumigated and non-fumigated samples was divided by the recovery factor KEC (Yao, 2006). Nitrogen (N) concentration was calculated as the difference of N concentrations to fumigated and non-fumigated samples through the colorimetric method, and the difference was divided by the recovery factor KEN (Yao, 2006). Phosphorus (P) concentrations of fumigated and non-fumigated soils were calculated through extracted by 0.5 mol L−1 NaHCO3 and the difference was divided by the recovery factor KEP . A correction for incomplete extraction of P released by CHCl3 was made by determining the percentage of recovery of a known quantity of P spiked to NaHCO3 solution followed by extraction of a non-fumigated soil. KEC , KEN and KEP were 0.45, 0.54 and 0.40 (Yao, 2006), respectively. Sucrase activity was measured after incubation of soil samples with sucrose solution for 24 h at 37 ◦ C, and determined spectrophotometrically at 508 nm (Yao, 2006). Urease activity was measured by the method of Yao (2006). A 7.5 mL citrate buffer (pH 6.7) and 10 mL of 10% urea substrate solution were added to 10 g soil, and subsequently the samples were incubated for 3 h at 38 ◦ C. Following filtration through filter papers, the filtrate was diluted, and NH3 -N concentrations were determined colorimetrically at 578 nm. Acid phosphatase activity was determined as the method of Lu (1999) described using ␳-nitrophenylphosphate as substrate and 0.1 mol L−1 Tris, pH 5.5 as buffer. Sucrase, urease and acid phosphatase activities were expressed as g Suc kg dry soil−1 , g NH3 kg dry soil−1 and mg PNFF kg dry soil−1 , respectively. Basal respiration (CO2 evolution) was measured by incubating fresh soil equivalent to 20 g dry weight at 28 ◦ C in 300-mL airtight jars. Respired CO2 was trapped in 0.1 mol L−1 NaOH solution, the CO3 2− was precipitated with BaCl2 and the excess OH− was titrated with 0.05 mol L−1 HCl using phenolphthalein indicator (Lu, 1999). Nitrogen (N) mineralization was determined by measuring the production of mineral N (NH4 + + NO3 − ) during incubation (Bao, 2000). Incubations were carried out with 20 g (dry weight equiv-

alent) of soil flooded with ultra-pure distilled water in an oven at 40 ◦ C for 7 days. Mineral N (NH4 + and NO3 − ) were extracted by shaking the incubated soil for 1 h with 50 mL 2 mol L−1 KCl (Bao, 2000), and were filtered and analyzed by FIAStar-5000 continuous-flow analyzer. The mineralization of soil organic N was calculated as the difference between mineral N contents before and after incubation. All soil samples were analyzed in triplicate. 2.3. Statistical analysis of data Statistical analysis was accomplished by ANOVA, and the differences among individual sites were determined using Duncan’s least significant difference (LSD) test at the 0.05 probability level. Soil chemical and microbial parameters were log-transformed before analysis to stabilize the variances. Then, linear correlation analyses were performed to determine relationships between heavy metals and these parameters, and the significance probability levels of the results were given at the p < 0.05 (*) and p < 0.01 (**), respectively. To classify soils by integrated microbial properties, data were analyzed by cluster analysis (CA) according to K-means partitioning method. Principal components analysis (PCA) was used to reduce the number of microbial variables by extracting the principal components separately. Canonical correlation analysis (CCA) was then carried out to investigate the dependent relationship between heavy metals and microbial parameters datasets. The significant level was set at ˛ = 0.05. The SAS 9.01 analytical software package was used for CA and CCA, and SPSS 11.5 software package was conducted for ANOVA, Duncan’s least significant difference test, linear correlation analysis, and PCA. 3. Results Soil pH, organic C, and total and available heavy metal contents are illustrated in Table 2. Total Cu, Zn and Pb contents in Site 17 were slightly lower than the soil background values of these heavy metals (Cu, 21.9 mg kg−1 ; Zn, 27.4 mg kg−1 ; Pb, 13.0 mg kg−1 ) in central Tibet, and total Cd contents was slightly higher than the soil background value of that (1.7 mg kg−1 ; Cheng and Tian, 1993). Thus, the Site 17 was used as the control for this study. The sampling sites varied greatly in soil pH, organic C and heavy metal contents due to their distances from the mine center. Soil pH varied from 5.3 to 6.5 (Table 2), and was significantly and negatively related to heavy metals (Table 4). In most cases, soil pH was significantly related to microbial parameters (Table 4). The range of soil organic C was

43%

47%

147

16%

25.9b 25.9b 7.5a 6.4a 28.4b 22.9b 48.8cd 41.6c 55.1d 43.9c 38.7c 26.8b 43.4c 41.4c 42.5c 37.1c 64.5e 0.11c 0.10bc 0.14c 0.15c 0.15c 0.10bc 0.06b 0.07b 0.07b 0.03a 0.06b 0.15c 0.08b 0.06b 0.05ab 0.06b 0.03a 62.0d 66.1d 59.9cd 64.2d 62.4d 63.3d 51.8c 62.1d 51.6c 32.0a 59.8cd 69.4d 63.7d 54.9c 60.5cd 53.2c 42.2b

Basal respiration (mg CO2 C kg−1 d−1 )

qCO2 d−1

N mineralization (mg kg−1 )

F.-P. Zhang et al. / Applied Soil Ecology 45 (2010) 144–151

36% 44% Means within a row followed by the different letters are significantly different at the level of 5%. MBC, microbial biomass C; MBN, microbial biomass N; MBP, microbial biomass P; qCO2 , metabolic quotient.

37% 40% 40% 35% Coefficient of variation

2.1ab 4.7d 3.7c 3.8c 3.7c 2.3b 3.6c 4.1cd 6.0e 4.8d 6.0e 1.9a 6.7ef 6.1e 6.2e 6.2e 7.0f 52.3c 58.4cd 51.6c 28.5a 43.3b 31.5a 62.6d 104.7f 126.2g 87.9e 63.5d 35.8ab 64.7d 76.9e 58.3cd 93.8f 128.0g 577.2b 692.1c 420.3a 429.9a 427.4a 607.4bc 921.0d 949.5d 729.9c 1009.16e 931.3d 474.6a 841.1c 960.7d 1122.5e 869.8d 1517.6f 10.0b 19.0d 5.3a 14.3c 15.5c 17.4cd 27.0ef 28.8f 25.2e 24.5e 27.2ef 6.0a 24.4e 22.8de 22.5de 10.7b 27.4f 0.11ab 0.16bc 0.09a 0.15b 0.12ab 0.10a 0.19c 0.19c 0.14b 0.25d 0.23cd 0.09a 0.16bc 0.18c 0.15b 0.20c 0.36d 3.8c 4.0c 4.0c 4.1c 2.7b 4.2c 4.6cd 4.5cd 5.2d 8.1f 6.1e 1.4a 4.1c 3.9c 3.8c 4.4c 7.2f 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Acid phosphatase (mg PNFF kg dry soil−1 ) Urease (g NH3 kg dry soil−1 ) Sucrase (g Suc kg dry soil−1 ) Sampling sites

Table 3 Changes in microbial characteristics in soils from different sampling sites of Lawu mine in central Tibet.

MBC (mg kg−1 )

MBN (mg kg−1 )

MBP (mg kg−1 )

Fig. 1. Dendrogram from K-means cluster method applied to soil microbial data.

4.2–12.9 g kg−1 . No significant correlation was found between soil total and available heavy metals and organic C (Table 4). There were the large variations in the contents of total Cu (13.05–495.88 mg kg−1 ), Zn (19.7–481.7 mg kg−1 ), Pb (1.1–135.7 mg kg−1 ) and Cd (3.7–59.4 mg kg−1 ), and available Cu (1.3–47.0 mg kg−1 ), Zn (3.3–98.3 mg kg−1 ), Pb (0.6–26.1 mg kg−1 ) and Cd (1.1–13.2 mg kg−1 ). Moreover, the total and available contents of Cu, Zn, Pb and Cd were higher in sites close to the mine center and decreased with the distances from the mine center. Total contents of Cu, Zn, Pb and Cd in Sites 1–16 were 3.5–38.0, 12.0–24.5, 2.6–19.5 and 2.4–15.8 times higher than those in the control, respectively, and available contents of Cu, Zn, Pb and Cd in Sites 1–16 were 5.7–36.7, 18.5–30.1, 13.5–45.0 and 2.7–12.2 times higher than those in the control, respectively. However, there was no increased trend of heavy metal contents along decreased altitude gradients around the mine center (Table 2). The effects of heavy metals on microbial parameters are shown in Table 3. As expected, soil MBC, MBN, MBP, sucrase activity, urease activity, acid phosphatase activity, and N mineralization significantly increased from around the mine center to about 200 m from the mine center, ranging from 420.3 to 1122.5 mg kg−1 , from 28.5 to 126.2 mg kg−1 , from 1.9 to 6.7 mg kg−1 , from 1.4 to 8.1 g Suc kg dry soil−1 , from 0.09 to 0.25 g NH3 kg dry soil−1 , from 5.3 to 28.8 mg PNFF kg dry soil−1 , and from 6.4 to 55.1 mg kg−1 , respectively. In contrast, soil basal respiration and qCO2 decreased from around the mine center to about 200 m from the mine center, ranging from 32.0 to 69.4 mg CO2 -C kg−1 d−1 and from 0.03 to 0.15 d−1 , respectively. In addition, in most cases, significant and negative correlations between MBC, MBN, MBP, sucrase activity, urease activity, acid phosphatase activity, and N mineralization with heavy metals were observed, while significant and positive correlations between basal respiration and qCO2 with heavy metals were found (Table 4). Cluster analysis (CA) showed a four-group clustering as the best clustering scheme for these soils (Fig. 1). The dendrogram clearly displayed that cluster 1 contained Sites 7, 8, 13, 14 and 16, cluster 2 included Sites 10, 11 and 15, cluster 3 comprised Sites 1–6, 9 and 12, and cluster 4 only contained Site 17. Also, MBC was the most significant variable defining the differences among clusters (Table 5). In present study, principal component analysis extracted only one principal component, Component 1, representing 72% of the original variances. Moreover, analysis on variables with significant loadings on Component 1 showed that MBC (R2 = 0.93) had the highest positive loadings on Component 1 followed by urease (R2 = 0.89), N mineralization (R2 = 0.87), qCO2 (R2 = −0.84),

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Table 4 Correlations between heavy metal with soil chemical and microbial characteristic of mining soils.

pH Organic C

pH

Organic C

1 −0.46**

Acid phosphatase

MBC

Sucrase

Urease

−0.46** 1

0.25 0.26

0.43** 0.15

0.50** 0.15

0.48** 0.14

MBN 0.36* 0.23

MBP 0.38* 0.20

Basal respiration 0.24 −0.20

N mineralization

qCO2

0.56** 0.12

0.42** −0.09

Total

Cu Zn Pb Cd

−0.38** −0.49** −0.31* −0.47**

−0.30* −0.29* −0.21 −0.17

−0.43** −0.35* −0.45** −0.59**

−0.32 −0.43** −0.05 −0.63**

−0.45** −0.20 −0.13 −0.75**

−0.43** −0.50** −0.08 −0.63**

−0.53** −0.67** −0.19 −0.56**

−0.29* −0.21 −0.10 −0.35*

0.43** 0.48** 0.20 0.55**

−0.48** −0.58** −0.06 −0.67**

0.42** 0.38** 0.11 0.65**

Available

Cu Zn Pb Cd

−0.38** −0.32* −0.20 −0.60**

−0.06 −0.23 −0.32* 0.06

−0.32* −0.54** −0.51** 0.52**

−0.43** −0.71** −0.47** −0.28*

−0.53** −0.57** −0.69** −0.75**

−0.22 −0.71** −0.83** −0.64**

−0.36* −0.68** −0.81** −0.60**

−0.10 −0.30* −0.48** −0.39*

0.33* 0.52** 0.66** 0.53**

−0.42* −0.73** −0.90** −0.69**

0.26 0.58** 0.80** 0.56**

MBC, microbial biomass C; MBN, microbial biomass N; MBP, microbial biomass P; qCO2 , metabolic quotient. * p < 0.05. ** p < 0.01.

Table 5 The ratio of between-cluster sum of square to total sum of square (R2 ) and the ratio of between-cluster sum of square to within-cluster sum of square (R2 /(1 − R2 )) for microbial variables in defining differences among clusters, n = 17. Items

R2

R2 /(1 − R2 )

Sucrase Urease Acid phosphatase MBC MBN MBP Basal respiration N mineralization qCO2

0.41 0.48 0.38 0.68 0.50 0.28 0.49 0.47 0.34

0.69 0.92 0.61 2.13 1.00 0.39 0.96 0.89 0.52

MBC, microbial biomass C; MBN, microbial biomass N; MBP, microbial biomass P; qCO2 , metabolic quotient.

MBN (R2 = 0.83), sucrase (R2 = 0.82), acid phosphatase (R2 = 0.79), basal respiration (R2 = −0.78) and MBP (R2 = 0.77). Canonical correlation analysis (CCA) showed that canonical correlations between the first heavy metal canonical variate (H-CV1) and the first microbial canonical variate (M-CV1), and the second heavy metal canonical variate (H-CV2) and the second microbial canonical variate (M-CV2) both were strong (R1 = 0.99, p < 0.001; R2 = 0.97, p < 0.01), while other pairs of canonical variates were not significant (T3 = 0.93, R4 = 0.86, R5 = 0.80, R6 = 0.65, R7 = 0.52, R8 = 0.15, p > 0.05). Moreover, redundancy analysis revealed that approximately 61% of standardized variances of heavy metal canonical variates were explained by microbial canonical variates, where 28% of the standardized variances of heavy metals (H-PCs) were explained by M-CV1, 19% by M-CV2, 5% by M-CV3, 4% by M-CV4, 2% by M-CV5, 1% by M-CV6, 1% by M-CV7 and 1% by M-CV8. Furthermore, the canonical coefficient of MBC for M-CV1 was 0.71, which were higher than other canonical coefficients of microbial parameters (0.24–0.59). 4. Discussion 4.1. Soil pH, organic C and heavy metal contents In present study, there were significantly negative correlations between soil pH and heavy metals (Table 4), suggesting that heavy metals led to mine soil acidification. Decreasing soil pH with increasing metal availability resulted from decreasing adsorption by variable charge colloids (Selin and Amacher, 1997). Moreover, soil pH was significantly correlated with microbial parameters (Table 4), which indirectly reflected inhibitory of heavy metals to microbial activities through influencing the availability of heavy

metals and their toxicity (Khan and Scullion, 2000). No significant correlation between soil total and available heavy metals and organic C (Table 4) was observed in our study, which was similar to the findings of Liao and Xie (2007) and Wang et al. (2007). Furthermore, the total and available contents of Cu, Zn, Pd and Cd decreased with the distances from the mine center. This might be explained by leaching, translocation, and accumulation of heavy metals released from the mining into the soils in different sites (Liao and Xie, 2007; Wang et al., 2009). Total or available heavy metals were higher in Sites 1–16 than those in the control, suggesting that the mine soils were severely polluted by heavy metals and might cause ecosystem problems. However, heavy metal contents did not decrease along altitude gradients around the mine center (Table 2), which was possibly related to the distribution of vegetation (Table 1). Higher vegetation biomass at reduced altitude sites might lead to higher uptake of heavy metals. 4.2. Effects of heavy metals on microbial characteristics In present study, significantly positive or negative correlations between microbial characteristics and heavy metals (Table 4) indicated the reduced activities of microorganisms with increasing heavy metal contents, which have been well documented in previous reports (Renella et al., 2003, 2005; Liao and Xie, 2007; Wang et al., 2007; Zhang et al., 2008; Papa et al., 2009). Soil microbial biomass, which plays an important role in nutrient cycling and ecosystem sustainability, has been found to be more sensitive to increased heavy metal concentrations in soils than total soil organic matter (Giller et al., 1998; Dai et al., 2004). There is now a considerable amount of evidence documenting a decrease in the soil microbial biomass as a result of heavy metal contamination (Liao and Xie, 2007; Wang et al., 2007; Papa et al., 2009). Our results demonstrated microbial biomass (C, N, and P) decreased markedly with increasing heavy metals (Table 4). The reasons are possibly due to microorganisms in soil under heavy metal stress diverting energy from growth to cell maintenance functions (Killham, 1985). Moreover, in contaminated soils, microorganisms need more energy to survive in unfavorable conditions. Therefore, a higher percentage of energy is lost, and less C, N and P are built into organic components (Mikanova, 2006). This assumption is supported by the higher basal respiration and metabolic quotient in contaminated soils (Table 3). Moreover, in our study, stronger correlations between microbial biomass (C, N, and P) with available heavy metals than with total heavy metals were observed (Table 4), demonstrating that toxicities to soil microorganisms are linked directly to heavy metals availability (Wang et al., 2007), and thus microbial biomass (C, N, and P) might be used as sensitive indicators of the soil pollutions

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by heavy metals. The results were inconsistent with the reports of Wang et al. (2009), who found that soil microbial biomass C did not correlate with heavy metals and is not proposed as sensitive indicator for evaluating the environmental effects of heavy metal pollution. However, reduced microbial biomass (C, N, and P) did not mean decreased diversity of soil microorganisms, because tolerant genotypes of some microorganisms with a high ‘evolutionary potential’ may sometimes develop within a few years (Tyler et al., 1989). Effects of soil pollution on enzyme activities are complex. The response of different enzymes to the same pollutant may vary greatly and the same enzyme may respond differently to different pollutants (He et al., 2003; Li et al., 2009). In our study, total heavy metals showed different effects on enzyme activities while available heavy metals all significantly inhibited enzyme activities (Table 4). In more cases, marked impacts of available heavy metals on enzyme activities than total metals were consistent with previously reported conclusions that these labile fractions are bioavailable (Li et al., 2009; Papa et al., 2009); moreover, various enzyme activities showed different responses on the same metal pollutant. We observed significant and different inhibitory of total Cu on sucrase and acid phosphatase activities, total Zn on sucrase and urease activities, and total Cd on all three enzyme activities measured; while total Pb only significantly inhibited sucrase activities, but not other enzyme activities (Tables 3 and 4). Li et al. (2009) also found that Cu, Mn, Pb and Cd affected organic C-acquiring enzymes activity (b-glucosidase and invertase), and total enzyme activity, but not xylanase and AlkP activity. The effects of metals on enzyme activities remain unclear. The adverse effect was explained by Ekenler and Tabatabai (2002) that metal ions may inactivate enzymes by reacting with sulfhydryl groups of enzymes to forming metal sulfides. Sulfhydryl groups in enzymes may serve as integral parts of the catalytic active sites or as groups involved in maintaining correct structural relationship of enzyme protein. Metal may also inhibit enzymes by complexing the substrate, or by reacting with the enzyme–substrate complex (Hinojosa et al., 2004). The activation may attribute to a shift to dominant microbial composition structure after the long-term soil stress. Lower enzyme activities may be also due to energy diversion into physiological adaptations necessary to tolerate heavy metals, such as synthesis of intra- and extracellular metal-sequestering proteins or saccharides, and biochemical reactions to precipitate or trap metals onto microbial surfaces (Renella et al., 2005). Significant correlation between soil pH and enzyme activities is not surprising, because the activities of these enzymes are significantly correlated with heavy metals (Table 4). In our study, enzyme activities inhibited by heavy metals indicated decreases of soil organic C, N and P cycles in Lawu mine, potentially causing available nutrient imbalance and soil quality degradation. Basal respiration has been largely used to estimate heavy metal toxicity (Dai et al., 2004). Our study indicated marked increase of basal respiration with increasing heavy metal contents (Tables 3 and 4). The results were inconsistent with the findings of Brookes (1995). They reported a decrease in CO2 -C evolution (ca. 30%) in presence of Cu, Ni, Zn and Cd. Dai et al. (2004) also found that the respiration rate was negatively correlated with Cu, Zn, Pb and Cd content. In our study, high basal respiration with increasing heavy metal contents was possible because microorganisms in less polluted soils used a higher percentage of consumed carbon for assimilation and thus a smaller percentage was released as CO2 in dissimilation processes; in contaminated soils, microorganisms needed more energy to survive in unfavorable conditions (Mikanova, 2006). Moreover, generally, lower soil organic C in the control than polluted soils might well explain lower basal respiration from unpolluted soils. Therefore, a higher percentage of consumed carbon was released as CO2 and less was built into

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organic components (Mikanova, 2006). In addition, the results further pointed out the shifts in physiological activities and metabolizable abilities of microorganisms, and the changes in ecological functions of microbial communities in mining soils as a result of heavy metal stress (Tyler et al., 1989), which led to more consumed energy for survival. In our study, basal respiration in contaminated soils in Lawu mine ranged from 31.95 to 69.44 mg CO2 -C kg−1 d−1 , which are far less than 480–960 mg CO2 -C kg−1 d−1 of basal respiration in soils in Lipu Cu mine (147–350 m above sea level of altitudes), Zhejiang Province, China (Long et al., 2003a), and 160–350 mg CO2 C kg−1 d−1 of basal respiration in soils in Gabuzkoa (Spain) contaminated with heavy metals from a low altitude mine spoil tip (Aceves et al., 1999), reflecting that compared to low altitude regions, soil microorganisms might develop quickly tolerance to heavy metals under extreme climates (low-tension, strongradiation, low-oxygen and cold) on the Tibetan Plateau. The qCO2 , i.e. basal respiration per unit biomass C, is an index for evaluating the toxic metal effects on soil microorganisms, and has been considered a sensitive ecophysiological indicator of heavy metal induced stress in soils (Renella et al., 2005). In our study, values of qCO2 have been shown to increase along with the increased contents of heavy metals (Table 3). The heavy metals contaminated soils had significantly higher qCO2 , indicating a greater energy requirement for maintenance (Brookes, 1995), a decline in substrate quality and eventually a decrease in microbial metabolic efficiency (Anderson and Domsch, 1990). This was confirmed by the significant and positive correlation between qCO2 and heavy metals (Table 4). A reduced metabolic efficiency of soil microflora in the presence of heavy metals was probably due to energy diversion into physiological adaptations necessary to tolerate heavy metals (Anderson and Domsch, 1990). Furthermore, the higher substrate-responsive qCO2 may indicate that microorganisms in contaminated soils were enduring greater stress. It has been reported that N mineralization was negatively correlated with heavy metal contents (Bååth, 1989; Dai et al., 2004). This indicated clearly the inhibitory effect of these metals (Cu, Zn, Cd and Pb) on N mineralization (Table 4). The results were inconsistent with the findings of Li et al. (2009) who found that increasing N mineralization under metal stress was mainly due to enhancement of N-related hydrolases and from NH4 + -N accumulation due to inhibited nitrification. Heavy metals generally exert an inhibitory action on soil microorganisms by displacing essential metal ions, blocking essential functional groups, or by modifying the active conformation of biological molecules (Doelman et al., 1994). Consequently, the activities of soil microorganisms would be reduced and oriented towards specific reactions of N-assimilation and reorganization rather than N mineralization (Minnich and McBride, 1986). Long et al. (2003b) also pointed out that nitrobacteria and ammonifier in relation to soil organic N decompositions were inhibited by heavy metal, thus reducing N mineralization. In most cases, total Pb contents were not significantly related to microbial characteristics (Table 4), possibly due that the effects of Pb on microbial characteristics were obscured by compound pollutions of Cu, Zn, Cd and Pb (Yang and Liu, 2001). Hinojosa et al. (2004) observed that Pb had very low and non-significant correlations with arylsulfatase, ␤-glucosidase, alkaline phosphatase, acid phosphatase, urease and dehydrogenase activities. Although some studies have reported that low contents of heavy metals could stimulate the activities of microorganisms (Bååth, 1989; Giller et al., 1998), we did not observe this phenomenon (Tables 2–4). The reasons were possibly because the functions of microbial communities are frangible under low-tension, strong-radiation, low-oxygen and cold climates (Li et al., 1999; Cai, 2002; Yao, 2006), and thus any adverse disturbance might reduce the activities of microorganisms.

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4.3. Multivariate analysis Cluster analysis (CA) is a method for dividing a large group of observations into smaller groups, by which observations within each group are relatively similar, while observations in different groups are relatively dissimilar (Lattin et al., 2003). In the present study, a K-means partitioning method was utilized to describe the heterogeneity of soils with different heavy metal pollutions using ten soil microbial parameters. The results suggested a four-group clustering as the best clustering scheme for these mining soils, which revealed that the mining soils by heavy metal pollution were highly discriminated by integrated soil microbial characteristics. Moreover, MBC was the most significant variable discriminating these clusters (Table 5), suggesting that the mining soils were highly distinguished by MBC (Table 5 and Fig. 1). Principal component analysis is a method for dimension reduction, which allows for re-expression of data with fewer variables while accounting for as much of the available information as possible (Lattin et al., 2003). Considering the large number of microbial variables, thus reduction was made by PCA while retaining as much as possible the original variances. In present study, the analysis showed that MBC had the highest positive loadings on Component 1, which further supported the previous statement that MBC significantly contributed to the discrimination in mining soils by heavy metal pollution (Table 5). Microbial communities are in close contact with soil microenvironments, and therefore are easily subjected to change following alteration of soil chemical properties (Corstanje et al., 2007). Thus, through altering soil microenvironments, mining activities are likely to affect the microbial community structure and function, which can be described by the changes in microbial parameters such as respiratory capacities, microbial biomass and extracellular enzymatic activities (Castillo and Wright, 2008). Canonical correlation analysis (CCA) indicated that two pairs of canonical variates were significant and approximately 47% of standardized variances of heavy metal canonical variates were explained by microbial canonical variates. This result further confirmed a close link between heavy metals and microbial characteristics (Table 4). Teng et al. (2005) also used CCA to assess the effects of heavy metals on microbial activities, and found that there existed significant canonical correlations (R1 = 0.89 or 0.93) between soil microorganism and heavy metal contents in two types of red earths. Moreover, the canonical coefficient of MBC for M-CV1 was higher than other canonical coefficients of microbial parameters. The results further showed that MBC were more sensitive to reflect changes in soil environment polluted by heavy metals. As described previously, the results of CA, PCA and CCA all demonstrated that MBC highly contributed to discriminate the polluted mining soils. Therefore, it may be postulated that MBC is a useful and sensitive indicator for assessing change in soil environmental quality in metal mine of central Tibet.

5. Conclusions Mining activities affected heavy metal contents of mine soils, thus influencing soil microbial characteristics. Multivariate analysis indicated that significant canonical correlations existed between soil heavy metals and microbial indexes with canonical coefficients of 0.97–0.99. The canonical coefficient of MBC for M-CV1 was 0.71, which was higher than other canonical coefficients of microbial parameters (0.24–0.59). One principal component, Component 1, was extracted, which represented 72% of the original variances; MBC (R2 = 0.93) had the highest positive loadings on Component 1. In addition, mining soils polluted by heavy metals were perfectly clustered into four groups, where MBC was the most significant

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