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Does arsenic play an important role in the soil microbial community around a typical arsenic mining area?
Environmental Pollution 213 (2016) 949e956
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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Does arsenic play an important role in the soil microbial community around a typical arsenic mining area?* Fan Wu a, b, Jun-Tao Wang a, Jun Yang c, **, Jing Li d, Yuan-Ming Zheng a, * a
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China University of Chinese Academy of Sciences, Beijing 100049, China c Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China d Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 January 2016 Received in revised form 9 March 2016 Accepted 21 March 2016 Available online 4 April 2016
Arsenic (As) can cause serious hazards to human health, especially in mining areas. Soil bacterial communities, which are critical parts of the soil ecosystem, were analyzed directly for soil environmental factors. As a consequence, it is of great significance to understand the ecological risk of arsenic contamination on bacteria, especially at the local scale. In this study, 33 pairs of soil and grain samples were collected from the corn and paddy fields around an arsenic mining area in Shimen County in Hunan Province, China. Significant differences were found between the soil nitrogen, As concentrations, and bacteria activities among these two types of land use. According to the structural equation model (SEM) analysis, compared with other environmental factors, soil As was not the key factor affecting the bacterial community, even when grain As was beyond the threshold of the national food hygiene standards of China. In the corn field, soil pH was the main factor dominating the bacterial richness, composition and grain As. Meanwhile, in the paddy field the soil total nitrogen (TN) and total carbon (TC) were the main factors impacting the bacterial richness, and the bacterial community composition was mainly affected by pH. The interactions between grain As and soil As were weak in the corn field. The bacterial communities played important roles in the food chain risk of As. The local policy of transforming paddy soil to dry land could greatly reduce the health risk of As through the food chain. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Land-use Basic soil properties Grain As Health risk Structural equation model (SEM)
1. Introduction As contamination has become a serious issue due to its toxicity and hazard to the environment and human health (Abernathy et al., 2003; Fendorf et al., 2010). Among all human activities, mining practices and wastewater irrigation are the main reasons for As contamination (Abedin et al., 2002; Han et al., 2002). Crops grown in contaminated soils can uptake As and transport it to the grains, and then a potential threat to human health would be caused through the food chain pathway (Jia et al., 2012). Microbes not only exist extensively in soil but also play important roles in the soil ecosystem. Nutrient cycling, detoxification and soil function maintenance are some of the main processes partaken
*
This paper has been recommended for acceptance by Kimberly Jill Hageman. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (J. Yang), [email protected] (Y.-M. Zheng). http://dx.doi.org/10.1016/j.envpol.2016.03.057 0269-7491/© 2016 Elsevier Ltd. All rights reserved.
by microbes (Filip, 2002; Bissett et al., 2013). Soil environment is essential to varied ecosystem service processes that are mediated by microbes (Bissett et al., 2013). Because some microbes are sensitive to environmental variations and can respond to these changes quickly, microbes are regarded as efficient bio-indicators of soil quality (Nielsen et al., 2002). The activity, abundance, diversity and community of microbes could be influenced by different environmental factors (Buckley and Schmidt, 2002), including As ez-Espino et al., 2009). Many reports about soil microbial (Pa geographic distribution have focused on the effects of environmental factors on soil microbial diversity. However, these studies were mainly conducted at regional or global scales. Some reports have noted that the changes in soil microbial richness and communities are closely related to disorders of the soil ecosystem function, and various bio-chemical processes in the soil would be affected. Thus, these diversity indexes could be used as indicators of environmental changes (Chaparro et al., 2012)dfor example, arsenic stress, particularly arsenic stress under long-term As contamination in the fielddand they can be beneficial to a better
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understanding of soil microbe responses to As toxicity. Consequently, the aims of this study were to assess how the activities, abundance, richness and community composition of soil bacteria respond to long-term As contamination in the farmland of a mining area and to identify the main factors determining these soil bacteria changes. Meanwhile, the effect of bacterial communities on the As accumulation in grains were also analyzed. We focused this investigation on a town in Shimen County, Hunan Province of China, most at the local scale. Considering the scaling effect, we tested whether the effects of soil environmental variables and As on soil bacteria at a local scale are different from those of larger-scale studies. The hypotheses were as follows: 1) As could be the main factor affecting the bacterial activities, abundance, richness, and community composition in different land-use types of the mining area, and 2) microbial processes might modify the As accumulation in the grains of crops. 2. Materials and methods 2.1. Site description and sample collection The research area (29.6403 N ~ 29.6657 N; 111.0146 E ~ 111.1252 E) is located in Shimen County, Hunan Province, China. This area is characterized by a typical subtropical monsoon climate zone. The annual average temperature was 16.7 C, and the annual average precipitation was 1540 mm. The soil type in this area is Ultisols (red soil) derived from quaternary red clay earth. This area features the largest realgar mine in Asia (Fig. 1). This mine was exploited approximately 1500 years ago, causing serious arsenic pollution in the surrounding areas. Soil samples were collected from the farmland around the mining area (Fig. 1). Due to the high risks of food chain exposure through rice, most of the paddy fields were changed to dry fields, especially near the realgar mine. Thus, we collected 33 soil samples. Among them, 20 samples were harvested from the corn field in August and distributed equally in the area near the realgar mine as far apart as possible. Additionally, 13 samples were harvested from the paddy field in September, and they were far from the realgar mine because of the limited distribution of the paddy field. At each site, crops (corn and rice plant) were uprooted, and soil was shaken off the roots to obtain 500 g of rhizosphere soil. Crop grains were collected simultaneously. All samples were stored in a
4 C refrigerated box and taken back to the laboratory. The soil samples were mixed completely and passed through a 2 mm sieve in the laboratory. Each sample was divided into three parts: one subsample was stored at 4 C for the measurement of the soil microbial biomass carbon (SMBC) and potential nitrification rate (PNR), one was stored at 80 C for DNA extraction, and one was air-dried for the analysis of soil physicochemical properties and the total concentration of As in the soil. The grain samples were washed and air dried for As measurement. 2.2. Soil physical and chemical properties The soil moisture content was measured after being dried in an oven at 105 C for 8 h. The soil pH was determined at an air-dried soil-to-water ratio of 1:2.5 with a pH conductor (Mettler-Toledo International Inc., Switzerland). The soil organic matter (OM) was measured by the K2Cr2O7 oxidation method (Schulte, 1995). The soil total carbon (TC) and total nitrogen (TN) were determined by an elemental analyzer (Vario EL III, Elementar, Germany). The soil ammonium nitrogen (NHþ 4 -N) and nitrate nitrogen (NO3 -N) concentration were extracted with a 2 M KCl solution and determined by a continuous flow analyzer (SANþþ, Skalar, Holland). 2.3. Soil and grain As concentrations For the soil total As measurement, 0.3 g of soil samples were digested with HNO3þHCl (12 mL, 3:1 v/v) following the method 3051A of the United States Environmental Protection Agency (USEPA) using a microwave digesting system (Mars X, CEM, USA). The solution was measured by inductively coupled plasma mass spectrometry (ICP-MS) (NexION 350, PerkinElmer, USA). Approximately 0.5 g of the grain samples were weighed into a flask, and 15 mL of HNO3 was added to it. After standing overnight, the samples were then digested on an electric heating plate with 2 mL of HClO4 and 0.5 mL of H2SO4 until the solutions were clear. After cooling, the solutions were transferred to a 50-mL volumetric flask for As measurement by a hydride-generation atomic fluorescence spectrometer (HG-AFS). 2.4. Soil microbial activity index For the analysis of SMBC, the soil samples were pretreated
Fig. 1. Study area and sampling sites in Shimen, Hunan Province.
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immediately after being brought back to the laboratory from the field. Then, a part of the moist soil (equivalent to 10 g oven-dried soil) was fumigated with ethanol-free CHCl3 in a total darkness vacuum dryer at 25 C for 24 h. The other part of the moist soil with the same weight was non-fumigated for contrast. The dissolved soil carbon was extracted with 0.5 M K2SO4 and then determined with a Liqui TOC analyzer (Vario TOC, Elementar, Germany). For the analysis of PNR, a phosphate buffer containing (NH4)2SO4 and KClO3 was added into each soil sample of 5 g. Then, each sample was cultured in darkness in an oscillator with a rotating speed of 180 rpm at room temperature (25 C) for 24 h. After the incubation, all samples were extracted with a 2 M KCl solution, and the extracts were measured with an ultraviolet spectrophotometer (Spectra Max M5, Molecular Devices, USA). 2.5. Soil DNA extraction Soil DNA was extracted from 0.5 g of each soil sample using the MoBio Powersoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA) following the manufacturer's protocol. The concentration and quality of the extracted DNA were determined by a NanoDrop ND-2000c UVeVis spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). 2.6. Quantitative PCR analysis and high-throughput sequencing of 16S rRNA gene The abundance of the bacterial 16S rRNA gene was amplified using the primer pairs BACT1369F (50 -CGGTGAATACGTTCYCGG-30 ) and PROK1492R (50 -GGWTACCTTGTTACGACTT-30 ) with the probe TM1389F (50 -CTTGTACACACCGCCCGTC-30 ) (Suzuki et al., 2000). The 25 mL PCR reaction mixture system contained 12.5 mL SYBR Premix Ex TaqTM (Takara Biotechnology, Dalian, China), 0.5 mL of each primer and 1 mL of probe, and 2 mL of 10-fold diluted DNA template. After even mixing, the reactions were determined on an iCycler iQ5 thermocycler (BioRad Laboratories, Hercules, CA, USA) with the following thermal profile: 95 C for 10 s, 40 cycles of 10 s at 95 C, and 1 min at 56 C. The bacterial 16S rRNA was amplified by the primer pair of Bakt_341F and Bakt_805R (Herlemann et al., 2011). The raw sequences obtained from Miseq high-throughput sequencing were analyzed with the pipeline of the Quantitative Insights into Microbial Ecology (QIIME) (Caporaso et al., 2010). Reads such as monomers and chimeras were needed wipe out using UPARSE which reports less incorrect bases than other methods (Edgar, 2013). With a threshold of 97% similarity, the sequences were clustered into operational taxonomic units (OTUs). The representative sequences of each OTU were classified by the RDP classifier (Wang et al., 2007). All representative sequences were aligned using PyNAST against the latest Greengenes database (DeSantis et al., 2006). A resampling procedure was performed at a sequencing depth of 31,182 before downstream analyses, and bacterial richness was characterized by the OTU counts at 97% sequence identity. The weighted UniFrac distance was employed as the measurement of bacterial community composition. 2.7. Statistical analysis Spearman's correlation analysis was performed to examine the relationship between As and soil physicochemical factors in SPSS 19 (IBM Co., Armonk, NY, USA). A t-test was used to compare SMBC, PNR, bacterial abundance and richness between the different landuse types. A regression analysis was conducted to the overall relationship between the bacterial indexes and environmental factors. A principal coordinates analysis (PCoA) was conducted to
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visualize the bacterial community composition based on the weighted UniFrac matrix. To explore the direct and indirect effects of bacterial activities, abundance, richness, community composition and As concentration in the soil and grains together with varied environmental factors, a structural equation model (SEM) was built. Before the operation, a conceptual model based on the relative knowledge of ecology and microbiology was needed. The SEM analysis was conducted using AMOS17.0 (Amos, Development Corporation, Meadville, PA, USA). 3. Results 3.1. Soil properties The land-use type exerts clear effects on soil properties (Table 1). Most of the basic soil properties showed great differences between these two land-use types. The concentrations of NO 3 -N in the corn soils were significantly higher than those in the paddy soils (P < 0.01), whereas the concentrations of NHþ 4 -N and TN were much lower in the corn soils than those in the paddy soils (P < 0.01). Moreover, soil pH, OM and TC showed differences between the corn and paddy soils. However, these differences were not significant. 3.2. Total As concentration in the soil and grain The total As concentrations in the soil and grain samples are shown in Table 2. The total concentrations of As in the corn soils ranged from 16.3 mg kg1 to 1023.3 mg kg1, much higher than those in the paddy soil with a range of 17.7 mg kg1 to 108.5 mg kg1 (P < 0.01). However, the As concentrations in the corn grains were significantly lower than those in the rice grains (P < 0.01). Additionally, the paddy field was attributed with a high health risk because all of the rice grains had higher concentrations than the threshold of the national food hygiene standards of China (MHSA, 2005) (0.15 mg kg1 and 0.2 mg kg1 for As in rice and corn grain, respectively). The Spearman rank-order correlation analysis showed that the grain As concentrations were notably correlated with soil As in the corn field (P < 0.05), whereas no relationship was observed between the soil and grain As concentrations in the paddy field. 3.3. Activities of soil bacterial community A significant difference in SMBC was observed between two land-use types (P < 0.01), i.e., the SMBCs in the corn field were much higher than those in the paddy field (Table 3). The SMBCs in the corn field increased with As concentrations ranging from 16.3 mg kg1 to 691.0 mg kg1 and then decreased until the highest As concentration of 1023.3 mg kg1, showing a significant correlation (P < 0.05). In addition, the soil TN was correlated with the SMBC (P < 0.01). In the paddy field, SMBCs decreased with increasing As concentration and then increased at the turning point of the As concentration of 74.9 mg kg1 (P < 0.01). The soil TC (P < 0.05) and TN (P < 0.01) had significant negative effects on the SMBC. The PNR was also significantly different under two land-use types (P < 0.05). The corn soil had a higher nitrification rate than the paddy soil. A correlation was found between PNR and the As concentration in the corn soil at 0.1levels. For the paddy soil, there was no significant correlation between PNR and the soil As concentration. Comparatively, the PNR was mainly correlated with soil pH at the 0.01 level, with TC and TN at the 0.05 level in the corn field, and with pH, TC, NHþ 4 -N and NO3 -N on PNR at 0.01 level in the paddy field.
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Table 1 The basic soil properties in different land-use types.
pH TN*** (%) TC (%) OM (mg$kg1) NHþ 4 -N*** (mg$kg1) NO 3 -N** (mg$kg1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy Corn Paddy Corn Paddy Corn Paddy Corn Paddy
20 13 20 13 20 13 20 13 20 13 20 13
4.28 4.92 0.11 0.16 1.02 1.20 21.0 24.3 7.57 16.7 9.17 2.88
7.90 7.52 0.22 0.23 2.51 3.73 42.3 75.9 15.1 56.4 55.2 23.1
6.60 5.65 0.14 0.19 1.42 1.61 30.1 35.2 10.7 23.6 17.5 9.64
6.31 5.94 0.15 0.19 1.49 1.86 31.1 40.4 10.8 28.4 21.1 11.3
1.05 1.00 0.03 0.02 0.38 0.75 7.20 16.0 1.92 11.8 11.6 6.95
Abbreviations: TN total nitrogen, TC total carbon, OM organic matter, and Std standard deviation. Significance of independent sample T-test: ** means P < 0.01, and *** means P < 0.001.
Table 2 The soil and grain As in different land-use types.
Soil As*** (mg$kg1) Grain As*** (mg$kg1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy
20 13 20 13
16.3 17.7 0.04 0.33
1023.3 108.5 0.26 0.87
214.3 67.0 0.11 0.40
344.0 67.0 0.13 0.48
277.1 25.5 0.07 0.18
Abbreviations: Std standard deviation. Significance of independent sample T-test: *** means P < 0.001.
Table 3 The soil microbial biomass carbon (SMBC) and potential nitrification rate (PNR) in different land-use types.
SMBC*** (mg$kg1) PNR* (mg$g1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy
20 13 20 13
212.4 14.7 0.05 0.02
789.9 318.2 4.50 1.53
426.9 136.6 0.82 0.13
437.9 133.2 1.03 0.39
146.1 74.9 1.12 0.46
Abbreviations: Std standard deviation. Significance of independent sample T-test: * means P < 0.05, *** means P < 0.001.
3.4. Abundance and richness of soil bacteria
Different phyla responded differently to the variation of the soil As concentration in different land-use types. The relative
The copy numbers of the bacterial 16S rRNA gene for the corn soils and the paddy soils ranged from 3.08 109 to 2.10 1010 and 1.04 1010 to 3.84 1010 copies$g1 dry soil, respectively, and there was a significant difference between these two land-use types (P < 0.01). The soil As concentrations were also significantly correlated with the copy numbers of bacteria for the paddy soils (P < 0.05). OTU numbers (97% identity threshold) were used to calculate the richness of the bacteria. A significant difference in bacterial richness was found between the corn soil and the paddy soil (P < 0.01). The interaction among the factors will be analyzed next using multivariate methods.
3.5. Composition of soil bacterial community The results of the PCoA derived from the weighted UniFrac distance based on the 97% OTU level of the bacterial community compositions between two land-use types demonstrated a significant difference in bacterial community composition between the corn soil and paddy soil (Fig. 2). The dominant phyla in both landuse types were mainly Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes, and so on, which accounted for 93.8e98.3% and 80.3e93.6% of the relative abundance in the corn field and paddy field, respectively (Fig. 3). Three phyla, Chlorobi, Cyanobacteria and Chlamydiae, were only detected in the paddy soils.
Fig. 2. Principal coordinates analysis (PCoA) of bacterial communities in the corn field and the paddy field. The community composition was characterized by the weighted UniFrac distance.
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Fig. 3. Relative abundance of soil bacteria on the phylum level in the corn field (a) and the paddy field (b).
abundance of Actinobacteria and Gemmatimonadetes in the corn soil was significantly higher compared with the paddy soil (P < 0.01), whereas the phyla of Verrucomicrobia and Nitrospirae were much lower (P < 0.01). For the corn soil, Bacteroidetes and Firmicutes presented a strong correlation with the soil As (P < 0.05). For the paddy soil, Nitrospirae and Chlamydiae had significant correlations with the gradient of As concentration (P < 0.05).
3.6. Impact factors of the activities, richness and community composition of soil bacteria
environmental factors and the bacterial community indexes (Fig. 4a, b). In the pooled model, land-use type was the most distinguished factor and could hide the effects of other factors; hence, two models were built for the two land-use types. For the corn soil, pH affected bacterial activities, richness and composition directly and significantly except SMBC (indirectly). Among them, richness was mainly controlled by soil pH, and composition was solely controlled by soil pH. Soil TC affected richness/abundance directly and PNR/SMBC indirectly, whereas soil TN contributed to richness and SMBC directly and to abundance and PNR indirectly. NO 3 -N affected bacterial activities (SMBC and PNR) in direct ways. Soil As had few effects on the microbial
SEMs were built to determine the relationship between the
Fig. 4. The structural equation models (SEM) showing the relationship among environmental factors, bacterial communities and grain As of the corn field (a) and the paddy soil (b). The solid arrows represent the positive effects while dotted arrows represent the negative effects. The size of the line denotes the strength of the effect. The models fitted the data well. (a) CIMN/DF ¼ 0.958; P ¼ 0.534; RMSEA ¼ 0.000; (b) CMIN/DF ¼ 0.956; P ¼ 0.523; RMSEA ¼ 0.000. Significance level: #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001. Abbreviations: TC total carbon, TN total nitrogen, SMBC soil microbial biomass carbon, PNR potential nitrification rate, Composition soil bacterial community composition.
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community but affected SMBC greatly. Grain As was mainly directly controlled positively by NO 3 -N and pH and negatively by abundance and TN. Soil TC and bacterial richness had indirect effects on the grain As. Soil As and bacterial composition had relatively weak influences on grain As. Thus, the basic soil properties played more important roles in modifying the bacterial communities and grain As in the corn soil of this mining area rather than soil As. For the paddy soil, pH only had a direct effect on NO 3 -N and bacterial community composition, and it was the most influential direct factor on community composition. In addition, it had an indirect effect on SMBC, bacterial abundance and richness. The soil TC exhibited direct effects on bacterial richness (with the highest coefficient) and community composition and negatively affected SMBC and bacterial abundance indirectly. Soil TN had direct effects on SMBC, bacterial abundance and richness, among which the effect on abundance was the largest. NO 3 -N only directly affected the bacterial richness in a positive way. Soil As had positive effects on SMBC, bacterial abundance and richness directly. The grain As of rice was influenced directly by soil As, TN and bacterial richness, whereas it was indirectly affected by soil pH, TC, and NO 3 -N. In total, soil As contributed more to the bacterial community and the grain As in the paddy soil compared with the corn soil. Additionally, the factors controlling grain As were also very different in two land-use types. The soil OM and NHþ 4 -N were not shown in the SEM because they had no significant effects on the bacterial community. 4. Discussion In this study, samples were collected from the farmland around a realgar mining area in Hunan Province. During the past several decades, rice was the main food crop of this area. However, paddy cultivation has led to a serious health hazard because As can accumulate easily in rice grains through this cultivation mode. After the 1970s, to decrease the health risk by the food chain pathway, paddy cultivation in the high soil As concentration area was changed to dry farming, mainly corn cultivation. Rice planting was restricted to limited areas, especially in the areas with low As. Therefore, after years of evolution, the soil bacterial community evolved. Under this condition, multiple environmental factors, including soil As, exerted different influences on soil bacteria, as demonstrated by the SEM results. Similarly, the grain As was also correlated with many environmental factors. 4.1. Effects of land-use on soil bacteria The activities, abundance, richness and community composition of soil bacteria were significantly different between the two types of land use. This was mainly because the land use could affect the bacterial community by modifying soil properties (Lauber et al., 2008; Kuramae et al., 2012; Xiang et al., 2014). It was also reported that the most important factor in structuring microbial communities was soil moisture saturation (Drenovsky et al., 2010). The phylum of Actinobacteria was significantly higher in the corn soil compared with the paddy soil because Actinobacteria had a lower relative abundance in the anaerobic conditions of the paddy soil (Shao et al., 2015). Moreover, the flooded condition of the paddy soil could increase the substrate utilization ability of soil bacteria and accelerate the turnover of the soil carbon (Pal and Broadbent, 1975). These differences in environmental conditions resulted in only three phyla, Chlorobi, Cyanobacteria and Chlamydiae, being detected in the paddy soils (Fig. 3). Further research is also needed to discover the exact reason, especially at a small scale as in the current study.
4.2. Effects of the soil properties on bacteria Basically, the soil physicochemical properties had significant direct or indirect effects on the soil bacteria as described in the results, consistent with many other reports which mostly investigated large scales (Fierer and Jackson, 2006; Chu et al., 2010; Hu et al., 2013; Wang et al., 2015). Among them, soil pH, TC and TN were the most important driving factors, and in different land-use types they presented different contributions to the soil bacteria assembly. In the corn field soil the pH directly affected composition, richness and PNR of soil bacteria, especially composition as the only controlling factor and richness as the highest impact coefficient (0.76); meanwhile, in the paddy field the pH contributed only negatively to composition, and no other direct effects on soil bacteria were found. Therefore, it seemed that pH was a scaleindependent factor in the soil bacteria assembly because it could not be neglected in multi-scale studies. By contrast, the bacterial richness in the paddy field was mainly affected by soil TC and TN, and the contribution from TC and TN to richness in the corn field was significant but less than that from pH. Further, the influence of TC was negative in dry land and positive in paddy land, whereas the influence of TN was positive in dry land and negative in paddy land. Our results were similar to the findings of Shen et al. (2013) whose study also conducted at the local scale demonstrated that the bacterial diversity, especially richness on Mt. Changbai was largely predicted by the soil pH, TC and TN (Shen et al., 2013). The effects from the soil TC and TN had seldom been reported in the previous studies conducted at large scales, for example, at a regional scale. Thus, the effects of soil properties on the soil bacteria should be related to the research scale (Martiny et al., 2006). At different scales, environmental factors present different contributions. In total, soil pH was an important factor in structuring the bacterial community both in dry land and paddy land at the local scale of the current study, which was similar to the findings within the previous studies (Wakelin et al., 2008; Chu et al., 2010; Rousk et al., 2010; Hu et al., 2013). However, in the corn soil, pH was the most dominant factor; meanwhile, in the paddy soil the direct effect of soil pH decreased, and TC and TN played important roles in determining the soil bacteria diversity. Moreover, soil TN contributed directly to SMBC in both land-use types and indirectly to PNR in the corn soil. NO 3 -N also had direct effects on SMBC and PNR in the corn soil. These findings were consistent with the previous research which found that soil properties including pH, the concentration of N, and so on, could affect the bacterial activities (Smolders et al., 2001; Yao et al., 2011; Chen et al., 2015). 4.3. Effects of the soil As on bacteria Soil As, an independent factor in the soil environment, had a stronger impact on the bacterial community in the paddy field than in the corn field according to the SEM analysis. In the paddy field, soil As had negative effects on SMBC, bacterial abundance and richness. However, in the corn field, soil As only positively affected SMBC. Although the concentrations of soil As were much higher in the corn field than in the paddy field, the toxicity of As under flooded conditions showed greater inhibition than that in dry land, which could be explained by the As toxicity being related to the species and fractionations of As in the soil (Bissen and Frimmel, 2003; Van Herreweghe et al., 2003; Zhao et al., 2013). Under the anaerobic conditions in the paddy soil, As was more toxic and mobile because more As(V) would be reduced to As(III) (Goldberg, 2002; Takahashi et al., 2004). In fact, As mainly showed stimulating effects on SMBC (positive effect in SEM) despite very high
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concentrations of As in the soil, indicating that the soil microbes had a stronger tolerance to As in dry land and that the transformation of the cultivation mode significantly decreased the As toxicity. Nevertheless, in the paddy soil As inhabited SMBC with lower concentrations, revealing that the SMBC was sensitive to As contamination (Edvantoro et al., 2003; Van Zwieten et al., 2003; Shade et al., 2012; Awasthi et al., 2014). Additionally, the species richness and abundance of soil bacteria were also affected by soil As in the paddy field. Das et al. (2013) found that bacterial abundance decreased significantly as As toxicity increased (Das et al., 2013). Thus, the stresses of As on soil bacteria were dominated by soil properties, which consequently affected the As toxicity (Marin et al., 1993; Goldberg, 2002; Jiang et al., 2005). When considering all environmental factors in this study, the influences of soil As receded greatly compared with the soil TC and TN (Fig. 4b). Consequently, we could not say that arsenic played an important role in the soil microbial community structure in this area. The soil bacterial composition did not show any links with soil As, but the relative abundance of some phyla showed some respected characteristics. Nitrospirae and Chlamydiae had correlations with the gradient of As concentration in the paddy soil (P < 0.05). Thus, bacteria phyla sensitive to As gradient should be investigated further in future studies about As eco-toxicity in the soil. 4.4. Grain As, food chain and health risk The As concentrations in rice grains were significantly higher than those in corn grains, and the rice As concentrations were all beyond the national standard of food security; however, the As concentrations in the paddy soil were much lower than those in the corn soil. The transformation of the cultivation mode in this area down-regulated the food chain risk of As. This was mainly because the soil As had weaker effects on the grain As in the corn field than it did in the paddy field. It also contributed less to grain As in the corn field compared with other soil properties, i.e., pH, NO 3 -N and TN, whereas in paddy field it was very different. The anaerobic conditions of the paddy soil led to a higher accumulation of As in grain, also resulting in the reductive dissolution of iron oxides/ hydroxides and the release of the absorbed As(V) (Xu et al., 2008), which led to an enhancement of the As bioavailability in anaerobic soil (Mandal and Suzuki, 2002; Chen et al., 2005; Weber et al., 2009; Stroud et al., 2011). In addition, according to the SEM analysis, the soil As affected the soil bacterial properties less in the dry land than in the paddy land. This led to less As transport from the soil to the grain through the process regulated by bacteria in the dry land. Therefore, in upland soil, As in the corn grain was affected more by soil properties (pH, TN and NO 3 -N) than by the soil As. In the paddy soil, the interactions between grain As and the other environmental factors (including soil As) were significant, and soil As could affect the As in grains through bacterial processes (showed as SMBC and bacterial richness). Hence, rational agronomic measures, such as reducing the soil As concentrations and modifying the soil conditions and relevant microbial functions, could be considered to reduce the health risk of As through the food chain, especially in paddy fields. The soil bacterial community played an important role in the food chain risk of As. 5. Conclusions In this study, bacterial responses to long-term As contamination were investigated around a typical As mining area. Land use was found to affect the soil bacterial community deeply and completely. Our findings also presented a high intensity of support for the
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intensive impact of long-term As contamination on soil bacteria, especially bacterial activities, abundance, richness and composition. However, when soil As was combined with soil properties, the soil pH, TC and TN were the main factors driving the bacterial communities. Our study also proved that the soil bacterial community played important roles in the As accumulation in grains. Thus, bacterial communities and various environmental factors should be considered comprehensively when assessing the human health risks of soil As contamination through the food chain. Acknowledgments The current work was financially supported by the projects with the grant No. KFJ-EW-STS-014 and KFJ-EW-ZY-005, and the Youth Innovation Promotion Association, Chinese Academy of Sciences. References Abedin, M.J., Cotter-Howells, J., Meharg, A.A., 2002. Arsenic uptake and accumulation in rice (Oryza sativa L.) irrigated with contaminated water. Plant Soil 240, 311e319. Abernathy, C.O., Thomas, D.J., Calderon, R.L., 2003. Health effects and risk assessment of arsenic. J. Nutr. 133, 1536Se1538S. Awasthi, A., Singh, M., Soni, S.K., Singh, R., Kalra, A., 2014. Biodiversity acts as insurance of productivity of bacterial communities under abiotic perturbations. ISME J. 2445e2452. Bissen, M., Frimmel, F.H., 2003. Arsenicda review. Part I: occurrence, toxicity, speciation, mobility. Acta hydroch. Hydrob 31, 9e18. Bissett, A., Brown, M.V., Siciliano, S.D., Thrall, P.H., 2013. Microbial community responses to anthropogenically induced environmental change: towards a systems approach. Ecol. Lett. 16, 128e139. Buckley, D.H., Schmidt, T.M., 2002. Exploring the Biodiversity of Soilda Microbial Rain Forest. Biodiversity of Microbial Life. Wiley-Liss Inc., New York, pp. 183e208. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7, 335e336. Chaparro, J.M., Sheflin, A.M., Manter, D.K., Vivanco, J.M., 2012. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fert. Soils 48, 489e499. Chen, Z., Wu, W., Shao, X., Li, L., Guo, Y., Ding, G., 2015. Shifts in abundance and diversity of soil ammonia-oxidizing bacteria and archaea associated with land restoration in a semi-arid ecosystem. Plos One. http://dx.doi.org/10.1371/ journal.pone.0132879. Chen, Z., Zhu, Y.G., Liu, W.J., Meharg, A.A., 2005. Direct evidence showing the effect of root surface iron plaque on arsenite and arsenate uptake into rice (Oryza sativa) roots. New Phytol. 165, 91e97. Chu, H., Fierer, N., Lauber, C.L., Caporaso, J., Knight, R., Grogan, P., 2010. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12, 2998e3006. Das, S., Jean, J.S., Kar, S., Liu, C.C., 2013. Changes in bacterial community structure and abundance in agricultural soils under varying levels of arsenic contamination. Geomicrobiol. J. 30, 635e644. DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., Andersen, G.L., 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microb. 72, 5069e5072. Drenovsky, R.E., Steenwerth, K.L., Jackson, L.E., Scow, K.M., 2010. Land use and climatic factors structure regional patterns in soil microbial communities. Glob. Ecol. Biogeogr. 19, 27e39. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996e998. Edvantoro, B.B., Naidu, R., Megharaj, M., Singleton, I., 2003. Changes in microbial properties associated with long-term arsenic and DDT contaminated soils at disused cattle dip sites. Ecotox. Environ. Safe 55, 344e351. Fendorf, S., Michael, H.A., van Geen, A., 2010. Spatial and temporal variations of groundwater arsenic in South and Southeast Asia. Science 328, 1123e1127. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. P. Natl. Acad. Sci. U. S. A. 103, 626e631. Filip, Z., 2002. International approach to assessing soil quality by ecologicallyrelated biological parameters. Agric. Ecosyst. Environ. 88, 169e174. Goldberg, S., 2002. Competitive adsorption of arsenate and arsenite on oxides and clay minerals. Soil Sci. Soc. Am. J. 66, 413e421. Han, F.X., Banin, A., Su, Y., Monts, D.L., Plodinec, J.M., Kingery, W.L., Triplett, G.E., 2002. Industrial age anthropogenic inputs of heavy metals into the pedosphere. Naturwissenschaften 89, 497e504.
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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Does arsenic play an important role in the soil microbial community around a typical arsenic mining area?* Fan Wu a, b, Jun-Tao Wang a, Jun Yang c, **, Jing Li d, Yuan-Ming Zheng a, * a
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China University of Chinese Academy of Sciences, Beijing 100049, China c Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China d Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 January 2016 Received in revised form 9 March 2016 Accepted 21 March 2016 Available online 4 April 2016
Arsenic (As) can cause serious hazards to human health, especially in mining areas. Soil bacterial communities, which are critical parts of the soil ecosystem, were analyzed directly for soil environmental factors. As a consequence, it is of great significance to understand the ecological risk of arsenic contamination on bacteria, especially at the local scale. In this study, 33 pairs of soil and grain samples were collected from the corn and paddy fields around an arsenic mining area in Shimen County in Hunan Province, China. Significant differences were found between the soil nitrogen, As concentrations, and bacteria activities among these two types of land use. According to the structural equation model (SEM) analysis, compared with other environmental factors, soil As was not the key factor affecting the bacterial community, even when grain As was beyond the threshold of the national food hygiene standards of China. In the corn field, soil pH was the main factor dominating the bacterial richness, composition and grain As. Meanwhile, in the paddy field the soil total nitrogen (TN) and total carbon (TC) were the main factors impacting the bacterial richness, and the bacterial community composition was mainly affected by pH. The interactions between grain As and soil As were weak in the corn field. The bacterial communities played important roles in the food chain risk of As. The local policy of transforming paddy soil to dry land could greatly reduce the health risk of As through the food chain. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Land-use Basic soil properties Grain As Health risk Structural equation model (SEM)
1. Introduction As contamination has become a serious issue due to its toxicity and hazard to the environment and human health (Abernathy et al., 2003; Fendorf et al., 2010). Among all human activities, mining practices and wastewater irrigation are the main reasons for As contamination (Abedin et al., 2002; Han et al., 2002). Crops grown in contaminated soils can uptake As and transport it to the grains, and then a potential threat to human health would be caused through the food chain pathway (Jia et al., 2012). Microbes not only exist extensively in soil but also play important roles in the soil ecosystem. Nutrient cycling, detoxification and soil function maintenance are some of the main processes partaken
*
This paper has been recommended for acceptance by Kimberly Jill Hageman. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (J. Yang), [email protected] (Y.-M. Zheng). http://dx.doi.org/10.1016/j.envpol.2016.03.057 0269-7491/© 2016 Elsevier Ltd. All rights reserved.
by microbes (Filip, 2002; Bissett et al., 2013). Soil environment is essential to varied ecosystem service processes that are mediated by microbes (Bissett et al., 2013). Because some microbes are sensitive to environmental variations and can respond to these changes quickly, microbes are regarded as efficient bio-indicators of soil quality (Nielsen et al., 2002). The activity, abundance, diversity and community of microbes could be influenced by different environmental factors (Buckley and Schmidt, 2002), including As ez-Espino et al., 2009). Many reports about soil microbial (Pa geographic distribution have focused on the effects of environmental factors on soil microbial diversity. However, these studies were mainly conducted at regional or global scales. Some reports have noted that the changes in soil microbial richness and communities are closely related to disorders of the soil ecosystem function, and various bio-chemical processes in the soil would be affected. Thus, these diversity indexes could be used as indicators of environmental changes (Chaparro et al., 2012)dfor example, arsenic stress, particularly arsenic stress under long-term As contamination in the fielddand they can be beneficial to a better
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understanding of soil microbe responses to As toxicity. Consequently, the aims of this study were to assess how the activities, abundance, richness and community composition of soil bacteria respond to long-term As contamination in the farmland of a mining area and to identify the main factors determining these soil bacteria changes. Meanwhile, the effect of bacterial communities on the As accumulation in grains were also analyzed. We focused this investigation on a town in Shimen County, Hunan Province of China, most at the local scale. Considering the scaling effect, we tested whether the effects of soil environmental variables and As on soil bacteria at a local scale are different from those of larger-scale studies. The hypotheses were as follows: 1) As could be the main factor affecting the bacterial activities, abundance, richness, and community composition in different land-use types of the mining area, and 2) microbial processes might modify the As accumulation in the grains of crops. 2. Materials and methods 2.1. Site description and sample collection The research area (29.6403 N ~ 29.6657 N; 111.0146 E ~ 111.1252 E) is located in Shimen County, Hunan Province, China. This area is characterized by a typical subtropical monsoon climate zone. The annual average temperature was 16.7 C, and the annual average precipitation was 1540 mm. The soil type in this area is Ultisols (red soil) derived from quaternary red clay earth. This area features the largest realgar mine in Asia (Fig. 1). This mine was exploited approximately 1500 years ago, causing serious arsenic pollution in the surrounding areas. Soil samples were collected from the farmland around the mining area (Fig. 1). Due to the high risks of food chain exposure through rice, most of the paddy fields were changed to dry fields, especially near the realgar mine. Thus, we collected 33 soil samples. Among them, 20 samples were harvested from the corn field in August and distributed equally in the area near the realgar mine as far apart as possible. Additionally, 13 samples were harvested from the paddy field in September, and they were far from the realgar mine because of the limited distribution of the paddy field. At each site, crops (corn and rice plant) were uprooted, and soil was shaken off the roots to obtain 500 g of rhizosphere soil. Crop grains were collected simultaneously. All samples were stored in a
4 C refrigerated box and taken back to the laboratory. The soil samples were mixed completely and passed through a 2 mm sieve in the laboratory. Each sample was divided into three parts: one subsample was stored at 4 C for the measurement of the soil microbial biomass carbon (SMBC) and potential nitrification rate (PNR), one was stored at 80 C for DNA extraction, and one was air-dried for the analysis of soil physicochemical properties and the total concentration of As in the soil. The grain samples were washed and air dried for As measurement. 2.2. Soil physical and chemical properties The soil moisture content was measured after being dried in an oven at 105 C for 8 h. The soil pH was determined at an air-dried soil-to-water ratio of 1:2.5 with a pH conductor (Mettler-Toledo International Inc., Switzerland). The soil organic matter (OM) was measured by the K2Cr2O7 oxidation method (Schulte, 1995). The soil total carbon (TC) and total nitrogen (TN) were determined by an elemental analyzer (Vario EL III, Elementar, Germany). The soil ammonium nitrogen (NHþ 4 -N) and nitrate nitrogen (NO3 -N) concentration were extracted with a 2 M KCl solution and determined by a continuous flow analyzer (SANþþ, Skalar, Holland). 2.3. Soil and grain As concentrations For the soil total As measurement, 0.3 g of soil samples were digested with HNO3þHCl (12 mL, 3:1 v/v) following the method 3051A of the United States Environmental Protection Agency (USEPA) using a microwave digesting system (Mars X, CEM, USA). The solution was measured by inductively coupled plasma mass spectrometry (ICP-MS) (NexION 350, PerkinElmer, USA). Approximately 0.5 g of the grain samples were weighed into a flask, and 15 mL of HNO3 was added to it. After standing overnight, the samples were then digested on an electric heating plate with 2 mL of HClO4 and 0.5 mL of H2SO4 until the solutions were clear. After cooling, the solutions were transferred to a 50-mL volumetric flask for As measurement by a hydride-generation atomic fluorescence spectrometer (HG-AFS). 2.4. Soil microbial activity index For the analysis of SMBC, the soil samples were pretreated
Fig. 1. Study area and sampling sites in Shimen, Hunan Province.
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immediately after being brought back to the laboratory from the field. Then, a part of the moist soil (equivalent to 10 g oven-dried soil) was fumigated with ethanol-free CHCl3 in a total darkness vacuum dryer at 25 C for 24 h. The other part of the moist soil with the same weight was non-fumigated for contrast. The dissolved soil carbon was extracted with 0.5 M K2SO4 and then determined with a Liqui TOC analyzer (Vario TOC, Elementar, Germany). For the analysis of PNR, a phosphate buffer containing (NH4)2SO4 and KClO3 was added into each soil sample of 5 g. Then, each sample was cultured in darkness in an oscillator with a rotating speed of 180 rpm at room temperature (25 C) for 24 h. After the incubation, all samples were extracted with a 2 M KCl solution, and the extracts were measured with an ultraviolet spectrophotometer (Spectra Max M5, Molecular Devices, USA). 2.5. Soil DNA extraction Soil DNA was extracted from 0.5 g of each soil sample using the MoBio Powersoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA) following the manufacturer's protocol. The concentration and quality of the extracted DNA were determined by a NanoDrop ND-2000c UVeVis spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). 2.6. Quantitative PCR analysis and high-throughput sequencing of 16S rRNA gene The abundance of the bacterial 16S rRNA gene was amplified using the primer pairs BACT1369F (50 -CGGTGAATACGTTCYCGG-30 ) and PROK1492R (50 -GGWTACCTTGTTACGACTT-30 ) with the probe TM1389F (50 -CTTGTACACACCGCCCGTC-30 ) (Suzuki et al., 2000). The 25 mL PCR reaction mixture system contained 12.5 mL SYBR Premix Ex TaqTM (Takara Biotechnology, Dalian, China), 0.5 mL of each primer and 1 mL of probe, and 2 mL of 10-fold diluted DNA template. After even mixing, the reactions were determined on an iCycler iQ5 thermocycler (BioRad Laboratories, Hercules, CA, USA) with the following thermal profile: 95 C for 10 s, 40 cycles of 10 s at 95 C, and 1 min at 56 C. The bacterial 16S rRNA was amplified by the primer pair of Bakt_341F and Bakt_805R (Herlemann et al., 2011). The raw sequences obtained from Miseq high-throughput sequencing were analyzed with the pipeline of the Quantitative Insights into Microbial Ecology (QIIME) (Caporaso et al., 2010). Reads such as monomers and chimeras were needed wipe out using UPARSE which reports less incorrect bases than other methods (Edgar, 2013). With a threshold of 97% similarity, the sequences were clustered into operational taxonomic units (OTUs). The representative sequences of each OTU were classified by the RDP classifier (Wang et al., 2007). All representative sequences were aligned using PyNAST against the latest Greengenes database (DeSantis et al., 2006). A resampling procedure was performed at a sequencing depth of 31,182 before downstream analyses, and bacterial richness was characterized by the OTU counts at 97% sequence identity. The weighted UniFrac distance was employed as the measurement of bacterial community composition. 2.7. Statistical analysis Spearman's correlation analysis was performed to examine the relationship between As and soil physicochemical factors in SPSS 19 (IBM Co., Armonk, NY, USA). A t-test was used to compare SMBC, PNR, bacterial abundance and richness between the different landuse types. A regression analysis was conducted to the overall relationship between the bacterial indexes and environmental factors. A principal coordinates analysis (PCoA) was conducted to
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visualize the bacterial community composition based on the weighted UniFrac matrix. To explore the direct and indirect effects of bacterial activities, abundance, richness, community composition and As concentration in the soil and grains together with varied environmental factors, a structural equation model (SEM) was built. Before the operation, a conceptual model based on the relative knowledge of ecology and microbiology was needed. The SEM analysis was conducted using AMOS17.0 (Amos, Development Corporation, Meadville, PA, USA). 3. Results 3.1. Soil properties The land-use type exerts clear effects on soil properties (Table 1). Most of the basic soil properties showed great differences between these two land-use types. The concentrations of NO 3 -N in the corn soils were significantly higher than those in the paddy soils (P < 0.01), whereas the concentrations of NHþ 4 -N and TN were much lower in the corn soils than those in the paddy soils (P < 0.01). Moreover, soil pH, OM and TC showed differences between the corn and paddy soils. However, these differences were not significant. 3.2. Total As concentration in the soil and grain The total As concentrations in the soil and grain samples are shown in Table 2. The total concentrations of As in the corn soils ranged from 16.3 mg kg1 to 1023.3 mg kg1, much higher than those in the paddy soil with a range of 17.7 mg kg1 to 108.5 mg kg1 (P < 0.01). However, the As concentrations in the corn grains were significantly lower than those in the rice grains (P < 0.01). Additionally, the paddy field was attributed with a high health risk because all of the rice grains had higher concentrations than the threshold of the national food hygiene standards of China (MHSA, 2005) (0.15 mg kg1 and 0.2 mg kg1 for As in rice and corn grain, respectively). The Spearman rank-order correlation analysis showed that the grain As concentrations were notably correlated with soil As in the corn field (P < 0.05), whereas no relationship was observed between the soil and grain As concentrations in the paddy field. 3.3. Activities of soil bacterial community A significant difference in SMBC was observed between two land-use types (P < 0.01), i.e., the SMBCs in the corn field were much higher than those in the paddy field (Table 3). The SMBCs in the corn field increased with As concentrations ranging from 16.3 mg kg1 to 691.0 mg kg1 and then decreased until the highest As concentration of 1023.3 mg kg1, showing a significant correlation (P < 0.05). In addition, the soil TN was correlated with the SMBC (P < 0.01). In the paddy field, SMBCs decreased with increasing As concentration and then increased at the turning point of the As concentration of 74.9 mg kg1 (P < 0.01). The soil TC (P < 0.05) and TN (P < 0.01) had significant negative effects on the SMBC. The PNR was also significantly different under two land-use types (P < 0.05). The corn soil had a higher nitrification rate than the paddy soil. A correlation was found between PNR and the As concentration in the corn soil at 0.1levels. For the paddy soil, there was no significant correlation between PNR and the soil As concentration. Comparatively, the PNR was mainly correlated with soil pH at the 0.01 level, with TC and TN at the 0.05 level in the corn field, and with pH, TC, NHþ 4 -N and NO3 -N on PNR at 0.01 level in the paddy field.
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Table 1 The basic soil properties in different land-use types.
pH TN*** (%) TC (%) OM (mg$kg1) NHþ 4 -N*** (mg$kg1) NO 3 -N** (mg$kg1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy Corn Paddy Corn Paddy Corn Paddy Corn Paddy
20 13 20 13 20 13 20 13 20 13 20 13
4.28 4.92 0.11 0.16 1.02 1.20 21.0 24.3 7.57 16.7 9.17 2.88
7.90 7.52 0.22 0.23 2.51 3.73 42.3 75.9 15.1 56.4 55.2 23.1
6.60 5.65 0.14 0.19 1.42 1.61 30.1 35.2 10.7 23.6 17.5 9.64
6.31 5.94 0.15 0.19 1.49 1.86 31.1 40.4 10.8 28.4 21.1 11.3
1.05 1.00 0.03 0.02 0.38 0.75 7.20 16.0 1.92 11.8 11.6 6.95
Abbreviations: TN total nitrogen, TC total carbon, OM organic matter, and Std standard deviation. Significance of independent sample T-test: ** means P < 0.01, and *** means P < 0.001.
Table 2 The soil and grain As in different land-use types.
Soil As*** (mg$kg1) Grain As*** (mg$kg1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy
20 13 20 13
16.3 17.7 0.04 0.33
1023.3 108.5 0.26 0.87
214.3 67.0 0.11 0.40
344.0 67.0 0.13 0.48
277.1 25.5 0.07 0.18
Abbreviations: Std standard deviation. Significance of independent sample T-test: *** means P < 0.001.
Table 3 The soil microbial biomass carbon (SMBC) and potential nitrification rate (PNR) in different land-use types.
SMBC*** (mg$kg1) PNR* (mg$g1)
Land-use
Number of samples
Minimum
Maximum
Median
Mean
Std
Corn Paddy Corn Paddy
20 13 20 13
212.4 14.7 0.05 0.02
789.9 318.2 4.50 1.53
426.9 136.6 0.82 0.13
437.9 133.2 1.03 0.39
146.1 74.9 1.12 0.46
Abbreviations: Std standard deviation. Significance of independent sample T-test: * means P < 0.05, *** means P < 0.001.
3.4. Abundance and richness of soil bacteria
Different phyla responded differently to the variation of the soil As concentration in different land-use types. The relative
The copy numbers of the bacterial 16S rRNA gene for the corn soils and the paddy soils ranged from 3.08 109 to 2.10 1010 and 1.04 1010 to 3.84 1010 copies$g1 dry soil, respectively, and there was a significant difference between these two land-use types (P < 0.01). The soil As concentrations were also significantly correlated with the copy numbers of bacteria for the paddy soils (P < 0.05). OTU numbers (97% identity threshold) were used to calculate the richness of the bacteria. A significant difference in bacterial richness was found between the corn soil and the paddy soil (P < 0.01). The interaction among the factors will be analyzed next using multivariate methods.
3.5. Composition of soil bacterial community The results of the PCoA derived from the weighted UniFrac distance based on the 97% OTU level of the bacterial community compositions between two land-use types demonstrated a significant difference in bacterial community composition between the corn soil and paddy soil (Fig. 2). The dominant phyla in both landuse types were mainly Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes, and so on, which accounted for 93.8e98.3% and 80.3e93.6% of the relative abundance in the corn field and paddy field, respectively (Fig. 3). Three phyla, Chlorobi, Cyanobacteria and Chlamydiae, were only detected in the paddy soils.
Fig. 2. Principal coordinates analysis (PCoA) of bacterial communities in the corn field and the paddy field. The community composition was characterized by the weighted UniFrac distance.
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Fig. 3. Relative abundance of soil bacteria on the phylum level in the corn field (a) and the paddy field (b).
abundance of Actinobacteria and Gemmatimonadetes in the corn soil was significantly higher compared with the paddy soil (P < 0.01), whereas the phyla of Verrucomicrobia and Nitrospirae were much lower (P < 0.01). For the corn soil, Bacteroidetes and Firmicutes presented a strong correlation with the soil As (P < 0.05). For the paddy soil, Nitrospirae and Chlamydiae had significant correlations with the gradient of As concentration (P < 0.05).
3.6. Impact factors of the activities, richness and community composition of soil bacteria
environmental factors and the bacterial community indexes (Fig. 4a, b). In the pooled model, land-use type was the most distinguished factor and could hide the effects of other factors; hence, two models were built for the two land-use types. For the corn soil, pH affected bacterial activities, richness and composition directly and significantly except SMBC (indirectly). Among them, richness was mainly controlled by soil pH, and composition was solely controlled by soil pH. Soil TC affected richness/abundance directly and PNR/SMBC indirectly, whereas soil TN contributed to richness and SMBC directly and to abundance and PNR indirectly. NO 3 -N affected bacterial activities (SMBC and PNR) in direct ways. Soil As had few effects on the microbial
SEMs were built to determine the relationship between the
Fig. 4. The structural equation models (SEM) showing the relationship among environmental factors, bacterial communities and grain As of the corn field (a) and the paddy soil (b). The solid arrows represent the positive effects while dotted arrows represent the negative effects. The size of the line denotes the strength of the effect. The models fitted the data well. (a) CIMN/DF ¼ 0.958; P ¼ 0.534; RMSEA ¼ 0.000; (b) CMIN/DF ¼ 0.956; P ¼ 0.523; RMSEA ¼ 0.000. Significance level: #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001. Abbreviations: TC total carbon, TN total nitrogen, SMBC soil microbial biomass carbon, PNR potential nitrification rate, Composition soil bacterial community composition.
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community but affected SMBC greatly. Grain As was mainly directly controlled positively by NO 3 -N and pH and negatively by abundance and TN. Soil TC and bacterial richness had indirect effects on the grain As. Soil As and bacterial composition had relatively weak influences on grain As. Thus, the basic soil properties played more important roles in modifying the bacterial communities and grain As in the corn soil of this mining area rather than soil As. For the paddy soil, pH only had a direct effect on NO 3 -N and bacterial community composition, and it was the most influential direct factor on community composition. In addition, it had an indirect effect on SMBC, bacterial abundance and richness. The soil TC exhibited direct effects on bacterial richness (with the highest coefficient) and community composition and negatively affected SMBC and bacterial abundance indirectly. Soil TN had direct effects on SMBC, bacterial abundance and richness, among which the effect on abundance was the largest. NO 3 -N only directly affected the bacterial richness in a positive way. Soil As had positive effects on SMBC, bacterial abundance and richness directly. The grain As of rice was influenced directly by soil As, TN and bacterial richness, whereas it was indirectly affected by soil pH, TC, and NO 3 -N. In total, soil As contributed more to the bacterial community and the grain As in the paddy soil compared with the corn soil. Additionally, the factors controlling grain As were also very different in two land-use types. The soil OM and NHþ 4 -N were not shown in the SEM because they had no significant effects on the bacterial community. 4. Discussion In this study, samples were collected from the farmland around a realgar mining area in Hunan Province. During the past several decades, rice was the main food crop of this area. However, paddy cultivation has led to a serious health hazard because As can accumulate easily in rice grains through this cultivation mode. After the 1970s, to decrease the health risk by the food chain pathway, paddy cultivation in the high soil As concentration area was changed to dry farming, mainly corn cultivation. Rice planting was restricted to limited areas, especially in the areas with low As. Therefore, after years of evolution, the soil bacterial community evolved. Under this condition, multiple environmental factors, including soil As, exerted different influences on soil bacteria, as demonstrated by the SEM results. Similarly, the grain As was also correlated with many environmental factors. 4.1. Effects of land-use on soil bacteria The activities, abundance, richness and community composition of soil bacteria were significantly different between the two types of land use. This was mainly because the land use could affect the bacterial community by modifying soil properties (Lauber et al., 2008; Kuramae et al., 2012; Xiang et al., 2014). It was also reported that the most important factor in structuring microbial communities was soil moisture saturation (Drenovsky et al., 2010). The phylum of Actinobacteria was significantly higher in the corn soil compared with the paddy soil because Actinobacteria had a lower relative abundance in the anaerobic conditions of the paddy soil (Shao et al., 2015). Moreover, the flooded condition of the paddy soil could increase the substrate utilization ability of soil bacteria and accelerate the turnover of the soil carbon (Pal and Broadbent, 1975). These differences in environmental conditions resulted in only three phyla, Chlorobi, Cyanobacteria and Chlamydiae, being detected in the paddy soils (Fig. 3). Further research is also needed to discover the exact reason, especially at a small scale as in the current study.
4.2. Effects of the soil properties on bacteria Basically, the soil physicochemical properties had significant direct or indirect effects on the soil bacteria as described in the results, consistent with many other reports which mostly investigated large scales (Fierer and Jackson, 2006; Chu et al., 2010; Hu et al., 2013; Wang et al., 2015). Among them, soil pH, TC and TN were the most important driving factors, and in different land-use types they presented different contributions to the soil bacteria assembly. In the corn field soil the pH directly affected composition, richness and PNR of soil bacteria, especially composition as the only controlling factor and richness as the highest impact coefficient (0.76); meanwhile, in the paddy field the pH contributed only negatively to composition, and no other direct effects on soil bacteria were found. Therefore, it seemed that pH was a scaleindependent factor in the soil bacteria assembly because it could not be neglected in multi-scale studies. By contrast, the bacterial richness in the paddy field was mainly affected by soil TC and TN, and the contribution from TC and TN to richness in the corn field was significant but less than that from pH. Further, the influence of TC was negative in dry land and positive in paddy land, whereas the influence of TN was positive in dry land and negative in paddy land. Our results were similar to the findings of Shen et al. (2013) whose study also conducted at the local scale demonstrated that the bacterial diversity, especially richness on Mt. Changbai was largely predicted by the soil pH, TC and TN (Shen et al., 2013). The effects from the soil TC and TN had seldom been reported in the previous studies conducted at large scales, for example, at a regional scale. Thus, the effects of soil properties on the soil bacteria should be related to the research scale (Martiny et al., 2006). At different scales, environmental factors present different contributions. In total, soil pH was an important factor in structuring the bacterial community both in dry land and paddy land at the local scale of the current study, which was similar to the findings within the previous studies (Wakelin et al., 2008; Chu et al., 2010; Rousk et al., 2010; Hu et al., 2013). However, in the corn soil, pH was the most dominant factor; meanwhile, in the paddy soil the direct effect of soil pH decreased, and TC and TN played important roles in determining the soil bacteria diversity. Moreover, soil TN contributed directly to SMBC in both land-use types and indirectly to PNR in the corn soil. NO 3 -N also had direct effects on SMBC and PNR in the corn soil. These findings were consistent with the previous research which found that soil properties including pH, the concentration of N, and so on, could affect the bacterial activities (Smolders et al., 2001; Yao et al., 2011; Chen et al., 2015). 4.3. Effects of the soil As on bacteria Soil As, an independent factor in the soil environment, had a stronger impact on the bacterial community in the paddy field than in the corn field according to the SEM analysis. In the paddy field, soil As had negative effects on SMBC, bacterial abundance and richness. However, in the corn field, soil As only positively affected SMBC. Although the concentrations of soil As were much higher in the corn field than in the paddy field, the toxicity of As under flooded conditions showed greater inhibition than that in dry land, which could be explained by the As toxicity being related to the species and fractionations of As in the soil (Bissen and Frimmel, 2003; Van Herreweghe et al., 2003; Zhao et al., 2013). Under the anaerobic conditions in the paddy soil, As was more toxic and mobile because more As(V) would be reduced to As(III) (Goldberg, 2002; Takahashi et al., 2004). In fact, As mainly showed stimulating effects on SMBC (positive effect in SEM) despite very high
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concentrations of As in the soil, indicating that the soil microbes had a stronger tolerance to As in dry land and that the transformation of the cultivation mode significantly decreased the As toxicity. Nevertheless, in the paddy soil As inhabited SMBC with lower concentrations, revealing that the SMBC was sensitive to As contamination (Edvantoro et al., 2003; Van Zwieten et al., 2003; Shade et al., 2012; Awasthi et al., 2014). Additionally, the species richness and abundance of soil bacteria were also affected by soil As in the paddy field. Das et al. (2013) found that bacterial abundance decreased significantly as As toxicity increased (Das et al., 2013). Thus, the stresses of As on soil bacteria were dominated by soil properties, which consequently affected the As toxicity (Marin et al., 1993; Goldberg, 2002; Jiang et al., 2005). When considering all environmental factors in this study, the influences of soil As receded greatly compared with the soil TC and TN (Fig. 4b). Consequently, we could not say that arsenic played an important role in the soil microbial community structure in this area. The soil bacterial composition did not show any links with soil As, but the relative abundance of some phyla showed some respected characteristics. Nitrospirae and Chlamydiae had correlations with the gradient of As concentration in the paddy soil (P < 0.05). Thus, bacteria phyla sensitive to As gradient should be investigated further in future studies about As eco-toxicity in the soil. 4.4. Grain As, food chain and health risk The As concentrations in rice grains were significantly higher than those in corn grains, and the rice As concentrations were all beyond the national standard of food security; however, the As concentrations in the paddy soil were much lower than those in the corn soil. The transformation of the cultivation mode in this area down-regulated the food chain risk of As. This was mainly because the soil As had weaker effects on the grain As in the corn field than it did in the paddy field. It also contributed less to grain As in the corn field compared with other soil properties, i.e., pH, NO 3 -N and TN, whereas in paddy field it was very different. The anaerobic conditions of the paddy soil led to a higher accumulation of As in grain, also resulting in the reductive dissolution of iron oxides/ hydroxides and the release of the absorbed As(V) (Xu et al., 2008), which led to an enhancement of the As bioavailability in anaerobic soil (Mandal and Suzuki, 2002; Chen et al., 2005; Weber et al., 2009; Stroud et al., 2011). In addition, according to the SEM analysis, the soil As affected the soil bacterial properties less in the dry land than in the paddy land. This led to less As transport from the soil to the grain through the process regulated by bacteria in the dry land. Therefore, in upland soil, As in the corn grain was affected more by soil properties (pH, TN and NO 3 -N) than by the soil As. In the paddy soil, the interactions between grain As and the other environmental factors (including soil As) were significant, and soil As could affect the As in grains through bacterial processes (showed as SMBC and bacterial richness). Hence, rational agronomic measures, such as reducing the soil As concentrations and modifying the soil conditions and relevant microbial functions, could be considered to reduce the health risk of As through the food chain, especially in paddy fields. The soil bacterial community played an important role in the food chain risk of As. 5. Conclusions In this study, bacterial responses to long-term As contamination were investigated around a typical As mining area. Land use was found to affect the soil bacterial community deeply and completely. Our findings also presented a high intensity of support for the
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intensive impact of long-term As contamination on soil bacteria, especially bacterial activities, abundance, richness and composition. However, when soil As was combined with soil properties, the soil pH, TC and TN were the main factors driving the bacterial communities. Our study also proved that the soil bacterial community played important roles in the As accumulation in grains. Thus, bacterial communities and various environmental factors should be considered comprehensively when assessing the human health risks of soil As contamination through the food chain. Acknowledgments The current work was financially supported by the projects with the grant No. KFJ-EW-STS-014 and KFJ-EW-ZY-005, and the Youth Innovation Promotion Association, Chinese Academy of Sciences. References Abedin, M.J., Cotter-Howells, J., Meharg, A.A., 2002. Arsenic uptake and accumulation in rice (Oryza sativa L.) irrigated with contaminated water. Plant Soil 240, 311e319. Abernathy, C.O., Thomas, D.J., Calderon, R.L., 2003. Health effects and risk assessment of arsenic. J. Nutr. 133, 1536Se1538S. Awasthi, A., Singh, M., Soni, S.K., Singh, R., Kalra, A., 2014. Biodiversity acts as insurance of productivity of bacterial communities under abiotic perturbations. ISME J. 2445e2452. Bissen, M., Frimmel, F.H., 2003. Arsenicda review. Part I: occurrence, toxicity, speciation, mobility. Acta hydroch. Hydrob 31, 9e18. Bissett, A., Brown, M.V., Siciliano, S.D., Thrall, P.H., 2013. Microbial community responses to anthropogenically induced environmental change: towards a systems approach. Ecol. Lett. 16, 128e139. Buckley, D.H., Schmidt, T.M., 2002. Exploring the Biodiversity of Soilda Microbial Rain Forest. Biodiversity of Microbial Life. Wiley-Liss Inc., New York, pp. 183e208. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7, 335e336. Chaparro, J.M., Sheflin, A.M., Manter, D.K., Vivanco, J.M., 2012. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fert. Soils 48, 489e499. Chen, Z., Wu, W., Shao, X., Li, L., Guo, Y., Ding, G., 2015. Shifts in abundance and diversity of soil ammonia-oxidizing bacteria and archaea associated with land restoration in a semi-arid ecosystem. Plos One. http://dx.doi.org/10.1371/ journal.pone.0132879. Chen, Z., Zhu, Y.G., Liu, W.J., Meharg, A.A., 2005. Direct evidence showing the effect of root surface iron plaque on arsenite and arsenate uptake into rice (Oryza sativa) roots. New Phytol. 165, 91e97. Chu, H., Fierer, N., Lauber, C.L., Caporaso, J., Knight, R., Grogan, P., 2010. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12, 2998e3006. Das, S., Jean, J.S., Kar, S., Liu, C.C., 2013. Changes in bacterial community structure and abundance in agricultural soils under varying levels of arsenic contamination. Geomicrobiol. J. 30, 635e644. DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P., Andersen, G.L., 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microb. 72, 5069e5072. Drenovsky, R.E., Steenwerth, K.L., Jackson, L.E., Scow, K.M., 2010. Land use and climatic factors structure regional patterns in soil microbial communities. Glob. Ecol. Biogeogr. 19, 27e39. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996e998. Edvantoro, B.B., Naidu, R., Megharaj, M., Singleton, I., 2003. Changes in microbial properties associated with long-term arsenic and DDT contaminated soils at disused cattle dip sites. Ecotox. Environ. Safe 55, 344e351. Fendorf, S., Michael, H.A., van Geen, A., 2010. Spatial and temporal variations of groundwater arsenic in South and Southeast Asia. Science 328, 1123e1127. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. P. Natl. Acad. Sci. U. S. A. 103, 626e631. Filip, Z., 2002. International approach to assessing soil quality by ecologicallyrelated biological parameters. Agric. Ecosyst. Environ. 88, 169e174. Goldberg, S., 2002. Competitive adsorption of arsenate and arsenite on oxides and clay minerals. Soil Sci. Soc. Am. J. 66, 413e421. Han, F.X., Banin, A., Su, Y., Monts, D.L., Plodinec, J.M., Kingery, W.L., Triplett, G.E., 2002. Industrial age anthropogenic inputs of heavy metals into the pedosphere. Naturwissenschaften 89, 497e504.
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