Marine Pollution Bulletin xxx (2015) xxx–xxx
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Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin Xin Ke a, Chunyong Wang a, Debing Jing b,c,⇑, Yun Zhang d,⇑, Haijun Zhang a a
College of Energy and Environment, Shenyang Aerospace University, Shenyang 110136, China College of Life Sciences, Capital Normal University, Beijing 100048, China c Xiang Yang Forestry Bureau, Xiangyang 441100, China d College of Land and Environment, Shenyang Agriculture University, Shenyang 110161, China b
a r t i c l e
i n f o
Article history: Received 25 February 2015 Revised 30 May 2015 Accepted 1 June 2015 Available online xxxx Keywords: Haihe River Basin Surface/subsurface sediment Modified PCR–DGGE Dominant sedimentary bacteria Comprehensive water quality
a b s t r a c t Sedimentary microorganisms can be used as a sensitive indicator of integrated aquatic environment quality assessment and indicate long-term water quality or toxicity. According to the Chinese National Standards of GB 3838-2002 and GB 18918-2002, the comprehensive water quality in Haihe River Basin has been described. Results showed that the comprehensive water quality in 6 sites, 4 sites, and 20 sites were good, bad, and medium. Furthermore, 162 dominant bacterial species were identified in surface and subsurface sediments in the 30 sampling sites. As revealed by two initial models constructed by logistic regression, the comprehensive water quality exhibited a pattern from good to bad as the ratio of the number of dominant bacterial species in surface sediments to that in subsurface sediments increased from 1 to 2.1. This finding possibly bridged a traditional gap between aquatic microbe indicators and water quality assessment or monitoring techniques. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Since the early 20th century, biological assessment of aquatic ecosystems has become an important standard tool used to monitor water quality because long-lived aquatic organisms can provide information of the ecological conditions in diverse aquatic environments in different regions (US EPA, 2002; Goodrich et al., 2005). Macro-invertebrates, fish, and periphyton are major aquatic assemblages utilized in biological assessment or monitoring of the health status of aquatic ecosystems (Martinho et al., 2008; Xu et al., 2009; Delpech et al., 2010; Dlamini et al., 2010; Gust et al., 2010; Moulin et al., 2010). As a more sensitive biological indicator, numerous aquatic microorganisms (bacteria, fungi, algae, protozoa, rotifers, cladocera and copepods) and their community compositions or ecosystem functions have greater potential in biological assessment or monitoring of aquatic quality or toxicity (Giller et al., 1998; Cao et al., 2006; Jiang, 2006; Peter et al., 2011). ⇑ Corresponding authors at: College of Life Sciences, Capital Normal University, Beijing 100048, China. Xiang Yang Forestry Bureau, Xiangyang 441100, China. Tel.: +86 10 6890 3456; fax: +86 10 6890 1456 (Debing Jing), College of Land and Environment, Shenyang Agriculture University, No. 120 of Dongling Street of Shenhe district, China (Yun Zhang). E-mail addresses: [email protected] (D. Jing), [email protected] (Y. Zhang).
Microorganisms in currents or sediments perform key ecological functions by actively mediating or regulating the transformation or transportation of nutrients, pollutants, and geochemically reactive elements in aquatic environments (Brandl and Hanselmann, 1991; Kieft et al., 1997; Mallet et al., 2004; Peter et al., 2011). In the same manner as fish adapts to different living conditions, diverse aquatic microorganisms exhibit different species-specific attributes that respond to various water pollution levels (Jiang, 2006). The survival or activities of some microbial strains are possibly suppressed immediately if their habitats are polluted or exposed to unfavorable conditions (Wagner-Dobler et al., 1992). Microorganisms suspended in currents are only an instantaneous indicator of water quality (Goodrich et al., 2005). To reveal the long-term impact of pollution caused by an overlying water column, we use sedimentary microorganisms as more efficient indicators (US EPA, 2002; Goodrich et al., 2005). As one of the ultimate sinks of all kinds of discharged pollutants, aquatic sediment provides a totally different ecological niche from that in the overlying water (Wagner-Dobler et al., 1992). Owing to their high species and functional diversity, sedimentary microorganisms are critical in intercepting and stabilizing pollutants and excessive nutrients or in degrading organic pollutants directly in aquatic environments (MacGillivray and Shiaris, 1994; Boschker et al., 2001; Wu et al., 2002; Konstantinidis et al., 2003; North et al.,
http://dx.doi.org/10.1016/j.marpolbul.2015.06.003 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
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X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
2004; Radl et al., 2005; Syakti et al., 2006). Although the use of sedimentary microorganisms to assess aquatic quality is widely recognized (Wagner-Dobler et al., 1992; Zhao et al., 2010), a rare identifiable relationship between sedimentary microorganisms and aquatic quality has been revealed except a positive and strong association of total phosphorous, total nitrogen, and ammonium–nitrogen contents with micro-eukaryotes and Gram-positive bacteria (Zhao et al., 2010). Several metals are also strongly associated with microbial community composition (Cao et al., 2006). Sediment microbial total biomass (by phospholipid fatty acid analysis) is positively correlated with organic carbon or total nitrogen contents in sediments (Córdova-Kreylos et al., 2006). Challenges have been encountered in the application of microorganisms as aquatic indicators (Radl et al., 2005). For instance, sedimentary microorganisms are a complex microbial assemblage; species identification is the basis of further studies. Traditional taxonomy requires separating various isolates into monocultures; however, only a small percentage (approximately < 1%) of species can be cultivated in laboratories because most of the anaerobic aquatic microbes can be cultured under obligate anaerobic conditions (Brück et al., 2010). Thus, the traditional phenotypic identification may lead to wrong conclusions (Das et al., 2006). Since the mid-1980s, new approaches and methodologies, such as polymerase chain reaction–denaturing gradient gel electrophesis (PCR–DGGE), in the field of molecular biology have provided new information on species identification of aquatic microbial assemblages (Antony et al., 2010; Garcia-Armisen et al., 2013). Genetic and phenotypic diversity in complex microbial assemblages can also be elucidated (Das et al., 2006; Wilmes et al., 2009; Héry et al., 2010). However, most of the aquatic microbes remain unavailable for physiological investigation under normal laboratory conditions because of their anaerobic nature (Caron, 2005). Thus, the lack of knowledge on basic physiological mechanisms or ecological functions of dominant unculturable microorganisms is another challenge for their application in aquatic assessment or monitoring (Wagner-Dobler et al., 1992; Héry et al., 2010). In addition, lateral gene transfer among microbial strains may confuse the relationships among taxa by molecular identification (Caron, 2005). The most feasible method is to avoid performing the aforementioned techniques to overcome such traditional gaps between aquatic microbial indicators and water quality assessment or monitoring. In this study, the ratio of the number of dominant bacterial species in surface sediments to that in subsurface sediments was determined for the first time to assess the long-term comprehensive water quality in Haihe River Basin, one of the seven large river
2. Materials and methods 2.1. Sample collection No specific permits were required for our field studies because these locations are not owned by private individuals or protected/regulated by any specific authority. These locations are not privately owned or protected, and the field studies did not involve endangered or protected species. Surface (5 cm in depth) and subsurface (20 cm in depth) sediment samples were collected from 30 sites in Haihe River Basin by using a columnar sediment sampler, with a global positioning system (Fig. 1). Approximately 500 mL of water sample, 50 g of surface sediments, and 50 g of subsurface sediments were sampled at each sampling site. After sampling, the samples were stored at 20 °C in a refrigerator. 2.2. Chemical analysis of water samples Total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N), Cr, Zn, Cu, Pb, and Cd concentrations of the water samples were analyzed according to the Chinese National Standard of ‘‘environmental quality standards for surface water (GB 3838-2002).’’ 2.3. Comprehensive water quality assessment According to GB 3838-2002, water resources of grades I–III are qualified as potable water sources, aquatic habitats for rare aquatic organisms or fishery, and swimming waters, respectively; water resources of grades IV and V are used as recreational water (without direct contact with human body) and industrial, agricultural, or sight-seeing water, respectively. To assess the comprehensive water quality quantitatively, we calculated the quotient of each chemical index based on grade V of GB 3838-2002 (Eq. (1)).
GX ¼ X=VX
ð1Þ
where X is the chemical index of the water sample, i.e., TP, TN, NH3-N, Cr, Zn, Cu, Pb or Cd; VX is the grade V of GB 3838-2002; GX is the quotient of the chemical index, i.e., TP/VTP, TN/VTN, NH3-N/VNH3-N, Cr/VCr, Zn/VZn, Cu/VCu, Pb/VPb, or Cd/VCd. The average value of the quotients, i.e., the comprehensive water quality index, Gaverage, was then calculated using Eq. (2).
Gaverage ¼ ðGTP þ GTN 0:5 þ GNH3-N 0:5 þ GCr þ GZn þ GCu þ GPb þ GCd Þ=i
ð2Þ
where i is the number of chemical indexes used in this study. The ranking criteria were determined based on Eqs. (3)–(5).
GðIII=VÞ ¼ ðIIITP =VTP þ IIITN =VTN 0:5 þ IIINH3-N =VNH3-N 0:5 þ IIICr =VCr þ IIIZn =VZn þ IIICu =VCu þ IIIPb =VPb þ IIICd =VCd Þ=7 ¼ 0:57 GðV=VÞ ¼ ðVTP =VTP þ VTN =VTN 0:5 þ VNH3-N =VNH3-N 0:5 þ VCr =VCr þ VZn =VZn þ VCu =VCu þ VPb =VPb þ VCd =VCd Þ=7 ¼ 1
ð3Þ ð4Þ
GðB=VÞ ¼ ðBTP =VTP þ BTN =VTN 0:5 þ BNH3-N =VNH3-N 0:5 þ BCr =VCr þ BZn =VZn þ BCu =VCu þ BPb =VPb þ BCd =VCd Þ=7 ¼ 2:18
ð5Þ
basins in China. Haihe River Basin has also been considered as the most polluted river basin in China. This basin covers the core area of Chinese political centers and the whole area of Bohai Economic Zone, one of the three poles of Chinese economy. To assess the long-term water quality of the flowing waters in the Haihe River Basin, we propose the use of sedimentary microbes as one of the most accurate indicators.
where B is the grade B in GB 18918-2002 (Chinese National Standard of ‘‘Discharge standard of pollutants for municipal wastewater treatment plant’’). The following criteria were used to indicate water quality: Gaverage < G(III/V) = 0.57, excellent (drinkable water sources); G(III/V) < Gaverage < G(V/V) = 1, good (industrial, agricultural, recreational, or sight-seeing water); G(V/V) < Gaverage < G(B/V) = 2.18,
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
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Fig. 1. Sampling sites in Haihe River Basin. (A – Tuhei River, B – Majia River, C – Zhangweixin River, D – Wei River, E – Zhang River, F – Nanyun River, G – Nandapai River, H – Beipei River, I – Ziyaxin River, J – Duliujian River, K – Hai River, L – Fuyangxin River, M – Fuyang River, N – Hutu River, O – Ziya River, P – Daqing River, Q – Tang River, R – Juma River, S – Yongding River, T – Sanggan River, U – Beiyun River, V – Yongdingxin River, W – Chaobai River, X – Bai River, Y – Chao River, Z – Jiyun River. Guanting, Miyun and Baiyangdian are reservoirs.).
medium; and Gaverage > G(B/V), bad, indicating water quality was worse than that of the discharge from municipal wastewater treatment plants. 2.4. Detection of the dominant bacteria in the sediment samples 2.4.1. Extraction of PCR-ready genomic DNA from soil samples Total DNA in the sediment samples were extracted using a Genomic DNA Extraction Kit (FastDNA SPIN Kit for Soil Sample, MP Biomedicals) following the manufacturer’s protocol. The purified DNA was dissolved in TE buffer and the concentration of DNA was measured using Nanodrop Spectrophotometer (Thermo scientific). 2.4.2. PCR of 16SrDNA V3 region in sedimentary bacteria Bacterial DNA was amplified using a primer with GC clamp (F338GC:50 -CGCCC GCCGCGCGCGGCGGGCGGGGCGGGGGCACGGG GGGACTCCTACGGGAGGCAGCAG-30 ) and R518 (50 -ATTACCGCGG CTGCTGG-3) (Duarte et al., 2010). The reaction mixture contained 2 lL of DNA (approximately 100 ng) and 0.5 lM of each primer, 22 lL of 2 Master Mix reactions (Promega) and water added to a final volume of 50 lL. PCR amplification was performed in a thermal cycler (PTC – 200 PCR instrument), as follows: 94 °C for 5 min; 30 cycles at 94 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; and 72 °C for 5 min. 2.4.3. Denaturing gradient gel electrophoresis (DGGE) DGGE analysis of the bacterial amplicons (50 lL – entire volume of a PCR reaction) was performed in 8% polyacrylamide with
40–60% urea-formamide denaturant gradient. Electrophoresis was performed in 1 TAE (Tris acetate 20 mM [pH 7.41], sodium acetate 10 mM, and sodium EDTA 0.5 mM) at 100 volts and 60 °C for 12 h using a Hoefer SE600 Chroma gels System. After electrophoresis, gels were fixed in fixative solution (10% ethanol, 5% acetic acid) for 15 min. After washed with water for twice; Gels were stained with silver dye for 15 min. DNA bands were developing for 5–7 min in a sodium hydroxide/formaldehyde development solution. Development was terminated in the fixative solution for 1 min. 2.4.4. 16S rDNA sequence analysis 16S rDNA sequence was analyzed by Beijing Biomed Science & Technology Development Co., Ltd. Separated DNA fragments were excised from the DGGE gels, placed in 40 lL of sterilized water and keep in 4 °C for 12–24 h. PCR was performed again using separated DNA fragment extractions as templates. PCR products were separated on a 1% agarose gel and visualized by ethidium bromide staining. The resulting bands were excised and purified using the Montage DNA Gel Extraction kit (instructions at http://www.biomed-tech.net). Purified DNA were inserted into pMD19-T vector and sequenced subsequently. Each sequence was identified using BLAST to compare with the sequences of known bacterial species in the GenBank database. Sequences that exhibited less than 95% identification by BLAST search were classified and the phylogenetic tree of the sequences was constructed using the Mega 5.0 Software.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
4
Sampling site
Long.
Lat.
TPa
TNa
NH3-Na
Cra
Zna
Cua
Pba
Cda
GTP
GTN
GNH3-N
GCr
GZn
GCu
GPb
GCd
Gaverage
Ranking degree
97# 77# 20# 173# 94# 76# 86# 83# 174# 38# 142# 36# 171# 140# 123# 31# 34# 39# 33# 169# 62# 35# 135# 17# 131# 126# 132# 67# 167# 144#
117.89 115.96 117.88 115.47 116.39 115.98 116.79 116.30 117.39 117.66 117.41 117.52 115.54 117.20 117.92 117.11 117.23 117.43 117.14 114.62 114.28 117.25 116.97 115.33 116.94 116.92 116.94 116.76 113.47 116.70
37.56 36.41 37.54 40.68 37.32 36.42 37.00 36.81 40.01 39.17 37.77 39.36 40.35 39.58 39.79 39.44 39.28 38.78 39.45 40.22 35.51 39.28 39.79 36.06 39.72 39.83 39.75 39.81 39.86 39.90
0.1135 0.2693 0.1172 0.2696 0.0691 0.2447 0.3212 0.3895 0.4927 0.5771 0.5235 0.6933 0.3102 1.2630 0.2927 0.9164 0.6013 1.3931 1.0080 2.2863 1.6294 0.5882 1.2832 1.4238 1.5967 1.4192 1.9917 2.1978 2.6260 4.1822
4.7201 3.2809 5.3988 – 3.1036 3.6329 4.8212 4.5776 5.9682 5.6540 6.3404 5.5720 6.9521 4.5990 15.1747 9.6552 9.8091 9.1773 8.5340 2.8637 7.8887 10.6591 12.6327 12.3489 12.5749 14.5445 13.0920 17.3824 32.5972 22.3673
1.3320 0.5234 0.9991 – 3.0546 1.7481 1.4569 2.9505 2.2335 3.6932 4.2548 3.7600 6.5519 3.1932 2.5491 4.5220 8.7313 2.1767 6.5037 2.3499 6.3391 10.0310 10.7359 7.9260 10.8852 11.5200 11.3987 11.6507 1.2244 3.51057
0.050 0.059 0.050 0.065 0.050 0.060 0.059 0.060 0.064 0.057 0.050 0.058 0.065 0.049 0.052 0.050 0.050 0.057 0.050 0.064 0.051 0.050 0.051 0.061 0.051 0.050 0.051 0.051 0.067 0.051
0.045 0.037 0.047 0.046 0.052 0.039 0.038 0.040 0.047 0.053 0.050 0.058 0.047 0.059 0.076 0.049 0.080 0.056 0.053 0.045 0.047 0.055 0.047 0.043 0.049 0.039 0.045 0.048 0.063 0.045
0.079 0.069 0.079 0.081 0.080 0.069 0.068 0.068 0.082 0.072 0.079 0.072 0.081 0.071 0.073 0.080 0.081 0.071 0.080 0.081 0.072 0.080 0.073 0.068 0.074 0.072 0.073 0.075 0.080 0.073
0.060 0.076 0.061 0.075 0.065 0.079 0.075 0.081 0.070 0.050 0.064 0.052 0.073 0.062 0.063 0.060 0.056 0.049 0.062 0.074 0.058 0.106 0.060 0.079 0.065 0.060 0.065 0.057 0.072 0.061
0.033 0.033 0.033 0.034 0.037 0.034 0.034 0.034 0.033 0.033 0.033 0.033 0.033 0.031 0.035 0.033 0.033 0.033 0.033 0.034 0.031 0.038 0.031 0.034 0.031 0.031 0.031 0.031 0.034 0.031
0.2837 0.6733 0.2931 0.6740 0.1727 0.6118 0.8031 0.9736 1.2318 1.4428 1.3088 1.7333 0.7754 3.1574 0.7316 2.2911 1.5032 3.4827 2.5200 5.7157 4.0735 1.4704 3.2081 3.5596 3.9917 3.5481 4.9793 5.4944 6.5650 10.455
2.36 1.64 2.699 – 1.552 1.816 2.411 2.289 2.984 2.827 3.170 2.786 3.476 2.300 7.587 4.828 4.905 4.589 4.267 1.432 3.944 5.330 6.316 6.174 6.287 7.272 6.546 8.691 16.300 11.18
0.666 0.2617 0.4995 – 1.5273 0.8741 0.7284 1.4752 1.1168 1.8466 2.1274 1.8800 3.2760 1.5966 1.2745 2.2610 4.3656 1.0883 3.2519 1.1750 3.1695 5.0155 5.3679 3.9630 5.4426 5.7600 5.6993 5.8254 0.6122 1.7553
0.50 0.59 0.50 0.65 0.50 0.60 0.59 0.60 0.64 0.57 0.50 0.58 0.65 0.49 0.52 0.50 0.50 0.57 0.50 0.64 0.51 0.5 0.51 0.61 0.51 0.50 0.51 0.51 0.67 0.51
0.023 0.019 0.024 0.023 0.026 0.020 0.019 0.020 0.024 0.027 0.025 0.029 0.024 0.030 0.038 0.025 0.040 0.028 0.027 0.023 0.024 0.028 0.024 0.022 0.025 0.020 0.023 0.024 0.032 0.023
0.079 0.069 0.079 0.081 0.080 0.069 0.068 0.068 0.082 0.072 0.079 0.072 0.081 0.071 0.073 0.080 0.081 0.071 0.08 0.081 0.072 0.080 0.073 0.068 0.074 0.072 0.073 0.075 0.080 0.073
0.60 0.76 0.61 0.75 0.65 0.79 0.75 0.81 0.70 0.50 0.64 0.52 0.73 0.62 0.63 0.6 0.56 0.49 0.62 0.74 0.58 1.06 0.60 0.79 0.65 0.60 0.65 0.57 0.72 0.61
3.3 3.3 3.3 3.4 3.7 3.4 3.4 3.4 3.3 3.3 3.3 3.3 3.3 3.1 3.5 3.3 3.3 3.3 3.3 3.4 3.1 3.8 3.1 3.4 3.1 3.1 3.1 3.1 3.4 3.1
0.8997 0.9088 0.9150 0.9297 0.9526 0.9765 1.0285 1.1077 1.1468 1.1783 1.2145 1.2239 1.2766 1.3451 1.4177 1.4771 1.5170 1.5400 1.5437 1.7004 1.7023 1.7301 1.9081 1.9311 2.0307 2.0508 2.2082 2.4331 2.8460 3.0343
Good Good Good Good Good Good Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Bad Bad Bad Bad
a mg L1; GTP = TP/VTP, GTN = TN/VTN, GNH3-N = NH3-N/VNH3-N, GCr = Cr/VCr, GZn = Zn/VZn, GCu = Cu/VCu, GPb = Pb/VPb, GCd = Cd/VCd; VTP, VTN, VNH3-N, VCr, VZn, VCu, VPb, VCd were the corresponding values of grade V of GB 3838-2002 (China National Standard of ‘‘environmental quality standards for surface water’’). Gaverage = (GTP + GTN 0.5 + GNH3-N 0.5 + GCr + GZn + GCu + GPb + GCd)/7.
X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
Table 1 Assessment of the comprehensive water quality at 30 sampling sites in Haihe River Basin.
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X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
2.5. Data analysis Multinomial logistic regression (in SPSS 10.0) was performed using the ranking degree and ln(surf/sub) as dependent and covariate variables, respectively. 3. Results and discussion 3.1. Comprehensive water quality assessment The chemical indexes of water quality at 30 sampling sites are listed in Table 1. For TP, TP concentrations in three sites (97#, 20#, and 94#) were found in the scope of grade III of GB 3838-2002 (1 mg/L), including an extreme TP value of 4.182 mg/L (144#). NH3-N concentrations in 2 sites (77# and 20#) were found in the scope of grade III (0.5–1 mg/L), 4 sites (97#, 76#, 86#, 167#) were found in the scope of grade V (1–2 mg/L), and 23 sites were found in the scope of grade B (2–15 mg/L). TN concentrations of 27 sites were found in the scope of grade B (2–20 mg/L), but 2 sites (167# and 144#) were beyond the scope of grade B (>20 mg/L). For heavy metals, Cr concentrations in 10 sites (97#, 20#, 94#, 142#, 140#, 31#, 34#, 33#, 35#, 126#) were found in the scope of grade III (95%). Nitrogen and phosphorous are the major limiting nutrients for aquatic living organisms (such as algae) worldwide; nitrogen and phosphorus enrichments significantly alter the community composition of ammonia-oxidizing bacteria (Lage et al., 2010). According to the Intermediate Disturbance Hypothesis, intermediate levels of disturbance likely promote higher levels of diversity. In aquatic habitats, water current usually moves or disturbs the surface layer of sediments. As a result, sedimentary bacteria found in depths of 5 cm obtain higher amounts of dissolved oxygen, nutrients, or pollutants. By contrast, those found in deeper habitats or in tough
Surf/sub Fig. 2. Tendency of the possibility that water quality was good, medium, or bad varying with surf/sub, i.e., ratio of the number of the dominant bacterium species in surface/subsurface sediments. (P(bad), P(good), or P(medium) indicate the probability that the comprehensive water quality was bad, good, or medium, respectively. P(bad) increased, whereas P(good) and P(medium) decreased as surf/sub increased particularly when surf/sub > 1.)
subsurface sediments remain anaerobic. In Haihe River Basin, a decrease in comprehensive water quality is mainly related to the increase in nitrogen and phosphorous concentrations in the water column (Table 1). As nitrogen and phosphorous supplies increased in the overlying water column, more diverse bacteria could colonize their habitats in the surface sediments, but intact subsurface sediments remained the preferred habitats of anaerobic species. Therefore, P (bad) increased; P (good) and P (medium) decreased as surf/sub (ratio of the number of dominant bacterium species in surface/subsurface sediments) increased (Fig. 2). These initial models using surf/sub were constructed to assess the comprehensive water quality in Haihe River Basin without devoting laborious effort and requiring high costs to probe the ecological function or metabolic characteristics of aquatic bacteria. If surf/sub could be used with other bio-assemblages utilized in biological assessment/monitoring of aquatic ecosystems (such as fish, aquatic plant, periphyton, and macro-invertebrates), the accuracy of biological assessment/monitoring of water quality or toxicity should be improved further. These models were constructed on the basis of natural sediments distributed across Haihe River Basin, indicating the wide application of these models in rivers, lakes, ditches, and reservoirs with different pollution situations.
4. Conclusions The comprehensive quality of water resource contaminated by compound pollutants could be assessed quantitatively by using Gaverage, an average value of the quotients between the concentrations of TP, TN, NH3-N, Cr, Zn, Cu, Pb, and Cd in water samples and their corresponding criteria of grade V of GB 3838-2002. By comparing Gaverage with the average quotients of the criteria of TP, TN, NH3-N, Cr, Zn, Cu, Pb, and Cd between grades III and V of GB 3838-2002 or between grades V and B of GB 18918-2002, the comprehensive water quality could be ranked qualitatively as excellent (potable water sources) when Gaverage < G(III/V), good (industrial, agricultural, recreational, or sight-seeing water) when G(III/V) < Gaverage < G(III/V) = 1, medium when 1 < Gaverage < G(B/V), and bad (worse quality than that of the discharge from municipal wastewater treatment plants) when Gaverage > G(B/V), respectively. The dominant bacterial species in surface (5 cm in depth) and subsurface (20 cm in depth) sediments were identified by performing a second-time PCR and colony counting to the standard PCR– DGGE. The comprehensive water quality in 6 sites (97#, 77#, 20#, 173#, 94#, 76#), 4 sites (132#, 67#, 167#, 144#), and 20 sites were good, bad, and medium. Either single chemical index (TP, TN, Pb, or Cd concentration) or comprehensive water quality of water resources at some sampling sites was beyond the scope of grade V or grade B. 162 dominant bacterial species were identified in surface and subsurface sediments in the 30 sampling sites and aerobic species (such as Streptomyces sp.), facultative anaerobic species (such as Bacillus sp.), and obligate anaerobic species (such as Clostridium sp.) were the dominant species. Using multinomial logistic regression, we constructed two initial mathematical models to reveal the quantitative relationship between the comprehensive water quality and the sedimentary bacterium indicator. Comprehensive water quality could be described as good, medium, or bad based on an increase in surf/sub from 1 and 1.8 to 2.1. This result occurred because the usual disturbance from the overlying eutrophic water column possibly carried higher amounts of nitrogen and phosphate to the surface sediments as comprehensive water quality decreased. As a result, more diverse bacteria could colonize their habitats in surface sediments, whereas the subsurface sediments remain as anaerobic habitats for anaerobic species.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
Acknowledgment This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment under Grant No. 2012ZX07202-004-02. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.marpolbul.2015. 06.003. References Antony, C.P., Kumaresan, D., Ferrando, L., Boden, R., Moussard, H., Scavino, A.F., et al., 2010. Active methylotrophs in the sediments of Lonar Lake, a saline and alkaline ecosystem formed by meteor impact. ISME J. 4 (11), 1470–1480. Boschker, H.T.S., Graaf, W., Kuter, M., Meyer-Reil, L.A., Cappenberg, T.E., 2001. Bacterial populations and processes involved in acetate and propionate consumption in anoxic brackish sediment. FEMS Microbiol. Ecol. 35 (1), 97–103. Brandl, H., Hanselmann, K.W., 1991. Evaluation and application of dialysis porewater samplers for microbiological studies at sediment-water interfaces. Aquat. Sci. 53 (1), 55–73. Brück, W.M., Brück, T.B., Self, W.T., Reed, J.K., Nitecki, S.S., McCarthy, P.J., 2010. Comparison of the anaerobic microbiota of deep-water Geodia spp. and sandy sediments in the Straits of Florida. ISME J. 4 (5), 686–699. Cao, Y., Cherr, G.N., Cordova-Kreylos, A.L., Fan, T.M., Green, P.G., Higashi, R.M., et al., 2006. Relationships between sediment microbial communities and pollutants in two California salt marshes. Microb. Ecol. 52 (4), 619–633. Caron, D.A., 2005. Marine microbial ecology in a molecular world: what does the future hold? Sci. Mar. 69 (S1), 97–110. Córdova-Kreylos, A.L., Cao, Y.P., Green, P.G., Hwang, H.M., Kuivila, K.M., LaMontagne, M.G., et al., 2006. Diversity, composition, and geographical distribution of microbial communities in California salt marsh sediments. Appl. Environ. Microbiol. 72 (5), 3357–3366. Das, S., Lyla, P.S., Khan, S.A., 2006. Marine microbial diversity and ecology: importance and future perspectives. Curr. Sci. 90 (10), 1325–1335. Delpech, C., Courrat, A., Pasquaud, S., Lobry, J., Le Pape, O., Nicolas, D., et al., 2010. Development of a fish-based index to assess the ecological quality of transitional waters: the case of French estuaries. Mar. Pollut. Bull. 60 (6), 908–918. Dlamini, V., Hoko, Z., Murwira, A., Magagula, C., 2010. Response of aquatic macroinvertebrate diversity to environmental factors along the Lower Komati River in Swaziland. Phys. Chem. Earth 35 (13), 665–671. Duarte, S., Pascoal, C., Alves, A., Correia, A., Cássio, F., 2010. Assessing the dynamic of microbial communities during leaf decomposition in a low-order stream by microscopic and molecular techniques. Microbiol. Res. 165 (5), 351–362. Faulwetter, J.L., Gagnon, V., Sundberg, C., Chazarenc, F., Burr, M.D., Brisson, J., et al., 2009. Microbial processes influencing performance of treatment wetlands: a review. Ecol. Eng. 35 (6), 987–1004. Garcia-Armisen, T., Anzil, A., Cornelis, P., et al., 2013. Identification of antimicrobial resistant bacteria in rivers: insights into the cultivation bias. Water Res. 47 (14), 4938–4947. Giller, K.E., Witter, E., McGrath, S.P., 1998. Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: a review. Soil Biol. Biochem. 30 (10), 1389–1414. Goodrich, C., Huggins, D.G., Everhart, R.C., Smith, E.F., 2005. Summary of state and national biological and physical habitat assessment methods with a focus on USEPA region 7 states. CPCBA (Central Plains Center for Bio-assessment) Report No. 135 of the Kansas Biological Survey. pp. 1–2. Gust, M., Buronfosse, T., Geffard, O., Mons, R., Queau, H., Mouthon, J., et al., 2010. In situ biomonitoring of freshwater quality using the New Zealand mudsnail Potamopyrgus antipodarum (Gray) exposed to waste water treatment plant (WWTP) effluent discharges. Water Res. 44 (15), 4517–4528. Héry, M., Sanguin, H., Fabiel, S.P., Lefebvre, X., Vogel, T.M., Paul, E., et al., 2010. Monitoring of bacterial communities during low temperature thermal treatment of activated sludge combining DNA phylochip and respirometry techniques. Water Res. 44 (20), 6133–6143.
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Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin Xin Ke a, Chunyong Wang a, Debing Jing b,c,⇑, Yun Zhang d,⇑, Haijun Zhang a a
College of Energy and Environment, Shenyang Aerospace University, Shenyang 110136, China College of Life Sciences, Capital Normal University, Beijing 100048, China c Xiang Yang Forestry Bureau, Xiangyang 441100, China d College of Land and Environment, Shenyang Agriculture University, Shenyang 110161, China b
a r t i c l e
i n f o
Article history: Received 25 February 2015 Revised 30 May 2015 Accepted 1 June 2015 Available online xxxx Keywords: Haihe River Basin Surface/subsurface sediment Modified PCR–DGGE Dominant sedimentary bacteria Comprehensive water quality
a b s t r a c t Sedimentary microorganisms can be used as a sensitive indicator of integrated aquatic environment quality assessment and indicate long-term water quality or toxicity. According to the Chinese National Standards of GB 3838-2002 and GB 18918-2002, the comprehensive water quality in Haihe River Basin has been described. Results showed that the comprehensive water quality in 6 sites, 4 sites, and 20 sites were good, bad, and medium. Furthermore, 162 dominant bacterial species were identified in surface and subsurface sediments in the 30 sampling sites. As revealed by two initial models constructed by logistic regression, the comprehensive water quality exhibited a pattern from good to bad as the ratio of the number of dominant bacterial species in surface sediments to that in subsurface sediments increased from 1 to 2.1. This finding possibly bridged a traditional gap between aquatic microbe indicators and water quality assessment or monitoring techniques. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Since the early 20th century, biological assessment of aquatic ecosystems has become an important standard tool used to monitor water quality because long-lived aquatic organisms can provide information of the ecological conditions in diverse aquatic environments in different regions (US EPA, 2002; Goodrich et al., 2005). Macro-invertebrates, fish, and periphyton are major aquatic assemblages utilized in biological assessment or monitoring of the health status of aquatic ecosystems (Martinho et al., 2008; Xu et al., 2009; Delpech et al., 2010; Dlamini et al., 2010; Gust et al., 2010; Moulin et al., 2010). As a more sensitive biological indicator, numerous aquatic microorganisms (bacteria, fungi, algae, protozoa, rotifers, cladocera and copepods) and their community compositions or ecosystem functions have greater potential in biological assessment or monitoring of aquatic quality or toxicity (Giller et al., 1998; Cao et al., 2006; Jiang, 2006; Peter et al., 2011). ⇑ Corresponding authors at: College of Life Sciences, Capital Normal University, Beijing 100048, China. Xiang Yang Forestry Bureau, Xiangyang 441100, China. Tel.: +86 10 6890 3456; fax: +86 10 6890 1456 (Debing Jing), College of Land and Environment, Shenyang Agriculture University, No. 120 of Dongling Street of Shenhe district, China (Yun Zhang). E-mail addresses: [email protected] (D. Jing), [email protected] (Y. Zhang).
Microorganisms in currents or sediments perform key ecological functions by actively mediating or regulating the transformation or transportation of nutrients, pollutants, and geochemically reactive elements in aquatic environments (Brandl and Hanselmann, 1991; Kieft et al., 1997; Mallet et al., 2004; Peter et al., 2011). In the same manner as fish adapts to different living conditions, diverse aquatic microorganisms exhibit different species-specific attributes that respond to various water pollution levels (Jiang, 2006). The survival or activities of some microbial strains are possibly suppressed immediately if their habitats are polluted or exposed to unfavorable conditions (Wagner-Dobler et al., 1992). Microorganisms suspended in currents are only an instantaneous indicator of water quality (Goodrich et al., 2005). To reveal the long-term impact of pollution caused by an overlying water column, we use sedimentary microorganisms as more efficient indicators (US EPA, 2002; Goodrich et al., 2005). As one of the ultimate sinks of all kinds of discharged pollutants, aquatic sediment provides a totally different ecological niche from that in the overlying water (Wagner-Dobler et al., 1992). Owing to their high species and functional diversity, sedimentary microorganisms are critical in intercepting and stabilizing pollutants and excessive nutrients or in degrading organic pollutants directly in aquatic environments (MacGillivray and Shiaris, 1994; Boschker et al., 2001; Wu et al., 2002; Konstantinidis et al., 2003; North et al.,
http://dx.doi.org/10.1016/j.marpolbul.2015.06.003 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
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X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
2004; Radl et al., 2005; Syakti et al., 2006). Although the use of sedimentary microorganisms to assess aquatic quality is widely recognized (Wagner-Dobler et al., 1992; Zhao et al., 2010), a rare identifiable relationship between sedimentary microorganisms and aquatic quality has been revealed except a positive and strong association of total phosphorous, total nitrogen, and ammonium–nitrogen contents with micro-eukaryotes and Gram-positive bacteria (Zhao et al., 2010). Several metals are also strongly associated with microbial community composition (Cao et al., 2006). Sediment microbial total biomass (by phospholipid fatty acid analysis) is positively correlated with organic carbon or total nitrogen contents in sediments (Córdova-Kreylos et al., 2006). Challenges have been encountered in the application of microorganisms as aquatic indicators (Radl et al., 2005). For instance, sedimentary microorganisms are a complex microbial assemblage; species identification is the basis of further studies. Traditional taxonomy requires separating various isolates into monocultures; however, only a small percentage (approximately < 1%) of species can be cultivated in laboratories because most of the anaerobic aquatic microbes can be cultured under obligate anaerobic conditions (Brück et al., 2010). Thus, the traditional phenotypic identification may lead to wrong conclusions (Das et al., 2006). Since the mid-1980s, new approaches and methodologies, such as polymerase chain reaction–denaturing gradient gel electrophesis (PCR–DGGE), in the field of molecular biology have provided new information on species identification of aquatic microbial assemblages (Antony et al., 2010; Garcia-Armisen et al., 2013). Genetic and phenotypic diversity in complex microbial assemblages can also be elucidated (Das et al., 2006; Wilmes et al., 2009; Héry et al., 2010). However, most of the aquatic microbes remain unavailable for physiological investigation under normal laboratory conditions because of their anaerobic nature (Caron, 2005). Thus, the lack of knowledge on basic physiological mechanisms or ecological functions of dominant unculturable microorganisms is another challenge for their application in aquatic assessment or monitoring (Wagner-Dobler et al., 1992; Héry et al., 2010). In addition, lateral gene transfer among microbial strains may confuse the relationships among taxa by molecular identification (Caron, 2005). The most feasible method is to avoid performing the aforementioned techniques to overcome such traditional gaps between aquatic microbial indicators and water quality assessment or monitoring. In this study, the ratio of the number of dominant bacterial species in surface sediments to that in subsurface sediments was determined for the first time to assess the long-term comprehensive water quality in Haihe River Basin, one of the seven large river
2. Materials and methods 2.1. Sample collection No specific permits were required for our field studies because these locations are not owned by private individuals or protected/regulated by any specific authority. These locations are not privately owned or protected, and the field studies did not involve endangered or protected species. Surface (5 cm in depth) and subsurface (20 cm in depth) sediment samples were collected from 30 sites in Haihe River Basin by using a columnar sediment sampler, with a global positioning system (Fig. 1). Approximately 500 mL of water sample, 50 g of surface sediments, and 50 g of subsurface sediments were sampled at each sampling site. After sampling, the samples were stored at 20 °C in a refrigerator. 2.2. Chemical analysis of water samples Total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N), Cr, Zn, Cu, Pb, and Cd concentrations of the water samples were analyzed according to the Chinese National Standard of ‘‘environmental quality standards for surface water (GB 3838-2002).’’ 2.3. Comprehensive water quality assessment According to GB 3838-2002, water resources of grades I–III are qualified as potable water sources, aquatic habitats for rare aquatic organisms or fishery, and swimming waters, respectively; water resources of grades IV and V are used as recreational water (without direct contact with human body) and industrial, agricultural, or sight-seeing water, respectively. To assess the comprehensive water quality quantitatively, we calculated the quotient of each chemical index based on grade V of GB 3838-2002 (Eq. (1)).
GX ¼ X=VX
ð1Þ
where X is the chemical index of the water sample, i.e., TP, TN, NH3-N, Cr, Zn, Cu, Pb or Cd; VX is the grade V of GB 3838-2002; GX is the quotient of the chemical index, i.e., TP/VTP, TN/VTN, NH3-N/VNH3-N, Cr/VCr, Zn/VZn, Cu/VCu, Pb/VPb, or Cd/VCd. The average value of the quotients, i.e., the comprehensive water quality index, Gaverage, was then calculated using Eq. (2).
Gaverage ¼ ðGTP þ GTN 0:5 þ GNH3-N 0:5 þ GCr þ GZn þ GCu þ GPb þ GCd Þ=i
ð2Þ
where i is the number of chemical indexes used in this study. The ranking criteria were determined based on Eqs. (3)–(5).
GðIII=VÞ ¼ ðIIITP =VTP þ IIITN =VTN 0:5 þ IIINH3-N =VNH3-N 0:5 þ IIICr =VCr þ IIIZn =VZn þ IIICu =VCu þ IIIPb =VPb þ IIICd =VCd Þ=7 ¼ 0:57 GðV=VÞ ¼ ðVTP =VTP þ VTN =VTN 0:5 þ VNH3-N =VNH3-N 0:5 þ VCr =VCr þ VZn =VZn þ VCu =VCu þ VPb =VPb þ VCd =VCd Þ=7 ¼ 1
ð3Þ ð4Þ
GðB=VÞ ¼ ðBTP =VTP þ BTN =VTN 0:5 þ BNH3-N =VNH3-N 0:5 þ BCr =VCr þ BZn =VZn þ BCu =VCu þ BPb =VPb þ BCd =VCd Þ=7 ¼ 2:18
ð5Þ
basins in China. Haihe River Basin has also been considered as the most polluted river basin in China. This basin covers the core area of Chinese political centers and the whole area of Bohai Economic Zone, one of the three poles of Chinese economy. To assess the long-term water quality of the flowing waters in the Haihe River Basin, we propose the use of sedimentary microbes as one of the most accurate indicators.
where B is the grade B in GB 18918-2002 (Chinese National Standard of ‘‘Discharge standard of pollutants for municipal wastewater treatment plant’’). The following criteria were used to indicate water quality: Gaverage < G(III/V) = 0.57, excellent (drinkable water sources); G(III/V) < Gaverage < G(V/V) = 1, good (industrial, agricultural, recreational, or sight-seeing water); G(V/V) < Gaverage < G(B/V) = 2.18,
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
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Fig. 1. Sampling sites in Haihe River Basin. (A – Tuhei River, B – Majia River, C – Zhangweixin River, D – Wei River, E – Zhang River, F – Nanyun River, G – Nandapai River, H – Beipei River, I – Ziyaxin River, J – Duliujian River, K – Hai River, L – Fuyangxin River, M – Fuyang River, N – Hutu River, O – Ziya River, P – Daqing River, Q – Tang River, R – Juma River, S – Yongding River, T – Sanggan River, U – Beiyun River, V – Yongdingxin River, W – Chaobai River, X – Bai River, Y – Chao River, Z – Jiyun River. Guanting, Miyun and Baiyangdian are reservoirs.).
medium; and Gaverage > G(B/V), bad, indicating water quality was worse than that of the discharge from municipal wastewater treatment plants. 2.4. Detection of the dominant bacteria in the sediment samples 2.4.1. Extraction of PCR-ready genomic DNA from soil samples Total DNA in the sediment samples were extracted using a Genomic DNA Extraction Kit (FastDNA SPIN Kit for Soil Sample, MP Biomedicals) following the manufacturer’s protocol. The purified DNA was dissolved in TE buffer and the concentration of DNA was measured using Nanodrop Spectrophotometer (Thermo scientific). 2.4.2. PCR of 16SrDNA V3 region in sedimentary bacteria Bacterial DNA was amplified using a primer with GC clamp (F338GC:50 -CGCCC GCCGCGCGCGGCGGGCGGGGCGGGGGCACGGG GGGACTCCTACGGGAGGCAGCAG-30 ) and R518 (50 -ATTACCGCGG CTGCTGG-3) (Duarte et al., 2010). The reaction mixture contained 2 lL of DNA (approximately 100 ng) and 0.5 lM of each primer, 22 lL of 2 Master Mix reactions (Promega) and water added to a final volume of 50 lL. PCR amplification was performed in a thermal cycler (PTC – 200 PCR instrument), as follows: 94 °C for 5 min; 30 cycles at 94 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; and 72 °C for 5 min. 2.4.3. Denaturing gradient gel electrophoresis (DGGE) DGGE analysis of the bacterial amplicons (50 lL – entire volume of a PCR reaction) was performed in 8% polyacrylamide with
40–60% urea-formamide denaturant gradient. Electrophoresis was performed in 1 TAE (Tris acetate 20 mM [pH 7.41], sodium acetate 10 mM, and sodium EDTA 0.5 mM) at 100 volts and 60 °C for 12 h using a Hoefer SE600 Chroma gels System. After electrophoresis, gels were fixed in fixative solution (10% ethanol, 5% acetic acid) for 15 min. After washed with water for twice; Gels were stained with silver dye for 15 min. DNA bands were developing for 5–7 min in a sodium hydroxide/formaldehyde development solution. Development was terminated in the fixative solution for 1 min. 2.4.4. 16S rDNA sequence analysis 16S rDNA sequence was analyzed by Beijing Biomed Science & Technology Development Co., Ltd. Separated DNA fragments were excised from the DGGE gels, placed in 40 lL of sterilized water and keep in 4 °C for 12–24 h. PCR was performed again using separated DNA fragment extractions as templates. PCR products were separated on a 1% agarose gel and visualized by ethidium bromide staining. The resulting bands were excised and purified using the Montage DNA Gel Extraction kit (instructions at http://www.biomed-tech.net). Purified DNA were inserted into pMD19-T vector and sequenced subsequently. Each sequence was identified using BLAST to compare with the sequences of known bacterial species in the GenBank database. Sequences that exhibited less than 95% identification by BLAST search were classified and the phylogenetic tree of the sequences was constructed using the Mega 5.0 Software.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
4
Sampling site
Long.
Lat.
TPa
TNa
NH3-Na
Cra
Zna
Cua
Pba
Cda
GTP
GTN
GNH3-N
GCr
GZn
GCu
GPb
GCd
Gaverage
Ranking degree
97# 77# 20# 173# 94# 76# 86# 83# 174# 38# 142# 36# 171# 140# 123# 31# 34# 39# 33# 169# 62# 35# 135# 17# 131# 126# 132# 67# 167# 144#
117.89 115.96 117.88 115.47 116.39 115.98 116.79 116.30 117.39 117.66 117.41 117.52 115.54 117.20 117.92 117.11 117.23 117.43 117.14 114.62 114.28 117.25 116.97 115.33 116.94 116.92 116.94 116.76 113.47 116.70
37.56 36.41 37.54 40.68 37.32 36.42 37.00 36.81 40.01 39.17 37.77 39.36 40.35 39.58 39.79 39.44 39.28 38.78 39.45 40.22 35.51 39.28 39.79 36.06 39.72 39.83 39.75 39.81 39.86 39.90
0.1135 0.2693 0.1172 0.2696 0.0691 0.2447 0.3212 0.3895 0.4927 0.5771 0.5235 0.6933 0.3102 1.2630 0.2927 0.9164 0.6013 1.3931 1.0080 2.2863 1.6294 0.5882 1.2832 1.4238 1.5967 1.4192 1.9917 2.1978 2.6260 4.1822
4.7201 3.2809 5.3988 – 3.1036 3.6329 4.8212 4.5776 5.9682 5.6540 6.3404 5.5720 6.9521 4.5990 15.1747 9.6552 9.8091 9.1773 8.5340 2.8637 7.8887 10.6591 12.6327 12.3489 12.5749 14.5445 13.0920 17.3824 32.5972 22.3673
1.3320 0.5234 0.9991 – 3.0546 1.7481 1.4569 2.9505 2.2335 3.6932 4.2548 3.7600 6.5519 3.1932 2.5491 4.5220 8.7313 2.1767 6.5037 2.3499 6.3391 10.0310 10.7359 7.9260 10.8852 11.5200 11.3987 11.6507 1.2244 3.51057
0.050 0.059 0.050 0.065 0.050 0.060 0.059 0.060 0.064 0.057 0.050 0.058 0.065 0.049 0.052 0.050 0.050 0.057 0.050 0.064 0.051 0.050 0.051 0.061 0.051 0.050 0.051 0.051 0.067 0.051
0.045 0.037 0.047 0.046 0.052 0.039 0.038 0.040 0.047 0.053 0.050 0.058 0.047 0.059 0.076 0.049 0.080 0.056 0.053 0.045 0.047 0.055 0.047 0.043 0.049 0.039 0.045 0.048 0.063 0.045
0.079 0.069 0.079 0.081 0.080 0.069 0.068 0.068 0.082 0.072 0.079 0.072 0.081 0.071 0.073 0.080 0.081 0.071 0.080 0.081 0.072 0.080 0.073 0.068 0.074 0.072 0.073 0.075 0.080 0.073
0.060 0.076 0.061 0.075 0.065 0.079 0.075 0.081 0.070 0.050 0.064 0.052 0.073 0.062 0.063 0.060 0.056 0.049 0.062 0.074 0.058 0.106 0.060 0.079 0.065 0.060 0.065 0.057 0.072 0.061
0.033 0.033 0.033 0.034 0.037 0.034 0.034 0.034 0.033 0.033 0.033 0.033 0.033 0.031 0.035 0.033 0.033 0.033 0.033 0.034 0.031 0.038 0.031 0.034 0.031 0.031 0.031 0.031 0.034 0.031
0.2837 0.6733 0.2931 0.6740 0.1727 0.6118 0.8031 0.9736 1.2318 1.4428 1.3088 1.7333 0.7754 3.1574 0.7316 2.2911 1.5032 3.4827 2.5200 5.7157 4.0735 1.4704 3.2081 3.5596 3.9917 3.5481 4.9793 5.4944 6.5650 10.455
2.36 1.64 2.699 – 1.552 1.816 2.411 2.289 2.984 2.827 3.170 2.786 3.476 2.300 7.587 4.828 4.905 4.589 4.267 1.432 3.944 5.330 6.316 6.174 6.287 7.272 6.546 8.691 16.300 11.18
0.666 0.2617 0.4995 – 1.5273 0.8741 0.7284 1.4752 1.1168 1.8466 2.1274 1.8800 3.2760 1.5966 1.2745 2.2610 4.3656 1.0883 3.2519 1.1750 3.1695 5.0155 5.3679 3.9630 5.4426 5.7600 5.6993 5.8254 0.6122 1.7553
0.50 0.59 0.50 0.65 0.50 0.60 0.59 0.60 0.64 0.57 0.50 0.58 0.65 0.49 0.52 0.50 0.50 0.57 0.50 0.64 0.51 0.5 0.51 0.61 0.51 0.50 0.51 0.51 0.67 0.51
0.023 0.019 0.024 0.023 0.026 0.020 0.019 0.020 0.024 0.027 0.025 0.029 0.024 0.030 0.038 0.025 0.040 0.028 0.027 0.023 0.024 0.028 0.024 0.022 0.025 0.020 0.023 0.024 0.032 0.023
0.079 0.069 0.079 0.081 0.080 0.069 0.068 0.068 0.082 0.072 0.079 0.072 0.081 0.071 0.073 0.080 0.081 0.071 0.08 0.081 0.072 0.080 0.073 0.068 0.074 0.072 0.073 0.075 0.080 0.073
0.60 0.76 0.61 0.75 0.65 0.79 0.75 0.81 0.70 0.50 0.64 0.52 0.73 0.62 0.63 0.6 0.56 0.49 0.62 0.74 0.58 1.06 0.60 0.79 0.65 0.60 0.65 0.57 0.72 0.61
3.3 3.3 3.3 3.4 3.7 3.4 3.4 3.4 3.3 3.3 3.3 3.3 3.3 3.1 3.5 3.3 3.3 3.3 3.3 3.4 3.1 3.8 3.1 3.4 3.1 3.1 3.1 3.1 3.4 3.1
0.8997 0.9088 0.9150 0.9297 0.9526 0.9765 1.0285 1.1077 1.1468 1.1783 1.2145 1.2239 1.2766 1.3451 1.4177 1.4771 1.5170 1.5400 1.5437 1.7004 1.7023 1.7301 1.9081 1.9311 2.0307 2.0508 2.2082 2.4331 2.8460 3.0343
Good Good Good Good Good Good Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Bad Bad Bad Bad
a mg L1; GTP = TP/VTP, GTN = TN/VTN, GNH3-N = NH3-N/VNH3-N, GCr = Cr/VCr, GZn = Zn/VZn, GCu = Cu/VCu, GPb = Pb/VPb, GCd = Cd/VCd; VTP, VTN, VNH3-N, VCr, VZn, VCu, VPb, VCd were the corresponding values of grade V of GB 3838-2002 (China National Standard of ‘‘environmental quality standards for surface water’’). Gaverage = (GTP + GTN 0.5 + GNH3-N 0.5 + GCr + GZn + GCu + GPb + GCd)/7.
X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
Table 1 Assessment of the comprehensive water quality at 30 sampling sites in Haihe River Basin.
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X. Ke et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx
2.5. Data analysis Multinomial logistic regression (in SPSS 10.0) was performed using the ranking degree and ln(surf/sub) as dependent and covariate variables, respectively. 3. Results and discussion 3.1. Comprehensive water quality assessment The chemical indexes of water quality at 30 sampling sites are listed in Table 1. For TP, TP concentrations in three sites (97#, 20#, and 94#) were found in the scope of grade III of GB 3838-2002 (1 mg/L), including an extreme TP value of 4.182 mg/L (144#). NH3-N concentrations in 2 sites (77# and 20#) were found in the scope of grade III (0.5–1 mg/L), 4 sites (97#, 76#, 86#, 167#) were found in the scope of grade V (1–2 mg/L), and 23 sites were found in the scope of grade B (2–15 mg/L). TN concentrations of 27 sites were found in the scope of grade B (2–20 mg/L), but 2 sites (167# and 144#) were beyond the scope of grade B (>20 mg/L). For heavy metals, Cr concentrations in 10 sites (97#, 20#, 94#, 142#, 140#, 31#, 34#, 33#, 35#, 126#) were found in the scope of grade III (95%). Nitrogen and phosphorous are the major limiting nutrients for aquatic living organisms (such as algae) worldwide; nitrogen and phosphorus enrichments significantly alter the community composition of ammonia-oxidizing bacteria (Lage et al., 2010). According to the Intermediate Disturbance Hypothesis, intermediate levels of disturbance likely promote higher levels of diversity. In aquatic habitats, water current usually moves or disturbs the surface layer of sediments. As a result, sedimentary bacteria found in depths of 5 cm obtain higher amounts of dissolved oxygen, nutrients, or pollutants. By contrast, those found in deeper habitats or in tough
Surf/sub Fig. 2. Tendency of the possibility that water quality was good, medium, or bad varying with surf/sub, i.e., ratio of the number of the dominant bacterium species in surface/subsurface sediments. (P(bad), P(good), or P(medium) indicate the probability that the comprehensive water quality was bad, good, or medium, respectively. P(bad) increased, whereas P(good) and P(medium) decreased as surf/sub increased particularly when surf/sub > 1.)
subsurface sediments remain anaerobic. In Haihe River Basin, a decrease in comprehensive water quality is mainly related to the increase in nitrogen and phosphorous concentrations in the water column (Table 1). As nitrogen and phosphorous supplies increased in the overlying water column, more diverse bacteria could colonize their habitats in the surface sediments, but intact subsurface sediments remained the preferred habitats of anaerobic species. Therefore, P (bad) increased; P (good) and P (medium) decreased as surf/sub (ratio of the number of dominant bacterium species in surface/subsurface sediments) increased (Fig. 2). These initial models using surf/sub were constructed to assess the comprehensive water quality in Haihe River Basin without devoting laborious effort and requiring high costs to probe the ecological function or metabolic characteristics of aquatic bacteria. If surf/sub could be used with other bio-assemblages utilized in biological assessment/monitoring of aquatic ecosystems (such as fish, aquatic plant, periphyton, and macro-invertebrates), the accuracy of biological assessment/monitoring of water quality or toxicity should be improved further. These models were constructed on the basis of natural sediments distributed across Haihe River Basin, indicating the wide application of these models in rivers, lakes, ditches, and reservoirs with different pollution situations.
4. Conclusions The comprehensive quality of water resource contaminated by compound pollutants could be assessed quantitatively by using Gaverage, an average value of the quotients between the concentrations of TP, TN, NH3-N, Cr, Zn, Cu, Pb, and Cd in water samples and their corresponding criteria of grade V of GB 3838-2002. By comparing Gaverage with the average quotients of the criteria of TP, TN, NH3-N, Cr, Zn, Cu, Pb, and Cd between grades III and V of GB 3838-2002 or between grades V and B of GB 18918-2002, the comprehensive water quality could be ranked qualitatively as excellent (potable water sources) when Gaverage < G(III/V), good (industrial, agricultural, recreational, or sight-seeing water) when G(III/V) < Gaverage < G(III/V) = 1, medium when 1 < Gaverage < G(B/V), and bad (worse quality than that of the discharge from municipal wastewater treatment plants) when Gaverage > G(B/V), respectively. The dominant bacterial species in surface (5 cm in depth) and subsurface (20 cm in depth) sediments were identified by performing a second-time PCR and colony counting to the standard PCR– DGGE. The comprehensive water quality in 6 sites (97#, 77#, 20#, 173#, 94#, 76#), 4 sites (132#, 67#, 167#, 144#), and 20 sites were good, bad, and medium. Either single chemical index (TP, TN, Pb, or Cd concentration) or comprehensive water quality of water resources at some sampling sites was beyond the scope of grade V or grade B. 162 dominant bacterial species were identified in surface and subsurface sediments in the 30 sampling sites and aerobic species (such as Streptomyces sp.), facultative anaerobic species (such as Bacillus sp.), and obligate anaerobic species (such as Clostridium sp.) were the dominant species. Using multinomial logistic regression, we constructed two initial mathematical models to reveal the quantitative relationship between the comprehensive water quality and the sedimentary bacterium indicator. Comprehensive water quality could be described as good, medium, or bad based on an increase in surf/sub from 1 and 1.8 to 2.1. This result occurred because the usual disturbance from the overlying eutrophic water column possibly carried higher amounts of nitrogen and phosphate to the surface sediments as comprehensive water quality decreased. As a result, more diverse bacteria could colonize their habitats in surface sediments, whereas the subsurface sediments remain as anaerobic habitats for anaerobic species.
Please cite this article in press as: Ke, X., et al. Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.06.003
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