Environmental Science Processes & Impacts Accepted Manuscript

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3 Carbon dioxide (CO 2) flooding of oil reservoirs has some merits such as extensive 4 adaptability, low cost, and high recoverability. However, the risk of CO 2 leakage during the 5 6 CO 2-EOR process is not a concern to take lightly. There are many sources of CO 2 emission during 7 this process, such as the CO 2 transportation process, oil production wells and the CO 2 injection

8 well. Studies have shown that elevated concentrations of CO 2 might modify soil properties 9 through altering the soil mineralogy, the pH of underground water and surface vegetation. These 10 11 changes could impact microbial communities and consequently ecosystems processes. 12 High-throughput sequencing was used to investigate the effects of CO 2 emission on the 13 composition and structure of soil bacterial communities. The diversity of bacterial community 14 notably decreased along the CO flux gradient and the composition of the community also varied Manuscript 15 2 16 along the gradient. These results could be useful for evaluating the impact of potential CO 2 17 leakages on ecosystems associated with CO 2-EOR processes. 18

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21 Accepted 22 23 24 25 26 27 28 Impacts

29 & 30 31 32 33 34 35 36

37 Processes 38 39 40 41 42 43

44 Science: 45 46 47 48 49 50 51 52 53 54

55 Environmental 56 57 58 59 60 Environmental Science: Processes & Impacts Page 2 of 18

1 2 1 Effects of CO leakage on soil bacterial community from simulated CO -EOR areas 3 2 2 [1,2] [1] [1] [1] [1,2] [1,2]* 4 2 Fu Chen , Yongjun Yang , Yanjun Ma , Huping Hou , Shaoliang Zhang , Jing Ma 5 3 1 Low Carbon Energy Institute, China University of Mining and Technology, Xuzhou, Jiangsu, 6 4 221008, China 7 2 8 5 School of Environment Science and Spatial Informatics, China University of Mining and 9 6 Technology, Xuzhou, Jiangsu, 221008, China 10 7 ﹡Corresponding author. E-mail address:[email protected]; tel.:+86051683883501; 11 12 8 fax:+86051683883501 13 9 14 10

15 Manuscript 16 11 17 12 18 13 19 20 14 21 15 22 16 23 Accepted 24 17 25 18 26 19 27 28 20 29 21 Impacts 30 22

31 & 32 23 33 24 34

35 25 36 26 37 27 38 39 28 40 29 Processes 41 30 42 43 31 44 32 45 33 46

47 34 Science: 48 35 49 36 50 51 37 52 38 53 39 54 55 40 56 41 57 42 58 59 43 Environmental 60 44 45

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1 2 46 Abstract: CO -EOR (enhance oil recovery) has been proposed as a viable option for flooding oil and 3 2 4 47 reducing anthropogenic CO2 contribution to the atmospheric pool. However, a potential risk of CO2 5 48 leakage from the process is the threat to the ecological system. High-throughput sequencing was used 6 49 to investigate the effects of CO emission on the composition and structure of soil bacterial 7 2 8 50 communities. The diversity of bacterial community notably decreased with increasing CO2 flux. The

9 51 composition of bacterial community varied along the CO2 flux, with increasing CO2 flux 10 52 accompanied by increases in the relative abundance of Bacteroidetes and Firmicutes phyla, but 11 12 53 decreases in the relative abundance of Acidobacteria and Chloroflexi phyla. Within the Firmicutes

13 54 phylum, the genus Lactobacillus increased sharply when the CO2 flux was at its highest point. Alpha 14 55 and beta diversity analysis revealed that differences in bacterial communities were best explained by

15 Manuscript 16 56 CO2 flux. The redundancy analysis (RDA) revealed that differences in bacterial communities were

17 57 best explained by soil pH value which related with CO2 flux. These results could be useful for 18 58 evaluating the risk of potential CO leakages on the ecosystems associated to CO -EOR process. 19 2 2 20 59 Keywords: High-throughput sequencing; CO2 enhance oil recovery; potential CO2 leakages; soil 21 60 bacterial communities; Lactobacillus; Redundancy analysis 22 61 23 Accepted 24 62 25 63 26 64 27 28 65 29 66 Impacts 30 67

31 & 32 68 33 69 34

35 70 36 71 37 72 38 39 73 40 74 Processes 41 75 42 43 76 44 77 45 78 46

47 79 Science: 48 80 49 81 50 51 82 52 83 53 84 54 55 85 56 86 57 87 58 59 88 Environmental 60 89 90

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1 2 91 Introduction 3 4 92 Recent global warming and climate change have been attributed to anthropogenic CO2 emissions

5 93 from the burning and consumption of fossil fuels. CO2 capture, utilization and sequestration (CCUS) 6 1,2 94 is a useful technology toward reducing such emissions from stationary sources . It has been proven 7 8 95 that CO2 can be used to enhance oil recovery (named CO2-EOR). As a promising technology,

9 96 CO2-EOR has been widely adopted by many oil companies around the world since it could improve 10 97 the recovery ratio of oil reservoir characterized by extensive adaptability, low cost and high 11 3,4 12 98 recoverability . However, the risk of CO2 leakage during the CO2-EOR process is a continuing

13 99 concern due to CO2 emission originating from CO2 transportation process, as well as oil production 14 5,6 100 and CO2 injection well .

15 Manuscript 16 101 Numerous studies have shown that elevated concentrations of CO2 can modify soil physical and

17 102 chemical properties, microbial respiration and activity. At a natural geological CO2 emission point in 18 103 Italy, Beaubien et al. found that soil pH significantly decreased near the CO exhaust port, also the 19 2 20 104 soil water content and O2 concentration decreased. They implied that decreasing soil pH value and

21 105 O2 concentration created an acidic soil environment, which inhibited the soil microbial activity and 22 106 enzyme activity, and had a great impact on the activity of the rhizosphere microorganisms 7. Pierce 23 Accepted 24 107 and Sjögersten have shown that elevated CO2 concentration influenced microbial biomass and

25 108 activity in different degree in a short term. The microorganism had a higher tolerance to higher CO2, 26 109 but the research did not involve soil microbial community structure and diversity of change 8. 27 28 110 Moreover, negative effects of high CO2 concentrations on soil microorganisms were observed by 29 detection of decreased microbial respiration rates 9. The study published in 2015 by Cunha involved the 111 Impacts 30 112 effect of CO2 seepage on microorganisms of soil surface in the Atlantic forest region. Results showed

31 & 32 113 that the soil microbial activity decreased with long term exposure to CO2, and the increase of CO2 33 114 concentration affected the microbial biomass, species richness and metabolic activity 10. These 34 changes can ultimately impact microbial communities and, consequently, the ecosystem function 11. 35 115 36 116 Elevated CO2 has also been shown to enhance plant biomass production thereby increasing the size 37 117 of root-associated microbial populations. Together this can result in a modification in plant-microbial 38 12 39 118 interactions . As a consequence, these changes may influence biological cycles and the overall 13-15 40 119 function of the ecosystem . Processes 41 120 As the most abundant and diverse group of all soil organisms, bacteria play an indispensable role in 42 43 121 soil ecology, particularly due to their central role in decomposition. Due to their enormous metabolic 16,17 44 122 versatility, they also serve as keystones in biogeochemical cycling . Soil bacterial community 45 123 composition associated with CO2 emission sites has been investigated using DNA fingerprinting, 46 quantitative polymerase chain reaction analyses (qPCR), polymerase chain reaction-denaturing 47 124 Science: 7,8,18,19 48 125 gradient gel electrophoresis (PCR-DGGE) and assessment on specific genes . Shen et al. have 49 126 given a comprehensive analysis of the soil bacterial community composition and diversity using the 50 20 51 127 Roche 454 pyrosequencing technique . Also, microbial community diversity in a real industrial 52 128 PAH-polluted soil was analyzed using this technique 21. However, few studies have investigated the 53 129 effects of increasing CO2 concentrations on soil bacterial communities through Illumina Miseq 54 55 130 sequencing, a more sensitive method to detect overall microbial community changes and minor 56 131 populations 22,23. The use of Illumina MiSeq has provided a sufficient number of sequences with 57 132 adequate length to enable extrapolations that estimate bacterial alpha diversity based on richness and 58 16 59 133 evenness . This approach has been applied to decipher the microbial communities in distinct Environmental 60 134 samples, such as samples taken from polychlorinated biphenyl contaminated environments 24, river 135 water 25 and iron mining soil 26.

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1 2 136 Changes in microbial community structure may affect both the below- and above-ground processes, 3 27 4 137 thus influencing ecosystem function . Microorganisms can be extremely sensitive to changes in soil

5 138 characteristics, thereby acting as good indicators of soil quality. An increase in CO2 concentration 6 139 could cause changes in soil biochemical conditions, which could lead to a shift in the functionality or 7 20,28-30 8 140 diversity of inhabiting microorganisms . The goal of the present study was to better understand

9 141 how elevated CO2 emissions affect the composition and structure of soil bacterial community in a 10 142 simulated CO gas vent, in order to assess the potential impact of CO leaking from the CO -EOR 11 2 2 2 12 143 process on the surface ecosystem. 13 144 Materials and Methods 14 145 Study site and experimental design

15 Manuscript 16 146 The study was carried out in an open field, at the CO2 experimental site (34°12′31.735″N, 17 147 117°08′08.328″E) within the campus of China University of Mining and Technology, in Xuzhou City, 18 148 Jiangsu Province, China (Fig. 1). The soil is haplic brown soil with CEC value 14.91±3.27 cmol/kg, 19 20 149 and granulometric composition is 15.12: 22.34: 9.15: 53.39 (<0.001mm: 0.001-0.005:

21 150 0.005-0.01: >0.01). This CO2 experimental site consists of a factorial experiment of 16 plots (2.5m × 22 th th 151 2.5m each) in which fluxes of CO2 were conducted from 15 July 2014 to 15 September 2014 and 4 23 Accepted 24 152 replicates for each treatment were assayed. Plots were filled from the bottom to the top with a 10 cm 25 153 coarse sand layer covered by 10 cm of fine sand, followed by a nano porous plate and 80-cm top soil 26 154 layer, respectively. The CO was injected from centre of the plot at 1.0 m below the surface. Four 27 2 28 155 sampling sites were selected according to the soil CO2 emission flux intensity. Flux intensity 29 categories consisted of the most extreme category (E, pure CO injection 1200 g∙m-2∙d-1), a high 156 2 Impacts 30 -2 -1 157 intensity category (H, pure CO2 injection 800 g∙m ∙d ), a medium intensity category (M, pure CO2

31 & -2 -1 -2 -1 32 158 injection 400 g∙m ∙d ), a low intensity category (L, pure CO2 injection 200 g∙m ∙d ), a control (C,

33 159 stands for background level, without CO2 injection) and a resilience category (R, after CO2 injection 34 terminated). Soil samples, one composite sample on three occasions from each plot, (10-30 cm depth) 35 160 th 36 161 were collected (15 September 2014) for L, M, H and E categories. The C and R soil samples were 37 162 collected at 15th July and 15th October 2014, respectively. The soil samples were packed in sterile 38 39 163 Ziploc bags, and the bags were sealed and transported to the laboratory for further analysis. A 40 164 fraction of the mixed sample was immediately treatment for biodiversity analysis. Another fraction Processes 41 165 was dried at room temperature for about a week and sieved through a 2-mm grid to remove stones 42 43 166 and visible plant fragments. A portion of this was ground and sieved through a 0.25-mm sieve for 44 167 physicochemical analysis. All samples were stored at 4 °C. Organic matter was measured using the 45 168 Walkley-Black wet oxidation method 31. For pH and electrical conductivity determination, 1 g of soil 46 sample was combined with 5 mL of deionized water and shaken for 5 min, and then allowed to stand 47 169 Science: − 48 170 for 1 hour prior to measurement. Nitrate content (NO3 ) was measured with a dual wavelength 49 171 spectrometry 32. 50 51 172 DNA extraction, PCR amplification and Illumina MiSeq sequencing 52 173 DNA was extracted from 0.5 g of sieved soil sample using FastDNATM SPIN Kit for Soil (MP 53 174 Biomedicals, Solon, OH, USA) according to the manufacturer’s instruction. The V4 regions of the 54 55 175 bacterial 16S rRNA gene were amplified using an Eppendorf thermal cycler (Model 5332). 56 176 Amplification conditions were as follows: An initial denaturation step at 98 °C for 5 min, followed 57 177 by 25 cycles of 98 °C for 30 s, 50 °C for 30 s, and 72 °C for 30 s and a final extension step at 72 °C 58 59 178 for 5 min. The primers used in the reaction were 520F (5’-barcode+ Environmental 60 179 GCACCTAAYTGGGYDTAAAGNG-3’)，802R (5’- TACNVGGGTATCTAATCC-3’). The barcode 180 is a seven-base sequence unique to each sample.

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1 2 181 The PCR reactions were performed in triplicate in a 25-μL mixture containing 5 μL of 5×Q5 3 4 182 Reaction Buffer, 5 μL of 5×Q5 GC high Enhancer, 2 μL of 2.5 mM dNTPs, 1 μL of each primer (10 5 183 μM), 0.25 μL of Q5 Polymerase, 8.75μL sterilizing ultrapure water and 2 μL of template DNA (20 6 184 ng/μL). Three PCR products per sample were pooled and purified with an AxyPrep DNA Gel 7 8 185 Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s 9 186 instructions. Quantification of purified PCR product was performed with a Quant-iTPicoGreen 10 187 dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Purified amplicons were pooled in equimolar 11 12 188 and paired-end sequenced (2×300) on an Illumina MiSeq platform (Personalbio, Shanghai) according 13 189 to a standard protocol. 14 190 Processing and analyzing of sequencing data

15 Manuscript 16 191 Raw FASTQ files were de-multiplexed and quality-filtered using QIIME (version 1.7.0) with the 17 192 following criteria: (1) 300-bp reads were truncated at any site that acquired an average quality score 18 193 of ＞20 over a 10-bp sliding window, and truncated reads shorter than 150 bp were discarded; (2) 19 20 194 Two nucleotide mismatch in primer matching and reads containing ambiguous characters were 21 195 removed; (3) Only overlapping sequences longer than 10 bp were assembled according to their 22 196 overlapped sequences. Reads that could not be assembled were discarded. Operational taxonomic 23 Accepted 24 197 units (OTUs) with 97 % similarity cutoff were clustered using UPARSE (version 7.1), and chimeric 25 198 sequences were identified and removed using UCHIME method. 26 199 Rarefaction analysis based on Mothur v.1.21.1 33 was conducted to reveal the diversity indices, Chao 27 28 200 richness estimate, Shannon diversity index were also estimated from OTUs using software Mothur. 29 Beta diversity analysis was performed using weighted UniFrac and Nonmetric multidimensional 201 Impacts 30 202 scaling (NMDS) index 34. The results of the redundancy analysis (RDA) were compared using the

31 & 32 203 Canoco windows for 4.5. 33 204 Results 34 35 205 Soil chemical characteristics and CO2 emission fluxes 36 206 No significant differences in soil properties were observed among low, medium, high and extreme

37 207 CO2 sites (Table 1). Soil pH ranged from mild alkaline in the control samples (7.90 ± 0.16) and 38 39 208 slightly acidic in the extreme sites (6.70 ± 0.27). Organic matter ranged from 0.70 ± 0.05 % in 40 209 control site to 0.84 ± 0.05 % in the extreme sampling site. Processes 41 − 210 Strong negative correlations were observed between the natural logarithm of CO2 flux and NO3 42 43 211 nitrogen (r = -0.991; p < 0.005) and EC value (r = -0.900; p < 0.005). Also, strong negative linear 44 212 correlations were observed between the CO2 flux and pH value (r = -0.984; p < 0.005). By contrast, a 45 213 positive correlation was detected between organic matter and ln of CO2 flux (r = 0.929; p < 0.005). 46

47 214 Sequencing results and alpha diversity measurements Science: 48 215 A total of 1121,986 reads were obtained from the 24 samples through Miseq sequencing analysis. 49 216 The libraries ranged in sizes, from 34,555 reads at site R3 to 69,433 reads at site C2. All rarefaction 50 51 217 curves tended to approach the saturation at any sequencing depth. The rarefaction curve indicated 52 218 that a large variation in the total number of retrieved OTUS was observed among samples (data not 53 219 shown). Compared to the high CO2 concentration soil (e.g., H1, H2, and E1, E2), all samples from 54 55 220 control and resilience sites had higher OTU density. The OTU densities of samples from the L and M 56 221 sites were also lower than the average OTU densities of the C and R sites. In addition, the average 57 222 OTUs of group R attained a higher value, but its average reads were significantly lower than those of 58 59 223 group M. Environmental 60 224 Moreover, the calculation of the alpha diversity species richness (Chao) and Shannon index

225 confirmed the decreasing diversity in the high CO2 flux sites, especially in group E, compared to

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1 2 226 group C (Fig. 2). The bacterial diversity of the 24 soil samples was analyzed by calculating the alpha 3 4 227 diversity indices. Chao 1 richness estimator values ranged from 4041 (E2) to 5851 (C3), whereas 5 228 Shannon diversity index varied between 6.39 (E2) and 7.06 (R4). Both index values decreased (Fig. 6 229 2), indicating that community richness and uniformity declined increasing CO fluxes. 7 2 8 230 Bacterial taxonomy composition 9 231 Sequences that could not be classified into any known group were assigned as unclassified. 10 232 Greengene (Release 13.8 ， http://greengenes.secondgenome.com/) was used as the annotation 11 12 233 database. The bacterial OTUs were assigned to 30 different phyla, 164 families, or 456 genera. Nine 13 234 of the 30 phyla comprised more than 90 % of the total read in every sample: Acidobacteria, 14 235 Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes, Proteobacteria,

15 Manuscript 16 236 Gemmatimonadetes and Verrucomicrobia. Actinobacteria was the most abundant group (Fig. 3A), 17 237 comprising approximately 35.51%. Proteobacteria consisted of 20.41 % in all libraries. However, 18 238 the proportion of Bacteroidetes in the different samples showed high variation, in the range of 2.9 to 19 20 239 21.10 %. The members from Bacteroidetes, Chloroflexi, Acidobacteria, Planctomycetes, Firmicutes, 21 240 Gemmatimonadetes and Verrucomicrobia comprised 12.18 %, 8.86 %, 5.48%, 3.01%, 5.44%, 2.28 % 22 241 and 2.83%, respectively, in all libraries. The average reads of the unclassified group accounted for 23 Accepted 24 242 2.26 %, but this value fluctuated for different sites. Phylogenetic analysis revealed clear differences

25 243 in community composition related to soil CO2 emission values (Fig. 3A). 26 244 The relative abundance of Bacteroidetes phylum increased along the CO gradient from 9.40-10.60% 27 2 28 245 in the control sampling site to 18.70-19.20% in the extreme sampling site, with Cytophagales being 29 the more abundant order within this phylum. However, the Bacteroidales order showed the most 246 Impacts 30 247 obvious increasing trend with respect to the CO2 flux (Fig. 3B). On the other hand, relative

31 & 32 248 abundance of Firmicutes phylum increased from control (3.50-4.20%) to extreme sampling sites 33 249 (8.70-9.70%). Moreover, the relative abundance of the genus Lactobacillus increased sharply from 34 1.10% to 6.95% (Fig. 3B). By contrast, the relative abundances of Acidobacteria and Chloroflexi 35 250 36 251 phyla decreased along the CO2 flux, from 17.70-20.80 % in the control to 6.20-10.50% in the 37 252 extreme sample sites. Anaerolineae, Chloroflexi and Thermomicrobia were the dominant genera in 38 39 253 the Chloroflexi phylum and their relative abundances decreased along the CO2 gradient (Fig. 3B). 40 254 Changes in the relative abundance of Actinobacteria and Proteobacteria phyla were not so obvious. Processes 41 255 Overall, the Actinobacteria class, which is mainly composed of the Actinomycetales order, was the 42 43 256 most abundant Actinobacteria. 44 257 Soil bacterial community comparison 45 258 Beta diversity analysis based on the quantitative weighted unifrac metric (Fig. 4A) and nonmetric 46 multidimensional scaling was performed (Fig. 4B). The unifrac metric provides a robust index of 47 259 Science: 48 260 community phylogenetic distances, whereas the nonmetric multidimensional scaling retains the 49 261 original ecological abundance-based similarity. Low, medium, high and extreme CO2 bacterial 50 51 262 communities were considered as a single group, and separated from the control communities by the 52 263 first axis. In contrast, the high and extreme CO2 bacterial communities were assigned into one group, 53 264 and separated from low and medium communities by the second axis. The calculated plot obtained 54 55 265 using the NMDS grouped bacterial communities following a similar pattern, with the first axis 56 266 explaining most of the variations. However, the low CO2 bacterial communities were divided into C 57 267 group. Fig. 4B also showed that bacterial communities sampled in the high and extreme CO2 sites 58 59 268 were phylogenetically closer than the group formed by bacterial communities sampled in the low and Environmental 60 269 medium CO2 sites. The bacterial communities were different from each other with respect to the CO2 270 flux categories (p < 0.001).

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1 2 271 The redundancy analysis (RDA) ordination diagram is shown in Fig. 5. The goodness of fit statistic 3 − 4 272 for environmental variables showed that ordination was highly correlated with CO2 and pH, NO3 5 273 nitrogen and EC value (p < 0.01) with long arrow (Fig. 5A). Among the environmental factors, pH, 6 − 274 NO nitrogen and EC value were highly correlated with Axis 1, which explains most of the variation 7 3 8 275 in the bacterial communities. This is not surprising that pH is correlated with CO2 as it was 9 276 previously commented. The pH arrow was the longest one, which might indicate that it was the most 10 277 important variable regarding bacterial community ordination. Also, bacterial community distribution 11 12 278 follows a gradient from low (control, low and medium samples) to high CO2 flux (high and extreme 13 279 samples). Moreover, organic matter distinguished category of low and high bacterial community 14 280 samples along Axis 2 (Fig. 5A).

15 Manuscript 16 281 In terms of bacterial community composition, the RDA also showed several OTUs particularly 17 282 characteristic of some communities since these OTUs were plotted around particular samples (Fig. 18 283 5B). Considering the frequent OTUs, high and extreme CO bacterial communities were associated 19 2 20 284 with OTUs related to Bacteroidetes and Firmicutes phyla: Bacteroidales (OTU0003 and OTU0004), 21 285 and Lactobacillus (OTU0009, OTU0010, OTU0011 and OTU0013). By contrast, control samples 22 286 were characterized by OTUs related to Acidobacteria-6 (OTU0006, OTU0017 and OTU0018) and 23 Accepted 24 287 Chloroflexi phylum: Gitt-GS-136(OTU0005), Thermomicrobia (OTU0008), Anaerolineae 25 288 (OTU0016) and Ellin6529 (OTU0001 and OTU0014). 26 Discussion 27 289 28 290 Possible impacts of CO2 leaks from the CCUS-EOR process on the ecosystem needs to be assessed 29 in order to understand how CO fluxes might affect soil bacterial communities before introducing 291 2 Impacts 30 292 this technology to oil displacement. For a better understanding of the effects of CO2 emissions on

31 & 32 293 soil bacterial communities, high-throughput 16S RNA gene sequencing was used to provide detailed

33 294 information on soil bacterial communities. Our results showed that simulated soil CO2 emissions 34 significantly altered the diversity and structure of bacterial communities, especially for certain 35 295 36 296 genera.

37 297 Within the area investigated, four sampling sites were designed according to CO2 fluxes. The sites 38 39 298 were defined as low, medium, high and extreme sites, with CO2 flux varying from the rate of 200 g -2 -1 −2 −1 40 299 m d at the low site to 1200 g m d at the extreme site. Along this gradient, soil pH decreased Processes 41 7 29 300 slightly, showing a negative correlation with CO2 flux. Beaubien et al. and Oppermann et al. have 42 43 301 reported decreasing pH values in CO2 leaking sites. H2CO3 is assumed to be formed by CO2 gas 44 302 which reacted with soil pore water or filled the pore space, which in turn lowered the pH of soil 45 303 solution 35,36. Thus in this study, it was speculated that decreases in pH might be attributed to the 46 acidification of soil water as a result of the dissolution of leaking CO gas. On the other hand, 47 304 2 Science: 21 48 305 McFarland et al. did not find significant variation in soil pH in response to CO2 flux. Moreover, 49 306 several other studies have revealed that different soil pHs often give rise to changes in soil microbial 50 37,38 51 307 communities across different locations . Organic matter is positively correlated to CO2 flux. 52 308 Miera et al. 39 also reported similar result, and inferred that it might be related to the low turnover of 53 309 organic matter under reducing conditions. The analyzed soil parameters were not statistically 54 55 310 significant among sampling sites, so we could assume that CO2 flux could be the main factor that led 56 311 to changing soil bacterial structure and composition. 57 312 Plant species have been reported to affect the diversity and composition of bacterial communities 16,40, 58 59 313 so we designed the experimental sampling sites without plant cultivation. Diversity and richness of Environmental 60 314 the bacterial communities decreased with increasing CO2 fluxes (Fig. 2). Decreases in diversity have 315 also been reported in other studies 38. However, several influential factors such as the use of different

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1 2 316 regions of the 16S rRNA genes, the sequence length and definition of OTU have made comparison of 3 41 4 317 diversity and richness estimates between different reports unrealistic . 5 318 The bacterial phyla in the soil represented in this study were consistent with the results from other 6 39,42-44 319 soils in that most sequences belonged to nine major phyla, Actinobacteria, Proteobacteria, 7 8 320 Bacteroidetes, Chloroflexi, Acidobacteria, Firmicutes, Planctomycetes, Gemmatimonadates and 9 321 Verrucomicrobia, although the last three phyla accounted for a lesser portion of the sequences. 10 322 Nevertheless, the distributions of sequences among and within phyla were different for different 11 12 323 sampling sites, indicating a possible effect of soil CO2 flux. Further examination of those phyla 13 324 revealed differences among some special bacterial species, either at the class or lower levels. The 14 325 Acidobacteria phylum did not show an obvious changing trend in the CO2 emission sites, which was

15 Manuscript 29 16 326 different from its increases in CO2 gas vents as suggested by Oppermann et al. . In Chloroflexi, the 17 327 relative abundance of Ellin6529 class, which accounts for the majority of the phyla, decreased 18 328 noticeably along the CO gradient. Two other dominant classes, Anaerolineae and Thermomicrobia, 19 2 20 329 also showed a declining trend. Thermomicrobium is known to exist as an aerobic Chemoheterotroph 45 21 330 . As the CO2 gradient increases, the proportion of O2 gas in the soil decreases. Decreases in the 22 331 number of bacteria from the Thermomicrobia phylum (Fig. 3) conformed to the CO2 gradient. On the 23 Accepted 39 24 332 contrary, Miera et al. have also reported that the relative abundance of Chloroflexi noticeably

25 333 increases along the CO2 gradient. Furthermore, the Bacteroidales order, which belongs to the 26 334 Bacteroidetes phylum also increased significantly. In a simulated underground storage site, Morales 27 46 28 335 et al. have also investigated the effects of elevated CO2 on microbial community richness and 29 composition using pyrosequencing analysis. They demonstrated the similar results with ours that 336 Impacts 30 337 bacterial richness decreased, and bacterial community composition shifted in response to CO2.

31 & 32 338 Moreover, their study also expressed that Bacteroidetes phylum was the most affected, with 11

33 339 OTUs either decreasing or increasing in response to CO2 enrichment. One the other side, the relative 34 abundance of the genus Lactobacillus, which was included in the Firmicutes phylum, increased 35 340 36 341 significantly. These results were the first report to demonstrate the presence of increasing

37 342 Lactobacillus along the CO2 gradient. It is difficult to explain why some bacteria increased or 38 39 343 decreased in the high and extreme communities. Since many reports of CO2 gas vent were aimed at 18,29 40 344 specific bacterial activities, such as methane production and sulphate oxidation . In CO2 rich Processes 41 345 hypoxic soils Šibanc et al. 47 reported that methanogenic taxa dominated with significant increase in 42 43 346 abundance of Methanomicrobia and anaerobic Chloroflexi and Firmicutes were also predominantly. 44 347 However, information about the presence of bacteria related to Chloroflexi and Firmicutes phyla are 45 348 still not sufficient available. The high-throughput 16S RNA gene sequencing used in this study has 46 provided extensive information about the taxa present in bacterial communities along the CO 47 349 2 Science: 48 350 gradient, but gave little insight into the functional role of the Lactobacillus and Lactococcus genera. 49 351 The ecological function of these taxa is not completely known and a more extensive research of its 50 51 352 metabolism and diversity might provide new information to complete this study. 52 353 The beta analysis results demonstrated that the microbial community composition significantly 53 354 changed under different CO2 flux pressures. This finding was consistent with the alpha analysis 54 55 355 results. The effect of soil properties on the community difference has been described. RDA result 56 356 demonstrated that the larger gap between C group and other groups under CO2 flux pressures. It is in 57 − 357 accordance with the high EC and NO3 nitrogen value in C group. This indicated that there might be 58 − 59 358 some correlation between high EC, NO3 nitrogen value and different community composition. In Environmental 60 359 Fig. 5B, the arrows of OTU0003, OTU0005, OTU0008, OTU0009 and OTU0010 were longer, which 360 indicated that these four OTUs were the most important variable OTUs regarding sample groups.

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1 2 361 The relationship between OTU and sample showed that the abundance order of OTU0005 and 3 4 362 OTU0008 in each sample was C>L>M>H>E. On contrast, the abundance order of OTU0003, 5 363 OTU0009 and OTU0010 was E>H>M>L>C. This result also confirmed that Bacteroidales, 6 364 Thermomicrobia and Lactobacillus were significantly impacted under different CO flux pressure. 7 2 8 365 Conclusion

9 366 Shifts in diversity and composition of bacterial communities in a simulated CO2 gradient area were 10 − 367 defined by soil CO flux. Parameters (NO nitrogen, EC and pH) correlated with CO flux have 11 2 3 2 12 368 provided some explanations for the differences among the low, medium, high and extreme CO2

13 369 bacterial communities. Little research has focused on understanding the impact of high soil CO2 14 370 fluxes on soil bacterial communities. New findings are considered important in the development of

15 Manuscript 16 371 remedial measures for handling CO2 leakage from CCUS-EOR. The data obtained here for the 17 372 characterization of soil communities, in particular the obvious changing trend related to Chloroflexi 18 373 and Firmicutes phyla warrant further research on this environmental pattern and the CO effect on 19 2 20 374 bacterial communities. In the future, such knowledge will be useful to understand the potential

21 375 effects of CO2 leakages from EOR sites on soil ecosystems. 22 376 Acknowledgements 23 Accepted 24 377 The authors acknowledge the Project supported by the Fundamental Research Funds for the Central 25 378 Universities (2014QNA18), the Key Projects in the National Science & Technology Pillar Program 26 379 during the Twelfth Five-year Plan Period (2012BAC24B05) and 27 28 380 the Key Laboratory of Coal-based CO2 Capture and Geological Storage, Jiangsu Province (2015B02). 29 There is no conflict of interest in this manuscript. 381 Impacts 30 382 References

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15 Manuscript 16 416 22. L. Oberauner, C. Zachow, S. Lackner, C. Högenauer, K.H. Smolle and G. Berg, Sci. Rep., 2013, 17 417 3, 4422-4427. 18 418 23. S. Uroz, P. Ioannidis, J. Lengelle, A. Cébron, E. Morin, M. Buée and F. Martin, PLOS ONE, 19 20 419 2013, 8, e55929. 21 420 24. X. Su, Q. Zhang, J. Hu, M.Z. Hashmi, L. Ding and C. Shen, Appl. Microbiol. Biot., 2015, 99, 22 421 1989-2000. 23 Accepted 24 422 25. C. Staley, T. Unno, T.J. Gould, B. Jarvis, J. Phillips, J.B. Cotner and M.J. Sadowsky, J. Appl. 25 423 Microbiol., 2013, 115, 1147-1158. 26 424 26. C. Hong, Y.X. Si, Y. Xing and Y. Li, Environ. Sci. Pollut. R., 2015, 22, 10788-10799. 27 28 425 27. B. Drigo, G.A. Kowalchuk and J.A. Van Veen, Biol. Fert. Soils, 2008, 44, 667-679. 29 28. R.H. Patil, J.J. Colls and M.D. Steven, Energy, 2010, 35, 1-5. 426 Impacts 30 427 29. B.I. Oppermann, W. Michaelis, M. Blumenberg, J. Frerichs, H.M. Schulz, A. Shippers, S.E.

31 & 32 428 Beaubien and M. Krüger, Geochim. Cosmochim. Ac., 2010, 74, 2697-2716. 33 429 30. J.M. West, J.M Pearce, P. Coombs, J.R. Ford, C. Scheib, J.J. Colls, K.L. Smith and M.D. Steven, 34 Energy Procedia, 2009, 9, 1863-1870. 35 430 36 431 31. A.C. Walkley and T.A. Black, Soil Sci., 1934, 37, 29-38. 37 432 32. R.J. Norman, J.C. Edberg and J.W. Stucki, Soil Sci. Soc. Am. J., 1985, 49, 1182-1185. 38 39 433 33. P. Schloss, S.L. Westcott, T. Ryabin, J.R. Hall, M. Hartmann, E.B. Hollister, R.A. Lesniewski, 40 434 B.B.Oakley, D.H. Parks, C.J. Robinson and other 5 authors, Appl. Environ. Microb., 2009, 75, Processes 41 435 7537-7541. 42 43 436 34. C. Lozupone, M. Hamady and R. Knight, BMC Bioinformatics, 2006, 7, 371. 44 437 35. Y. Wei, M. Maroto-Valer and M.D. Steven, Energy Procedia, 2011, 4, 3224-3230. 45 438 36. H.P. Raavendra, In: Ali, M. (Ed.), Diversity of Ecosystems, InTech, 2012, 27-50. 46 37. C.L. Lauber, M. Hamady, R. Knight and N. Fierer, Appl. Environ. Microb., 2009, 75, 5111-5120. 47 439 Science: 48 440 38. J. Rousk, E. Bååth, P.C. Brookes, C.L. Lauber, C. Lozupone, G. Caporaso, R. Knight and N. 49 441 Fierer, ISME J., 2010, 4, 1340-1351. 50 51 442 39. L.E.S. Miera, P. Arroyo, E.L. Calabuig, J. Falagán and G. Ansola, Int. J. Greenh. Gas. Con., 2014, 52 443 29, 176-184. 53 444 40. G. Berg and K. Smalla, FEMS Microbiol. Ecol., 2009, 68, 1-13. 54 55 445 41. C. Will, A. Thurmer, A. Wollherr, H. Nacke, N. Herold, M. Schrumpf, J. Gutknecht, T. Wubet, 56 446 F.Buscot and R. Daniel, Appl. Environ. Microb., 2010, 76, 6751-6759. 57 447 42. C.L. Lauber, K.S. Ramirez, Z. Aanderud, J. Lennon and N. Fierer, ISME J., 2013, 7, 1641-1650. 58 59 448 43. V. Acosta-Martínez, S. Dowd, Y. Sun, D. Wester and V. Allen, Appl Soil Ecol., 2010, 45, 13-25. Environmental 60 449 44. J. Frerichs, B.I. Oppermann, S. Gwosdz, I. Möller, M. Herrmann and Krüger M., FEMS 450 Microbiol. Ecol., 2013, 84, 60-74.

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1 2 451 45. D. Wu, J. Raymond, M. Wu, S. Chatterji, Q. Ren, J.E. Graham, D.A. Bryant, F. Robb, A. Colman, 3 4 452 L.J. Tallon and other 4 authors, PLOS ONE, 2009, 4, e4207. 5 453 46. S.E. Morales and W.E. Holben, Soil Boil. Biochem., 2014, 76, 286-296. 6 454 47. N. Šibanc, A.J. Dumbrell, I. Mandić-Mulec and I. Maček, Soil Boil. Biochem., 2014, 68, 7 8 455 348-356. 9 456 10 457 11 12 458 13 459 14 460

15 Manuscript 16 461 17 462 18 463 19 20 464 21 465 22 466 23 Accepted 24 467 25 468 26 469 27 28 470 29 471 Impacts 30 472

31 & 32 473 33 474 34

35 475 36 476 37 477 38 39 478 40 479 Processes 41 480 42 43 481 44 482 45 483 46

47 484 Science: 48 485 49 486 50 51 487 52 488 53 489 54 55 490 56 491 57 492 58 59 493 Environmental 60 494 495

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1 2 496 TABLE 1. Soil physicochemistry and soil CO flux in sampling point. C = control; L = 3 2 4 497 Low CO2 sites; M = Medium CO2 sites; H = High CO2 sites; E = Extreme CO2 sites; R= 5 498 Resilience. 1, 2, 3, 4 stand for 4 parallel sample plots of each CO2 flux level. 6 − Sampling point pH EC Organic NO3 7 matter (%) (mg/kg) 8 9 C1 7.83 255 0.65 5.99 10 Control C2 8.06 226 0.75 6.10 11 C3 7.73 239 0.64 5.62 12 C4 7.97 242 0.74 6.59 13 L1 7.66 188 0.89 2.83 14 Low L2 7.60 154 0.77 2.97

15 Manuscript L3 7.70 175 0.84 2.79 16 17 L4 7.74 179 0.79 2.91 18 M1 7.47 192 0.76 1.87 19 Medium M2 7.58 173 0.81 1.91 20 M3 7.50 187 0.84 1.85 21 M4 7.42 185 0.78 1.88 22 H1 7.31 183 0.83 1.55

23 High H2 7.16 179 0.87 1.27 Accepted 24 H3 7.25 172 0.80 1.37 25 H4 7.18 175 0.78 1.42 26 E1 6.59 188 0.83 1.19 27 Extreme E2 6.98 179 0.75 1.02 28 E3 6.78 165 0.89 1.13 29 E4 6.43 181 0.87 1.08 Impacts 30 R1 7.51 140 0.87 0.11

31 & 32 Resilience R2 7.3 156 0.75 0.09 33 R3 7.2 167 0.65 0.10 34 R4 7.37 160 0.62 0.45 35 499 36 500 37 38 501 39 502 40 Processes 503 41 42 504 43 505 44 506 45 46 507

47 508 Science: 48 509 49 50 510 51 511 52 512 53 54 513 55 514 56 515 57 58 516 59 517 Environmental 60 518 519 12

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

15 Manuscript 16 17 18 19 20 21 22

23 Accepted 24 25 26 520 27 28 521 FIG. 1. Location map and studied area photos 29

522 Impacts 30 523

31 & 32 524 33 525 34 35 526 36 527 37 528 38 39 529 40 530 Processes 41 531 42 43 532 44 533 45 46

47 Science: 48 49 50 51 52 53 54 55 56 57 58 59 Environmental 60

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1 2 3 6000 4 5 6 5000 7 8 9 4000 10

11 3000 12 13

14 2000 RichnessChao index 15 Manuscript 16 17 1000 18 19 0 20 C1 C2 C3 C4 L1 L2 L3 L4M1M2M3M4H1H2H3H4 E1 E2 E3 E4 R1 R2 R3 R4 21 534 sample sites 22

23 Accepted 24 25 7 26 27 6 28 29 30 5 Impacts

31 & 32 4 33 34 3 35 36 2

37 diversity Shannon index 38 39 1 40 Processes 41 42 0 C1 C2 C3 C4 L1 L2 L3 L4M1M2M3M4H1H2H3H4 E1 E2 E3 E4 R1 R2 R3 R4 43 sample sites 44 535 45 536 FIG. 2. Richness (Chao index) and diversity (Shannon diversity index) plotted against 46

47 537 CO2 flux (C = control; L = Low CO2 sites; M = Medium CO2 sites; H = High CO2 sites; Science: 48 538 E = Extreme CO2 sites; R= Resilience. 1, 2, 3, 4 stand for 4 parallel sample plots of each 49 539 CO2 flux level.) 50 51 52 53 54 55 56 57 58 59 Environmental 60

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1 2 3 A 4 5 6 7 8 9 10 11 12 13 14

15 Manuscript 16 17 18 19 20 21 22

23 Accepted 24 25 26 27 540 28 29 B 30 Impacts

31 & 32 33 34 35 36 37 38 39 40 Processes 41 42 43 44 45 46

47 Science: 48 49 50 51 541 52 542 FIG. 3. Taxonomic distributions of soil samples. A, Phylum distribution level; B, 53 543 dominant genus in Chloroflexi, Firmicutes and Bacteroidetes(C = control; L = Low CO2 54 55 544 sites; M = Medium CO2 sites; H = High CO2 sites; E = Extreme CO2 sites. 1, 2, 3, 4 56 545 stand for 4 parallel sample plots of each CO2 flux level.) 57 58 59 Environmental 60

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1 2 3 4 A 5 6 7 8 9 10 11 12 13 14

15 Manuscript 16 17 18 19 20 21 22

23 Accepted 24 25 546 26 27 28 B 29 30 Impacts

31 & 32 33 34 35 36 37 38 39 40 Processes 41 42 43 44 45 46

47 Science: 48 49 547 50 51 548 FIG. 4. Beta diversity analysis plots derived from Unifrac distances (A) and NMDS (B) 52 549 (C = control; L = Low CO2 sites; M = Medium CO2 sites; H = High CO2 sites; E = 53 550 Extreme CO2 sites. 1, 2, 3, 4 stand for 4 parallel sample plots of each CO2 flux level.) 54 55 56 57 58 59 Environmental 60

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1 2 3 A 4 5 6 7 8 9 10 11 12 13 14

15 Manuscript 16 17 18 19 20 21 22 551 23 Accepted 24 25 B 26 27 28 29 30 Impacts

31 & 32 33 34 35 36 37 38 39 40 Processes 41 42 43 44 552 45 553 FIG. 5. Redundancy analysis ordination plot showed the relationship between the 46

47 554 bacteria community and soil properties (A) or some frequent OTUs (B) (C = control; L Science: 48 555 = Low CO2 sites; M = Medium CO2 sites; H = High CO2 sites; E = Extreme CO2 sites. 1, 49 556 2, 3, 4 stand for 4 parallel sample plots of each CO2 flux level.) 50 51 557 52 53 54 55 56 57 58 59 Environmental 60

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