Effect of simulated acid rain on soil CO2, CH4 and N2O emissions and microbial communities in an agricultural soil

Geoderma 366 (2020) 114222

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Effect of simulated acid rain on soil CO2, CH4 and N2O emissions and microbial communities in an agricultural soil

T

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Ziqiang Liua, Dengfeng Lia, Jiaen Zhanga,b,c,d, , Muhammad Saleeme, Yan Zhanga, Rui Maa, Yanan Hea, Jiayue Yanga, Huimin Xianga,b,c, Hui Weia,b,c,d a

Department of Ecology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China c Guangdong Provincial Key Laboratory of Eco-circular Agriculture, South China Agricultural University, Guangzhou 510642, China d Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China e Department of Biological Sciences, Alabama State University, Montgomery, AL 36104, USA b

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Naoise Nunan

Since the advent of industrialization and urbanization, acid rain has emerged as one of the quintessential global environmental issues. However, the effects of acid rain on carbon (C) and nitrogen (N) cycles of terrestrial ecosystems are still far from fully understood, though some studies have reported the sensitivity of living organisms and soil physicochemical properties to acidic conditions. Herein, we conducted intact soil core experiments to understand the effects of artificial acid rains of pH 5.0, 4.0 and 3.0 on soil CO2, CH4, and N2O fluxes and microbial communities in an agricultural soil of southern China. We did not detect any effect of acid rain on CO2 and N2O fluxes as compared to the control; however, acid rain of pH 3.0 significantly reduced the cumulative CH4 flux from the soil. Most noticeably, both acid rains of pH 4.0 and pH 3.0 significantly increased the total amount of soil microbial phospholipid fatty acids (PLFAs) by increasing the PLFA contents of gram-positive bacteria, actinomycetes, fungi, and arbuscular mycorrhizal fungi, though all the acid rain treatments did not change the relative abundance of microbial groups. In addition, both CO2 and CH4 fluxes negatively correlated with the total amount of soil microbial PLFAs; however, the N2O flux positively correlated to soil NO3−-N contents (p < 0.05). These results confirm the recent theoretical predictions that N-addition (e.g., by acid rain) may alter microbial C utilization pattern by allocating more C to the microbial biomass than to respiration. Overall, our results demonstrated that acid rain substantially altered the soil microbial biomass, and reduced the cumulative CH4 flux from the agricultural soil during the experimental period. Given these findings, we suggest further research to investigate the responses of soil greenhouse gas emissions and microbial communities to longterm acid rain exposures in the context of climate change.

Keywords: Acid deposition Greenhouse gas emissions Microorganisms Phospholipid fatty acids (PLFAs) analysis Agricultural soils

1. Introduction A worldwide increase in urbanization and energy demands has caused several environmental problems (Liu et al., 2019). Acid rain is one of the major environmental problems and it may affect the earth’s biodiversity and human wellbeing (Liu et al., 2017; Singh and Agrawal, 2008). The acid rain mainly results from the acidic gas emissions such as sulfur dioxide (SO2) and nitrogen oxides (NOX) (Xu et al., 2015). In North America and Europe, the emission of SO2 decreased from 1990 to 2016 due to several policy measures, such as Clean Air Act and Convention on Long-range Transboundary Air Pollution; however, the NOX emission has been increasing due to the increasing number of vehicles (The European Monitoring and Evaluation Programme (EMEP); The ⁎

U.S. Environmental Protection Agency (EPA)). Meanwhile, in Asia, the emissions of SO2 and NOX have been rapidly increasing due to the intensive urbanization and industrialization (Balasubramanian et al., 1999; Kita et al., 2004; Sant’Anna-Santos et al., 2006; Wei et al., 2020). For instance, in China, the annual S and N emissions from coal combustion, fertilizer applications and livestock have increased significantly (Larssen et al., 2011). As a result, the acid rain frequently occurs in southern and southwestern China (Larssen et al., 2006), and the vulnerable areas experience acid rain with the pH values below 5.0 (Ministry of Ecology and Environment of the People's Republic of China, 2019). Therefore, parts of China have emerged as the third acid rain hotspot following northeast America and central Europe (Liu et al., 2017). Furthermore, the acid rain is projected to increase in East Asia in

Corresponding author at: Department of Ecology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China. E-mail address: [email protected] (J. Zhang).

https://doi.org/10.1016/j.geoderma.2020.114222 Received 10 December 2019; Received in revised form 13 January 2020; Accepted 22 January 2020 0016-7061/ © 2020 Elsevier B.V. All rights reserved.

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how acid rain affects greenhouse gas emissions from soil. Therefore, in this study, we investigated the effect of acid rain on soil greenhouse gas emissions and microbial communities in an agricultural soil. Given the experimental use of intact soil cores having undisturbed structure in recent laboratory experiments (Doroski et al., 2019; Mastrocicco et al., 2019), we conducted a short-term experiment (in 12 intact cores for 180 days) to elucidate the impact of acid rain (with a pH of 5.0, 4.0 and 3.0) on soil CO2, CH4 and N2O emissions and microbial communities. We hypothesize that acid rain will alter greenhouse gas emissions from the soil by changing the microbial community composition and biomass, due to the acid rain induced changes in nutrients availability and soil edaphic properties such as pH, etc.

the future due to increasing air pollution (Network Center for EANET, 2019). Since global change phenomena may impact microbial-driven ecosystem functioning (Harper et al., 2005; Saleem, 2015). Therefore, it is timely to evaluate the impact of acid rain on soil properties such as greenhouse gas emissions and microbial communities, especially in acid-vulnerable regions. For instance, in southern China that is one of the high-risk areas with respect to acid rain pollution, there is a need for more researches that address the impacts of acid rain on soil ecosystems (Liu et al., 2019). Although some studies have investigated the impact of acid rain on soil greenhouse gas emissions from forest, farmland, grassland and wetland soil ecosystems (Chen et al., 2012; Q. Li et al., 2019; Y. Li et al., 2019; Liang et al., 2013; Zheng et al., 2018), a consensus has not yet emerged regarding responses of soil ecosystems to the acid rain. For instance, acid rain may increase (Wang et al., 2018; Zhang et al., 2011), decrease (Liang et al., 2013; Wang et al., 2018; Zhang et al., 2011), and/or have no effect on soil greenhouse gas emissions (Liang et al., 2013). A field experiment conducted in the northern subtropical secondary forest showed that acid rain (both of pH 4.5 and pH 3.5) increased soil CO2 flux in the non-growth period (3 months), most likely due to the anthropogenic N input (i.e., NO3− carried by acid rain) which nevertheless acted as a fertilizer and thus increased the root respiration (Wang et al., 2018; Zhang et al., 2011). However, a continuous high-intensity acid rain (pH 2.5) significantly reduced the CO2 emission in the growth period (9 months) due to the soil nutrients (e.g., Ca2+, Mg2+, K+, and Na+) deficiency, aluminum toxicity, and lower microbial activity (Zhang et al., 2011). In another study, Liang et al. (2013) reported that acid rain significantly reduced CO2 emission from the soils of mixed conifer-broadleaf and broadleaf forests; however, it did not affect soil CO2 emission in the pine forest, thus suggesting the influence of vegetation and soil types on acid rain effects. Apart from soil and vegetation factors, the acid rain properties, for instance, the SO42-/NO3− ratio may also influence the soil CO2 emission (Liu et al., 2017). Overall, previous studies have mostly focused on the CO2 emission while simultaneous investigations of the impact of acid rain on soil CH4, CO2, and N2O emissions are scarce (Wang et al., 2018). Given that CH4 and N2O contributions to global warming are higher due to their higher radiative forcing (IPCC, 2014), therefore, it is critical to collectively address the effect of acid rain on the soil CH4, CO2, and N2O emissions. In recent years, some researchers have also investigated the impact of biotic and abiotic factors such as, vegetation type (Han et al., 2014), shoot biomass (Han et al., 2014), root biomass (Chen et al., 2015a,2015b; Liang et al., 2013; Liang et al., 2016), litter decomposition (Wang et al., 2013; Wu et al., 2016), microorganisms (Liang et al., 2013; Liang et al., 2016), and soil properties (e.g., pH, temperature, moisture, electrical conductivity, texture, C/N ratio) on soil greenhouse gas emissions (Liang et al., 2013; Ouyang et al., 2008; Wang et al., 2018; Wei et al., 2014). Among these, the vegetation type plays an important role in the soil CO2 emission, while the root and shoot biomass demonstrated significant linear relationships with the CO2 flux in a previous study (Han et al., 2014). Similarly, the litter decomposition also contributed to about 22.3% of total soil CO2 efflux (Wang et al., 2013). Moreover, the microbial biomass is also considered as an indicator of greenhouse gas emission in the forest and peatland soils (Liang et al., 2013; Liang et al., 2016; Lozanovska et al., 2016). Regarding abiotic factors, both soil temperature and moisture significantly impact CO2 flux from the soil (Liang et al., 2016; Maucieri and Borin, 2017). Furthermore, Lang et al. (2011) reported that the cumulative CO2 emissions positively and negatively correlated with the soil nutrients availability (e.g., water-soluble organic C and N, C/N ratio) and pH, respectively. By contrast, the cumulative CH4 and N2O emissions showed positive and negative correlations with soil pH and nutrients availability (e.g., total C and N contents and C/N ratio), respectively (Fender et al., 2013; Lang et al., 2011). Given the prevalence of acid rain and its adverse consequences, it remains understudied whether and

2. Materials and methods 2.1. Collection of soil cores On July 2, 2017, we collected 12 undisturbed soil cores using PVC pipes (inner diameter = 60 cm, depth = 40 cm) from Zengcheng Teaching and Research Farm (113°38′ E, 23°14′ N) of South China Agricultural University in Guangdong, China. We inserted PVC pipes into the soil by an excavator, and then dug out the soil cores from the outside of the PVC pipe with shovels and placed them on plastic plates. The sampling region experiences a typical subtropical monsoon climate and has a mean annual air temperature of 22 °C and an annual precipitation of 1976.8 mm (Wei et al., 2018). This region has witnessed severe acid rain events with the average acid rain frequency of 35.2% and the annual acid rain precipitation of 623.1 mm during 2011–2015 (Department of Ecology and Environment of Guangdong province, 2019; Guangdong Meteorological Bureau, 2019). The soil in this region is classified as lateritic red soil (Wei et al., 2018). The soil contained 77.23% sand, 11.72% silt and 11.04% clay. With a pH of 5.9 (Wei et al., 2018), the soil contained 8.13 and 15.59 mg/kg of ammonium- and nitrate-N, respectively. The total C, N and P contents of soil were 11.67, 1.97 and 0.41 g Kg−1, respectively, thus representing the soil C/N, C/P and N/P ratios as 5.91, 30.31 and 5.20, respectively. The methods used to measure the above-mentioned variables are presented in Section 2.5. 2.2. Experimental design After collection, we moved these 12 soil cores to a greenhouse (temperature: 6.9 ~ 39.7℃, air humidity: 31.5 ~ 98.3%) in the experimental farm (113°21′ E, 23°10′ N) of Guangdong Provincial Key Laboratory of Eco-circular Agriculture. During equilibration, we applied equal amounts of local tap water as needed to keep soil cores moist. After three months of equilibration, we randomly divided these cores into four treatments, with three replicates per treatment: CK (control), A1 (acidity 1, pH = 5.0), A2 (acidity 2, pH = 4.0), A3 (acidity 3, pH = 3.0). To simulate the actual ion composition of acid rain in Guangdong province, the ions concentration (μeq l−1) SO42- to NO3− was set as 4 : 1 (Ministry of Ecology and Environment of the People's Republic of China, 2012). We prepared acid rain solutions by mixing 0.5 mol/L H2SO4 and 0.5 mol/L HNO3 at 2:1 mol ratio, and the pH values were adjusted to 5.0, 4.0 and 3.0 by adding the local tap water (the average pH approximates to 7.5). We calibrated pH values with the portable Pro10 pH meter (YSI Inc./Xylem Inc., Yellow Springs, OH, USA). The total amount of acid rain for each soil core was 623.1 mm, which is equivalent to the mean annual amount of acid rain from 2011 to 2015 in Guangdong (Department of Ecology and Environment of Guangdong province, 2019; Guangdong Meteorological Bureau, 2019). To ensure that all soil cores are infiltrated by the acid rain solution without leaching from bottom during the experimental period, we sprayed a 5 L solution of acid rain to each soil core, after every 5 days, by using a sprinkler from October 2017 to April 2018 (total 36 times in 180 days). Simultaneously, we applied the same doses 2

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determined by the two-wavelength ultraviolet spectrometry method (Wang et al., 2012). The soil total carbon (TC) and total nitrogen (TN) contents were analyzed by dry combustion at 950℃ in the Vario TOC element analyzer (Elementar, Hanau, Germany).

of tap water to the CK treatment. 2.3. Measurement of greenhouse gases and environmental parameters From each soil core, we used the static opaque chamber to collect four gas samples within 30 min with an interval of 10 min to analyze the concentrations of greenhouse gases (CO2, CH4, and N2O) using a gas chromatograph equipped with a flame ionization detector (FID) and an electron capture detector (ECD). Before sampling, we removed the vegetation (mainly are Oxalis pes-caprae L. and Lygodium japonicum (Thunb.) Sw.) within PVC collars to decrease the influence of gas emissions from the vegetation. The fluxes of CO2, CH4 and N2O were calculated by the slope of the linear regression between the concentrations of each gas and the sampling time intervals. The cumulative CO2, CH4, and N2O fluxes were calculated by multiplying the fluxes of each gas at each measurement by the time intervals, and then summing these values (details could be referred to Li et al., 2019). The gas samples were analyzed once every 15 days. At each sampling time, the soil temperature (T) and electrical conductivity (EC) at 20 cm depth of each soil core were also recorded in situ with Handheld Meter AZS-100 (Beijing Aozuo Ecological Instrument Co., Ltd., China).

2.6. Phospholipid fatty acid (PLFA) analysis The total microbial biomass and the biomass of specific groups as well as the microbial community structure were determined by PLFA analysis following the established protocols (Bossio and Scow, 1998; Chen et al., 2019b, 2019a). Briefly, 8 g of freeze-dried soil from each sample was used to extract phospholipids by a buffer solution (phosphate buffer: chloroform: methanol = 0.8:1:2). The phospholipids were then separated by the chloroform, acetone and methanol in a 3 mL silica column (ANPEL Laboratory Technologies Inc., Shanghai, China). Finally, the phospholipid fatty acids were collected and detected by the Agilent 7890A GC equipped with the Sherlock Microbial Identification System (Version 6.2, MIDI Inc., Newark, Delaware, USA). The individual PLFAs concentrations (nmol g−1) were calculated according to the internal standard 19:0. The PLFAs, which are used as markers for specific microbial groups are listed in Table S1. In addition, the ratios of gram positive to negative bacterial PLFAs (G+/G−), fungal to bacterial PLFAs (F/B), saturated to monounsaturated PLFAs (S/M) and cyclopropyl to precursor PLFAs (Cy/Pre) were calculated as the soil microbial stress indicators (Wu et al., 2009).

2.4. Soil sampling At the end of the experiment, we collected five surface soil cores (0–10 cm) from each soil core with a 2.5 cm-diameter soil drill, and then thoroughly mixed to form a composite sample. We sieved (2 mm) all fresh soil samples immediately to remove large stones and root residues, and then divided each sample into two portions. The first portion was air-dried for determining soil physicochemical properties (i.e., soil pH, exchangeable acid, H+, and Al3+, available P, available K, alkaline N, ammonium-N, nitrate-N, total C and N). We freeze-dried the second portion of soil samples for the phospholipid fatty acid (PLFA) analysis.

2.7. Statistical analysis To compare greenhouse gases (CO2, CH4, and N2O) fluxes among acid rain treatments at various time intervals, we used repeated measures analysis of variances (ANOVA). When the main effects of ANOVA were significant, the significance of treatment effects at each sampling time was tested by one-way ANOVA with post-hoc Duncan or GamesHowell in SPSS 25.0 (IBM Corp., New York, USA). One-way ANOVA was also used to compare the cumulative greenhouse gases fluxes, global warming potential (GWP), soil physicochemical properties and microbial biomass among treatments. The significance level was set at p < 0.05. The CO2 is used as the reference gas to estimate the GWP. We transformed the cumulative CH4 and N2O fluxes to the CO2 equivalents by multiplying by 28 and 265, respectively, based on the IPCC’s 100year time horizon estimates (IPCC, 2014). The GWP was then calculated as: GWP = cumulative CO2 flux + 28 × cumulative CH4 flux + 265 × cumulative N2O flux. The principal component analysis (PCA) was performed to investigate differences in soil microbial community structure among treatments on the basis of PLFA profiles. The redundancy analysis (RDA) was performed to reveal the relationships among soil greenhouse gas emissions, microbial community parameters and physicochemical properties. The relationships between soil greenhouse gas emissions, microbial community and physicochemical properties were determined using Pearson correlations. The PCA and RDA were performed using “vegan” package in R (Oksanen et al., 2019). Moreover, we used a linear model to determine the relationships between greenhouse gas emissions and environmental parameters (soil T, EC) with lm function in R, in which the soil T, EC and their interactions were included as explanatory variables. Prior to modeling, we checked the normality of data using histograms or Q-Q plots of the residuals, and the data was log (ln)-transformed to improve the normality if necessary.

2.5. Soil physicochemical properties analysis We determined the soil physicochemical properties following established methods (Lu, 2000). Briefly, the soil moisture was determined by oven-drying at 105 °C for 6–8 h. The soil pH was measured in a 1:2.5 soil/water suspension with Pro10 pH meter (YSI Inc./Xylem Inc., Yellow Springs, OH, USA). We extracted the soil exchangeable acid, H+ and Al3+ (EA, E H+ and E Al3+) by leaching 10.00 g air-dried soil with 250 mL 1.0 mol/L KCl solution, and their contents were determined by titrating with 0.02 mol/L NaOH standard solution. The soil available P was extracted by adding 50 mL HCl-NH4F solution to 5.0 g air-dried soil, followed by shaking at 150 ~ 180 rpm for 30 min. Then, the supernatant was filtered through a phosphorus-free filter paper, and the available P contents were determined by UV spectrophotometer (UV1750, Shimadzu International Trading (Shanghai) Co., Ltd., China). Similarly, the soil available K was extracted by adding a 50 mL 1.0 mol/ L ammonium acetate solution to 5.00 g air-dried soil, followed by shaking at 120 rpm for 30 min. Then, the supernatant was filtered through a filter paper, and the available K contents were determined by using a flame photometer. The soil alkaline N was determined by adding 0.2 g ferrous sulfate powder to the 2.00 g air-dried soil, and the contents were determined by using the alkaline solution diffusion method. The soil ammonium-N (NH4+-N) was extracted by adding 50 mL 2 mol/L KCl solution to 10 g air-dried soil, followed by shaking at 165 rpm for 1 h. Then, the supernatant was filtered through a filter paper, and the ammonium-N contents were determined by the indophenol blue colorimetry. The soil nitrate-N (NO3−-N) was extracted by adding 50 mL 1.0 mol/L KCl solution to 10.00 g air-dried soil, followed by shaking at 180 ~ 240 rpm for 1 h. Then, the supernatant was filtered through a filter paper, and the nitrate-N contents were

3. Results 3.1. Soil physicochemical properties and greenhouse gases fluxes The A3 treatment significantly reduced the soil pH and TN relative to the CK treatment (p < 0.05, Table 1). Both CO2 and N2O fluxes 3

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0.08 0.22 0.48 0.53

3.2. Soil microbial biomass and structure

5.32 ± 5.34 ± 5.95 ± 6.18 ± 0.119 ns ns

± ± ± ±

11.01 11.61 11.80 11.03 0.384

Both A2 and A3 treatments significantly increased the total PLFAs contents as compared to the CK (p < 0.05, Fig. 2a). The acid rain promoted the growth of gram-positive bacteria, actinomycete, fungi and AMF. Particularly, the A2 and A3 significantly increased the PLFAs of gram-positive bacteria and actinomycetes, and the A2 and A3 significantly increased the AMF and fungal PLFAs, respectively (p < 0.05, Fig. 2b). However, the acid rain treatments did not change the relative proportions of microbial groups as compared to the CK (Table S2). Moreover, the PLFAs of SO42−-reducing and methane-oxidizing bacteria, and the ratios of G+/G-, F/B, S/M and Cy/Pre did not differ across treatments (Fig. 2c and d). The PCA analysis was used to determine differences in microbial community composition in different treatments. The first two principal components accounted for 90.3% of the total variance in microbial community composition, among which the first principal component (PC1) explained 72.9%, and the second principal component (PC2) explained 17.4% of the total variance (Fig. 3). Moreover, the composition and biomass of microbial communities in various treatments gradually separated along PC1, and there were significant differences between A3 and CK, A1, A2 tested by ANOVA of the loadings of PC1 (p < 0.05, Fig. 3).

3.97 ± 6.39 ± 5.30 ± 5.67 ± 0.194 ns 4.96 ± 5.17 ± 5.59 ± 5.87 ± 0.635 ns 54.37 58.69 63.15 62.14 0.352 46.13 44.82 52.29 56.92 0.546

ns

3.3. Relationships between microbial groups and soil physicochemical properties

1.99 0.75 1.36 0.02

± ± ± ±

8.59 2.69 7.69 5.07

ns

AN mg kg−1

± ± ± ±

3.32 7.15 1.80 2.24

NH4+-N mg kg−1

0.58 0.09 0.25 0.85

NO3−-N mg kg−1

0.24 1.40 0.19 0.13

TC g kg−1

TN g kg−1

0.03b 0.14ab 0.01b 0.02a

TC/TN

0.06 0.31 0.24 0.35 2.07 ± 2.19 ± 1.98 ± 1.79 ± 0.006**

differed significantly at various time intervals; however, the treatment effect, and the interactions between treatment and sampling time did not affect the gases fluxes (Table 2, Fig. S1a and c). There were no significant differences in the cumulative CO2 and N2O fluxes among treatments (Fig. 1a and c). However, there was a significant effect of acid rain treatment on the CH4 fluxes (p = 0.029, Table 2). Moreover, the A3 treatment significantly reduced the cumulative CH4 fluxes as compared to the CK treatment (p < 0.05, Fig. 1b). However, we did not observe any difference of GWP among acid rain treatments (Fig. S2).

AK mg kg−1

0.14 0.13 0.29 0.25

0.23 ± 0.27 ± 0.19 ± 0.28 ± 0.293 ns

0.04 0.01 0.05 0.02

0.24 ± 0.48 ± 0.63 ± 0.74 ± 0.316 ns

0.08 0.05 0.24 0.25

4.43 ± 3.25 ± 6.86 ± 3.97 ± 0.277 ns

The RDA analysis showed the relationships between soil microbial groups and physicochemical properties (Fig. 4a). We determined the relationships of soil physicochemical properties such as, acidification parameters (i.e., soil pH, exchangeable acid and Al3+), nutrients indices (i.e., available P, available K, total C, total N, and C/N ratio) and soil electrical conductivity (EC) with the PLFAs of various microbial groups. RDA analysis presented the relationships between microbial community parameters and soil properties, and the correlation analysis further demonstrated more-quantitative relationships between these variables. Among these, the total PLFAs, gram-positive bacterial PLFAs and actinomycetal PLFAs positively and negatively correlated with the C/N ratio and soil pH, respectively (p < 0.05, Fig. 4a, Fig. 5a). The PLFAs of AMF, SRB, gram-positive and -negative bacteria positively correlated with the soil available K contents (p < 0.05, Fig. 4a, Fig. 5a). Moreover, gram-positive bacterial, actinomycetal and SRB PLFAs positively correlated with soil exchangeable Al3+ (p < 0.05, Fig. 5a). However, the total PLFAs and fungal PLFAs negatively correlated with the total N (p < 0.05, Fig. 5a). In addition, we found significant positive correlations among total PLFAs, gram-positive bacterial PLFAs, MOB PLFAs, G+/G-, S/M and Cy/Pre ratios and soil EC (p < 0.05, Fig. 4a, Fig. 5a).

0.04b 0.07ab 0.15ab 0.01a

0.27 ± 0.84 ± 0.82 ± 1.02 ± 0.159 ns

AP mg kg−1 E Al3+ cmol kg−1 E H+ cmol kg−1 EA cmol kg−1

5.21 ± 4.84 ± 4.76 ± 4.68 ± 0.003**

3.4. Linking greenhouse gas fluxes to soil microbial groups and soil physicochemical properties We found significant correlations between CO2 flux and some soil properties (T, EC), and between N2O flux and soil T (p < 0.05, Table 3). Moreover, both CO2 and CH4 fluxes negatively correlated with the total PLFAs; however, the N2O flux positively correlated with the

CK A1 A2 A3 P values

pH

Table 1 Soil physicochemical properties among different treatments at the end of the experiment (mean ± SE, n = 3). CK: control, A1: acid rain with pH of 5.0, A2: acid rain with pH of 4.0, A3: acid rain with pH of 3.0. EA: exchangeable acid; E H+: exchangeable H+; E Al3+: exchangeable Al3+; AP: available phosphorus; AK: available potassium; AN: alkaline nitrogen; NH4+-N: ammonium nitrogen; NO3−-N: nitrate nitrogen; TC: total carbon; TN: total nitrogen. Different lowercase letters within a column indicate significant differences (p < 0.05) among treatments. ** Different at p < 0.01, * Different at p < 0.05, ns Non-different.

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Table 2 Results from the repeated measures ANOVA for CO2, CH4 and N2O fluxes. CO2 flux

Treatment Time Treatment*Time

CH4 flux

N2O flux

df

F value

p

df

F value

p

df

F value

p

3 12 36

0.225 28.846 0.812

0.876 < 0.001 0.750

3 12 36

6.167 1.471 0.944

0.029 0.203 0.565

3 12 36

0.387 5.503 0.727

0.767 < 0.001 0.852

correlations between GWP and MOB PLFAs, and fungal/bacterial PLFA ratio (p < 0.05, Fig. 5b). 4. Discussion In our study, the acid rain treatments did not affect the soil CO2 emission (Fig. S1a, Fig. 1), which might be due to either buffering potential of soil and/or the duration of acid rain (Liang et al., 2013; Liang et al., 2016). It has been well established that soil properties such as cation exchange capacity (CEC), soil organic matter (SOM) and clay content play critical roles in alleviating the negative effects of exogenous acid inputs (Wei et al., 2020; Yang et al., 2020). However, soil acid-buffering capacity is soil type-specific. In lateritic red soil (the soil type in our study), soil CEC and clay content may be the dominant factors to affect the soil acid-buffering capacity to a great extent (Wei et al., 2020). Regarding the acid rain effects on the CO2 emission, soil microbes potentially contribute to the CO2 emission due to their metabolic activities such as decomposition of organic matter (Liang et al., 2016; Wang et al., 2017). Therefore, it is commonly anticipated that once the microbial biomass reduces, then the decomposition of organic matter and CO2 emission will decreases (Chen et al., 2016, 2019b, 2019a; D. Chen et al., 2015; S. Chen et al., 2015). However, contrary to most prior studies describing a negative effect of acid rain on microbial biomass (Liang et al., 2016; Liu et al., 2017), the soil microbial biomass did not decrease, rather, it significantly increased in our study (Fig. 2a). These results suggested that exogenous acid input positively influenced the soil microbial populations; however, the CO2 emission did not change under short-term acid rain exposure. In addition, some studies have reported that the relationship between soil microbial biomass and soil respiration is complex (Martin et al., 2015; Riggs et al., 2015; Schimel, 2003). For instance, there is a perception that N-enrichment in a N-limited soil may lead to a relatively higher C allocation to the microbial cellular growth than to respiratory or extracellular enzyme activities, thus causing a greater microbial C-assimilation efficiency (biomass) and consequently, a reduced or unaltered respiration (Riggs et al., 2015; Schimel, 2003). However, a high-intensity acid rain significantly inhibited the cumulative CH4 fluxes while a mild acid rain showed no significant effects on cumulative CH4 fluxes (Fig. 1b). Both SO42- and NO3− mediated redox reactions could reduce CH4 production due to their inhibitory effects and/or competition of NO3− and SO42−- reducing bacteria with methanogen (Bodegom and Stams, 1999; Eriksson et al., 2010). In addition, SO42−- reducing bacteria could participate in anaerobic oxidation of CH4 by utilizing CH4 as the carbon or energy source (Smemo and Yavitt, 2011). Interestingly, in our study, we found that the PLFAs of both SO42−- reducing and methane-oxidizing bacteria tended to increase (Fig. 2c), which might have reduced the cumulative CH4 fluxes by excluding/suppressing the methanogens and increasing CH4 oxidation (Eriksson et al., 2010). In addition, Ye et al. (2012) reported that CH4 production was more sensitive to changes in soil pH than CO2 production, and low pH could reduce CH4 production by mainly inhibiting both methanogenesis pathways (hydrogenotrophic and acetolactic CH4 production). These could be the possible explanations for the unaltered CO2 emission but decreased CH4 emission under the same acid rain addition. Furthermore, Eriksson et al. (2010) reported that SO42− deposition significantly reduced the CH4 production in the mire

Fig. 1. The cumulative greenhouse gases fluxes among treatments. CK: control, A1: acid rain with pH of 5.0, A2: acid rain with pH of 4.0, A3: acid rain with pH of 3.0. Error bars indicate the standard error of means (n = 3), different lowercase letters across treatments indicate significant differences (p < 0.05).

soil NO3−-N content (p < 0.05, Fig. 4b, Fig. 5b). The cumulative CO2 and CH4 fluxes were negatively related to the MOB PLFAs and fungal/ bacterial PLFAs ratio; however, the cumulative N2O flux positively correlated to the soil alkaline N and NO3−-N contents (p < 0.05, Fig. 4b, Fig. 5b). Moreover, we also found significant negative 5

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Fig. 2. The total PLFAs contents (a), specific microbial group PLFAs contents (b, c) and the ratios of PLFAs of various microbial groups (d) among treatments. CK: control, A1: acid rain with pH of 5.0, A2: acid rain with pH of 4.0, A3: acid rain with pH of 3.0. “G+: gram-positive bacteria”; “G−: gram-negative bacteria”; “A: actinomycete”; “F: fungi”; “AMF: arbuscular mycorrhizal fungi”; “SRB: SO42−- reducing bacteria”; “MOB: methane-oxidizing bacteria”; “B: bacteria”; “S: saturated PLFAs”; “M: monounsaturated PLFAs”; “Cy: cyclopropyl PLFAs”; “Pre: precursor PLFAs”. Error bars indicate the standard error of means (n = 3), different lowercase letters across treatments indicate significant differences (p < 0.05).

Fig. 3. Principal component analysis (PCA) of PLFA data based on the 12 soil cores. Error bars indicate the standard errors (n = 3). CK: control, A1: acid rain with pH of 5.0, A2: acid rain with pH of 4.0, A3: acid rain with pH of 3.0.

instance, low concentrations of SO42− that may undergo a rapid turnover don’t necessarily decrease CH4 production (Eriksson et al., 2010; Vile et al., 2003). That is why we did not observe a significant effect of mild acid rain exposure on cumulative CH4 fluxes. Moreover, the soil NO3−-N contents positively correlated with the N2O flux though acid rain did not affect the soil N2O fluxes (Fig. S1c, Fig. 1c). Previous studies showed that acid rain-induced soil acidification had a positive, negative, or no effect on N2O emissions, depending on soil edaphic factors such as initial pH, moisture, temperature, NO3− concentration, available organic carbon and microbial activities (Fan et al., 2017; Heinen, 2006; Lozanovska et al., 2016; Pu et al., 2001; Simek et al., 2000; Sitaula et al., 1995). In our case, the unaltered N2O emission might be ascribed to the indistinctive differences in soil nutrients availability (e.g., NO3−-N content) in the acid rain treatments. Interestingly, acid rain increased the total, gram-positive bacterial, actinomycetal and fungal PLFAs (Fig. 2). Partly similar to our study, some studies have reported a positive effect of reduced soil pH on the soil microbial community parameters under subtropical conditions (Enowashu et al., 2009; Ham et al., 2010; Liu et al., 2017). We anticipate that acid rain enriched in N acted as a fertilizer, and thus stimulated soil microbes (Liu et al., 2017). However, several studies have also reported that reduction in the soil pH and base cations (e.g., Na+, K+, Ca2+, Mg2+) decreased the total and bacterial PLFAs in the grassland and forest soils (Chen et al., 2019b, 2019a; D. Chen et al., 2015; S. Chen et al., 2015; Grayston et al., 2001; Högberg et al., 2006; Rousk et al., 2009, 2011), which is inconsistent with our findings. On the basis of observed effects of acid rain on soil microbial community, we think there are two interconnected plausible interpretations to explain our observations. First, the acid rain-induced “fertilization effect” minimized the inhibitory effect of soil acidification on microbial communities in a short term. Second, the negative effect of excess H+ seemed to be slightly stronger than the fertilization effect. With

surface by altering the abundance of SO42−- reducing bacteria and methanogens. However, the effect of SO42− deposition on CH4 production depends, among others, mainly on its concentrations. For 6

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Fig. 4. Redundancy analysis (RDA) of soil microbial groups and physicochemical properties (a), greenhouse gas emissions and the selective PLFA biomarkers, and physicochemical properties (b). G+: gram-positive bacteria; G−: gram-negative bacteria; A: actinomycete; F: fungi; AMF: arbuscular mycorrhizal fungi; SRB: SO42−reducing bacteria; MOB: methane-oxidizing bacteria; B: bacteria; S: saturated PLFAs; M: monounsaturated PLFAs; Cy: cyclopropyl PLFAs; Pre: precursor PLFAs. EA: exchangeable acid; E Al3+: exchangeable Al3+; AP: available phosphorus; AK: Available potassium; AN: alkaline nitrogen; NO3−-N: nitrate nitrogen; TC: total carbon; TN: total nitrogen; EC: soil electrical conductivity.

resistance against soil acidic conditions. Although our study does not provide the direct evidence to prove this prediction, but we suggest that acid rain-induced changes in the AMF might help plants in a short term. Thus, our results suggest that further studies are needed to discern the consequences of cumulative effects of acid rains on soil microbial communities and soil ecosystem functioning. Compared with the field experiments, the results obtained from our intact soil core experiment may have certain limitations. First, we studied only 12 soil cores for 180 d under semi-natural conditions. Second, the simulated acid rain exposure does not reflect the intensity and frequency of physicochemical effects of natural rain on the studied soil. The observations obtained from the controlled experiments may vary from those obtained from in situ experiments, nevertheless, these

decreasing soil pH, the contents of available nutrients such as P, K, and N tended to increase (Table 1). Correspondingly, the concentrations of soil exchangeable acid, H+, and Al3+ also tended to increase (Table 1). Thus, we suggest that shifts in the soil microbial community structure and abundance result from the effects of acid rain-induced nutrient availability rather than the toxicity of H+ and Al3+ on the soil microbes during the experimental period. Apart from the effect of acid rain on total, gram-positive bacterial, actinomycetal and fungal PLFAs, it significantly increased the abundance of AMF, while opposite results were reported by a recent study (Liu et al., 2017). The positive effect of acid rain on AMF might have broad implications. For instance, very recently, He et al. (2019) reported that the colonization of plant roots by AMF could induce plant

Fig. 5. Pearson correlations between the specific PLFA groups and selective soil properties, and environmental parameters (a), and between greenhouse gases emissions and the selective PLFA biomarkers, and soil properties (b). Total: the total PLFAs; G+: gram-positive bacteria; G−: gram-negative bacteria; A: actinomycete; F: fungi; AMF: arbuscular mycorrhizal fungi; SRB: SO42−- reducing bacteria; MOB: methane-oxidizing bacteria; B: bacteria; S: saturated PLFAs; M: monounsaturated PLFAs; Cy: cyclopropyl PLFAs; Pre: precursor PLFAs. EA: exchangeable acid; E Al3+: exchangeable Al3+; AP: available phosphorus; AK: Available potassium; AN: alkaline nitrogen; NO3–-N: nitrate nitrogen; TC: total carbon; TN: total nitrogen; EC: soil electrical conductivity. The numbers in cells represent the Pearson correlation coefficients (r) between the two parameters, with * and ** indicating significant correlations at the level of p < 0.05 and p < 0.01, respectively. Only significant correlations (p < 0.05) are shown. 7

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Table 3 The relationships between greenhouse gases fluxes and environmental parameters with linear model. T: soil temperature; EC: soil electrical conductivity. The signs of estimates indicate the positive or negative relationships with the responses. EC, CO2 and N2O fluxes were log-transformed to meet the normality. “-” indicates there are no interactions between T and EC. CO2 flux

Intercept T EC T*EC

CH4 flux

N2O flux

Estimate

SE

t value

p

Estimate

SE

t value

p

Estimate

SE

t value

p

2.286 0.039 −0.650 0.030

0.285 0.011 0.296 0.011

8.007 3.567 −2.198 2.787

< 0.0001 0.0005 0.0296 0.0061

−15.210 −0.214 −7.855 –

48.712 1.855 12.290 –

−0.312 −0.115 −0.639 –

0.755 0.908 0.524 –

3.135 −0.029 0.108 –

0.361 0.014 0.091 –

8.690 −2.116 1.182 –

< 0.0001 0.0361 0.2392 –

kinds of controlled experiments may help us predict the responses of soil greenhouse gas emissions and microbial communities to anthropogenic phenomena such as acid rain in a short term. Meanwhile, we suggest that controlled experiments such as intact soil core incubation studies should include experimental conditions that mimic natural soil and environmental factors in situ.

explain the contrasting responses of microbial and root respiration to experimental soil acidification. Soil Biol. Biochem. 90, 139–147. Chen, D., Li, J., Lan, Z., Hu, S., Bai, Y., 2016. Soil acidification exerts a greater control on soil respiration than soil nitrogen availability in grasslands subjected to long-term nitrogen enrichment. Funct. Ecol. 30, 658–669. Chen, D., Xing, W., Lan, Z., Saleem, M., Wu, Y., Hu, S., et al., 2019b. Direct and indirect effects of nitrogen enrichment on soil organisms and carbon and nitrogen mineralization in a semi-arid grassland. Funct. Ecol. 33, 175–187. Chen, D., Saleem, M., Cheng, J., Mi, J., Chu, P., Tuvshintogtokh, I., et al., 2019. Effects of aridity on soil microbial communities and functions across soil depths on the Mongolian Plateau. Funct. Ecol. 0, 1–11. Chen, S., Shen, X., Hu, Z., Chen, H., Shi, Y., Liu, Y., 2012. Effects of simulated acid rain on soil CO2 emission in a secondary forest in subtropical China. Geoderma 189–190, 65–71. Chen, S., Zhang, X., Liu, Y., Hu, Z., Shen, X., Ren, J., 2015b. Simulated acid rain changed the proportion of heterotrophic respiration in soil respiration in a subtropical secondary forest. Appl. Soil Ecol. 86, 148–157. Department of Ecology and Environment of Guangdong province https://www.gdep.gov. cn/ (accessed on 6 June, 2019). Doroski, A.A., Helton, A.M., Vadas, T.M., 2019. Greenhouse gas fluxes from coastal wetlands at the intersection of urban pollution and saltwater intrusion: A soil core experiment. Soil Biol. Biochem. 131, 44–53. Enowashu, E., Poll, C., Lamersdorf, N., Kandeler, E., 2009. Microbial biomass and enzyme activities under reduced nitrogen deposition in a spruce forest soil. Appl. Soil Ecol. 43, 11–21. Eriksson, T., Öquist, M.G., Nilsson, M.B., 2010. Production and oxidation of methane in a boreal mire after a decade of increased temperature and nitrogen and sulfur deposition. Global Change Biol. 16 (7), 2130–2144. Fan, J., Xu, Y., Chen, Z., Xiao, J., Liu, D., Luo, J., et al., 2017. Sulfur deposition suppressed nitrogen-induced soil N2O emission from a subtropical forestland in southeastern China. Agr. Forest Meteorol. 233, 163–170. Fender, A., Gansert, D., Jungkunst, H.F., Fiedler, S., Beyer, F., Schützenmeister, K., et al., 2013. Root-induced tree species effects on the source/sink strength for greenhouse gases (CH4, N2O and CO2) of a temperate deciduous forest soil. Soil Biol. Biochem. 57, 587–597. Grayston, S.J., Griffith, G.S., Mawdsley, J.L., Campbell, C.D., Bardgett, R.D., 2001. Accounting for variability in soil microbial communities of temperate upland grassland ecosystems. Soil Biol. Biochem. 33, 533–551. Guangdong Meteorological Bureau http://gd.cma.gov.cn/ (accessed on 6 June, 2019). Ham, Y., Kobori, H., Kang, J., Kim, J.H., 2010. Ammonium nitrogen deposition as a dominant source of nitrogen in a forested watershed experiencing acid rain in central Japan. Water Air Soil Pollut. 212 (1–4), 337–344. Han, G., Xing, Q., Luo, Y., Rafique, R., Yu, J., Mikle, N., 2014. Vegetation types alter soil respiration and its temperature sensitivity at the field scale in an estuary wetland. PLoS One 9 (3), e91182. Harper, C.W., Blair, J.M., Fay, P.A., Knapp, A.K., Carlisle, J.D., 2005. Increased rainfall variability and reduced rainfall amount decreases soil CO2 flux in a grassland ecosystem. Global Change Biol. 11 (2), 322–334. He, L., Xu, J., Hu, L., Ren, M., Tang, J., Chen, X., 2019. Nurse effects mediated by acidtolerance of target species and arbuscular mycorrhizal colonization in an acid soil. Plant Soil 441, 161–172. Heinen, M., 2006. Simplified denitrification models: Overview and properties. Geoderma 133 (3–4), 444–463. Högberg, M.N., Högberg, P., Myrold, D.D., 2006. Is microbial community composition in boreal forest soils determined by pH, C-to-N ratio, the trees, or all three? Oecologia 150 (4), 590–601. IPCC Anthropogenic and natural radiative forcing. In Intergovernmental Panel On Climate Change, Ed. Cambridge University Press: Cambridge, 2014; pp. 659–740. Kita, I., Sato, T., Kase, Y., Mitropoulos, P., 2004. Neutral rains at Athens, Greece: A natural safeguard against acidification of rains. Sci. Total Environ. 327 (1–3), 285–294. Lang, M., Cai, Z., Chang, S.X., 2011. Effects of land use type and incubation temperature on greenhouse gas emissions from Chinese and Canadian soils. J. Soil. Sediment. 11 (1), 15–24. Larssen, T., Lydersen, E., Tang, D.G., He, Y., Gao, J.X., Liu, H.Y., et al., 2006. Acid rain in China. Environ. Sci. Technol. 40 (2), 418–425. Larssen, T., Duan, L., Mulder, J., 2011. Deposition and leaching of sulfur, nitrogen and calcium in four forested catchments in China: Implications for acidification. Environ. Sci. Technol. 45 (4), 1192–1198.

5. Conclusion A high-intensity acid rain significantly decreased the soil cumulative CH4 fluxes though it did not affect soil CO2 and N2O fluxes. However, acid rain increased soil microbial biomass by promoting the growth of gram-positive bacteria, actinomycete, fungi, and arbuscular mycorrhizal fungi, but it did not change the relative proportions of microbial groups. Our findings provide evidence of how and why greenhouse gases emission and soil microbial communities respond to acid rain under controlled conditions. However, it is essential to determine the long-term impacts of acid rain on greenhouse gas emissions and microbial communities using robust experiments under natural and semi-natural experimental conditions representing different soil, vegetation, and regional climatic factors. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the National Natural Science Foundation of China [grant numbers U1701236, 31500401]. We are grateful to Dr. Xiaoge Han, Dr. Yanxia Nie, Ms. Yongxia Jia and Mr. Weiren Wang from South China Botanical Garden, Chinese Academy of Sciences for their helps on the analyses of greenhouse gases emissions and microbial community. In addition, we are grateful to the handling editor and two anonymous reviewers for their constructive comments that helped us improve the quality of this manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2020.114222. References Balasubramanian, R., Victor, T., Begum, R., 1999. Impact of biomass burning on rainwater acidity and composition in Singapore. J. Geophys. Res.-Atmos. 104 (D21), 26881–26890. Bodegom, P.M.V., Stams, A.J.M., 1999. Effects of alternative electron acceptors and temperature on methanogenesis in rice paddy soils. Chemosphere 39 (2), 167–182. Bossio, D.A., Scow, K.M., 1998. Impacts of carbon and flooding on soil microbial communities: Phospholipid fatty acid profiles and substrate utilization patterns. Microb. Ecol. 35, 265–278. Chen, D., Wang, Y., Lan, Z., Li, J., Xing, W., Hu, S., et al., 2015a. Biotic community shifts

8

Geoderma 366 (2020) 114222

Z. Liu, et al.

Schimel, J., 2003. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol. Biochem. 35 (4), 549–563. Simek, M., Cooper, J.E., Picek, T., Santrůcková, H., 2000. Denitrification in arable soils in relation to their physico-chemical properties and fertilization practice. Soil Biol. Biochem. 32 (1), 101–110. Singh, A., Agrawal, M., 2008. Acid rain and its ecological consequences. J. Environ. Biol. 29 (1), 15–24. Sitaula, B.K., Bakken, L.R., Abrahamsen, G., 1995. N-fertilization and soil acidification effects on N2O and CO2 emission from temperate pine forest soil. Soil Biol. Biochem. 27 (11), 1401–1408. Smemo, K.A., Yavitt, J.B., 2011. Anaerobic oxidation of methane: An underappreciated aspect of methane cycling in peatland ecosystems? Biogeosciences 8, 779–793. The European Monitoring and Evaluation Programme (EMEP) Officially reported emission trends. http://www.emep.int/ (accessed on 21 January, 2019). The U.S. Environmental Protection Agency (EPA) Our nation's air: Air quality improves as America grows. https://gispub.epa.gov/air/trendsreport/2018 (accessed on 21 January, 2019). Vile, M.A., Bridgham, S.D., Wieder, R.K., 2003. Response of anaerobic carbon mineralization rates to sulfate amendments in a boreal peatland. Ecol. Appl. 13 (3), 720–734. Wang, Q., He, T., Wang, S., Liu, L., 2013. Carbon input manipulation affects soil respiration and microbial community composition in a subtropical coniferous forest. Agr. Forest Meteorol. 178–179, 152–160. Wang, J., Pan, X., Liu, Y., Zhang, X., Xiong, Z., 2012. Effects of biochar amendment in two soils on greenhouse gas emissions and crop production. Plant Soil 360, 287–298. Wang, C., Wang, W., Sardans, J., An, W., Zeng, C., Abid, A.A., et al., 2018. Effect of simulated acid rain on CO2, CH4 and N2O fluxes and rice productivity in a subtropical Chinese paddy field. Environ. Pollut. 243, 1196–1205. Wang, Q., Yu, Y., He, T., Wang, Y., 2017. Aboveground and belowground litter have equal contributions to soil CO2 emission: an evidence from a 4-year measurement in a subtropical forest. Plant Soil 421, 7–17. Wei, H., Guenet, B., Vicca, S., Nunan, N., Asard, H., AbdElgawad, H., et al., 2014. High clay content accelerates the decomposition of fresh organic matter in artificial soils. Soil Biol. Biochem. 77, 100–108. Wei, H., Liu, Y., Xiang, H., Zhang, J., Li, S., Yang, J., 2020.. Soil pH responses to simulated acid rain leaching in three agricultural soils. Sustainability-Basel 12 (1), 280. Wei, H., Ma, R., Zhang, J., Saleem, M., Liu, Z., Shan, X., Yang, J., Xiang, H., et al., 2020. Crop-litter type determines the structure and function of litter-decomposing microbial communities under acid rain conditions. Sci. Total Environ. 713, 136600. Wei, H., Zhang, K., Zhang, J., Li, D., Zhang, Y., Xiang, H., 2018. Grass cultivation alters soil organic carbon fractions in a subtropical orchard of southern China. Soil Till. Res. 181, 110–116. Wu, J., Liang, G., Hui, D., Deng, Q., Xiong, X., Qiu, Q., et al., 2016. Prolonged acid rain facilitates soil organic carbon accumulation in a mature forest in Southern China. Sci. Total Environ. 544, 94–102. Wu, Y., Ma, B., Zhou, L., Wang, H., Xu, J., Kemmitt, S., et al., 2009. Changes in the soil microbial community structure with latitude in eastern China, based on phospholipid fatty acid analysis. Appl. Soil Ecol. 43 (2–3), 234–240. Xu, H., Zhang, J., Ouyang, Y., Lin, L., Quan, G., Zhao, B., et al., 2015. Effects of simulated acid rain on microbial characteristics in a lateritic red soil. Environ. Sci. Pollut. R. 22, 18260–18266. Yang, Y., Wang, Y., Peng, Y., Cheng, P., Li, F., Liu, T., 2020... Acid-base buffering characteristics of non-calcareous soils: Correlation with physicochemical properties and surface complexation constants. Geoderma 360, 114005. Ye, R., Jin, Q., Bohannan, B., Keller, J.K., McAllister, S.A., Bridgham, S.D., 2012. pH controls over anaerobic carbon mineralization, the efficiency of methane production, and methanogenic pathways in peatlands across an ombrotrophic–minerotrophic gradient. Soil Biol. Biochem. 54, 36–47. Zhang, Y., Wang, L., Chen, S., Hu, Z., Shen, X., Shi, Y., 2011. Effects of simulated acid rain on soil respiration in a northern subtropical secondary forest (in Chinese). China Environ. Sci. 31 (9), 1541–1547. Zheng, S., Bian, H., Quan, Q., Xu, L., Chen, Z., He, N., 2018. Effect of nitrogen and acid deposition on soil respiration in a temperate forest in China. Geoderma 329, 82–90.

Li, Y., Wang, Y., Wang, Y., Wang, B., 2019. Effects of simulated acid rain on soil respiration and its component in a mixed coniferous-broadleaved forest of the three gorges reservoir area in Southwest China. For. Ecosyst. 6, 32. Liang, G., Liu, X., Chen, X., Qiu, Q., Zhang, D., Chu, G., et al., 2013. Response of soil respiration to acid rain in forests of different maturity in southern china. PLoS One 8 (4), e62207. Li, Q., Zhang, X., Gao, J., Song, M., Liang, J., Yue, Y., 2019. Effects of N addition frequency and quantity on Hydrocotyle vulgaris growth and greenhouse gas emissions from wetland microcosms. Sustainability-Basel 11 (6), 1520. Liang, G., Hui, D., Wu, X., Wu, J., Liu, J., Zhou, G., et al., 2016. Effects of simulated acid rain on soil respiration and its components in a subtropical mixed conifer and broadleaf forest in southern China. Environ. Sci.-Proc. Imp. 18 (2), 246–255. Liu, Z., Yang, J., Zhang, J., Xiang, H., Wei, H., 2019. A bibliometric analysis of research on acid rain. Sustainability-Basel 11 (11), 3077. Liu, X., Zhang, B., Zhao, W., Wang, L., Xie, D., Huo, W., et al., 2017. Comparative effects of sulfuric and nitric acid rain on litter decomposition and soil microbial community in subtropical plantation of Yangtze River Delta region. Sci. Total Environ. 601–602, 669–678. Lozanovska, I., Kuzyakov, Y., Krohn, J., Parvin, S., Dorodnikov, M., 2016. Effects of nitrate and sulfate on greenhouse gas emission potentials from microform-derived peats of a boreal peatland: A C-13 tracer study. Soil Biol. Biochem. 100, 182–191. Lu, R., 2000. Analytical methods of soil and agricultural chemistry. China Agricultural Science and Technology Press, Beijing. Martin, S.L., Clarke, M.L., Othman, M., Ramsden, S.J., West, H.M., 2015. Biochar-mediated reductions in greenhouse gas emissions from soil amended with anaerobic digestates. Biomass Bioenerg. 79, 39–49. Mastrocicco, M., Colombani, N., Soana, E., Vincenzi, F., Castaldelli, G., 2019. Intense rainfalls trigger nitrite leaching in agricultural soils depleted in organic matter. Sci. Total Environ. 665, 80–90. Maucieri, C., Borin, M., 2017. CO2 emissions and maize biomass production using digestate liquid fraction in two soil texture types. T. Asabe 60 (4), 1325–1336. Ministry of Ecology and Environment of the People's Republic of China Bulletin on the State of China's Ecological Environment in 2012. Ministry of Ecology and Environment of the People's Republic of China http://www.mee. gov.cn/ (accessed on 6 June, 2019). Network Center for EANET EANET data on the acid deposition in the east asian region. https://monitoring.eanet.asia/document/public/index (accessed on 26 September, 2019). Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., et al. Vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan (accessed on 6 June, 2019). Ouyang, X., Zhou, G., Huang, Z., Liu, J., Zhang, D., Li, J., 2008. Effect of simulated acid rain on potential carbon and nitrogen mineralization in forest soils. Pedosphere 18 (4), 503–514. Pu, G., Saffigna, P.G., Xu, Z., 2001. Denitrification, leaching and immobilisation of 15Nlabelled nitrate in winter under windrowed harvesting residues in hoop pine plantations of 1–3 years old in subtropical Australia. Forest Ecol. Manag. 152 (1–3), 183–194. Riggs, C.E., Hobbie, S.E., Bach, E.M., Hofmockel, K.S., Kazanski, C.E., 2015. Nitrogen addition changes grassland soil organic matter decomposition. Biogeochemistry 125, 203–219. Rousk, J., Brookes, P.C., Baath, E., 2009. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl. Environ. Microb. 75 (6), 1589–1596. Rousk, J., Brookes, P.C., Baath, E., 2011. Fungal and bacterial growth responses to N fertilization and pH in the 150-year ‘Park Grass’ UK grassland experiment. Fems Microbiol. Ecol. 76 (1), 89–99. Saleem, M., 2015. Microbiome ecosystem ecology: Unseen majority in an anthropogenic ecosystem. In: Saleem, M. (Ed.), Microbiome Community Ecology: Fundamentals and Applications. Springer International Publishing, Cham, pp. 1–11. Sant’Anna-Santos, B.F., Da Silva, L.C., Azevedo, A.A., de Araujo, J.M., Alves, E.F., Da Silva, E.A.M., et al., 2006. Effects of simulated acid rain on the foliar micromorphology and anatomy of tree tropical species. Environ. Exp. Bot. 58 (1–3), 158–168.

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